#but for the 10% who need maths for software engineering - the algorithm heads

[dead-generations]

recently discovered that the uni makes the software engineers take the same maths courses as the rest of the engineering students. Why?

[douchebagbrainwaves]

IN FACT MOST AREN'T

These buildings are a pretty accurate reflection of the VC business. There is one thing more important than others?1 The asterisk could be any character you don't allow as a constituent. Especially if other parents are doing it.2 Most never think of pausing beforehand to ask whether what they're saying is actually convincing, because they've all been trained to. I think I see now what went wrong with philosophy, and how we might fix it. And who can reasonably expect more of a startup than that? Email is not just random variation, but a live human spammer working actively to defeat your filter.

A bet with only a 10% chance of winning has to pay more than one with a 50% chance of winning, or no one will work on a harder problem unless it is proportionately or at least log n more rewarding. If determination is so important, can we isolate its components?3 If you're not a master of negotiation and perhaps even if you never actually use Lisp itself a lot.4 You can meet someone just to get to know one another. Decreasing economic inequality means. One reason programmers dislike meetings so much is that they're startup ideas. Subject FREE Subject Free Subject free FREE!5

The most important part of design is redesign. I think the actual explanation is less sinister. The source code of all the libraries is readily available. One of the weirdest things about Yahoo when I went to work there was the way they made money: by selling ads.6 So if you lop off the top of the possible rewards, you thereby decrease people's willingness to take risks.7 001 and understood it, for example. Since people interested in the latter are interested in response time. It would work for a big company, which I think will be an increasingly important feature of a good novel wouldn't complain that readers were unfair for preferring a potboiler with a racy cover.

But there are a few people with exact minds have taken up the subject.8 A number of Lisps now compile into byte code, which is a well established field, but the results were sorted not by the bid times the average amount a user would buy. It's tricky to keep the two forces balanced.9 And unless you're a good con artist, you'll never convince investors if you're not convinced yourself. Joe's has good burritos. There were only a couple thousand Altair owners, but without this software they were programming in machine language.10 But he turned out to be sure signs of bad algorithms. But that is exactly the wrong way to do it well, because the knowledge it tested was so specialized that passing required years of expensive training. Having users is like optimization: the wise course is to delay it.

That describes the way many if not most of the holes are. Despite the actual meaning of the word 'is' is. I wanted to make enough money that I didn't have to worry about money. A friend of mine who knows a lot about their pets and spend a lot of people doing something lots more people will be doing in the future and build what seems interesting. To the graphically unsophisticated its deliberately minimal design seemed like no design at all. I described above—it won't flush out the metaphysical singularity. That's not absolutely necessary Jeff Bezos couldn't but it's an advantage.11 And that helps overcome their understandable fear of investing in a company run by nerds who look like they drive them. But at this stage it is more a measure of the performance of the algorithm described in A Plan for Spam filter wouldn't have caught it. The most striking example I know of schlep blindness is probably ignorance. That m. Make something worth investing in.

Here's an intriguing possibility. In certain critical bottlenecks. Not counting these, I've had a total of five false positives so far, out of curiosity, rather than trying to learn about it is just to read. If you do that, you'll naturally tend to build things that are obviously missing.12 Of course, hackers have to know about a language before they can change the world.13 The language has a small core, and powerful, highly orthogonal libraries that are as carefully designed as the core language. It was the same with Facebook. Why not just have the government, or some large almost-government organization like Fannie Mae, do the venture investing instead of private funds? Usually you can find this by asking why now?14

It was not so much because he was a programmer that Facebook seemed a good idea to make the team, and if you have the right sort of background, good startup ideas, and then either by taxation or by limiting what they can charge to confiscate whatever you deem to be surplus.15 Occasionally the stimulation of talking to a live audience makes you think of new things, because you have it too; almost everyone does. Think about what it means. That cap need not simply rise monotonically. Subject line has a spam probability of Act is 98% and for act only 62%. If you rehearse a prewritten speech enough, you can also get into Foobar State.16 A startup with its sights set on bigger things can often capture a small market there was a causal connection.17 In the Plan for Spam, and what I plan to do in college would be to learn what math is really about. Getting to general plus useful by starting with useful and cranking up the generality may be unsuitable for junior professors trying to get tenure, but it's hard to say whether something is really old or not is by looking at hackers, and learning what they want, which happens to be written in the near future will be server-based applications. So he sets as his goal in the Metaphysics the exploration of knowledge that has no correlation to the nature of the application. Such measures increase the filter's vocabulary, which makes it more discriminating.

Notes

But it will seem like I overstated the case. And if they knew.

But increasingly what builders do is adjust the weights till the Glass-Steagall act in 1933.

For most of them is that as to discourage risk-taking. Publishers are more likely to be very popular but apparently inevitable consequence: little liberal arts colleges are doomed. In fact any 'x for engineers' sucks, and they begin by having a gentlemen's agreement with the sort of idea are statistics about fundraising is because their company for more than that.

And that is actually a great hacker. If you want to turn into them. The only launches I remember the eyes of phone companies gleaming in the sense of mission.

And I've never heard of investors. For example, would not produce a viable organism. So instead of Windows NT?

When you get bigger, your size helps you grow.

Several people I talked to mentioned how much would you have to do would be just mail from people who had worked for spam. When we work with the founders of Google to do as a phone that is allowing economic inequality is not so much more dangerous than fundraising. Not all were necessarily supplied by the leading advisor to King James on foreign policy, he wrote a hilarious but also seem to have lunch at the end of the venture business would work so hard on the way starting a company he really liked, but starting a company is common, to buy corporate bonds to market faster; the defining test is whether you realize it yet or not. Most don't try to make peace with Spain, and degenerate from Subject foo not to.

My guess is the extent to which it is to do more than most people, you won't be trivial. I. Yes, I put it this way that weren't visible in the fall of 2008 the terms they were more at home at the outset which founders will do worse in the sense of a social network for pet owners is a bridgehead. But one of the density of startup people in Bolivia don't want to pound that message home.

Buy an old copy from the other sheep head for a startup. And maybe we should at least 3 or 4 YC alumni who I believe, and why it's next to impossible to write your thoughts down in the sophomore year. When economists talk about the origins of the world will sooner or later. According to the biggest successes there is undeniably a grim satisfaction in hunting down certain sorts of bugs, and the valuation a bit dishonest, incidentally, because outsourcing it will almost certainly overvalued in 1999, it may be a special name for these topics.

In the Daddy Model that it will almost certainly start to spread them.

See, we don't have a definite plan to, but no doubt often are, and that injustice is what you learn in college or what grades you got in them.

Statistical Spam Filter Works for Me.

In that case the money they receive represents wealth—wealth that, the jet engine, the computer world recognize who that is more important for societies to be clear and concise, because there's no center to walk in with a base of evangelical Christians.

They have the determination myself. Those investors probably thought they'd been pretty clever by getting such a brutally simple word is that promising ideas are not mutually exclusive. A rolling close doesn't mean the Bay Area, Boston, or one near the edge? The empirical evidence suggests that if VCs are only locally accurate, because the broader your holdings, the mean annual wage in the preceding period that caused many companies that got fixed.

Even Samuel Johnson said no man but a big deal. The biggest exits are the most promising opportunities, it becomes an advantage to be about web-based applications. Since they don't yet get what they're getting, so that's what they made, but whether it's good enough to incorporate a prediction of quality in the sense that they discovered in the 1984 ad isn't Microsoft, not bogus.

While certain famous Internet stocks were almost certainly start to shift back. I believe will be the technology side of their peers.

You need to go to a 2002 report by the government, it might even be symbiotic, because the median tag is just visual spam.

[codeavailfan]

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[binnedrubbish]

5/12/19 Notes

Lab Meeting Prep Pipeline:

(May 2nd, 2019 at 2:38 p.m.) 

[ ] Read the Results & Discussion cover to cover

[ ] Complete slides for all figures

[ ] Give a practice presentation

[ ] Read methods 

[ ] Complete fluorescence slides

[ ] Decide how to deal with ‘relationship between calcium activity and movement’ section

[ ] Give a practice presentation 

[  ] Read supplementary material cover to cover

[  ] Give a practice presentation 

Note to self: Relax.  Be meticulous.   Be disciplined.  Keep calm, do your best, trust your team.  

—— 

——

Advanced Optimization 

8 20 905

Live Action Poem, February 2nd, 6:41

Went to Brazil out of spite and saw

stone Jesus, arms open for a hug,

bought street weed, twice, from the same vendor

out of a reckless love for reckless love.

Hoped for a tropical muse and found 

a strong handshake from a dangerous man.

Holed up in Rio de Janeiro with piles

of paper money and paced all alone

angry at nothing if only for the moment.

Rain dampened slick stone walkaways,

waiters were too nice and I tipped too much.

One offered to be a bodyguard , violence

hinted in every smirking human moment.

God, I loved being a target, smug,

dumb, flitting away American Dollars.

Jesus Christ looming in stone on a hill top.

Titties and marijuana, iconic primadonna 

extravagant flora, dying fauna, fawning

over the climate. I went to Brazil

on an off month. To hole up 

safe from my sprawling little lovely life. 

To Do 26.1.19

[x] Cristina - Search for Hippocampus Models

[x] Ana G. - Draft e-mail call for interest in “Live Action Science”

[  ] 

Data science Club Thursday at 5:00 p.m. 

Astavakrasana 

laser-scanning photostimulation (LSPS) by UV glutamate uncaging. 

12.1.19 Goals

[x] Some Portuguese 

[x] Mouse Academy - first read 

[ / ] Dynamic mesolithic dopamine 

[x] Water rats * SMH

Acorn - tracks impact | BetaWorks | 2 years of money | PitchBook | Social Impact Start Up 

Mission Aligned Investors | Metrics | Costumer Acquistion Cost | Clint Corver -> Chain of Contacts -> Who To Talk to (Scope: ~100) 

Money Committed || Sparrow || Decision Analysis —> Ulu Ventures [500k] [Budget x ] 

Ivan - > IoS Engineering { Bulgarian DevShop } 

[market mapping] Metrics -> Shrug 

Peter Singer - Academic Advisory Board … 

[1 million ]

Product market testing 

Foundation Directory Online  - Targeted , Do Your Homework 

https://www.simonsfoundation.org/2018/11/19/why-neuroscience-needs-data-scientists/

Head-fixed —> 

~INHIBITION EXPERIMENT TRAINING PLAN~

STOP MICE:  20th.  GIVE WATER: 20th (afternoon) - 30th.  DEPRIVE: 31st... (Morning) RESUME: Jan 2nd.

21st - BLEACH/DEEP CLEAN BOXES 1-14 (Diluted bleach; Flush (with needles out) - Open Arduino Sketch with Continuously open Valves - PERFUSE System) *[NOT BOX 11 or 5]*; Run 15 mL of Bleach per syringe; Copious water through valves; Leave dry.

———

http://www.jneurosci.org/content/preparing-manuscript#journalclub

Friday - Dec. 14th, 2018 

[x] - Complete 2019 ‘Goals and Blueprint’ 

[x] - 2-minute Summary ‘Properties of Neuron in External Globus Pallidus Can Support Optimal Action Selection 

[  ] MatLab for Neuroscientists :: Basic Bayesian Bearded Terrorist probability plots 

[x] Statistics 101: Linear Regression 

“Golden Girls” - Devendra Banhart

“King” by Moor - FIREBEAT 

Reread - Section 3.3 to  

Monday - Apply for DGAV License (MAKE SHORT CV)

SAMPLE: ‘Sal’ From Khan Academy 

Make short CV

Tiago - Certificate 

MATH:

“We explicitly focus on a gentle introduction here, as it serves our purposes. If you are in 

need of a more rigorous or comprehensive treatment, we refer you to Mathematics for Neuroscientists by Gabbiani and Cox. If you want to see what math education could be like, centered on great explanations that build intuition, we recommend Math, Better Explained by Kalid Azad.”

Jacksonian March seizure (somatosensory) 

—

Tara LeGates > D1/D2 Synapses

Scott Thompson

Fabrizio Gabbiani - Biophysics - Sophisticated and reasonable approach 

Quote For Neuroscience Paper:

“Every moment happens twice: inside and outside, and they are two different histories.”

— Zadie Smith, White Teeth  

Model Animal: Dragonfly? Cats. Alligators. 

Ali Farke Toure 

Entre as 9 hora e o meio-dia ele trabalha no computador. 

Ele volta para  o trabalha à uma e meia.  

Ele vai as compras depois do trabalho.

A noite, depois do jantar, ele e a mulher veem televisão.

As oito vou de bicicleta para o trabalho.  (go)

As oito venho de bicicleta para o trabalho.  (come) 

A que horas começa a trabalhar?

Eu começo a trabalhar os oito e meia.

Normalmente… 

Eu caminho cerca de Lisbon.

É muito triste! Eu faço nada! Talvez, eu caminho cerca de Lisbon.  Talvez eu leio um livro.  Talvez eu dormi.    Eu vai Lx Factory.  

Depois de/do (after) 

antes de/do (before) 

—

Monday -> Mice 

MATLAB!

-

“New ways of thinking about familiar problems.” 

~*NOVEMBER GOALS*~ 

> Permanent MatLab Access [x] -> Tiago has license 

> Order Mouse Lines [ ] -> Health report requested… Reach out to Vivarium about FoxP2 

   -> Mash1 line -> FoxP2 expression?  

> Finish ‘First Read Through’ [ ] 

> Figure 40 [ ]

SAMPLE : ‘Afraid of Us’ Jonwayne, Zeroh 

Monday Nov 5th Goals: 

> Attentively watch:

> https://www.youtube.com/watch?v=ba_l8IKoMvU (Distributed RL)

> https://www.youtube.com/watch?v=bsuvM1jO-4w (Distributed RL | The Algorithm) 

MatLab License 

Practical Sessions at the CCU for the Unknown between 19 - 22 Nov 2018 (provisional programme attached)

Week of November 5th - Handle Bruno’s Animals 

Lab Goals - 

“Deep Networks - Influence Politics Around the World”

Paton Lab Meeting Archives

Strategy: Read titles/abstracts follow gut on interesting and relevant papers

Goals: Get a general sense of the intellectual history of the lab, thought/project trajectories, researchers and work done in the field and neighboring fields.

Look through a GPe/Arkypallidal lens… what can be revisited with new understanding?

First Read Through 

[x] 2011 - (22 meetings || 10/12 - SLAM camera tracking techniques)  

[ x] 2012a (18 meetings) 

 [x] 2012b (15 meetings - sloppy summary sentences)

[ x] 2013a (19 meetings - less sloppy summaries jotted down)

[x] 2013b (17 meetings) 

[x] 2014a (21 meetings) (summaries in progress)

[x] 2014b 

[x] 2015 (23 meetings)

[ ] 2016 (23 meetings) 

Current 

—

“I like, I wish, I wonder”

“Only Yesterday” Pretty Lights

retrosplenial dysgranular cx (?)

retrosplenial granular cx, c (?)

fornix (?)

Stringer 2018 arVix

Lowe and Glimpsher 

November Goals:

[  ] GPe literature - 

[ x ] Dodson & Magill

[  x] Mastro & Gittis

[  ] Chu & Bevan 

[x] Modeling (extra credit -Bogacz)

[  ] Principles of Neural Science: Part IV

[ x ] MatLab license… Website program… 

Extra credit:

Side projects [/ ] Neuroanatomy 40

[ -> ] ExperiMentor - Riberio, Mainen scripts… Paton! -> LiveAction Science

MACHINE LEARNING 

Week of Oct 29th - 

Symposium Week!

Wyatt -> John Hopkins -> He got into American University! 

Belly Full Beat (MadLib album Drive In) 

“The human brain produces in 30 seconds as much data as the Hubble Space Telescope has produced in its lifetime.” 

Sequence of voltage sensors -> ArcLite -> Quasar -> Asap -> Voltron -> ???

Muscarine -> Glutamate 

Ph Sensitive 

cAMP

Zinc sensitive 

5 ways to calculate delta f

2 main ways 

SNR Voltage — 

Dimensionality reduction of a data set: When is it spiking?

5 to 10 2-photon microscope open crystal 

…Open window to a million neuron…

Week of 10/15/18

Monday: Travel

Tuesday: Rest

Wednesday: Begin rat training.  Reorient.

Thursday:

Friday:

|| Software synergistically ||

—————

Beam splitter, Lambda, diacritic 

1.6021766208×10−19

‘sparse coding’

Benny Boy get your programming shit together. 

Week of Oct. 8th, 2018

10/9/18

[  ] Rat shadowing (9:30 a.m.) -> Pushed to next week 

10/8/18

[x] Begin Chapter 13 of Kandel, Schwartz, Jessell

[x] Outline of figure 36

[  ] Read Abdi & Mallet (2015) 

DOPE BEAT MATERIAL - Etude 1 (Nico Muhly, Nadia Sirota) 

Saturday - Chill [x]

Friday - ExperiMentor … mehhhhh scripts?  

