#ai vs ml

[naya-mishra]

This article highlights the key difference between Machine Learning and Artificial Intelligence based on approach, learning, application, output, complexity, etc.

[taslimursunybilas]

Can Artificial Intelligence Replace Human Writers? Unveiling the True Potential of ChatGPT and Google Bard

[sexhaver]

sorry for the Talos-Principle-2-posting but like. okay, first, for context so i don't sound insane, the plot of the series is that around 2020ish, global warming released a disease from the arctic permafrost that drove humanity to extinction within a few years. before going extinct, a team of scientists managed to set up an iterative simulation that would run in a loop until it produced a "true" AI, then upload that AI to a physical robot body to ensure sentient life still exists in the universe.

the first game was that iterative simulation, and the "true" ending has you become the first AI to break out of the simulation and return to the physical world. the sequel follows the society that gives rise to, the members of which are interestingly always referred to as "humans" despite being entirely non-biological.

the central conflict of the second game is between two factions. the ruling conservative faction points to the mistakes of their biological ancestors (i.e. us) as a natural consequence of expanding too far and/or wielding too much power; for example, global warming releasing the aforementioned plague from permafrost. the opposing faction counters by pointing out that we aren't necessarily doomed to repeat the past; in fact, knowing the mistakes our predecessors made enables us to avoid them and succeed where our forebears failed.

this rhetoric felt really familiar to me and i couldn't put my finger on why until a couple hours in. then it hit me: "power is inherently evil and corrupts those who wield it and should be avoided", "we can't do [x] because we tried it in the past and it failed"... this is just bog-standard anarchist vs ML tumblr [none pizza with] left beef that's been getting rehashed since 2013

[kuroo-suno]

Hey, random question but ik you read some shoujo manga. Do you have any recommendations?

omg i sure do!! sorry if this is long i just really love shoujo ( ‘́⌣’̀) completed series are at the top and currently releasing are after, and i wrote a little about each one (though it's based on memory so it's not exactly detailed).

hopefully at least one of these is enjoyable! i've read loads more but these are some that stick out ♥

Completed:

Chorokute Kawaii Kimi ga Suki

☆ Intimidating, misunderstood sweetheart ML and an endearingly goofy and super-weak-to-romantic-gestures FL

Horimiya

☆ one of my all-time favourites UGH. FL and ML are both completely different outside of school but in vastly different ways and thanks to a chance encounter, they meet and discover that perhaps they both have a little more to offer than what's on the surface ♥♥♥♥

Kanojo ga Kawaisugite Ubaenai

☆ demon ML comes to the human world disguised as a high school student to complete a test to become the next demon lord and the FL has never had friends before and couldn't read any romantic (or warning) signs if they were printed on the inside of her eyelids. ML is also sooo weird and awkward and provides a lot of comic relief

Kawaisugiru Danshi ga Ouchi de Matteimasu

☆ FL is a star employee, but an absolute scrub once she gets home so she offers her friend/ML to live with her rent-free in exchange for his cooking and cleaning services (and to keep her from getting involved with losers)

Living no Matsunaga-san

☆ FL moves into her uncle's boarding house while her parents care for her grandmother and her roommates are quite the array of individuals, including the older ML who shows he cares by being a crabby little nag-machine

Natsuaki-kun wa Kyou mo Kokuhaku Shitai

☆ Aloof and seemingly disinterested ML (main character) pines HARD for the world's cutest girl and is desperately trying to confess

Tsubaki-chou Lonely Planet

☆ i wish i could read this for the first time again tbh. FL gets a job as a live-in housekeeper for a famous author (ML) to help pay back her dad's debts. ML is icy and distant, but FL has a heart of gold fr. This series made me laugh and cry numerous times and reading the last chapter was so bittersweet because i loved getting a conclusion but didn't want it to end yk? (from the same author as hirunaka no ryuusei and uruwashi no yoi no tsuki so no surprise it's 10/10)

Ongoing:

Daifuku-chan to Ouji-sama

☆ Small-town girl moves to the city for university (and to find new love). One of her boarding house roommates is a 10/10 but their first meeting doesn't exactly leave the best impression~

Hikaeme ni Itte mo, Kore wa Ai

☆ one of my all-time favourites?? Super school-focused, perpetually stressed FL comes across the battered and bruised delinquent ML in the rain and patches him up. He makes sure to repay the favour (insert saluting emoji here)

Kaoru Hana wa Rin to Saku

☆ modern day romeo/juliet situation except no one dies! ML and FL are from rival schools (lower-class, bad grades vs wealthy scholars) and are perceived very differently. ML is soft and sweet despite his appearance and encounters FL as a customer at his family's cake shop. She treats him differently than others have (read: like a human person) and oooooo it's so sugary!!