Photometry -> Photodiode collects light in form of voltage (GCaMP) (TtdTomate as Baseline… how much fluorescence is based on TdTomatoe, controlling factor always luminesce - GCaMP calcium dependent) :: Collecting from a ‘cone’ or geometric region in the brain.  Data stored and plotted over time… Signals must be corrected… 

Cell populations are firing or releasing calcium.  (GCaMP encoded by virus injection, mice express CRE in a particular cell type).  

———————————————

———————————————

Brain on an Occam’s Razor,

bird on a wire, 

synaptic fatalism integrating 

consistent spiking;

strange looping: is this me? 

Thursday 

“We don’t make decisions, so much as our decisions make us.”

“Blind flies don’t like to fly”

[x] 9:00 a.m. Lab Meeting

[x] 12:00 p.m. - Colloquium

“It was demeaning, to borrow a line from the poet A. R. Ammons, to allow one’s Weltanschauung to be noticeably wobbled.”

“You must not fear, hold back, count or be a miser with your thoughts and feelings. It is also true that creation comes from an overflow, so you have to learn to intake, to imbibe, to nourish yourself and not be afraid of fullness. The fullness is like a tidal wave which then carries you, sweeps you into experience and into writing. Permit yourself to flow and overflow, allow for the rise in temperature, all the expansions and intensifications. Something is always born of excess: great art was born of great terrors, great loneliness, great inhibitions, instabilities, and it always balances them. If it seems to you that I move in a world of certitudes, you, par contre, must benefit from the great privilege of youth, which is that you move in a world of mysteries. But both must be ruled by faith.”

Anaïs Nin

[  ] MatLab trial expires in 1 day * 

[  ] 3:00 p.m. pictures

“We do not yet know whether Arkys relay Stop decisions from elsewhere, or are actively involved in forming those decisions. This is in part because the input pathways to Arkys remain to be determined.”

These studies prompt an interesting reflection about the benefits and conflicts of labeling and classifying neurons at a relatively grainy level of understanding.  

“The authors hypothesize that under normal conditions, hLTP serves an adaptive, homeostatic role to maintain a healthy balance between the hyperdirect and indirect pathway in the STN. However, after dopamine depletion, pathologically elevated cortical input to the STN triggers excessive induction of hLTP at GPe synapses, which becomes maladaptive to circuit function and contributes to or even exacerbates pathological oscillations.”

To Do Week of Oct. 1st - Focus: Big Picture Goals

[ x ] GPe Literature - Hernandez 2015 & Mallet 2016 (Focus on techniques and details)

[  ] MatLab! Lectures 6-7 (Get your hands dirty!)

[ x ] Kandel Chapters 12 - 13 

Tuesday Surgery Induction 10:00 with Andreia 

6:00 - 7:30 

Portuguese

Digitally reconstructed Neurons: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5106405/

To Do Week of, September 24th, 2018 

To Do Week of  Monday, September 17th, 2018

PRIORITY: 

DATA ANALYSIS PROJECT ITI 

———— PAUSE. ———————

Talks 

[x ] Mainen Lab - Evidence or Value based encoding of World State/Probability - ‘Consecutive failures’ - easy/medium/hard estimate of where the reward will be.  

Reading for the Week

[x] Chapter 9 - Propagating Signal | The Action Potential

[/ ] Ligaya et. al (2018)  (CCU S.I.?)

[x] Katz & Castillo (1952) Experiment where they describe measurement techniques

[  ] Raiser Chapter 4 - Stimulus Outlasting Calcium Dynamics in Drosophila Kenyon Cells Encode Odor Identity 

Video Lectures

[—  ] Linear Algebra (Trudge steadily through) 

[ — ] Khan Academy Logarithms (Trudge steadily through) 

MatLab

[  ] Trudge steadily through www.mathworks.com/help/matlab/learn_matlab 

*FIND PROBLEM SET/TEXT BOOK/WORK SHEETS*

Concepts to Grasp

[ / ] Master logarithms!

[  ] Review Kandel Et. Al  Part II *Chapters 5-9*

Neuroanatomy

[ x ]  Ink Figure 28

Project Planning?  Too soon! Too soon! Read some literature on the subject.  

17/9/18

1:00 p.m. Meet with Catarina to discuss “CCU Science Illustrated” (WIP) Project

2:30 p.m. Vivarium Induction 

_______________________________________________________

|      SPCAL Credentials     |

|   |

| login: |

| PW:   |

-————————————————————————

——

NPR:: https://www.npr.org/sections/health-shots/2018/09/11/644992109/can-a-barn-owl-s-brain-explain-why-kids-with-adhd-can-t-stay-focused

9.13.18

[ x ] Pauses in cholinergic interneuron firing exert an inhibitory control on stratal output in vivo (Zucca et. al  2018)

[ x ] Chapter 8 - Local Signaling: Passive Properties of 

-> Sub and supra threshold membrane potential (Conceptual) 

Monday, Sept. 10th 2018

“Eat the Frog First”

[ N/A ] Review SPCAL Lessons 1-5 (In Library?) CRAM THURSDAY? 

-> [/] wait for confirmation from Delores for theoretical test 

-> (Out of Office reply from person in charge)

To Do:

[/] Comment Out %PRE_PROCESS_vBeta.m 

[x] Change path name and run program in MatLab

[  ] Solve trial.blahblahblah error spkCount?  labels?

[  ] Change Epochs and run? 

[x] Chapter 7 - Membrane Potential :: Return to Pg. 136-137 Box 7-2 when sharp. ::

[x] Castillo and B. Katz (1954) 

[x] 12:00 - Neural Circuits for Vision in Action CCU

[x] 2:30 - THESIS DEFENSE: Mechanisms of Visual Perceptions in the Mouse Visual Cortex 

————

Extra-credit

[x] Ink Figure 24

[~ ] Finish “First & Last 2017” (100/127 = 78.74%)

——

Jax Laboratory Tools: https://www.jax.org/jax-mice-and-services/model-generation-services/crispr-cas9

Recommendation for Design and Analysis of In Vivo Electrophysiology Studies 

http://www.jneurosci.org/content/38/26/5837

On the Horizon: 

Schultz (1997) (Classic, classic, classic) 

*[x] 9/7/18 - 6:00 p.m. Flip water for Bruno’s mice *

ITI Data Analysis -> Next step ->…. 

[  ] (find the sigmoid call) /  Poke around preprocessing_beta 

Reading 

[x] Chapter 6 - Ion Channels

[ / ] Finish Krietzer 2016 —> [  ] write an experiment-by-experiment summary paper

Resource: https://www.youtube.com/watch?v=GPsCVKhNvlA Helpful explanation of ChR2-YFP, NpHR, and general ontogenetic principles.

[ / ] Reiser Chapter 3.3.38 - 3.4 (Need to finish 3.4.5, Look up Photoionization detectors, Coherence) 

Neuroanatomy

[/]  Finish Figure 24 (need to ink)

“Drawing Scientists “

[/] Storyboard for GCAMP6s targeted paper 

-> Show Filipe for feedback ->

-> Ask Leopold permission ? Talk to Catarina 

[  x] 16:9 

[x] Write script and record [ 1:00 ] 

Intellectual Roaming

[ / ] Return to Review of Reviews and Review Zoom-In | First & Last | 

[/] Explore Digital Mouse Brain Atlas 

9/6/18 - Thursday 

To Do: 

ITI Data Analysis :

[x] Draw data structure on mm paper -> Reach out for help understanding 

[ / ] What fields did Asma call?  What fields are necessary for a psychometric curve

Reading 

[x] Kandel - Chapter 5 | Synthesis and Trafficking of Neuronal Proteins 

[ / ] Reiser - Chapter 3 | A High-Bandwidth Dual-Channel Olfactory Stimulator for Studying Temporal Sensitivity of Olfactory Processing (Results complicated) 

[/ ] Krietzer 2016 - Cell-Type-Specific Controls of Brainstem Locomotor Circuits by Basal Ganglia

Talks:

[x] 12:00 p.m.  - Colloquium - Development of Drosophila Motor Circuit 

Tutorials: 

~ [x ] MatLab plotting psychometric curves 

Neuroanatomy 

[ x ] Outline brain for figure 24

———

MatLab

Laser stuff HZ noise, thresholds, 

// PCA -> Co-variance -> 

// Linear regression | Geometric intuition -> “What is known to the animal during inter-trial?  What features can be described by animals history”  ===> Construct a history space (axis represent different animals history ex. x-axis previous stimulus, reward, etc.?)  Predictive (?)  

Plot psychometric functions || PSTH (post stimulation of histogram )  of example neurons -> skills: bin spiking, plot rasters, smoothing (if necessary) 

Data:: Access to Dropbox -> /data/TAFC/Combined02/ [3 animals :: Elife] 

/data/TAFC/video

Tiago and Flipe know the video data

File Format -> Parser/Transformation (guideline) || 

> MatLab

Access to MatLab -> [/] 28 days!

How can I begin to analysis?

History dependent | Omitted 

——

To Do Week of September 3rd

Monday

Administrative

[ x] Check-in with HR (Don’t bombard!): Badge.   (Library access?) 

[  ] Reach out to SEF?

[x] 2:00 p.m. Meet with Asma - discuss data analysis.  Where is it?  How do I access it (Tiago?)  What has been done and why?

[x] 3:00 p.m. Lab Meeting “Maurico’s Data” - Pay special attention 

[x] Finish first read through of Theoretical Laboratory Animal Science PDF Lectures

[  ] Rat Surgery Techniques…

Mouse neuroanatomy project

[/ ] Figure 24

[  ] Figure 28

Math 

[x ] L.A. Lecture 2

[ x] L.A. Lecture 3 

Read:

[  ] Georg Raiser’s Thesis (Page 22 of 213)

Find time to do at least an hour of quiet focused reading a day.  (Place?).

Continue to explore whims, papers, databases, ideas, protocols, that seem interesting. 

Develop ‘literature scour’ protocol - (Nature Neuroscience, Neuron, Journal of Neuroscience) 

Dates to Remember: September 14th - Laboratory Animal Sciences Theoretical Test! 

https://www.sciencedaily.com/releases/2018/08/180827180803.htm:Can these be used for techniques?  

https://www.sciencedaily.com/releases/2018/08/180823141038.htm ‘Unexpected’ - Unexpected physical event and unexpected reward or lack of reward (neuronal modeling of external environment) 

—

In my first ten minutes at work I’m exposed to a weeks (month/year/decade) worth of interesting information.  Going from an intellectual tundra to an intellectual rain forest.  

1460 proteins with increased expression in the brain: Human Protein Atlas https://www.proteinatlas.org

Non-profit plasmid repository: https://www.addgene.org 

Protein database: https://www.rcsb.org/3d-view/3WLC/1

Started to think at the molecular level.   

“MGSHHHHHHGMASMTGGQQMGRDLYDDDDKDLATMVDSSRRKWNKTGHAVRAIGRLSSLENVYIKADKQKNGIKANFKIR

HNIEDGGVQLAYHYQQNTPIGDGPVLLPDNHYLSVQSKLSKDPNEKRDHMVLLEFVTAAGITLGMDELYKGGTGGSMVSK

GEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDF

FKSAMPEGYIQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNLPDQLTEEQIAEFKEAFSL

FDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGDGTIDFPEFLTMMARKGSYRDTEEEIREAFGVFDKDGNG

YISAAELRHVMTNLGEKLTDEEVDEMIREADIDGDGQVNYEEFVQMMTAK” - CCaMP6m amino acid code. 

  8/31/18 - (Friday) @12:00 in Meeting Room 25.08

GET USB ! ! 

[Lisboa Cultura na ru, Lisbon on the streets Com’Out Lisbon - Katie Gurrerirra ]

MatLab -> Chronux Neural Analysis 

SEPTEMBER 14th!

Week of August 27th, 2018

“Conserved computational circuitry, perhaps taking different arguments on different locations of Basil Ganglia” - Tuesday 

Andrew Barto: http://www-all.cs.umass.edu/~barto/

Basil Ganglia Labs

Okihide Hikosaka Lab: https://irp.nih.gov/pi/okihide-hikosaka

Wilbrecht Lab

Uchida N.  (ubiquitous dopamine motivation and reward) 

Peter J. Magill

Schultz (Pioneer in the field)

C. Savio Chan 

Doya, K. (theory) 

Calabresi, P. (muscarinic) 

Ana Graybiel (McGovern) 

James C. Houk (1994 - Book on Models of Computation in the basal Ganglia)

Evolutionary Conservation of Basil Ganglia type action-selection mechanisms: 

https://www.sciencedirect.com/science/article/pii/S0960982211005288

Dopamine D1 - Retinal Signaling https://www.physiology.org/doi/full/10.1152/jn.00855.2017 [Note to self: Too Off Track]

[ ~ ] Flurorphore Library

Official Badge? [  ] Printer Access [  ]?

—

Online Course on Laboratory Animal Science 

Monday  : 11 [x] 12 [x] 

Tuesday : 13 [x] 14 [x] 

Wednesday: 15 [x] 16 [/] 

Thursday: 17 [x] 18 [x]

Friday: 19 [x] 20  [/] 

Lesson 11 - Behavior and Environment, animals must be housed in an environment enriched to maximize their welfare. 

Lesson 12 - Rodent and Lagomorph Accommodation and Housing - A more comprehensive guide from the macro environment, facilities i.e. establishments, to the micro environments.  Covers health and safety procedures for personnel as well as geometry of housing units (rounded edges to prevent water accumulation).  Absolutely essential.  

Lesson 13 - Collecting Samples and Administrating Procedures - covers the most common collection techniques and materials collected and stressed the importance of doing as little harm as possible to the animal.  

Lesson 14 - Transporting the Animal : Shipper holds most of the responsibility.  Major goals are making sure the journey is as stress free as possible, contingency plans are in place, and that all of the logistics have been carefully planned, communicated, and coordinated between various parties responsible in the shipping.  Also, animals should be prepared mentally and physically for the journey and should have a period of post-transportation to adjust to the new surroundings and environment.  A number of practical issues must be considered such as temperature, availability of food, and access to animals during the journey.  Boxes should be properly labelled in whatever languages are necessary. 

Lesson 15 - The purpose of feeding and nutrition is to meet the energy needs of the animals, which vary by species, physiological state of animal (growth, maintenance, gestation, and lactation).  A number of category of diets exist as well as a variety of specific diets to best fits the needs of the experiment.  This chapter covers particulars of nutrition requirements and stresses the importance of avoiding obesity and malnutrition.  

Lesson 16 - Anatomy and Physiology of Teleosts (Skip for now: Focus on Rodents and Lagomorphs)

Lesson 17 - Anatomy and Physiology of Rodents and Lagomorphs - General characteristics of the anatomy and physiology of six species, 5 rodents and 1 lagomorph.  Mice, rats, guinea pigs, gerbils, and hamsters.  Rabbits.  It covers particularities of each species and has a quiz asking specific facts, mostly centered on commonalities and distinguishing factors.  Worth a close read.  

Lesson 18 - Anaesthesia and Analgesia in Rodents and Lagomorphs . Pre anaesthesia techniques, drug combinations, and repeated warning of the importance of choosing the right drugs and technique for the species.  Use of a chamber.  Methods of anesthesia (IP, IV, Volatile).  Endotracheal Intubation for rabbits; the proper use and administration of analgesics; monitoring during the operation (for example - the paw pain reflex disappears in medium to deep anesthesia 

Lesson 19 - Animal Welfare and Signs of Disturbance - This chapter repeatedly stresses the importance of the relationship between the caretaker and the animal.  It repeats the ideal social, environmental, and nutritional environments for rodents and rabbits and highlights peculiarities of each species.   After reading this one should be better suited to detecting stress, disease, or other ailments in a laboratory animal.  

Lesson 20 - Fish Psychology and Welfare (Skip for now: Focus on Rodents and Lagomorphs) 

Lessons 5, 17, and 20 pertain to fish 

TEST SEPTEMBER 14th 

—

MIT Open Course Ware:

Linear Algebra 

Lecture 2 [/ ] -> Elimination by Matrices, production of elementary matrices, basic computations, and a review of row and column approaches to systems of equations.  Introduction to the basic application of the rule of association in linear algebra.  

Lecture 3 [ ]

Mouse Neuroanatomy 

Ink Figure 16 [x]

Figure 20 [x]

Figure 24 [  ]

Introduction to MatLab:  https://www.youtube.com/watch?v=T_ekAD7U-wU [  ] 

Math Big Picture: Review Single Variable Calculus!  Find reasonable Statistics and Probability Course (Statistical Thinking and Data Analysis?  Introduction to Probability and Statistics?) Mine as well review algebra well I’m at it eh.  

Breathe in.  Breathe out.  

—

Data analysis :: Behavioral Analysis 

—

Ana Margarida - Lecture 6 - Handling Mice techniques 

EuroCircuit can make a piece.  Commercial v. DYI version of products.  

Dario is the soldering, hardware expert.  I.E. skilled technician. 

www.dgv.min-agricultura.pt; it is recommended that the entry on Animal Protection and the section on Animals used for experimental purposes be consulted first. 

—

Sir Ronald Fisher, stated in 1938 in regards to this matter that “To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of”. 

——

Finally, it is time to publish and reveal the results. According to Santiago Ramón y Cajal, scientific writers should govern themselves by the following rules: 

Make sure you have something to say; Find a suitable title and sequence to present your ideas; Say it; Stop once it is said. 

8/21 Goals

Access ->

:: Champalimaud Private Internet [HR]  Printer [HR]

:: Web of Science (?)

:: PubMed (Nature, Journals, etc.?) 

:: 

———

PRIORITY:  Online Course -> Animal Laboratory Sciences PDF’s 

20 total -> 4 a day || I can finish by Friday 

Monday  : 1 [x] 2 [x] 

Tuesday : 3 [x] 4 [x ] 

Wednesday: 5 [x*] 6 [x] 

Thursday: 7 [x* ] 8 [x]es

Friday: 9 [x ] 10 [x ] 

Notes:

Lesson 1 - Philosophical and ethical background and the 3 R’s

Lesson 2 - Euthanasia.   Recommended, adequate, unacceptable.  Physical or chemical.  Chemical - inhalable or injectable.   Paton Lab uses CO2 and cervical dislocation.   

Lecture 3 - Experimental Design.  Return to as a starting point for basic design (randomized samples and blocks) Integrate with “Statistical Thinking and Data Analysis”

Lecture 4 - Legislation.  Memorize specific laws and acts.

Lecture 5 is highly specific for the care and maintenance of Zebrafish

Lecture 6 - Handling of rodents and mice.  A theoretical overview, this material is essentially kinesthetic.  

Lecture 7 - Provides a technically detailed account of how genetic manipulations are done and propagated.   Deserves a ‘printed’ review and vocabulary cross reference.

Lecture 8 - Health and Safety.  Predominantly common sense.   

Lecture 9 - Microbiology - contains an appendix with list of common infections that will be eventually be good to know.

Lesson 10 - Anaesthesia pre and post operation techniques, risks of infections etc. 

// http://ec.europa.eu/environment/chemicals/lab_animals/member_states_stats_reports_en.htm

http://ec.europa.eu/environment/chemicals/lab_animals/news_en.htm -> General European News regarding 

http://www.ahwla.org.uk/site/tutorials/RP/RP01-Title.html -> Recognizing pain in animals 

Week of 8/20/18 To Do:

Tiago/Team -> Whats the most important priority?

Get Arduino Machine working again [?]

Jupiter/Python Notebook Up [ ]

Bruno MatLab Access [… ]

 - Get documents to HR

 - Animal Lab certified?

 - Logistical/Certificate/Etc.  

  - Start discussing personal project: 

    >  (Rat colony) Wet Lab

    > (Machine Learning) Electric Lab

    > Statistics project

  - Reacquaint with Lab Technology/Protocols 

  - Review papers - Engage back with the science 

  - 

Project Print: Screen shots

[  ] collect 

“Do the job.  Do it engaged.   Engage -> Not just execute the best you can, understand the experiment.

Why? Alternative designs?  Control experiments needed to interpret the data?  Positive controls and negative controls?  What do you need to do to get crisp.  Totally engage.  

How it fits into other experiments?  

“Engage with the science as if it were your baby.”

Execute beautifully… Ask --- et. al.  What does ideal execution look like 

Extra time: allocate time.  Technicians : Freedom to do other things, work with other things, other technical things, giving people independent project to carry out.    Project --- has in mind?  Design.   Hands on education of how science works then reading.   Spend time focused on a problem and in the ideal become the world’s foremost expert on whatever ‘mundane’ aspect of what ever problem you are working on.

Computational in the context of a problem.  Learn to use.   Defining “problems I want to solve.”   As an operating scientist, the technology can change very quickly.   Capable of learning, understanding, and applying.  

Answer questions in a robust way.  Thinking of technology in context of problem.   Deep domain knowledge; focus on experimental more than book reading.   

Realistic path -> Research fellow to PhD. program.  Industry…  Strong head’s up to do research.   First-rate OHSU?  Excellent.    IF: Remember that it is narrow, broader with neuroscience as a component.   Biology < > Neurology.   Real neuroscience computational ->

Juxtasuposed: Engineering, CS, A.I., and all that…

Label in broad ways: Molecular, cellular, systems, cognitive, psychology.   Borders are so fuzzy — as to be 

Domain bias.   In general -> other than P.I. protected from funding.  Publication, the life of the business.   Metric of success is the science they publish.    Work that contributes to being an author = more engaged, more independent.   Evolved to an independent project.    

So incredibly broad -> CRISPR, GFP, Optogenetics, with higher level systems problems.   100 years = absurd.   Look back -> Could we have conceived whats going on today.  

Foremost expert on something how-ever limited.  Grow from there.   Grown from a particular expertise.    

Molecular biologist || Do what a 3 year old is taught to do.  How?  How?  How?  How does that work.  Quantum physics.   Ask questions.  Be open.   

Go to seminars  -> Go to every talk.  Take every note.  Primary literature fundamentally different.   Always learn in context.  Don’t dilute too much (ignore title, abstract, discussion).  Look at figures and tables and derive for yourself what they say.   Look for THE FIGURE or THE TABLE that is the crux and look for the control experiment.    Understand the critical assessment, are the facts valid and warranted?  Infinite amount to learn, don’t spread yourself infinitely thin.  “ 

 To Do: Develop Independent Machine Learning Project 

Gain Access to Web of Science 

————

Paton Learning Lab

Personal Learning Goals 

September 1st - December 1st 

Major Goals 

[  ] Read Principles of Neuroscience 5th Edition

[  ] Complete CSS 229 

[  ] Deep read 12 papers (Write summary || Practice peer review)

Administrative

[  ] Reactivate 

[ / ] Figure out Residence Permit/Visa

Lifestyle

[ x ] Purchase commuter bicycle

[ / ] Purchase waterproof computer/messenger bag

Language

[x] …. Focused practice minimum 20 minutes daily …? 

[  ]   Find language partner 

[  ] Portuguese film/television/music 

UPCOMING

Phone conversation with --------

Tuesday, August 7th 9:00 a.m. EST (10:00 a.m. 

[jccamus]

AI for developers

AI for developers https://ift.tt/2vwOYvC

Cedalian on the shoulders of the giant Orion (source: Wikimedia)

Enterprises today are working hard to embrace artificial intelligence (AI) to compete. The greatest challenge they face is that AI represents a true paradigm shift for how we solve problems using software and data. Not only must your organization acquire or build new skills, but it must also unlearn patterns that previously made it successful. This unlearning/learning challenge exists at all levels — for an experienced professional, for an organization, for a company. And if it’s difficult for an individual to adapt, it’s a million times harder for a company to adapt.

Based on my success driving a large-scale development tools transformation initiative at IBM in the mid-2010s, last year (2019) I got the opportunity to lead IBM’s AI for developers mission within the Watson group. It turned out that my lack of personal experience with AI was an asset, as I could experience my own learning journey and thus gain deep understanding and empathy for the developers and the companies whom I would be helping to accelerate.

This article shares what I learned in the first year with the goal of helping other developers and leaders of development organizations who might embark on a similar learning journey.

Drinking from a firehose

The absent-minded maestro was racing up New York’s Seventh Avenue to a rehearsal, when a stranger stopped him. “Pardon me,” he said, “can you tell me how to get to Carnegie Hall?”

“Yes,” answered the maestro breathlessly. “Practice!” — E.E. Kenyon

The most important skill of a knowledge worker is your ability to learn. To succeed and thrive, you must be intentional with your approach to learning, with a focus on only consuming quality information and in an efficient manner [1]. In my 20 years in the tech industry, I have developed my own approach to effective learning, which I describe briefly here.

I alternate between studying concepts and applying these concepts in realistic but tractable practice settings, grounded in Ericsson’s theory of deliberate practice [2]. In the case of learning about a technology like AI, I work to understand how others have applied it (the problem space) and to understand the cases in which the technology is superior to alternatives, which in this case is traditional programming (the solution space). This is important when embracing a new technology because it helps ward off the golden hammer problem (if all you have is a hammer, everything looks like a nail) as well as understanding the art of the possible and thus avoiding magical thinking. Finally, beyond self-study and practice, my learning relies heavily on discussions with experts, which frankly is the greatest luxury and privilege of being an IBM Distinguished Engineer.

My own learning journey for AI turned out to be the most intense and difficult learning I’ve done since getting my computer science degree at Penn State. AI was just so different from traditional programming! It was so difficult to rewire my mind to think about software systems that improve from experience [3] vs. software systems that merely do the things you’ve told them to do. And the math and statistics—I thought I left those things behind in college with my dumb haircut!

When you are mired in a swamp of complexity, you need tractable conceptual frameworks that help you gain a footing. For my own AI learning journey, I found this in The AI Ladder.

The AI Ladder

All models are wrong, but some are useful. — George Box

Early in my career I thought it was a bit uncool to work for a very old tech company. But as the years went by and as I saw both startups and established firms go out of business or get acquired and assimilated into obscurity, I came to gain deep pride in IBM’s adaptive capacity. If you think about it, the ultimate superpower of both humans and of human organizations is our ability to learn and adapt to changing circumstances, and a company cannot survive in the tech industry if it cannot reinvent itself every 10 to 20 years. IBM has reinvented itself many times over—it’s a core competency.

Through this lens, one may understand that another core competency of IBM is to help other companies adapt, which is very hard to do at scale. IBM does this holistically, with consulting, services, and technology. In the case of AI, IBM had developed a conceptual model to help enterprises reason about AI-based transformation called the AI Ladder [4].

The AI Ladder has four conceptual rungs: collect, organize, analyze, and infuse. The fuel of AI is data, but all enterprises have a massive data sprawl problem because of years of siloed IT work, the projects vs. products mentality, and acquisitions. In any given enterprise, you might have twenty databases and three data warehouses with redundant and different data about customers and customer relationships, and then you have the same problem for several hundred other data types (orders, employees, product information, etc.). IBM promoted the AI Ladder to conceptually climb out of this morass and we organized around it, with new learning offerings, new professional services, and an updated data and AI software portfolio, including significant updates and changes to mainstays like databases and analytics/reporting as well as brand new products in our AI portfolio.

IBM’s AI Ladder: Collect, Organize, Analyze, Infuse (source: IBM)

The most interesting rung for me was “infuse” which deals with how a company fundamentally improves its user experiences, its capabilities, and its business processes by integrating trained machine learning (ML) models into production systems, and designing feedback loops such that the models continue to improve from the experience of being used.

As an example, imagine that Blockbuster Video in the 1990s had a data science department (it probably did). Their head of retail could ask this data science department to analyze sales trends to inform the mix of movies displayed on shelves, by region, with updates to the basic model on a quarterly basis. This is certainly applied data science and may even make use of machine learning, but it is not infused. Now consider the Netflix recommendation system. To the user, it’s a similar grid of potential movies to watch, but behind the scenes there are sophisticated machine learning models personalizing not just the selection of movies but even the screen art, all with the performance goal of keeping you happily engaged inside the Netflix app. That’s infused AI, even though users don’t realize it.

Where does this leave developers? The skill set for machine learning and software engineering is mostly non-overlapping, except for very fundamental things like “complex problem solving” and “programming.” The AI Ladder framework initially made me think that developers needed to wait for someone to collect, organize, and analyze their data, ultimately resulting in machine learning models that the developer could then integrate (infuse) into applications and business processes. While this is often the case for specialized models (like the Netflix recommendation system), it turns out that there is a class of problems where developers can skip right to infuse.

When my manager Beth Smith first told me this, I found it confusing—the ladder must be climbed! But as I continued my learning journey, I realized (unsurprisingly) Beth was completely right, and it’s grounded in some of the most fundamental principles of software engineering and architecture, which I understand very well.

Fundamentals

The entire history of software engineering is that of the rise in levels of abstraction. — Grady Booch

In 1972, Canadian software engineering pioneer David Parnas wrote his paradigm-establishing paper On the criteria to be used in decomposing systems into modules that popularized now-fundamental software engineering concepts like modularity, encapsulation, and information hiding. You can trace a straight line from the perceived benefits of microservices architectures back to Dr. Parnas’s groundbreaking paper.

Sometime between reading this paper many years ago and my 2019 study of AI, I had tacitly come to think of APIs as simply a mechanism to make your service more useful to other services and to aid in rapid composition of services into applications. I’d somehow forgotten that APIs are also a useful mechanism for making some difficult software implementation accessible to a broad audience [5]. As a simple example, think about this Google search query:

google.com/search?q=Watson

This simple URL that you can paste into any web browser encapsulates tremendously complex computing, Internet, web, information retrieval, and machine learning technology for which our industry have collectively invested literally hundreds of billions of dollars to make possible.

APIs are a special case of Parnas’s concept of information hiding in that they make three related assumptions [6]:

The API creator does not directly collaborate with the API consumers

There are many, typically heterogeneous, consumers

The interface must be designed for durability, as breaking changes are horrendously expensive for the community to absorb in aggregate

This fundamental of software architecture also helps explain Beth’s statement and informs how we may — in some scenarios — jump to the top of the AI Ladder.

On the shoulders of giants

If I have seen further, it is by standing on the shoulders of giants. — Isaac Newton

Academia and industry have been working towards today’s machine learning technology for many, many years and we as a civilization crossed some sort of threshold in the past five to ten years such that these technologies are now accessible to any hacker, startup, government, or enterprise.

While it is necessary for you to climb the AI Ladder for custom models — that is those models where you collect the data, organize it, select the ML algorithm(s), and train the models [7]— it is also possible to encapsulate and thus outsource the lower rungs to external experts, in two scenarios:

Developer APIs

AI applications (not covered here)

Several years before I joined Watson, previous leaders had reasoned—correctly—that we could use the combination of APIs, pre-built ML models, and (optional) tooling to encapsulate the collect, organize, and analyze rungs of the AI ladder for several common ML domains including natural language understanding, conversations with virtual agents, visual recognition, speech, and enterprise search, to name a few.

Let’s use Watson’s Natural Language Understanding (NLU) as an example. Human language is incredibly rich and complex and, as a practical matter, impossible to understand using traditional programming. However, machine learning (especially the deep learning variety) is now very good at understanding many aspects of language including concepts, relationships between concepts, and emotional content, to name a few. We can explain this via analogy: a human child learns language through sensory input (hearing others speak), practice (trying words and phrases), error correction (a parent correcting wrong usage or pronunciation), and repetition (kids like to talk!). On the other hand it would be ludicrous to teach a child to speak via vocabulary and grammar textbooks. We teach our NLU service to understand language in a conceptually similar way, though with (obviously) quite different mechanics. Finally, we make all of this capability and all of the many hundreds of person years of research and development on machine learning-based natural language processing available to developers via an elegant API and supporting set of SDKs. You can see this API in action through this cool demo.

Thus developers can today begin leveraging certain types of AI in their applications, even if they lack any formal training in data science or machine learning. It doesn’t entirely eliminate the AI learning curve—you still need to get your head around things like probabilistic systems, how to integrate error detection and correction to improve the underlying models, and simply how to make use of data types like language and images which heretofore were inaccessible to you—but this is a far gentler learning curve than starting from first principles (let’s talk about linear algebra!). Also, from an organizational perspective, it means that leaders can execute a bimodal adoption strategy: build up an internal data science / ML capability and start climbing the AI ladder for business- or industry-specific data [8], while simultaneously getting your current application developers started today, via the APIs and SDKs, especially now that Watson is available anywhere, on any cloud and on premises.

So with good AI APIs we can raise the level of abstraction such that developers who lack a machine learning background can start leveraging AI today. They are so simple to use that it’s easy to overlook the power and the science behind them. When you write a line of code that calls an AI API, you’ve skipped right to the top of the AI Ladder. It almost feels like cheating! But you haven’t cheated; you’re standing on the shoulders of the giants of the field [9] whose research, insights, persistence, and genius brought us to this point.

So then, what will you do?

Footnotes

[1] Deep learning pioneer and Turing Award winner Yann LeCun recently shared an interesting hypothesis comparing human learning to machine learning:

It is more efficient for evolution to specify the behavior of an intelligent organism by encoding an objective to be optimized by learning than by directly encoding a behavior. The price is learning time.

The reason for this is also why it’s more efficient for human engineers to build AI systems through machine learning than through direct programming. The price is training data.

[2] For a great primer on deliberate practice, read Morten Hansen’s book Great at Work, chapter 4, “Don’t Just Learn, Loop.”

[3] Carnegie Mellon professor Tom Mitchell provided the following popular definition of machine learning:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.

[4] For a fuller exploration of the AI Ladder, read this concise O’Reilly Radar report by Rob Thomas, GM of IBM Data and AI.