Super no Ura de Yani Suu Futari *technically seinen but........

☆ 45 y/o ML's only joys in life are going to the convenience store to talk to his favourite bubbly cashier and smoking out back with an edgy young woman who he's definitely more familiar with than he realizes..........

Uruwashi no Yoi no Tsuki

☆ FL presents much less feminine than her peers earning her the nickname of "prince" and she's always treated as such. But then she meets ML who is a fellow """prince""" and he sees her for who she is and appreciates her beauty both inside and out~

Yamaguchi-kun wa Warukunai

☆ FL has an unfortunate run-in with a creep on the train en route to her first day of high school and is saved by her delinquent classmate (ML). he's in everyone's bad books but the more she learns about him, the more determined she gets to change his reputation

Yubisaki to Renren

☆ FL is a deaf and lovely, but sheltered, college student and has a chance encounter with the ML, who is much more worldly and adventurous than she is annnnd cue tears fr

i almost added about 600 000 000 000 more but i'll hold back for now LOL if you have any recs or want to talk shoujo, please don't hesitate to reach out!!

[fishboneart]

Fishbone #010

the human cost rolls downhill

5 source images, 9 layers.

I hate having to write this. I hate that this is a thing that happened to be written about.

In early 2024 a private virtual clinic providing medical care for a vulnerable and underserved patient demographic allegedly replaced 80% of its human staff with machine learning software.

As far as I can find this hasn't been reported on in the media so far and many of the details are currently not public record. I can't confirm how many staff were laid off, how many quit, how many remain, and how many of those are medics vs how many are admin. I can't confirm exact dates or software applications. This uncertainty about key details is why I'm not naming the clinic. I don't want to accidentally do a libel.

I'm not a journalist and ancestors willing researching this post is as close as I'll ever have to get. It's been extremely depressing. The patient testimonials are abundant and harrowing.

What I have been able to confirm is that the clinic has publicly announced they are "embracing AI," and their FAQs state that their "algorithms" assess patients' medical history, create personalised treatment plans, and make recommendations for therapies, tests, and medications. This made me scream out loud in horror.

Exploring the clinic's family of sites I found that they're using Zoho to manage appointment scheduling. I don't know what if any other applications they're using Zoho for, or whether they're using other software alongside it. Zoho provides office, collaboration, and customer relationship management products; things like scheduling, videocalls, document sharing, mail sorting, etc.

The clinic's recent Glassdoor reviews are appalling, and make reference to increased automation, layoffs, and hasty ai implementation.

The patient community have been reporting abnormally high rates of inadequate and inappropriate care since late February/early March, including:

Wrong or incomplete prescriptions

Inability to contact the clinic

Inability to cancel recurring payments

Appointments being cancelled

Staff simply failing to attend appointments

Delayed prescriptions

Wrong or incomplete treatment summaries

Unannounced dosage or medication changes

The clinic's FAQ suggests that this is a temporary disruption while the new automation workflows are implemented, and service should stabilise in a few months as the new workflows come online. Frankly I consider this an unacceptable attitude towards human lives and health. Existing stable workflows should not be abandoned until new ones are fully operational and stable. Ensuring consistent and appropriate care should be the highest priority at all times.

The push to introduce general-use machine learning into specialised areas of medicine is a deadly one. There are a small number of experimental machine learning models that may eventually have limited use in highly specific medical contexts, to my knowledge none are currently commercially available. No commercially available current generation general use machine learning model is suitable or safe for medical use, and it's almost certain none ever will be.

Machine learning simply doesn't have the capacity to parse the nuances of individual health needs. It doesn't have the capacity to understand anything, let alone the complexities of medical care. It amplifies bias and it "hallucinates" and current research indicates there's no way to avoid either. All it will take for patients to die is for a ML model to hallucinate an improper diagnosis or treatment that's rubber stamped by an overworked doctor.