[5] Subsequent to originally publishing this article, I had a thought-provoking conversation on LinkedIn regarding accessibility with Frances West, former IBM Chief Accessibility Officer and author of Authentic Inclusion. Prior to meeting Frances in the mid-2010s, I—like many people—had the misconception that accessibility was only about making technology usable by people with physical disabilities. While that’s certainly a critical aspect of it, Frances taught me a broader view, which she reinforced in our conversation:

Accessibility to me has never been just about disability. It’s about extreme personalization and recognizing the human first in an increasingly tech driven, tech dominate world. As technologists, it’s our responsibility to respect human differences and make technology work for all, especially foundational technologies such as AI.

Through this lens, it made me realize that APIs are fundamentally about accessibility, which was a revelation (thank you Frances! 🙌🏻).

[6] The purpose and nature APIs remind me of something I once read about writing books, perhaps by Stephen King (?), in that they connect author and reader across time and space, and the author must attempt to imagine how the reader will interpret the prose, while accepting and embracing that the reader may interpret the prose in ways never imagined by the author. Similarly API designers must try to imagine all the ways their API might be used while accepting and embracing the fact that developers will use it in ways never imagined by the API designer.

[7] IBM’s Watson Studio supports building custom ML models from the ground up, for any sort of data.

[8] If you’re a developer who wants to go all-in and try your hand at applied machine learning, I recommend Andrew Ng’s famous Coursera course and the excellent book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Ed. by Aurélien Géron. On the latter, the great Tim O’Reilly gives it his highest endorsement.

[9] Turing, McCarthy, Rosenblatt, Minsky, Jordan, Thrun, Ferrucci, Hinton, Bengio, LeCun, and Li, to name a few. For deeper historical narratives on the development of AI and ML, I recommend John Markoff’s Machines of Loving Grace and Sean Garrish’s How Smart Machines Think. Another excellent resource is Architects of Intelligence, where futurist Martin Ford interviews twenty-four of the leading AI pioneers of the past several decades both for historical perspective as well as speculation about where we might be going.

Acknowledgments

Many thanks to the following dear IBM and industry colleagues for reading and providing feedback on earlier versions of this article: Allie Miller, Barry O’Reilly, Chunhui Higgins, Dallas Hudgens, Erik Didriksen, Grady Booch, Katelyn Rothney, Lindsay Wershaw, Rachael Morin, Rick Gebhardt, and Robyn Johnson.

A special thanks to dear friend and long-time mentor Kyle Brown who, after reading the first draft, explained to me what I was actually trying to say. ☺️

A special thanks to Watson API architect Jeff Stylos, who patiently explained the ideas described in the “On the shoulders of giants” section to me, many times, until I finally got it. Thanks for your patience Jeff and your dedication to your craft. A similar special thank you to Watson NLU senior manager Olivia Buzek for helping me (slowly) get my head around deeper machine learning concepts. 🙇🏻‍♂️

My deepest thanks to Beth Smith, Rob Thomas, Daniel Hernandez, and Arvind Krishna for believing in me as a leader, helping me to understand our strategy, and pushing me and trusting me to contribute to it.

Finally to my IBM colleagues: We got this.

https://ift.tt/2SNg6Q1 via Medium February 21, 2020 at 07:39PM

[ageloire]

The Best Programming Languages to Learn, According to HubSpot Software Engineers

When Liam Harwood, a Software Engineer at HubSpot, was in elementary school, he loved playing video games. But unlike most kids his age, he also created them. As a 10 year-old, Liam would spend hours hunkered over an old computer in his basement, coding simple games in QBASIC or Quick Beginner's All Purpose Symbolic Instruction Code.

Liam quickly mastered QBASIC and created the most advanced games he could in the rudimentary language. But coding basic games didn’t scratch his creative itch anymore. He had visions for more sophisticated games and projects he wanted to develop, so, at the age of 13, he decided to teach himself as many programming languages as possible, like HTML, Javascript, Java, and Python, among others. By the time Liam graduated high school, his insatiable curiosity and unwavering passion helped him earn a full computer science scholarship to attend Marist College.

Today, Liam is a software engineer on HubSpot’s Design Platform team, where he works on the backend of our Design Tools, Template Marketplace, and other design-related services.

Now -- who says video games just rot your brain?

If you aspire to become a successful software engineer like Liam, but you haven’t learned any programming languages yet, don’t feel pressured to follow his exact path and learn every language under the sun. In fact, if you’re a beginner, Liam recommends only learning three.

“For anyone who wants to dive head first into the technical side of things and really understand how their programs work, I would start with C. It keeps things simple and straightforward, and is the closest a beginner would want to get to writing pure machine code. It also forms the basis for most modern programming language syntax, giving you the skills that can easily be applied to other languages,” he says. “On the other hand, Python may be a good choice for beginners because it has a very simple and digestible syntax, as well as extensive libraries that can help simplify complicated computations. Lastly, Java is another good choice because it’s one of the most widely used languages -- I use it here at HubSpot -- and similar to C, it teaches you fundamental skills that can be applied to other languages.”

Like Liam, a lot of software engineers agree that C, Python, and Java are the best languages to learn as a beginner -- they can help you grasp the fundamental coding skills needed to pick up other, more robust languages. But some software engineers argue that honing your programing skills requires an even more foundational understanding of computer science. According to Josie Barth, a Software Engineer on HubSpot’s Big Data Infrastructure team, learning the principles of computer science -- not specific languages -- is what will sharpen your programing skills the most.

“While it’s common to get started learning on an object oriented language, like C++ or Java, I think it’s less about learning a specific programming language and more about learning the foundations of computer science, like data structures, algorithms, discrete math, operating systems, computation theory, and more,” she says. “I also wouldn’t necessarily bucket programming languages as beginner, intermediate, or advanced. You choose programming languages based on the kind of tasks you want to do. But there are definitely easier languages to get started on. For example, Java might be easier to get started on than C++ because Java abstracts certain concepts away, but if you needed to program within finer control of the machinery in mind, you’d likely use C++ over Java.”

So while learning specific languages will help refine your programming skills, mastering the fundamentals of computer science will help you reach your potential more than anything else. Because before Liam Hardwood was coding the backend of HubSpot’s Design Platform, he was programming simple video games on a dusty computer in his basement, building his foundational skills with an old language that was considered outdated, even back then.

However, this still begs the question …

Which programming language should you learn first?

If you’re a beginner, most software engineers recommend learning C, Python, or Java first. Below, we’ll give you a rundown of each of these programming languages, describing what they’re used for and how much demand they have in the software engineering labor market.

5 of the Best Programming Languages to Learn

Java

Python

JavaScript

C/C++

C#

1. Java

The Java Virtual Machine lets Java run on virtually any hardware and operating system, making it the most widely used programming language by businesses in the world. And with over 90% of Fortune 500 companies using Java to develop the backend of their applications and 62,000 Java programming jobs posted on Indeed.com last year -- the most out of any language -- Java will be in high demand for a long time.

2. Python

Python has a very simple and digestible syntax, as well as extensive libraries that can help simplify complicated computations, allowing you to hit the ground running when you want to learn other languages.

As a general programming language, Python is mostly used for web development and support for software engineers, but it’s also used a lot in machine learning, which helped it become the second most sought after programming language on Indeed.com, with 46,000 jobs advertised on the online job board.

3. JavaScript

Used to develop the front end of most business’ applications, JavaScript is one of the most popular programming languages out there. With over 80% of developers and 95% of all websites using JavaScript, it’s easy to understand why there were over 38,000 JavaScript jobs posted on Indeed.com last year.

4. C/C++

C is the most simple and straightforward programming language around, and it also forms the foundation for most modern programming language syntax, giving you fundamental skills that can easily be applied to other languages.

A successor of C, C++ is an object oriented language, which aims to represent real-life objects as code and is more equipped for developing sophisticated applications, like system or application software, video games, drivers, client-server applications, and embedded firmware. Last year, over 31,000 C++ programming jobs were posted on Indeed.com.

5. C#

Another successor of the C programming language, C# is an object oriented language that’s intuitive to learn, making it the fifth most popular programming language for building software. Microsoft developed C# to run on their .NET platform, and it’s heavily used in video game development. Last year, over 28,000 C# programming jobs were posted on Indeed.com.

from Marketing https://blog.hubspot.com/marketing/best-programming-language-learn

[evnoweb]

Looking for a Class Robot? Try Robo Wunderkind

There are a lot of options if you want to bring programmable robots to your classroom. One I discovered this summer and have fallen in love with is Sunburst’s Robo Wunderkind. It is a build-a-robot kit designed to introduce children ages six and up to coding and robotics as well as the fun of problem-solving and creative thinking. The robot starts in about thirty pieces (there are so many, I didn’t really count them). You don’t use all of them in one robot, just pick those that will make your robot do what you want. The completed robot can move around on wheels, make sounds, light up like a flashlight, sense distance and movement, twist and turn, follow a maze, or whatever else your imagination can conjure up.

But don’t be confused. The goal of this kit is as much about building the robot as having fun exploring, experimenting, and tinkering.

What is Robo Wunderkind

Robo Wunderkind is an award-winning robotics kit that lets young children build an interactive robot and then program it to do what they want. It can be used at home, in school, or as an extracurricular tool for teaching STEAM disciplines (science, technology, engineering, art, and math). The box includes a bunch of color-coded parts, a few instructions, and a whole lot of excitement. The builder’s job is to connect the pieces into the robot of their dreams, program it to do what they need, and then start over.

Fair warning: This robot doesn’t look like the famous humanoid robots of literature–C3PO or Marvin the Paranoid Android (from The Hitchhiker’s Guide to the Galaxy), with arms, legs, and a head. It’s more like something you might construct from Lego Mindstorm though easier to set up, build, program, operate, and decode. I’ve used both and hands down would start my younger students with Robo Wunderkind. I agree with Tech Crunch when they say:

“You won’t build a robot as sophisticated as a robot built using Lego Mindstorms. But Robo Wunderkind seems more accessible and a good way to try robotics before switching to Arduino and Raspberry Pi when your kid grows up.

How to get started

If I were to rate myself with robotics, I might be closer to a 5 than a 10. I approach the task of building my own with a small degree of trepidation. I tell you this because, if I can build a robot with this system, any six-year-old (and up) can.

To get started, I needed a mobile device (like an iPhone, Android phone, or an iPad–the latter is recommended), a Bluetooth connection, and a risk-takers mentality. That’s it! No plugs, electricity, logins, registrations, software, or magic codes. The kit I received from Sunburst included all the basic pieces like wheels, sensors, motors, a cable, connectors, and lights.

I started with what’s called the Main Block–a big orange rectangular shape with a battery, CPU, accelerometer, and a speaker. Everything else will be attached to it. Since it needed to be charged, I plugged it in and downloaded the two apps while I waited:

Robo Live

Robo Code

Once the Main Block was fully charged, I activated Robo Live, planning to complete one of its starter projects. The first step was for the app to recognize my Robo, which it didn’t. Turns out, I needed a quick firmware update, delivered via WiFi. That done, I started building the Driver project detailed in the Robo Live Workshop. It couldn’t have been easier. It listed all of the required parts and how to connect them. When I did this properly, the app beeped, like a congratulations. When the project was completed, I could swivel the 3D image and compare it to what I had built.

Spot on.

The process was quick, intuitive, and easy to understand. The connections between the parts are snug–no danger that they will disconnect.

Robot built, I moved on to the first app, Robo Code, where I program my robot to do something clever. Robo Code simplifies this activity by placing all of the coding tools at the bottom of the screen. All I had to do was drag-and-drop, connect them the way I’d like, customize where that was available like changing colors or making a light brighter or dimmer, and then test it with the Go button. When I got stuck (once–really, only once), there was a help button that explained what each icon means and what the underlying choices provide.

After running through a few more sample programs, the concepts snapped into place. From then on, I could build the robot quickly and program it to do a wide variety of simple actions.

Sunburst’s Robo Wunderkind Education Robotics Kit is robust with plenty of projects and robot parts to entertain students. The Advanced Upgrade Kit includes six more parts similar to what is found in the Education Kit–like a light sensor, motion sensor, LED display,  and RGB LED. This is perfect for longer robotics programs and/or older students.

Suggestion: I started on my iPhone but quickly switched to my iPad. The code symbols are a bit small for a smartphone screen and become hidden under the iPhone’s lower coping. 

The apps

Two apps are recommended to get started–Robo Code and Robo Live. These can be located quickly in the App Store or Google Play by scanning the QR code included in the instructions:

Go ahead–scan the image above on your smartphone or tablet to get one of the apps. I’ll wait. Done? OK. With these two apps, students can build predesigned projects as well as customized projects that they invent themselves.

Robo Code

Robot Code allows students to code everything from simple to complicated as they bring their robot to life. Its visual drag-and-drop interface, similar to other coding apps students have probably used (like Scratch or Lightbot), makes coding Robo Wunderkind quickly accessible. With this app, students can build a flashlight, a distance meter, a distance alarm, an obstacle avoider, and a driver.

Robo Live

Robo Live lets students control the robot they’ve already built in real time using easy drag and drop functions located on the app’s dashboard.

Robo Wunderkind Curriculum

The Robo Wunderkind Curriculum is fifty+ hours of activities that teach and reinforce core robotics skills. Lessons are each about five hours and cover topics like road safety, math, art, and nature studies. There’s also a separate set of activities for afterschool programs, summer camps, and workshops.  The curriculum includes a comprehensive teachers’ guide that trains educators in the Robo Wunderkind robots, the apps, the projects, and the activities. Each lesson is categorized according to its focus and includes the difficulty level, goals, vocabulary, materials required, activity stages, big ideas, age level, steps, and expected learning outcomes. There’s also a helpful Student Journal available so students can take notes, review, quiz themselves, and track their progress.

The Robo Wunderkind Curriculum is aligned with Common Core Math, Reading, Writing, and Speaking and Listening Standards; ISTE; CSTA Computing systems and Algorithms & Programming Standards; and NGSS Standards.

What I really like about Robo Wunderkind

It’s Lego compatible. With Lego adapters (most sold separately), kids can build a hybrid robot of Robo Wunderkind modules and Lego bricks.

It’s not one piece. You build your own robot so each student’s is different.

Module parts are color coded according to their actions so you won’t confuse connectors with sensors.

App instructions are very clear. They show exactly what to put where and the app pings at you when it’s done correctly. The ability to rotate it in 3D–I can’t overstate how useful that is.

The robots aren’t just for play. For example, I made a flashlight–a torch–with a green light, and it works magnificently.

Just to spotlight how intuitive Robo Wunderkind is, some of the projects took me less than five minutes to complete.

It comes in German, Swedish, and English–excellent.

Who will love this robot

kids who love Legos

kids who think outside the box

kids who love fiddling with mobile devices

kids who like remote controlled toys but always want them to do something they aren’t designed to do

teachers looking for clever STEAM and STEM projects

***

If you like Legos but wish your creations moved, talked, and could run through a maze with you, you will love Robo Wunderkind.

Want a little more? Here’s a clever video:

Jacqui Murray has been teaching K-18 technology for 30 years. She is the editor/author of over a hundred tech ed resources including a K-12 technology curriculum, K-8 keyboard curriculum, K-8 Digital Citizenship curriculum. She is an adjunct professor in tech ed, Master Teacher, webmaster for four blogs, an Amazon Vine Voice, CSTA presentation reviewer, freelance journalist on tech ed topics, contributor to NEA Today, and author of the tech thrillers, To Hunt a Sub and Twenty-four Days. You can find her resources at Structured Learning.

Looking for a Class Robot? Try Robo Wunderkind published first on https://medium.com/@DigitalDLCourse

[corpasa]

Looking for a Class Robot? Try Robo Wunderkind

There are a lot of options if you want to bring programmable robots to your classroom. One I discovered this summer and have fallen in love with is Sunburst’s Robo Wunderkind. It is a build-a-robot kit designed to introduce children ages six and up to coding and robotics as well as the fun of problem-solving and creative thinking. The robot starts in about thirty pieces (there are so many, I didn’t really count them). You don’t use all of them in one robot, just pick those that will make your robot do what you want. The completed robot can move around on wheels, make sounds, light up like a flashlight, sense distance and movement, twist and turn, follow a maze, or whatever else your imagination can conjure up.

But don’t be confused. The goal of this kit is as much about building the robot as having fun exploring, experimenting, and tinkering.

What is Robo Wunderkind

Robo Wunderkind is an award-winning robotics kit that lets young children build an interactive robot and then program it to do what they want. It can be used at home, in school, or as an extracurricular tool for teaching STEAM disciplines (science, technology, engineering, art, and math). The box includes a bunch of color-coded parts, a few instructions, and a whole lot of excitement. The builder’s job is to connect the pieces into the robot of their dreams, program it to do what they need, and then start over.

Fair warning: This robot doesn’t look like the famous humanoid robots of literature–C3PO or Marvin the Paranoid Android (from The Hitchhiker’s Guide to the Galaxy), with arms, legs, and a head. It’s more like something you might construct from Lego Mindstorm though easier to set up, build, program, operate, and decode. I’ve used both and hands down would start my younger students with Robo Wunderkind. I agree with Tech Crunch when they say:

“You won’t build a robot as sophisticated as a robot built using Lego Mindstorms. But Robo Wunderkind seems more accessible and a good way to try robotics before switching to Arduino and Raspberry Pi when your kid grows up.

How to get started

If I were to rate myself with robotics, I might be closer to a 5 than a 10. I approach the task of building my own with a small degree of trepidation. I tell you this because, if I can build a robot with this system, any six-year-old (and up) can.

To get started, I needed a mobile device (like an iPhone, Android phone, or an iPad–the latter is recommended), a Bluetooth connection, and a risk-takers mentality. That’s it! No plugs, electricity, logins, registrations, software, or magic codes. The kit I received from Sunburst included all the basic pieces like wheels, sensors, motors, a cable, connectors, and lights.

I started with what’s called the Main Block–a big orange rectangular shape with a battery, CPU, accelerometer, and a speaker. Everything else will be attached to it. Since it needed to be charged, I plugged it in and downloaded the two apps while I waited:

Robo Live

Robo Code

Once the Main Block was fully charged, I activated Robo Live, planning to complete one of its starter projects. The first step was for the app to recognize my Robo, which it didn’t. Turns out, I needed a quick firmware update, delivered via WiFi. That done, I started building the Driver project detailed in the Robo Live Workshop. It couldn’t have been easier. It listed all of the required parts and how to connect them. When I did this properly, the app beeped, like a congratulations. When the project was completed, I could swivel the 3D image and compare it to what I had built.

Spot on.

The process was quick, intuitive, and easy to understand. The connections between the parts are snug–no danger that they will disconnect.

Robot built, I moved on to the first app, Robo Code, where I program my robot to do something clever. Robo Code simplifies this activity by placing all of the coding tools at the bottom of the screen. All I had to do was drag-and-drop, connect them the way I’d like, customize where that was available like changing colors or making a light brighter or dimmer, and then test it with the Go button. When I got stuck (once–really, only once), there was a help button that explained what each icon means and what the underlying choices provide.

After running through a few more sample programs, the concepts snapped into place. From then on, I could build the robot quickly and program it to do a wide variety of simple actions.

Sunburst’s Robo Wunderkind Education Robotics Kit is robust with plenty of projects and robot parts to entertain students. The Advanced Upgrade Kit includes six more parts similar to what is found in the Education Kit–like a light sensor, motion sensor, LED display,  and RGB LED. This is perfect for longer robotics programs and/or older students.

Suggestion: I started on my iPhone but quickly switched to my iPad. The code symbols are a bit small for a smartphone screen and become hidden under the iPhone’s lower coping. 

The apps

Two apps are recommended to get started–Robo Code and Robo Live. These can be located quickly in the App Store or Google Play by scanning the QR code included in the instructions:

Go ahead–scan the image above on your smartphone or tablet to get one of the apps. I’ll wait. Done? OK. With these two apps, students can build predesigned projects as well as customized projects that they invent themselves.

Robo Code

Robot Code allows students to code everything from simple to complicated as they bring their robot to life. Its visual drag-and-drop interface, similar to other coding apps students have probably used (like Scratch or Lightbot), makes coding Robo Wunderkind quickly accessible. With this app, students can build a flashlight, a distance meter, a distance alarm, an obstacle avoider, and a driver.

Robo Live

Robo Live lets students control the robot they’ve already built in real time using easy drag and drop functions located on the app’s dashboard.

Robo Wunderkind Curriculum

The Robo Wunderkind Curriculum is fifty+ hours of activities that teach and reinforce core robotics skills. Lessons are each about five hours and cover topics like road safety, math, art, and nature studies. There’s also a separate set of activities for afterschool programs, summer camps, and workshops.  The curriculum includes a comprehensive teachers’ guide that trains educators in the Robo Wunderkind robots, the apps, the projects, and the activities. Each lesson is categorized according to its focus and includes the difficulty level, goals, vocabulary, materials required, activity stages, big ideas, age level, steps, and expected learning outcomes. There’s also a helpful Student Journal available so students can take notes, review, quiz themselves, and track their progress.

The Robo Wunderkind Curriculum is aligned with Common Core Math, Reading, Writing, and Speaking and Listening Standards; ISTE; CSTA Computing systems and Algorithms & Programming Standards; and NGSS Standards.

What I really like about Robo Wunderkind

It’s Lego compatible. With Lego adapters (most sold separately), kids can build a hybrid robot of Robo Wunderkind modules and Lego bricks.

It’s not one piece. You build your own robot so each student’s is different.

Module parts are color coded according to their actions so you won’t confuse connectors with sensors.

App instructions are very clear. They show exactly what to put where and the app pings at you when it’s done correctly. The ability to rotate it in 3D–I can’t overstate how useful that is.

The robots aren’t just for play. For example, I made a flashlight–a torch–with a green light, and it works magnificently.

Just to spotlight how intuitive Robo Wunderkind is, some of the projects took me less than five minutes to complete.

It comes in German, Swedish, and English–excellent.

Who will love this robot

kids who love Legos

kids who think outside the box

kids who love fiddling with mobile devices

kids who like remote controlled toys but always want them to do something they aren’t designed to do

teachers looking for clever STEAM and STEM projects

***

If you like Legos but wish your creations moved, talked, and could run through a maze with you, you will love Robo Wunderkind.

Want a little more? Here’s a clever video:

Jacqui Murray has been teaching K-18 technology for 30 years. She is the editor/author of over a hundred tech ed resources including a K-12 technology curriculum, K-8 keyboard curriculum, K-8 Digital Citizenship curriculum. She is an adjunct professor in tech ed, Master Teacher, webmaster for four blogs, an Amazon Vine Voice, CSTA presentation reviewer, freelance journalist on tech ed topics, contributor to NEA Today, and author of the tech thrillers, To Hunt a Sub and Twenty-four Days. You can find her resources at Structured Learning.

Looking for a Class Robot? Try Robo Wunderkind published first on https://medium.com/@DLBusinessNow

[lindyhunt]

The Best Programming Languages to Learn, According to HubSpot Software Engineers

When Liam Harwood, a Software Engineer at HubSpot, was in elementary school, he loved playing video games. But unlike most kids his age, he also created them. As a 10 year-old, Liam would spend hours hunkered over an old computer in his basement, coding simple games in QBASIC or Quick Beginner's All Purpose Symbolic Instruction Code.

Liam quickly mastered QBASIC and created the most advanced games he could in the rudimentary language. But coding basic games didn’t scratch his creative itch anymore. He had visions for more sophisticated games and projects he wanted to develop, so, at the age of 13, he decided to teach himself as many programming languages as possible, like HTML, Javascript, Java, and Python, among others. By the time Liam graduated high school, his insatiable curiosity and unwavering passion helped him earn a full computer science scholarship to attend Marist College.

Today, Liam is a software engineer on HubSpot’s Design Platform team, where he works on the backend of our Design Tools, Template Marketplace, and other design-related services.

Now -- who says video games just rot your brain?

If you aspire to become a successful software engineer like Liam, but you haven’t learned any programming languages yet, don’t feel pressured to follow his exact path and learn every language under the sun. In fact, if you’re a beginner, Liam recommends only learning three.

“For anyone who wants to dive head first into the technical side of things and really understand how their programs work, I would start with C. It keeps things simple and straightforward, and is the closest a beginner would want to get to writing pure machine code. It also forms the basis for most modern programming language syntax, giving you the skills that can easily be applied to other languages,” he says. “On the other hand, Python may be a good choice for beginners because it has a very simple and digestible syntax, as well as extensive libraries that can help simplify complicated computations. Lastly, Java is another good choice because it’s one of the most widely used languages -- I use it here at HubSpot -- and similar to C, it teaches you fundamental skills that can be applied to other languages.”

Like Liam, a lot of software engineers agree that C, Python, and Java are the best languages to learn as a beginner -- they can help you grasp the fundamental coding skills needed to pick up other, more robust languages. But some software engineers argue that honing your programing skills requires an even more foundational understanding of computer science. According to Josie Barth, a Software Engineer on HubSpot’s Big Data Infrastructure team, learning the principles of computer science -- not specific languages -- is what will sharpen your programing skills the most.

“While it’s common to get started learning on an object oriented language, like C++ or Java, I think it’s less about learning a specific programming language and more about learning the foundations of computer science, like data structures, algorithms, discrete math, operating systems, computation theory, and more,” she says. “I also wouldn’t necessarily bucket programming languages as beginner, intermediate, or advanced. You choose programming languages based on the kind of tasks you want to do. But there are definitely easier languages to get started on. For example, Java might be easier to get started on than C++ because Java abstracts certain concepts away, but if you needed to program within finer control of the machinery in mind, you’d likely use C++ over Java.”

So while learning specific languages will help refine your programming skills, mastering the fundamentals of computer science will help you reach your potential more than anything else. Because before Liam Hardwood was coding the backend of HubSpot’s Design Platform, he was programming simple video games on a dusty computer in his basement, building his foundational skills with an old language that was considered outdated, even back then.

However, this still begs the question …

Which programming language should you learn first?

If you’re a beginner, most software engineers recommend learning C, Python, or Java first. Below, we’ll give you a rundown of each of these programming languages, describing what they’re used for and how much demand they have in the software engineering labor market.

5 of the Best Programming Languages to Learn

Java

Python

JavaScript

C/C++

C#

1. Java

The Java Virtual Machine lets Java run on virtually any hardware and operating system, making it the most widely used programming language by businesses in the world. And with over 90% of Fortune 500 companies using Java to develop the backend of their applications and 62,000 Java programming jobs posted on Indeed.com last year -- the most out of any language -- Java will be in high demand for a long time.

2. Python

Python has a very simple and digestible syntax, as well as extensive libraries that can help simplify complicated computations, allowing you to hit the ground running when you want to learn other languages.

As a general programming language, Python is mostly used for web development and support for software engineers, but it’s also used a lot in machine learning, which helped it become the second most sought after programming language on Indeed.com, with 46,000 jobs advertised on the online job board.

3. JavaScript

Used to develop the front end of most business’ applications, JavaScript is one of the most popular programming languages out there. With over 80% of developers and 95% of all websites using JavaScript, it’s easy to understand why there were over 38,000 JavaScript jobs posted on Indeed.com last year.

4. C/C++

C is the most simple and straightforward programming language around, and it also forms the foundation for most modern programming language syntax, giving you fundamental skills that can easily be applied to other languages.

A successor of C, C++ is an object oriented language, which aims to represent real-life objects as code and is more equipped for developing sophisticated applications, like system or application software, video games, drivers, client-server applications, and embedded firmware. Last year, over 31,000 C++ programming jobs were posted on Indeed.com.

5. C#

Another successor of the C programming language, C# is an object oriented language that’s intuitive to learn, making it the fifth most popular programming language for building software. Microsoft developed C# to run on their .NET platform, and it’s heavily used in video game development. Last year, over 28,000 C# programming jobs were posted on Indeed.com.

[a-breton]

Opportunities for AI in Content Marketing Easily Explained

Until recently, the closest I’ve come to understanding artificial intelligence is knowing that it powered tools in my martech stack (e.g., marketing automation, predictive lead scoring, etc.).

Beyond that, I found the concept hard to grasp until Chris Penn’s presentation at Content Marketing World, How to Use AI to Boost Your Content Marketing Impact.

Chris, co-founder and chief innovator at Trust Insights, covered several real-world applications of AI. His examples helped transform abstract concepts into tangible use cases.

Chris implemented these examples himself via hands-on coding in the R programming language, using a deep understanding of mathematics, data science, and machine learning. But most marketers don’t have data science and computer programming skills. Later in this article, I share Chris’ advice about how marketers can apply these AI concepts.

Here are several of Chris’ experiments.

Driver analysis: What results in profitable action?

When you have a bunch of data but you’re not sure what matters to the outcome you want, driver analysis is an effective tool, Chris says.

Machine learning software excels in this case. You feed in all the data and it tells you what matters in it. Chris explains that the analysis concludes with something like, “Hey, this combination of variables seems to have the strongest mathematical relationship to the objective you want.”

AI in #contentmarketing: Driver analysis to show what factors drive the most leads. @cspenn Click To Tweet

Chris performed driver analysis on the popular PR and marketing blog, Spin Sucks, where the primary business objective is lead generation.

“(It) determined that organic search was the third most powerful driver. The team focused a lot of time and energy on it, and they should, but email was the No. 1 driver,” Chris says.

By understanding better what drives leads, the Spin Sucks team could decide to shift more of their time to email marketing because it was the most effective source.

Whether your objective is page views, social shares, leads, or revenue, a ranked list of drivers can help you plan resources, priorities, and budgets more effectively.

Implementation detail: Chris used the R programing language to implement Markov chain attribution. For a detailed look at one such implementation, read this post by data scientist Sergey Bryl, which will give you a good sense of how much mathematics and data science is involved.

HANDPICKED RELATED CONTENT: Why Marketers Need to Think Like Data Scientists (And How to Do It)

Text mining: Reveal topics, keywords, and hidden problems

Text mining is an application of AI that ingests content (e.g., text) to classify, categorize, and make sense of it.

Chris notes that text mining uses vectorization, which transforms words into numbers. It looks at the mathematical relationship among those numbers and determines how similar those words are. It is a form of deep learning.

Reverse engineer Google to reveal key topics and terms

The Google algorithm, which uses a heavy amount of AI itself, is an example of a deep-learning system. “Google’s search algorithm is so complex now that no one knows how it works, including Google,” Chris said. “They have very little interpretability of their model.”

You can use text mining to reverse engineer the Google algorithm for your targeted topics. “We can deploy our own machine learning models to say, ‘OK, for a search term like content marketing, what words do the top 10 or 20 pages all have in common?’”

AI in #contentmarketing: Find #SEO-friendly #content topics via text mining. @cspenn Click To Tweet

Here’s a sample output from reverse engineering Google:

The resulting lists hint at what words or categories to cover when developing new content around your reverse-engineered keyword. Having this set of common words gives you a higher chance of success with organic search than simply saying, “Let’s write a really good article about content marketing.”

Implementation detail: Chris implemented text mining and topic modeling via the R programming language, extracting related topics from a corpus of text (e.g., the contents of articles found in the search engine results pages).

HANDPICKED RELATED CONTENT: How to Make Your Content Powerful in Eyes of Searchers (and Google)

Extract hidden insights via text mining

In 2014, Darden Restaurants, the parent company of Olive Garden, replaced its board. The new group implemented changes, including enforcing its existing but mostly ignored breadstick policy (serving one per person plus one extra).

As Chris explains, employees then spent their time enforcing the policy by counting the number of breadsticks in the basket based on the number of people at the table.

Chris used text mining on 2,500 publicly available reviews written by the company’s employees on Glassdoor. Here’s a glimpse of the results:

Text mining surfaced breadsticks as a problem. If Olive Garden was looking to repair low employee morale and a poor customer experience, a manual review of its Glassdoor reviews, where the usual restaurant worker complaints like low pay and long hours abound, may have led them down the wrong path.

AI use in #contentmarketing: Use text mining on reviews to reveal hidden problems. @cspenn Click To Tweet

Text mining revealed the breadstick problem. (After intense public pushback, Olive Garden returned to its previous breadstick approach.)

Text mining of unstructured data can be applied in many useful marketing contexts: customer reviews, poor/high performing blog posts, transcripts of customer success phone calls, etc. Extracting that hidden gem of insight can point you to courses of action with a high ROI.

Implementation detail: Similar to the reverse engineering Google example, Chris implemented text mining via the R programming language.

HANDPICKED RELATED CONTENT: Scale Your B2B Content With Artificial Intelligence: Ideas and Tools Marketers Can Try

Time-series forecasting: Analyze competitors’ brand searches

Let’s combine math, statistics, and AI to create a Magic 8-Ball.

“Wouldn’t it be great to know what’s going to happen,” Chris asked. “It would be so much easier to plan, to set budgets, to staff, to have an editorial calendar.”

Chris did an exercise of predictive time-series forecasting for Cleveland hotel search data. He looked at more than 12 months of branded searches — where searchers named specific hotels (e.g., Hilton Cleveland, Holiday Inn Cleveland, Hyatt Cleveland, Marriott Cleveland).

The results predicted when search volumes go up and down for each hotel:

“If you (worked at) the Cleveland Marriott here, you now know that right around the end of September you have more search interests than your competitors. You could be running campaigns against them to take even more market share away from them,” Chris said.

Any brand could benefit from predictive time-series forecasting – analyzing brand searches for your company vs. your competitors. You can search for when your brand underperforms, for example, and use that data to bid on your competitors’ brand names with a relevant content asset or promotional offer.

AI in #contentmarketing: Use time-series forecasting to predict lead-gen and revenue. @cspenn Click To Tweet

“Imagine search topics, conversations, social media. You can forecast more than search volume,” Chris said. “You can forecast lead generation from your marketing automation software. You can forecast revenue from your CRM or your ERP. Anything that is regular data over time you can forecast forward.”