Yet despite the fact that it is not and will never be fit for purpose, general use machine learning has been pushed fait accompli into the medical lives of real patients, in service to profit. Whether the clinic itself or the software developers or both, someone is profiting from this while already underserved and vulnerable patients are further neglected and endangered.

This is inevitable by design. Maximising profit necessitates inserting the product into as many use cases as possible irrespective of appropriateness. If not this underserved patient group, another underserved patient group would have been pressed, unconsenting, into unsupervised experiments in ML medicine--and may still. The fewer options and resources people have, the easier they are to coerce. You can do whatever you want to those who have no alternative but to endure it.

For profit to flow upwards, cost must flow downwards. This isn't an abstract numerical principle it's a deadly material fact. Human beings, not abstractions, bear the cost of the AI bubble. The more marginalised and exploited the human beings, the more of the cost they bear. Overexploited nations bear the burden of mining, manufacture, and pollution for the physical infrastructure to exist, overexploited workers bear the burden of making machine learning function at all (all of which I will write more about another day), and now patients who don't have the option to refuse it bear the burden of its overuse. There have been others. There will be more. If the profit isn't flowing to you, the cost is--or it will soon.

It doesn't have to be like this. It's like this because humans made it this way, we could change it. Indeed, we must if we are to survive.

[pearl484-blog]

Replay's Senti-monsters

One thing that Pearl and Fire Opal like to do is have me listen to their ideas about their ML fanfic to the point of annoyance.

Their favorite one, Replay, is a Peggy Sue fic where Adrien goes back in time with a Peacock amd teams up with his past self from Origins.

They like to call it a retelling or a re-imagining of the Spiderman clone saga. But really it only explores to the themes raised by it.

However, there is thing I find interesting

In Replay, Senti-monsters are treated as magic's answers to robots. They range from barely more sentient than a toaster to fully sapient. (Sentience vs sapience)

One planned reoccuring plot point is that the Adriens want to create a sapient senti-monster. Future!Adrien wishes to do this by upgrading the "AI", for lack of a better description, of his sentimonster while Past!Adrien keeps trying to make new ones to be sentient.

There is one scene that was cut from Feast (yes, they plan stuff way out and need to focus on their current friggin' chapter) in which Mayura wakes Feast and threatens it to make it comply and Future!Adrien is insanely jealous that Fu's first attempt to make a sentimonster is not only able to recognize a genuine threat to its life, but make a decision and respond without hesitation.

Fu later tries to comfort him, which is awkward as Fu would prefer to talk about ANYTHING else besides his trauma about making a sentimonster that ate his entire life.

He tries to cheer Adrien up by saying he had a lifetime of magical training before he made it, and Adrirn pointing out that he was basically like what? 11? 12? How is he supposed to compete with that of Mayura can make a perfectly sapient Ladybug on her first try and a 12 year old can make a sentient sentimonster?

It's actually good angst and introspection, even if most of the humor comes from future!Adrien being jealous that his mostly obedient senti-monster is nothing like the ravenous mouth attached to a black hole that is Fu's monster.

[erisluna35ocblog]

Character Art, Notes and other Info:

General:

Meaning of Names

Notes on main ships

Notes on affection

Shizuke's Canon Events

Characterization Across AUs:

Fiona Kuznetsov

Keagan Gerald Aurelio Ashworth

Natalia Hale

Shizuke Midorikawa

Blair Crawford

Blake Crawford

Damien Guerrero

Zephyr Ryder

ML AU:

ML AU Intro

ML AU Timeline

Shizuke and Blair ML AU first concept art

Shizuke and Blair emoting 1

Shizuke emoting 2 Blair emoting 2

ML AU love square chart

LadyBlair and ShizuChat ships

Shizuke and Blair N2CatS flashbacks

Shizuke and Blair akumasonas (?)

Natalia and Blake akumasonas

Natalia's boyfriend is too tall

Team Roles (mainly Shizuke and Blair)

Kaji Fuyu and Blake ML AU first concept art

Blake Kaji and Fuyu ML AU digital art

Concept Scenes for BTaL

Full Cast ML AU Chibis

Ai and Chihiro Tsurugi

How Chloe Bourgeois influences a good chunk of my cast

Picture Compilation of Blair compared to my Lila art

Blair as Li Impiratrice

N2Cats as of Chapter 16

ShizuBlair Compilation (ML AU):

ShizuBlair are not on a date

ShizuBlair are finally dating

Blair is sick. Love sick.