Implementation detail: Chris used R to process five years of Google search data, then implemented a statistical method called autoregressive integrated moving average (ARIMA).

How content marketers can try these AI uses

I know what some of you must be thinking about now:

“Wait, really?”

“The data science and probability are over my head.”

“I’m too busy and can’t possibly learn to do this myself.”

These reactions are understandable. The good news is that you have options. And you don’t need to learn the deep nuts and bolts covered earlier.

Chris offered three recommendations for marketers thinking about approaching AI.

Do it yourself. This approach fits for the small percentage of marketers who have a genuine interest in data science and machine learning. You should be interested in going deep with math, statistics, and probability – and comfortable writing code.

If you decide to go this route, Chris suggests checking out Google’s Machine Learning Crash Course, available free online, which takes you through 40-plus exercises, 25 lessons, real-world case studies, and lectures from Google researchers.

Chris notes that IBM Watson Studio has an intuitive, drag-and-drop user interface. While Watson does enable programmers to write code on its platform, the UI can be useful for marketers who are not inclined to write code.

For those interested in coding, Chris recommends learning the R and Python languages, which form the basis for a lot of AI tools and libraries. Be prepared to spend six to 12 months to learn the programming language and another six to 12 months to learn the data science.

If you’re just getting started with coding, the Dummies franchise has books that may be useful: R for Dummies and Python for Dummies.

Tap your staff data scientist. The second option applies to larger organizations that employ data scientists (e.g., Google, Facebook, and Uber). “Staff with data science skills are quantitatively inclined and know how to use the technology properly, so they can be of great help,” Chris said.

Think back to the use cases I mentioned. For text mining or time-series forecasting, in-house data scientists will understand your objectives and goals, build the right models, then implement the necessary codes.

Outsource. This option works for organizations that don’t have AI and data science talent in-house. The answer is to outsource to the experts: people or agencies with the necessary AI know-how and experience.

Chris puts it this way: “Agencies and consultants can help you use the methodologies. You can do small projects on a one-off basis. If the need is ongoing or more frequent, they can help you build software that runs when you need it to.”

Next steps

No matter which of the three options makes sense for you, there’s one thing I urge all marketers to do: Learn about AI and understand the role it plays in marketing technology.

While you don’t need to understand Markov chain attribution or how to program in R, you need to know enough to determine where and how AI can help your marketing. Basic AI knowledge will also help you better evaluate vendor solutions and claims.

Think about the kind of knowledge you need to buy a computer. You don’t need to be a chip designer, but you need to know the difference between a 32-bit and 64-bit processor and whether a 1.5 GHz processor is better than a 2.7 GHz processor. With AI, when a vendor says, “Our predictive analytics solution uses the latest AI techniques,” you need to know how to question the claim and how to distinguish fluff from reality.

HANDPICKED RELATED CONTENT: Are You Really Smart About How AI Works in Marketing?

Since AI is a topic often covered in business, marketing, and technology publications, I’m soaking up as much as I can. Next, I’ll probably enroll in some free, online courses in machine learning.

What about you? What’s your interest level in AI for marketing and how are you staying informed and educated?

Here’s an excerpt from Chris’ talk:

Further your tech skills in 2019 by attending ContentTECH Summit in April. Register today using code BLOG100 to save $100. 

Cover image by Joseph Kalinowski/Content Marketing Institute

from http://bit.ly/2VHL0sx

[lucyariablog]

Opportunities for AI in Content Marketing Easily Explained

Until recently, the closest I’ve come to understanding artificial intelligence is knowing that it powered tools in my martech stack (e.g., marketing automation, predictive lead scoring, etc.).

Beyond that, I found the concept hard to grasp until Chris Penn’s presentation at Content Marketing World, How to Use AI to Boost Your Content Marketing Impact.

Chris, co-founder and chief innovator at Trust Insights, covered several real-world applications of AI. His examples helped transform abstract concepts into tangible use cases.

Chris implemented these examples himself via hands-on coding in the R programming language, using a deep understanding of mathematics, data science, and machine learning. But most marketers don’t have data science and computer programming skills. Later in this article, I share Chris’ advice about how marketers can apply these AI concepts.

Here are several of Chris’ experiments.

Driver analysis: What results in profitable action?

When you have a bunch of data but you’re not sure what matters to the outcome you want, driver analysis is an effective tool, Chris says.

Machine learning software excels in this case. You feed in all the data and it tells you what matters in it. Chris explains that the analysis concludes with something like, “Hey, this combination of variables seems to have the strongest mathematical relationship to the objective you want.”

AI in #contentmarketing: Driver analysis to show what factors drive the most leads. @cspenn Click To Tweet

Chris performed driver analysis on the popular PR and marketing blog, Spin Sucks, where the primary business objective is lead generation.

“(It) determined that organic search was the third most powerful driver. The team focused a lot of time and energy on it, and they should, but email was the No. 1 driver,” Chris says.

By understanding better what drives leads, the Spin Sucks team could decide to shift more of their time to email marketing because it was the most effective source.

Whether your objective is page views, social shares, leads, or revenue, a ranked list of drivers can help you plan resources, priorities, and budgets more effectively.

Implementation detail: Chris used the R programing language to implement Markov chain attribution. For a detailed look at one such implementation, read this post by data scientist Sergey Bryl, which will give you a good sense of how much mathematics and data science is involved.

HANDPICKED RELATED CONTENT: Why Marketers Need to Think Like Data Scientists (And How to Do It)

Text mining: Reveal topics, keywords, and hidden problems

Text mining is an application of AI that ingests content (e.g., text) to classify, categorize, and make sense of it.

Chris notes that text mining uses vectorization, which transforms words into numbers. It looks at the mathematical relationship among those numbers and determines how similar those words are. It is a form of deep learning.

Reverse engineer Google to reveal key topics and terms

The Google algorithm, which uses a heavy amount of AI itself, is an example of a deep-learning system. “Google’s search algorithm is so complex now that no one knows how it works, including Google,” Chris said. “They have very little interpretability of their model.”

You can use text mining to reverse engineer the Google algorithm for your targeted topics. “We can deploy our own machine learning models to say, ‘OK, for a search term like content marketing, what words do the top 10 or 20 pages all have in common?’”

AI in #contentmarketing: Find #SEO-friendly #content topics via text mining. @cspenn Click To Tweet

Here’s a sample output from reverse engineering Google:

The resulting lists hint at what words or categories to cover when developing new content around your reverse-engineered keyword. Having this set of common words gives you a higher chance of success with organic search than simply saying, “Let’s write a really good article about content marketing.”

Implementation detail: Chris implemented text mining and topic modeling via the R programming language, extracting related topics from a corpus of text (e.g., the contents of articles found in the search engine results pages).

HANDPICKED RELATED CONTENT: How to Make Your Content Powerful in Eyes of Searchers (and Google)

Extract hidden insights via text mining

In 2014, Darden Restaurants, the parent company of Olive Garden, replaced its board. The new group implemented changes, including enforcing its existing but mostly ignored breadstick policy (serving one per person plus one extra).

As Chris explains, employees then spent their time enforcing the policy by counting the number of breadsticks in the basket based on the number of people at the table.

Chris used text mining on 2,500 publicly available reviews written by the company’s employees on Glassdoor. Here’s a glimpse of the results:

Text mining surfaced breadsticks as a problem. If Olive Garden was looking to repair low employee morale and a poor customer experience, a manual review of its Glassdoor reviews, where the usual restaurant worker complaints like low pay and long hours abound, may have led them down the wrong path.

AI use in #contentmarketing: Use text mining on reviews to reveal hidden problems. @cspenn Click To Tweet

Text mining revealed the breadstick problem. (After intense public pushback, Olive Garden returned to its previous breadstick approach.)

Text mining of unstructured data can be applied in many useful marketing contexts: customer reviews, poor/high performing blog posts, transcripts of customer success phone calls, etc. Extracting that hidden gem of insight can point you to courses of action with a high ROI.

Implementation detail: Similar to the reverse engineering Google example, Chris implemented text mining via the R programming language.

HANDPICKED RELATED CONTENT: Scale Your B2B Content With Artificial Intelligence: Ideas and Tools Marketers Can Try

Time-series forecasting: Analyze competitors’ brand searches

Let’s combine math, statistics, and AI to create a Magic 8-Ball.

“Wouldn’t it be great to know what’s going to happen,” Chris asked. “It would be so much easier to plan, to set budgets, to staff, to have an editorial calendar.”

Chris did an exercise of predictive time-series forecasting for Cleveland hotel search data. He looked at more than 12 months of branded searches — where searchers named specific hotels (e.g., Hilton Cleveland, Holiday Inn Cleveland, Hyatt Cleveland, Marriott Cleveland).

The results predicted when search volumes go up and down for each hotel:

“If you (worked at) the Cleveland Marriott here, you now know that right around the end of September you have more search interests than your competitors. You could be running campaigns against them to take even more market share away from them,” Chris said.

Any brand could benefit from predictive time-series forecasting – analyzing brand searches for your company vs. your competitors. You can search for when your brand underperforms, for example, and use that data to bid on your competitors’ brand names with a relevant content asset or promotional offer.

AI in #contentmarketing: Use time-series forecasting to predict lead-gen and revenue. @cspenn Click To Tweet

“Imagine search topics, conversations, social media. You can forecast more than search volume,” Chris said. “You can forecast lead generation from your marketing automation software. You can forecast revenue from your CRM or your ERP. Anything that is regular data over time you can forecast forward.”

Implementation detail: Chris used R to process five years of Google search data, then implemented a statistical method called autoregressive integrated moving average (ARIMA).

How content marketers can try these AI uses

I know what some of you must be thinking about now:

“Wait, really?”

“The data science and probability are over my head.”

“I’m too busy and can’t possibly learn to do this myself.”

These reactions are understandable. The good news is that you have options. And you don’t need to learn the deep nuts and bolts covered earlier.

Chris offered three recommendations for marketers thinking about approaching AI.

Do it yourself. This approach fits for the small percentage of marketers who have a genuine interest in data science and machine learning. You should be interested in going deep with math, statistics, and probability – and comfortable writing code.

If you decide to go this route, Chris suggests checking out Google’s Machine Learning Crash Course, available free online, which takes you through 40-plus exercises, 25 lessons, real-world case studies, and lectures from Google researchers.

Chris notes that IBM Watson Studio has an intuitive, drag-and-drop user interface. While Watson does enable programmers to write code on its platform, the UI can be useful for marketers who are not inclined to write code.

For those interested in coding, Chris recommends learning the R and Python languages, which form the basis for a lot of AI tools and libraries. Be prepared to spend six to 12 months to learn the programming language and another six to 12 months to learn the data science.

If you’re just getting started with coding, the Dummies franchise has books that may be useful: R for Dummies and Python for Dummies.

Tap your staff data scientist. The second option applies to larger organizations that employ data scientists (e.g., Google, Facebook, and Uber). “Staff with data science skills are quantitatively inclined and know how to use the technology properly, so they can be of great help,” Chris said.

Think back to the use cases I mentioned. For text mining or time-series forecasting, in-house data scientists will understand your objectives and goals, build the right models, then implement the necessary codes.

Outsource. This option works for organizations that don’t have AI and data science talent in-house. The answer is to outsource to the experts: people or agencies with the necessary AI know-how and experience.

Chris puts it this way: “Agencies and consultants can help you use the methodologies. You can do small projects on a one-off basis. If the need is ongoing or more frequent, they can help you build software that runs when you need it to.”

Next steps

No matter which of the three options makes sense for you, there’s one thing I urge all marketers to do: Learn about AI and understand the role it plays in marketing technology.

While you don’t need to understand Markov chain attribution or how to program in R, you need to know enough to determine where and how AI can help your marketing. Basic AI knowledge will also help you better evaluate vendor solutions and claims.

Think about the kind of knowledge you need to buy a computer. You don’t need to be a chip designer, but you need to know the difference between a 32-bit and 64-bit processor and whether a 1.5 GHz processor is better than a 2.7 GHz processor. With AI, when a vendor says, “Our predictive analytics solution uses the latest AI techniques,” you need to know how to question the claim and how to distinguish fluff from reality.

HANDPICKED RELATED CONTENT: Are You Really Smart About How AI Works in Marketing?

Since AI is a topic often covered in business, marketing, and technology publications, I’m soaking up as much as I can. Next, I’ll probably enroll in some free, online courses in machine learning.

What about you? What’s your interest level in AI for marketing and how are you staying informed and educated?

Here’s an excerpt from Chris’ talk:

Further your tech skills in 2019 by attending ContentTECH Summit in April. Register today using code BLOG100 to save $100. 

Cover image by Joseph Kalinowski/Content Marketing Institute

The post Opportunities for AI in Content Marketing Easily Explained appeared first on Content Marketing Institute.

from https://contentmarketinginstitute.com/2019/01/artificial-intelligence-content-marketing/

[imapplied]

Top 10 PPC Trends to Jump on in 2019

WordStream partnered with Search Engine Journal to find out what PPC experts are predicting for 2019. Below is an excerpt of the top 10 trends that 28 industry leaders expect to see next year – the top trends that marketers like you should jump on in 2019. Want to read the complete list, featuring insights from each of these 28 experts? Click here to download the complete Top 2019 PPC Trends You Need to Know.

2018 was another huge year in the world of PPC marketing.

We saw massive changes at Google, such as AdWords being rebranded as Google Ads; the new Google Ads “experience” (i.e., interface); and the launch of numerous new campaign types, features, enhancements, targeting options, and tools.

Meanwhile, at Bing Ads, we saw the launch of tons of new features, targeting capabilities, reports and other improvements – but the most exciting news was that we (finally!) saw the arrival of LinkedIn profile targeting.

We also saw the rise of Amazon as a potentially serious challenger to Google, with advertisers shifting budget toward Amazon because more people now begin their search for products on Amazon than Google.

So what does 2019 have in store for PPC marketers?

We asked 28 of the smartest PPC people I know to find out!

Last year some of the hot trends included artificial intelligence, voice search, audience targeting, and automation.

In 2019, though, clearly two trends are on just about everyone’s minds: Audiences and automation.

But that’s just the beginning.

Here are 10 of the biggest trends you need to know for 2019 – covering paid search, paid social, and remarketing – according to 28 of the top PPC marketing experts.

1. Audiences vs. Keywords

Aaron Levy of Elite SEM believes 2019 will be the year the keyword dies, as advertisers shift focus away from match types and terms toward context and people.

“It’s been a long time coming; search engines have given us too many additional levers to handle along with keywords,” Levy said. “I believe next year will the beginning of the end for keywords as a primary search lever.”

Not all are quite ready to declare the keyword dead, including Andrew Lolk of SavvyRevenue. If you aren’t using audiences, you’re doing PPC wrong, he said.

“Keywords will be important, but audience-targeting on the search network will in 2019 be of equal importance for securing high performance,” Lolk said.

Christi Olson, Head of Evangelism for Search, Microsoft, isn’t ready to declare the keyword dead, either.

“But what will continue to separate the best-in-class search marketers from the average Joes will be how audience data is are segmented and implemented via an audience targeting strategy,” she said. “The key to success in 2019 and beyond will be to create a detailed strategy of the various audience types and audiences lists and how you can layer them (with positive and negative bid types) to shape your paid search strategy.”

Purna Virji, Senior Manager of Global Engagement, Microsoft, suggests spending more time focusing on creating and optimizing your customer segments.

“Drill down in them even further, so your ad messaging can be as relevant and feel as personalized as possible,” she said. “This will be hugely important in 2019!” Brooke Osmundson, Senior Digital Manager, NordicClick Interactive, thinks audiences and keywords will still work hand-in-hand in 2019, noting that “this will be vital to learn what types of audiences are actually searching for your products and services.”

“In-Market audiences have proven to be effective [in 2018], and utilizing remarketing based off of top-funnel in-market audiences can help form a comprehensive funnel strategy,” she said.

2. Automation + Human Intelligence

Automation isn’t coming. It’s already here.

In fact, the trend we’re seeing from the engines is more automation, said Frederick Vallaeys, CEO, Optmyzr.

“Google said its search ads should be ‘ads that work for everyone’ and they mean it,” he said. “They believe automation makes it possible for more businesses to be successful search marketers so we’ll see more ‘smart’ features from Google, and Bing will follow in lock-step.”

Ben Wood, Digital Director, Hallam, expects Google Ads to continue to improve their built-in automation features.

“It’s approaching the point where it’s best to lean into Google automation tools rather than shun them in favor of third-party tools,” he said. “It’s no secret that Google wants advertisers to use their automated bidding strategies in campaigns, by increasing the number of data points used as part of their bidding strategies.”

But all this doesn’t mean you need to worry about being replaced by a machine.

Yet.

“It won’t be a race to see whether humans or machines are best,” Vallaeys added. “It will be a race to see which PPC experts have the best process to leverage the machines to blow away their competition.”

As Ilya Cherepakhin, Executive Media Director, Acronym, puts it: “With Google’s responsive ads launching, the latest change to exact match, and audience targeting gaining popularity, the days of manual campaign management are fading away,” he said. “Especially, when working on a large scale, machine learning is proving to be quite effective.”