Blair is a Demiromantic

Shizuke carries a picture of "Hawkmoth"

ShizuBlair kiss

Barbie Mugshot Meme

Shizuke is moving on

Keagan selling pictures of Shizuke to Chat Noire

OCs and their Canon Counterparts for N2CatS:

Blair and Adrien (N2CatS official cover pic in FF.Net)

Blair and Lila

Blake and Lila

Shizuke and Marinette

Keagan and Blake and Alya

OC Design Notes (ML AU):

Shizuke Blair Blake Natalia

The Past Gen Heroes (ML AU):

Bianca the Vixen

Travis the Peacock

William the Butterfly

Jordan the Bee

The parents on set

ML AU vs OG Story Notes:

Shizuke Blair and Kaji, ML AU vs OG Story character evolution

ShizuBlair, ML AU vs OG Story dynamic

Shizuke, ML AU vs OG Story characterization

ML Reverse AU:

Blair as a villain notes

Shizuke as a villain

Shizuke vs Blair on redemption

If they had a Reverse Special

The Good Guys

Reverse!AU Shizuke Midorikawa pic

Reverse!AU Blair Crawford pic

Reverse!AU Fiona Kuznetsov pic

Reverse!AU Blake Crawford pic

Reverse!AU Keagan Gerald Aurelio Ashworth pic

Scene: Blair is perfectly fine

OG Story:

OG Story Main Cast

OG Story Character Archetypes

OG Story finale outfits

Natalia the Sea Witch

Lucia the Sorceress Princess

Lucia and her Puppets concept art

OG Story Keagan's opinions on ShizuBlair

OG ShizuBlair based on a picrew

Headshots: Crawford Parents Side Characters batch 1 Side Characters batch 2

Otome Game AU:

OG notes and art

Revamp notes

[naya-mishra]

[aiinstitutedelhi]

Artificial Intelligence vs Machine Learning

Both AI and ML have gained significant attention in recent years, as they hold the potential to revolutionize various industries and reshape our everyday lives. 

To understand the differences between AI and ML, it's essential to understand artificial intelligence and machine learning.

[tutort-academy]

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HackerRank and LeetCode are popular online competitive programming platforms for software engineers who are looking to practice for their technical interviews👩‍💻

Both platforms are great ways to provide an easy way to practice common algorithmic and data structure problems in preparation for an interview 💻

➡️ Let's see the difference between HackerRank and LeetCode👆

➡️ If you want to start your career into the field of data science, ML and AI visit here-- www.tutort.net 📍

[sorrowfulsoul]

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A Aa Ab Ac Ad Ae Af Ag Ah Ai Aj Ak Al Am An Ao Ap Aq Ar As At Au Av Aw Ax Ay Az

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C Ca Cb Cc Cd Ce Cf Cg Ch Ci Cj Ck Cl Cm Cn Co Cp Cq Cr Cs Ct Cu Cv Cw Cx Cy Cz