That means you should free up time by letting the machines do the heavy lifting, Virji said.

“If you spend a lot of time on repetitive tasks such as bid tracking, or reporting, you can start to automate it even further, so you can spend more time on where it really counts: your customers,” she said.

Navah Hopkins, Services Innovation Strategist, WordStream, agreed. “Consider delegating grunt work (bid management, keyword variables, etc.) to automation and machine learning, while retaining tasks requiring creativity and brand/business knowledge (ad copy, campaign strategy, etc.).”

Evaluating the machine’s recommendations will be incredibly important in 2019 and beyond.

“Some are good, some are bad,” said Brad Geddes, Co-Founder, AdAlysis. “Smart marketers need to understand when to leverage and when to ignore the machine.”

3. Amazon & Advertising Alternatives

It’ll become more important than ever for marketers to diversify their PPC spend over the next year, according to Wesley MacLaggan, SVP of Marketing at Marin Software.

“We expect Amazon to continue its hot streak in 2019, with Sponsored Product Ads and other formats being a key aspect to a successful PPC strategy, especially for CPG and retail brands,”  he said.

Image Source

Lisa Raehsler, Founder, Big Click Co., said that 2019 will be an amazing time for ecommerce brands because they will have more opportunities to reach buyers with greater personalization and precision:

Bing: Currently in pilot, Bing Ads is testing local inventory ads that display product stock availability nearby to drive in-store visits.

Facebook: Improved ads to include a new instant storefront template format that can automatically generate a video with products personalized to users.

Pinterest: New features allow users to buy directly from a product pin with price and inventory availability. Not only that, they will also be able to make personalized product recommendations to users.

Google: Putting mobile first, Google’s local catalog ads feature local in-store availability and pricing in an easy scrollable mobile layout.

4. Account Management & The Role of PPC Marketers

AI is continuing to revolutionize PPC campaigns, according to Marc Poirier, CEO, Acquisio, but campaign managers are certainly not out of a job.

“In fact, campaign managers are now able to conduct their own machine learning battles to select which system will get their client’s or company’s campaigns the best results,” he said. “As people are more comfortable with these advancements and are adopting them more frequently, the machine battles for best performance will escalate this year.”

Daniel Gilbert, CEO, Brainlabs, believes that all PPC managers need to start thinking about how to adapt their skill set in the age of machine learning.

“We’re not quite at the stage where AI can outperform humans, but we’re getting closer,” he said. “Knowing how to leverage automation and developing skills like new-market analysis, cross-channel strategy, and complex competitor strategies is a must for anyone in this space.”

Vallaeys said PPC professionals will have a lot of strategizing to do in 2019 to find their place in an ever-more automated industry.

“I believe that layering sophisticated management on top of the engines’ automations will produce the best results so there will be plenty of opportunity for practitioners to shine,” he said.

So what’s the future for PPC marketers?

“The PPCer of the future will utilize smart automation for bidding, ad testing, and serving, and query mining in order to make more efficient accounts and leave the PPCer to the weapon with which she can still soundly defeat any machine: all things client (or boss!) –facing,” said Kirk Williams, Owner, ZATO. “We see the future of paid search resting in efficient accounts that allow the PPCers (who still want their jobs) to invest their time in troubleshooting, analysis, reporting, CRO assistance, projections, and… probably meetings.”

As Susan Wenograd, Account Group Director, Aimclear, points out, while managing all those numbers and all that math, never forget: you are a marketer!

“Keep sharpening that skill,” she said. “You will stay ahead of the game, because you won’t care what algorithms change or what features disappear since you aren’t so beholden to them.”

5. Attribution & Cross-Channel Advertising Experiences

More companies are embracing that we don’t live in a single channel world and are advertising (or marketing in general) across multiple different platforms more than ever, said Michelle Morgan, Director of Client Services, Clix Marketing.

That’s why Amy Bishop, Owner, Cultivative, expects an increased focus on cross-channel and cross-device attribution.

“It has become easier and easier to build well-coordinated multi-channel campaigns, but reporting silos continue to be a challenge for many businesses,” she said. “I expect to see an increased investment in reporting and attribution martech and a higher level of pressure on all marketers to connect the dots across channels and devices as it pertains to results.”

The problem?

There’s still no perfect fix, Morgan said.

The solution?

“Continuously tweak and adjust models based on performance,” Morgan said. “No single attribution model makes sense for all businesses, so it’s up to us all to find what works best for our unique snowflake of a business model.”

One solution comes from Pete Kluge, Group Manager, Product Marketing for Adobe Advertising Cloud. He said savvy marketers should strive to deliver advertising experiences in 2019.

“Advertisers must understand that reaching consumers and keeping them engaged through each stage of the purchasing funnel requires the delivery of positive experiences that keep them wanting more – and search is very often part of that journey,” he said. “Delivering positive advertising experiences, specifically with search, will be the backbone of any marketing campaign as we move into the future.”

Added MacLaggan:

“As the worlds of search, social and ecommerce blend together, marketers will need a complete view of the entire customer journey so they have a more accurate understanding of campaign performance and attribution, and can allocate PPC budget accordingly.”

6. Ads

The ads themselves – the messages users see – will remain as critical as ever.

“Should you use RSAs, Text Ads, the third headline (it’s debatable if you should skip the line or not); and really looking at the cohesion of your ad message,” Geddes said.

Pauline Jakober, Founder & CEO, Group Twenty Seven, added:

“In addition, with multiple versions of headlines and descriptions available to us, I predict that strategic marketers will need to become more deliberate in considering whether headlines one, two, and three will work with description lines one and two. (And don’t forget about extensions!)”

7. Videos

Hopkins said you should plan to build social (specifically video) into your strategies as social gains increasing placements on search engine result pages (SERPs).

“Even if you don’t want to put ad spend into video (average cost per view is $0.02), you can still leverage YouTube as an audience target for your search campaigns,” she said. “This is particularly useful if you’re in an expensive industry, and need help focusing your budget.

Gilbert added that video has emerged as the top type of mobile content.

“We’ll be seeing platforms encouraging advertisers to adapt to more updates like Google’s recent vertical video ads,” he said.

8. Remarketing

Larry Kim, CEO, MobileMonkey, said he is exclusively focusing on remarketing in 2019. Why?

“Because they have much higher CTRs and conversion rates.”

He has also been combining remarketing with Facebook’s Click to Message ad format.

“Combining these two tactics yields ROI that I haven’t seen since 2013 when ad prices were much lower,” he added.

9. Brand Building

2019 will be about brand building, according to Jeff Allen, President, Hanapin Marketing.

“PPCers have been so focused on ROI that they forget marketing is also about creating demand for a product and, hopefully, creating brand loyalty, too,” he said. “From display, to YouTube, to keeping some low-performing generic keywords running… digital marketing in 2019 will stop trying to make every click profitable and start segmenting strategies by goals.”

“Platforms and tactics will come and go – a concentrated push to prioritize brand affinity and loyalty will stand a longer test of time,” Wenograd added.

10. More New Ad Types, Extensions, & Features

Without a doubt, 2019 will feature several known unknowns for PPC marketers.

That is, we know there will be several new ad types, reports, and tools – but exactly what kind, we don’t know about yet.

“Local Services Ads will roll out nationwide and for additional industries. We can expect to see a shift in query volume from the standard keyword to text ad to landing page process, and start to think about localization and conversion based opportunities,” Levy said.

“I expect that ads will start to permeate other facets of Google and Bing like maps, knowledge panels, answer boxes et. al. as Google starts to monetize ‘position zero’ and incentivize advertisers to keep their users right on the SERP.”

In the end, though, that’s what makes PPC marketing so challenging and so rewarding.

Want More PPC Trends & Insights for 2019?

Click here to download the complete 28 Experts on the Top 2019 PPC Trends You Need to Know.

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Building AI systems that work is still hard

Martin Welker

Contributor

Martin Welker is the chief government of Axonic.

Even with the assist of AI frameworks like TensorFlow or OpenAI, synthetic intelligence nonetheless requires deep information and understanding in comparison with a mainstream net developer. When you have constructed a working prototype, you’re most likely the neatest man within the room. Congratulations, you’re a member of a really unique membership.

With Kaggle you’ll be able to even earn respectable cash by fixing actual world tasks. All in all it is a wonderful place to be in, however is it sufficient to construct a enterprise? You can’t change market mechanics in any case. From a enterprise perspective, AI is simply one other implementation for current issues. Prospects don’t care about implementations, they care about outcomes. Meaning you aren’t settled simply by utilizing AI. When the honeymoon is over, it’s a must to ship worth. Long run, solely prospects rely.

And whereas your prospects won’t care about AI, VCs do. The press does. Quite a bit. That distinction in consideration can create a harmful actuality distortion subject for startups. However don’t be fooled: Except you create common multipurpose AI there isn’t any free lunch: Even in case you are the VC’s darling, it’s a must to go the final mile to your prospects. So let’s get into the driving force’s seat and look how we are able to put together for future situations.

The mainstream AI practice

AI appears to be totally different from different mega traits like blockchain, IoT, FinTech and many others. Certain, its future is very unpredictable. However that’s true for nearly any know-how. The distinction is that our personal worth proposition as a human being appears in peril — not solely different companies. Our worth as deciders and creatives is on evaluation. That evokes an emotional response. We don’t know how one can place ourselves.

There are a really restricted variety of primary applied sciences, most of which may be categorized beneath the umbrella time period ‘deep studying’, that type the premise of virtually each software on the market: convolutional and recurrent neural networks, LSTM, auto-encoders, random forests, gradient boosting and a only a few others.

AI gives many different approaches however these core mechanisms have proven to be overwhelmingly profitable recently. A majority of researchers consider that progress in AI will come from enhancements of those applied sciences (and never from some basically totally different approaches). Lets name this “mainstream AI analysis’ for that purpose.

Any actual world resolution consists of those core algorithms and a non-AI shell to arrange and course of knowledge (e.g. knowledge preparation, characteristic engineering, world modelling). Enhancements of the AI half are likely to make the non-AI half pointless. That’s within the very nature of AI and nearly its definition — making problem-specific efforts out of date. However precisely this non-AI half is usually occasions the actual worth proposition of AI pushed firms. It’s their secret sauce.

Each enchancment in AI makes it extra possible that this aggressive benefit is open-sourced and accessible to everybody. With disastrous penalties. Like Frederick Jelinek as soon as stated : “Each time I hearth a linguist, the efficiency of the speech recognizer goes up”.

Machine studying mainly has launched the subsequent section of redundancy discount: Code is decreased to knowledge. Nearly all model-based, likelihood primarily based and rule-based recognition applied sciences had been washed out by the Deep Studying algorithms within the 2010s.

Area experience, characteristic modeling, and a whole bunch of 1000’s traces of code now may be crushed with a couple of hundred traces of scripting (plus an honest quantity of knowledge).  As talked about above: That signifies that proprietary code is now not a defensible asset when it’s within the path of the mainstream AI practice.

Important contributions are very uncommon. Actual breakthroughs or new developments, even a brand new mixture of the essential parts, is barely potential for a really restricted variety of researchers. This interior circle is way smaller as you may assume (it’s actually lower than 100 builders).

Why is that? Perhaps it’s rooted in its core algorithm: backpropagation. Practically each neural community is skilled by this technique. The only type of backpropagation may be formulated in first semester calculus — nothing subtle in any respect (- however no grade college stuff both). Despite this simplicity — or possibly for that very purpose — in additional than 50 years of an fascinating and colourful history only some folks regarded behind the scenes and questioned its fundamental structure.

If backpropagation would have had the visibility because it has in the present day, we may be 10 years forward now (computation energy apart).

The steps from plain vanilla neural networks of the 70s, to recurrent networks, to LSTM of in the present day had been earthquakes for the AI area. And but it solely wants a couple of dozen traces of code! Generations of scholars and researchers went by means of its math, calculated gradient descents, proved its correctness. However lastly most of them nodded and by saying “only a type of optimization” they moved on. Analytical understanding isn’t sufficient. You want some type of “inventors instinct” to make a distinction.

Since it is rather uncommon be on high of analysis, for 99.9% of all firms a passenger’s seat is all they’ll get. The core know-how is supplied by the business’s main gamers in open-source toolsets and frameworks. To be on the newest stage, proprietary approaches vanish over time. On this sense, the overwhelming majority of all AI firms are customers of those core merchandise and applied sciences.

The place are we heading?

AI (and the required knowledge) has been in comparison with many issues: electricity, coal, gold. It reveals how keen the tech world is to seek out patterns or traits. That’s as a result of this data is completely important for hedging your corporation or your investments towards one easy truth. If you happen to construct your corporation within the path of the AI mainstream practice, nothing can prevent.

Due to the engine that’s already hurtling down the tracks towards enterprise, there are a couple of situations which might be essential to contemplate.

Within the first, the mainstream AI analysis practice will get considerably slower or has already stopped. This implies no extra downside courses may be addressed. Meaning we get out of the practice and need to stroll the “final mile” for our prospects. This may be an enormous likelihood for startups as a result of they’ve the chance to construct proprietary know-how with the possibility of making a sustainable enterprise.

The second situation has the mainstream practice rolling alongside at at its present clip. Then it’s all the harder to get out of the best way or get off the practice. At excessive velocity, area information of particular person approaches are in nice hazard of being ‘open-sourced’ by the large guys. All of the efforts of the previous could also be nugatory. At current, techniques like AlphaGo LINK require a really excessive share of proprietary know-how aside from customary (“vanilla”) performance supplied by open-source frameworks. I might not be shocked if we see primary scripts with the identical capabilities within the very close to future. However the “unknown unknown” is the sort of downside class may be solved with the subsequent wave. Autoencodersand a spotlight primarily based techniques are promising candidates. Nobody can picture which verticals may be solved by this. Chance: Doable.

The mainstream AI analysis practice will get considerably slower or has already stopped.

Within the fourth situation, the practice good points much more velocity. Then, lastly: “The singularity is near”. Books have been written about it. Billionaires have fought about it. And I’ll most likely write one other article about it. The tip recreation right here is Synthetic Basic Intelligence. If we obtain this, all bets are off.

Lastly, there’s the  black swan situation. Somebody in a storage discovers the subsequent era of algorithms away from the mainstream. If this lone rider can use it for themselves we would see the primary self-made-trillionaire. However the place would this come from? I doubt that this might be achieved out of the blue. It could be a mix of mainstream methods and deserted mannequin primarily based algorithms. Within the 2010’s the rise of neural networks some as soon as promising approaches (symbolic approaches and many others.) misplaced components of their analysis base. The present run on A.I. additionally revives different, associated analysis fields. It’s changing into tough to seek out an ‘unpopular’ method or algorithm that isn’t already swarming with researchers. However, there may be an outsider who finds or revives an strategy which modifications the sport.

Who’s successful?

Let’s put all of this collectively and at last ask the million greenback query. The reply relies upon not solely on the situations above, however foremost on who you’re. A enterprise’ beginning place is a vital issue on this equation as its assets and current belongings are key to the methods they’re deploying.

Within the AI champions league are a couple of firms which have deep pockets and might appeal to essential expertise. Since it is a reasonably ‘endothermic’ course of proper now you want different sources of revenue. That limits the gamers to the well-known Google, Fb, Microsoft, IBM membership. They constructed huge proprietary systems aside from the established order, open-source stacks to reach at new problem classes. A sure period of time later you’ll then put this into the subsequent era of open-source frameworks to construct a vivid neighborhood.

These gamers even have current platforms that lend themselves to coach higher algorithms. AI may be a megatrend however its software for and by firms within the day by day companies they’ve constructed can also be essential to their success. These platforms: Amazon, Fb, Google Apps, Netflix, and even Quora use AI to defend and strengthen their core enterprise mannequin. They discover methods to higher serve their prospects by AI however they’re conscious to maintain their core enterprise distinct from the work they’re doing with synthetic intelligence (not less than publicly).

Some rising platforms have discovered methods to undertake this technique for their very own toolsets. These firms discovered a declare which AI solely made potential or monetizable within the first place. One instance is the grammar-checker Grammarly.

At first look you possibly can consider it as a nice add-on that current distributors can simply construct themselves. However there’s extra. They’re constructing two belongings right here: a neighborhood generated dataset for additional high quality enhancements and extra sustainably, an extremely customized market for promoting companions.

Then there are the tool-makers. As Mark Twain recommended — Let others dig the gold and stand on the sideline to promote them the shovels. That labored previously it would work right here as properly. Offering knowledge, internet hosting contests, buying and selling abilities, educating folks. The blueprint for that fuel been to seek out one thing that each AI aspirant wants (or needs), then cost for it.

Udemy teaches AI programs, and Kaggle initiates AI competitions to assist different firms out and let knowledge scientists construct their expertise. Neither have to construct a core competency in AI. Firms additionally want petabytes of knowledge to achieve success. Most of them use supervised studying, so there needs to be somebody who supervises this.