D Da Db Dc Dd De Df Dg Dh Di Dj Dk Dl Dm Dn Do Dp Dq Dr Ds Dt Du Dv Dw Dx Dy Dz

E Ea Eb Ec Ed Ee Ef Eg Eh Ei Ej Ek El Em En Eo Ep Eq Er Es Et Eu Ev Ew Ex Ey Ez

F Fa Fb Fc Fd Fe Ff Fg Fh Fi Fj Fk Fl Fm Fn Fo Fp Fq Fr Fs Ft Fu Fv Fw Fx Fy Fz

G Ga Gb Gc Gd Ge Gf Gg Gh Gi Gj Gk Gl Gm Gn Go Gp Gq Gr Gs Gt Gu Gv Gw Gx Gy Gz

H Ha Hb Hc Hd He Hf Hg Hh Hi Hj Hk Hl Hm Hn Ho Hp Hq Hr Hs Ht Hu Hv Hw Hx Hy Hz

I Ia Ib Ic Id Ie If Ig Ih Ii Ij Ik Il Im In Io Ip Iq Ir Is It Iu Iv Iw Ix Iy Iz

J Ja Jb Jc Jd Je Jf Jg Jh Ji Jj Jk Jl Jm Jn Jo Jp Jq Jr Js Jt Ju Jv Jw Jx Jy Jz

K Ka Kb Kc Kd Ke Kf Kg Kh Ki Kj Kk Kl Km Kn Ko Kp Kq Kr Ks Kt Ku Kv Kw Kx Ky Kz

L La Lb Lc Ld Le Lf Lg Lh Li Lj Lk Ll Lm Ln Lo Lp Lq Lr Ls Lt Lu Lv Lw Lx Ly Lz

M Ma Mb Mc Md Me Mf Mg Mh Mi Mj Mk Ml Mm Mn Mo Mp Mq Mr Ms Mt Mu Mv Mw Mx My Mz

N Na Nb Nc Nd Ne Nf Ng Nh Ni Nj Nk Nl Nm Nn No Np Nq Nr Ns Nt Nu Nv Nw Nx Ny Nz

O Oa Ob Oc Od Oe Of Og Oh Oi Oj Ok Ol Om On Oo Op Oq Or Os Ot Ou Ov Ow Ox Oy Oz

P Pa Pb Pc Pd Pe Pf Pg Ph Pi Pj Pk Pl Pm Pn Po Pp Pq Pr Ps Pt Pu Pv Pw Px Py Pz

Q Qa Qb Qc Qd Qe Qf Qg Qh Qi Qj Qk Ql Qm Qn Qo Qp Qq Qr Qs Qt Qu Qv Qw Qx Qy Qz

R Ra Rb Rc Rd Re Rf Rg Rh Ri Rj Rk Rl Rm Rn Ro Rp Rq Rr Rs Rt Ru Rv Rw Rx Ry Rz

S Sa Sb Sc Sd Se Sf Sg Sh Si Sj Sk Sl Sm Sn So Sp Sq Sr Ss St Su Sv Sw Sx Sy Sz

T Ta Tb Tc Td Te Tf Tg Th Ti Tj Tk Tl Tm Tn To Tp Tq Tr Ts Tt Tu Tv Tw Tx Ty Tz

U Ua Ub Uc Ud Ue Uf Ug Uh Ui Uj Uk Ul Um Un Uo Up Uq Ur Us Ut Uu Uv Uw Ux Uy Uz

V Va Vb Vc Vd Ve Vf Vg Vh Vi Vj Vk Vl Vm Vn Vo Vp Vq Vr Vs Vt Vu Vv Vw Vx Vy Vz

W Wa Wb Wc Wd We Wf Wg Wh Wi Wj Wk Wl Wm Wn Wo Wp Wq Wr Ws Wt Wu Wv Ww Wx Wy Wz

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[agox]

Last night I got high and wrote a program to find all of the unused elemental symbols. It turns out there’s 585 of them and it’s much less interesting the next day.

One interesting finding: I couldn’t be bothered to generate all possible one and two letter strings, so I asked ChatGPT to write me code that would do all that for me. It worked surprisingly well!