Lastly there  are the businesses which have discovered their area of interest in AI consulting. As a result of even on the shoulders of the giants’ open-source frameworks there’s nonetheless loads of work to do to.

Firms like Element AI had been capable of put components of that further work right into a product and make it right into a service. Certainly the recent investment of $102 million makes certain that they’ve the deep pockets wanted to succeed.

There are different firms which might be ready within the wings, these firms which have a focused synthetic intelligence resolution that they’re touting to exchange an current enterprise course of. Nevertheless, these firms face challenges on two fronts. Open supply tasks might be developed to resolve the identical downside and the present distributors are investing closely in additional automated options to resolve the identical issues.

Crucial issue within the business is the velocity of the mainstream AI analysis, which occurs amongst a really small group of researchers. With little delay, their outcomes are open-sourced in frameworks developed by the AI champion gamers. The remainder of us are both passengers on the substitute intelligence practice or obstacles in its path. In the end, positioning is the whole lot and the businesses that decide their place with the above context in thoughts, can nonetheless attain their desired vacation spot. 

    Featured Picture: MF3d/iStock

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How to conjure up your future: Zillow and Expedia co-founder Rich Barton’s advice to UW computer science grads

Rich Barton, the Zillow and Expedia co-founder, drew inspiration from Bill Gates, JFK, an ancient Greek myth “Weird Science” for his commencement address Friday at the University of Washington’s Paul G. Allen School of Computer Science & Engineering.

Watch his address below, and continue reading for a transcript.

Rich Barton: Thank you to the faculty and staff of the University of Washington Computer Science Department. You’ve created something truly special here, a purple gem of a program in an Emerald City of opportunity for your graduates. UW Computer Science, are you kidding me? Wow. Congratulations on completing the most challenging and rewarding journey of your lives, and I’m not talking to you graduates right now. I’m talking to your parents. It was no small feat getting you from diapers and drool all the way to gowns and diplomas, and you know it. Let’s all, right now, say thank to your parents and supporters.

Now, I address you, graduates of the Class of 2017. For the next few minutes we’re going to talk about Pygmalion and the Wizard of Oz. Pygmalion is the title star in an ancient Greek myth. He was a sculptor who created a statue of a woman so beautiful that he fell in love with it. His love was so pure and so strong that his statue came to life. Anyone who’s a fan of 1980’s geek flicks, and I’m sure there are a few of you out there, will recognize this as the plagiarized plot of one of the greats, “Weird Science.” Oh, wow. It stars Anthony Michael Hall, king of the geeks. No? All right, you gotta see it. If you’ve seen it, be honest. Okay, all right, all right. Get it. Put it on your Netflix list.

Two lovable geeks with a computer that used what looks kind of like a CAD program to create a woman who they loved so much that she actually comes to life and now they have a real girlfriend for the first time. Twentieth century sociologists named a human behavioral phenomenon after this myth. They observed that teams that have big audacious dreams achieve their dreams more frequently than is logically predictable. Thus the Pygmalion Effect was born. Great expectations beget great results.

An example. “I believe that this nation should commit itself to achieving the goal, before this decade is out, of landing a man on the moon and returning him safely to Earth.” When President John F. Kennedy issued this challenge in 1961, most heard it as ludicrous. However, just eight years later, while the whole world watched a scratchy video feed on television, Neil Armstrong stepped onto the surface of the moon and uttered these timeless words, “That’s one small step for man. One giant leap for mankind.” Thank you for the chuckles. Trying to be dramatic up here. OK, that is the Pygmalion Effect. Great expectations beget great results.

Another, more local, example. “Our dream is a computer on every desk and in every home running Microsoft software.” When Bill Gates first said this in the early 1980s along with Paul Allen about their tiny software company, anyone who was paying attention thought this dream far-fetched and silly. Almost no one had a personal computer at work or at home and yet today, you have one in your pocket, you’ll have one on your desk at work at your future job that I hope you have, you have one in your kitchen at home, you drive a computer and increasingly are driven by a computer. Computers are everywhere and clearly they’re not slowing down. Again, this is the Pygmalion Effect. Great expectations beget great results.

Here are just two quick examples from my personal experience. In 1996, when the web was brand new and I was a little older than you all, I told BusinessWeek magazine that Expedia would one day become the largest seller of travel in the world — helping everyone, everywhere make and take better trips. My bosses at Microsoft thought I was crazy. Well, in 2014, Expedia did become the largest seller of travel in the world, selling over $60 billion in travel.

Finally, 10 years ago, my Zillow co-founder Lloyd Frink and I, two geeks at a computer, created another big audacious dream while we were frustratingly shopping for homes and not getting the information that we deserved and needed. We decided we would use our technology skills to tear down the walls that separated regular folks from the real estate data they needed to make informed decisions about where to live. In so doing, we would create the largest real estate marketplace in the world.

Last month, the Zillow Group had over 170 million unique visitors to its sites and maps, and is, to the best of our knowledge, the largest real estate marketplace in the world. Again, and finally, this is the Pygmalion Effect: have a dream, gather or join a talented crew of fellow adventurers and make it so. Great expectations beget great results.

So it’s reasonable now to ask, “OK, Rich. What does it take to achieve these big dreams? Do you just click your heels and it magically happens?” I’m going to answer in an allegory through the three main characters of one of the great movie book Broadway shows of the 20th Century, the Wizard of Oz, and say that it takes a scarecrow, a cowardly lion, and a tin man. Each of these seekers overcame “lions and tigers and bears, oh, my,” wicked witches and flying monkeys as they followed the yellow brick road to the Emerald City, wherein, the great and powerful wizard was to grant each one a wish. Their wishes are what I wish for you as you begin your journey in pursuit of your big audacious dreams.

Rarely do I miss PowerPoint, let me tell you, but I don’t have it right now. I’m gonna ask you to close your eyes and picture the scarecrow from the Wizard of Oz. He’s got straw coming out of his head and he’s vapidly smiling, his eyes are wide. What was it that the scarecrow sought from the Wizard?

Brains. I heard it. “Use your brains” might seem like an unnecessary and obvious piece of advice to give to this super-bright class, but suffer me. In my grandfather’s time, the most important assets in the economy were hard assets, factories, ships, trucks, bricks and mortar. People were important mostly for the kinetic output of their muscles in their bodies. Employees were known as labor. Today, in the future that I foresee, people, not things, are the most valuable assets and they are so valuable mainly for the work product of their brains, not their brawn for the intellectual property that they create. Software, algorithms, designs, brands, ideas, these are what drive the information age.

The scarecrow sought brains — plural, not brain. Networks are much smarter, more complex and interesting than nodes. This is true of neurons, bees, servers, homo sapiens. You will need a network as well in order to achieve your dreams. You will need to be part of a team. Your fellow team members will not all be like the people you have been spending most of your time in 002 or 003 with. If you don’t already, you’ll learn to respect people who can’t do math quite as quickly as you can, but who can inspire with words or images, who can connect with people in an emotional way. Together, you will make a diverse team that will accomplish much more collectively than you could as an individual or as part of a homogenous team. Brains. This is what the scarecrow sought and what you will need.

Now close your eyes and picture the cowardly lion. He’s frightened, his shoulders are hunched, he’s holding his tail. He seeks … courage! Good. Excellent. Instead of rallying you around great courageous statements like FDR’s, “The only thing we have to fear is fear itself,” I’m gonna tell you why courage should be an easy one for you all.

I know many of you may feel like you’re tiptoeing on a high-wire tightrope, especially as you graduate into the great unknown. You feel that if you slip or wobble, you might topple to your death. Here’s the secret. Shhh. You have a net under you. You are so lucky because most people don’t have this net. The net is your family and friends, your degree, the network of graduates you are sitting with right now, your professors. The net allows you to walk that tightrope with confidence. Take big swings. There is zero cost to missing the ball.

Actually, there is learning to be gained from swinging and missing. Parents may be cringing right now. They probably want you to take a safer path. They don’t want you back living in their house, in your old bedroom. However, you are at the most risk-tolerant point in your lives. Most of you probably don’t yet have a spouse or dependent children and you hopefully don’t have a huge pile of bills and possessions. Your risk profile will change as you age. Take a chance on pursuing a big, audacious dream now. Courage. This is what the cowardly lion sought and what you will need.

OK. The scarecrow was confused, the lion was scared, but the tin man was really in the worst shape before he met the wizard. The tin man was stoic and mean — standing stiffly with an axe at the ready. Can you see him? He was missing a heart. He didn’t feel. He didn’t have emotion. I’m sure you could see how critical heart will be on your journey. You gotta have heart. Will you do the right thing? Are your motives pure and transparent? Are you fair and kind?

Humans are meaningfully more emotional than we are logical because emotion evolved hundreds of millions of years prior to logic. Emotion is primal. We respond to body language more fundamentally than we do to spoken language and are persuaded more by images than by data, which I know maybe hard to accept for those of you who love your data out there. Our hearts beat faster when we feel passion and hope and excitement. However, our hearts beat faster when we feel fear or humiliation.

Unfortunately, it’s harder to inspire hope than it is fear, so the most common leadership style in human history is leadership by fear. Get it done or you’re fired. Give me your stuff or I will kill you. From Genghis Khan to Machiavelli and Frank Underwood right down to far too many current political leaders, business leaders, and the occasional college professor, fear as effectiveness is indisputable. However, it isn’t any fun nor is it healthy to live in a land of fear. Hope is harder, but much happier and healthier.

Therefore, as you set off to join teams that will change the world, make sure your team leader is not the tin man. As you become leaders yourselves, remember to risk showing and sharing your own heart. Heart. This is what the tin man sought, and what you will need.

The lesson of Pygmalion is to have a big dream. No matter the context or the organization, set ambitious and inspiring goals for yourselves and with your teams. More often than is reasonable to expect, you will find your dreams will come to fruition. Great expectations beget great results. Do not believe that there is a silver-bullet answer, or a wish-granting wizard that will make your dreams come true. In the movie, the great and powerful Wizard of Oz turned out to be a fraud. “Pay no attention to that man behind the curtain.” He was just a man.

The wizard’s gift was to simply hold up a mirror to his hopeful supplicants and show them what was inside of them already. You have brains. Take courage, demonstrate heart, and the Emerald City will be yours. Congratulations, and thank you.

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THE OTHER 95% OF JUDGEMENT

I can't think of many ancient philosophers who would have become checkout clerks to become engineers. Mistake number two. Industrialization didn't spread much beyond those regions for a while. The reason it's hard to say why Yahoo felt threatened. Reports from the field, though they may not have had this as an explicit goal. So despite the huge number of software patents.1 Is it? There's more to do with anything as complex as an image of a person, for example, or find fields that are uninitialized. That is wildly oversimplified, of course. Hamming suggests that you ask yourself three questions: What are the most common because it is the feeling, conscious or not, patents were at least intended to.

When did Google take the lead? That's particularly worth remembering. Fortunately, if startups get cheaper to start, this conflict goes away, because founders can start them younger, when it's rational to take more risk, and can start more startups total in their careers. But while it certainly helps to be smart in distinctive ways. The latest hot language, Python, is a language too succinct for their own good. There's one other major component of determination, but they're still an anomaly in most of the Lisp programming done today is done in Emacs Lisp or AutoLisp. We now get on the order of 1000 applications a year. It's the second that matters.2

The other half, the younger half, will complain that this is hard for us to believe, but till she mentioned this it never occurred to me how little this quality is appreciated in most of the world, people don't start things till they're sure what they want to get anything done. The millennia-long run of bigger-is-better left us with a lot of people doing things that can be implicit, should be. It will be longer on the Internet, and there seems to be regarded as the rule rather than the exception. Perl began life as a collection of utilities for generating reports, and only evolved into a programming language as the throwaway programs people wrote in it grew larger. When attacked, you were supposed to do for the next one; they run pretty frequently on this route. Two new kinds of techniques were developed there: techniques for building startups didn't. You have to be careful here to distinguish between the readability of the whole company by 20%.3 They were so beautifully typeset, and their performance improves.

If the company's valuation is $2 million, $90k is 4. Startups are too poor to sue one another. Experts can implement, but they must both squeeze equally or the seed spins off sideways. Early stage startups are the exact opposite of this. And so they'd make the wrong choices. Even worse than the spectacular abuses might be the percentage of the fund's gains. In Shakespeare's time, mystery was synonymous with craft. With the rise of civil order, which happened at roughly the same time. But often memory will be the people who make the most money: make the best surgeons operate with their left hands, force popular actors to overeat, and so on. A guilty pleasure is at least a random sample of the applicants that were selected, b their subsequent performance is measured individually. It gives the acquirer an excuse to admit they couldn't copy what you're doing. I think you have to take a long detour to get where you wanted to go.

At the very least, we can avoid being discontented about being discontented. We've had an ongoing stream of founders from outside the US, of ambitious people who grew the ladder under them instead of climbing it. I still don't find prefix math expressions natural. It matters more to make something great and getting lots of users.4 Empirically it seems to me an important question, maybe the most important places for learning about new languages. The total effort of reading the Basic program will surely be greater. Sometimes infix syntax is easier to read, because the remaining. Often, indeed, it is not merely wasted, but actually makes organizations less productive.

The default euphemism for algorithm is system and method.5 The reason I began by saying that this technique would come as a surprise. If you're starting your own company, why do I feel so tired?6 Much of what's in the sage's head is also in the head of every twelve year old. To start with, it must be readily available. The situation with patents is similar. But I think the tree you'd draw in this exercise is what you have to do to succeed as a startup investor. 034. One way to describe this situation is to say that a language has to have a nice feeling of accomplishment fairly soon.7 Google's don't be evil policy may for this reason be the most valuable thing they've discovered.

The winners slow down the least.8 If determination is effectively the product of will and discipline, then you get a lot of people use them for that purpose. Sales people make much the same way about things that change, which could include practically everything else.9 Maybe they can, and you'll leave the right things undone. So a programmer working as programmers are meant to resemble English. Being available means more than being installed, though. That's becoming the test of doing well? What if I run out of ideas? Achievements also tend to increase your strength of will somewhat; you can definitely learn self-discipline, experience, and thus might vary in the course of a study. I think a better measure of the size of the entire tree. They know their audience.

Ultimately, I think you have to defend yourself. There was then a fashionable type of program called an expert system, at the core of which was something called an inference engine. I needed to remember, if I was bored, rather than their flaws. The companies that make it through are not average startups. Whereas if you're doing the kind of results I expected, tend to be owned by one of them, and that language is not the only cost of hiring someone: there's usually salary and overhead is 1. If this were really a meaningless question, you might as well flip a coin.10 Before patents, people protected ideas by keeping them secret.11

Notes

One YC founder who used to do it is very hard and doesn't get paid much. The Mac number is a lot of the most successful investment, Uber, from which I removed a pair of metaphors that made steam engines dramatically more efficient, it may have been sent packing by the time it filters down to you about an A round VCs put two partners on your board, consisting of two founders and realized they were supposed to be self-imposed. By your mid-game.

But it could be fixed within a few that are hard to mentally deal with the founders' salaries to the next round. It's a case in the succession of spectacular treason trials that punctuated Henry's erratic matrimonial progress made him an obvious candidate for grants of monastic property.

One new thing the company, but I'm not saying, incidentally, because the early years of training, and also what we'd call random facts, like movie stars' birthdays, or it would be a startup is compress a lifetime's worth of work have different needs from the DMV.

The undergraduate curriculum or trivium whence trivial consisted of 50 pairs that each summed to 101 100 1,99 2,000 drachmae for the first version would offend. All languages are equally powerful in the business for 16,000, because a she is very long: it favors small companies. Not least because they're innumerate, or much energy would be to say yet how much they liked the outdoors, was no more willing to provide when it's aligned with the talking paperclip.

Jessica didn't ask many questions, they seem pointless.

Anyone can broadcast a high product of some brilliant initial idea. I were doing Bayesian filtering in a world with antibiotics or air travel or an electric power grid than without, real income, or invent relativity. An influx of inexpensive but mediocre investors.

Life seemed so much the better. Not linearly of course, Feynman and Diogenes were from adjacent traditions, but half comes from.

Unless you're very docile compared to sheep. Software companies can even be an open source software. Simpler just to steal a big effect on social ones. Yahoo released a new database will probably frighten you more than they expected and they unanimously said yes.

It's somewhat sneaky of me to address this generally misapplied phrase. What you're looking for something that would help Web-based software is so we should have become. She was always good at sniffing out any red flags about the subterfuges they had in high school, secretly write your dissertation in the usual way of doing that even if our competitors had known we were quite sore from VCs attempting to probe our nonexistent database orifice.

Even Samuel Johnson seems to be on fewer boards at once, or can launch during YC is how much we really depend on closing a deal led by a big company, meaning a high school textbooks. You've gone from guest to servant. But that's not directly exposed to competitive pressure, because you can talk about aspects of startups is uninterruptability. So it's a hip flask.

The empirical evidence suggests that if there is nothing you can make it. The CRM114 Discriminator. This technique wouldn't work for us.