['a', 'd', 'e', 'g', 'h', 'j', 'l', 'm', 'q', 'r', 't', 'x', 'z', 'aa', 'ab', 'ad', 'ae', 'af', 'ah', 'ai', 'aj', 'ak', 'an', 'ao', 'ap', 'aq', 'av', 'aw', 'ax', 'ay', 'az', 'bb', 'bc', 'bd', 'bf', 'bg', 'bj', 'bl', 'bm', 'bn', 'bo', 'bp', 'bq', 'bs', 'bt', 'bu', 'bv', 'bw', 'bx', 'by', 'bz', 'cb', 'cc', 'cg', 'ch', 'ci', 'cj', 'ck', 'cp', 'cq', 'ct', 'cv', 'cw', 'cx', 'cy', 'cz', 'da', 'dc', 'dd', 'de', 'df', 'dg', 'dh', 'di', 'dj', 'dk', 'dl', 'dm', 'dn', 'do', 'dp', 'dq', 'dr', 'dt', 'du', 'dv', 'dw', 'dx', 'dz', 'ea', 'eb', 'ec', 'ed', 'ee', 'ef', 'eg', 'eh', 'ei', 'ej', 'ek', 'el', 'em', 'en', 'eo', 'ep', 'eq', 'et', 'ev', 'ew', 'ex', 'ey', 'ez', 'fa', 'fb', 'fc', 'fd', 'ff', 'fg', 'fh', 'fi', 'fj', 'fk', 'fn', 'fo', 'fp', 'fq', 'fs', 'ft', 'fu', 'fv', 'fw', 'fx', 'fy', 'fz', 'gb', 'gc', 'gf', 'gg', 'gh', 'gi', 'gj', 'gk', 'gl', 'gm', 'gn', 'go', 'gp', 'gq', 'gr', 'gs', 'gt', 'gu', 'gv', 'gw', 'gx', 'gy', 'gz', 'ha', 'hb', 'hc', 'hd', 'hh', 'hi', 'hj', 'hk', 'hl', 'hm', 'hn', 'hp', 'hq', 'hr', 'ht', 'hu', 'hv', 'hw', 'hx', 'hy', 'hz', 'ia', 'ib', 'ic', 'id', 'ie', 'if', 'ig', 'ih', 'ii', 'ij', 'ik', 'il', 'im', 'io', 'ip', 'iq', 'is', 'it', 'iu', 'iv', 'iw', 'ix', 'iy', 'iz', 'ja', 'jb', 'jc', 'jd', 'je', 'jf', 'jg', 'jh', 'ji', 'jj', 'jk', 'jl', 'jm', 'jn', 'jo', 'jp', 'jq', 'jr', 'js', 'jt', 'ju', 'jv', 'jw', 'jx', 'jy', 'jz', 'ka', 'kb', 'kc', 'kd', 'ke', 'kf', 'kg', 'kh', 'ki', 'kj', 'kk', 'kl', 'km', 'kn', 'ko', 'kp', 'kq', 'ks', 'kt', 'ku', 'kv', 'kw', 'kx', 'ky', 'kz', 'lb', 'lc', 'ld', 'le', 'lf', 'lg', 'lh', 'lj', 'lk', 'll', 'lm', 'ln', 'lo', 'lp', 'lq', 'ls', 'lt', 'lw', 'lx', 'ly', 'lz', 'ma', 'mb', 'me', 'mf', 'mh', 'mi', 'mj', 'mk', 'ml', 'mm', 'mp', 'mq', 'mr', 'ms', 'mu', 'mv', 'mw', 'mx', 'my', 'mz', 'nc', 'nf', 'ng', 'nj', 'nk', 'nl', 'nm', 'nn', 'nq', 'nr', 'ns', 'nt', 'nu', 'nv', 'nw', 'nx', 'ny', 'nz', 'oa', 'ob', 'oc', 'od', 'oe', 'of', 'oh', 'oi', 'oj', 'ok', 'ol', 'om', 'on', 'oo', 'op', 'oq', 'or', 'ot', 'ou', 'ov', 'ow', 'ox', 'oy', 'oz', 'pc', 'pe', 'pf', 'pg', 'ph', 'pi', 'pj', 'pk', 'pl', 'pn', 'pp', 'pq', 'ps', 'pv', 'pw', 'px', 'py', 'pz', 'qa', 'qb', 'qc', 'qd', 'qe', 'qf', 'qg', 'qh', 'qi', 'qj', 'qk', 'ql', 'qm', 'qn', 'qo', 'qp', 'qq', 'qr', 'qs', 'qt', 'qu', 'qv', 'qw', 'qx', 'qy', 'qz', 'rc', 'rd', 'ri', 'rj', 'rk', 'rl', 'rm', 'ro', 'rp', 'rq', 'rr', 'rs', 'rt', 'rv', 'rw', 'rx', 'ry', 'rz', 'sa', 'sd', 'sf', 'sh', 'sj', 'sk', 'sl', 'so', 'sp', 'sq', 'ss', 'st', 'su', 'sv', 'sw', 'sx', 'sy', 'sz', 'td', 'tf', 'tg', 'tj', 'tk', 'tn', 'to', 'tp', 'tq', 'tr', 'tt', 'tu', 'tv', 'tw', 'tx', 'ty', 'tz', 'ua', 'ub', 'uc', 'ud', 'ue', 'uf', 'ug', 'uh', 'ui', 'uj', 'uk', 'ul', 'um', 'un', 'uo', 'up', 'uq', 'ur', 'us', 'ut', 'uu', 'uv', 'uw', 'ux', 'uy', 'uz', 'va', 'vb', 'vc', 'vd', 've', 'vf', 'vg', 'vh', 'vi', 'vj', 'vk', 'vl', 'vm', 'vn', 'vo', 'vp', 'vq', 'vr', 'vs', 'vt', 'vu', 'vv', 'vw', 'vx', 'vy', 'vz', 'wa', 'wb', 'wc', 'wd', 'we', 'wf', 'wg', 'wh', 'wi', 'wj', 'wk', 'wl', 'wm', 'wn', 'wo', 'wp', 'wq', 'wr', 'ws', 'wt', 'wu', 'wv', 'ww', 'wx', 'wy', 'wz', 'xa', 'xb', 'xc', 'xd', 'xf', 'xg', 'xh', 'xi', 'xj', 'xk', 'xl', 'xm', 'xn', 'xo', 'xp', 'xq', 'xr', 'xs', 'xt', 'xu', 'xv', 'xw', 'xx', 'xy', 'xz', 'ya', 'yc', 'yd', 'ye', 'yf', 'yg', 'yh', 'yi', 'yj', 'yk', 'yl', 'ym', 'yn', 'yo', 'yp', 'yq', 'yr', 'ys', 'yt', 'yu', 'yv', 'yw', 'yx', 'yy', 'yz', 'za', 'zb', 'zc', 'zd', 'ze', 'zf', 'zg', 'zh', 'zi', 'zj', 'zk', 'zl', 'zm', 'zo', 'zp', 'zq', 'zs', 'zt', 'zu', 'zv', 'zw', 'zx', 'zy', 'zz']

[nomorepixels]

Sometimes you’ll meet people who go ‘pfft, what’s the deal with AI cores in GPUs, they should get rid of it’ without fully understanding what hardware like Tensor cores could do. Sure there’d be the usual nVidia vs AMD camps battling whether DLSS or FSR is better. IMHO, FSR is a cheaper, easier to implement feature that costs next to nothing to implement. DLSS has to be trained months using large data number crunchers to produce good results. But therein lies the conceit: FSR is a fixed algorithm. DLSS is a machine-learning (ML) implementation. Which means it can be trained to be better. A lot better.

Let’s forget about 3D stuff for now and go back to what this blog mainly covers about: old 2D visuals. Currently a lot of emulators have lightweight pixel scalers such as xBRz and HQ4x that attempt to upscale visuals so that they don’t look so square on modern LCD screens. This is because some people were old enough to remember looking at the visuals on CRT screens that were somewhat fuzzy via a combination of phosphor bloom and scanlines. These filters were great when they were introduced in zSNES because it upscaled the lowres console graphics to twice the resolution, but ultimately still displayed on a CRT monitor. In the modern era where HD LCD is the norm, these filters look horrendous. In a twist of irony, many resort to using scanline filters in an attempt to resurrect the original feel of the visuals with varying success.

The past recent years came the introduction of machine learning processing, which utilise neural networks to twist the previous paradigm of having humans come up with the algorithms of how pixels should be upscaled. Instead, the computer itself comes up with the methodology by comparing the input, expected output and its own predictions over a hundred thousand iterations. The result of this can be incredible, depending on what data was fed to the computer to achieve this objective. The downside is this upscaling takes a lot of GPU power, roughly clocking about 5-10 fps for a 4x upscale of a 320x240 pixel visual.

Companies that believe ML is the future commit to allocating a significant chunk of their chip silicone real-estate to AI-centric cores, such as nVidia and Apple (or ARM in general). Meanwhile, AMD is in the firm believe that their Compute Units are sufficient enough to handle ML tasks. However, them dropping out of developing a ML-based upscaler for their GPUs (even with heavy promotion with their X-Box partner Microsoft, supposedly to be done via DirectML) and coming out with FSR instead... puts doubt into this notion.

Nvidia is investing for things to come, despite the initial missteps in ML tech such as the first implementation of DLSS. Right now we already other interesting implementations of ML tech such DLAA and RTX voice, so who knows, when GPUs become fast enough to do 2D ML upscaling at 60fps, then the drivers, libraries, data and models are already ready for mass implementation.

[strelles-universe]

Kujhikoslan, the Complete Grammar Guide

Of all the groups in Strelles, the Fox Skulks were the first societies to approach the Sky Kingdoms upon realizing that they had made generous strides in preserving and recovering the Old Tongues. Their language is deeply precious to them and though they respect the decision of the gods to stop the meaningless aggression all species directed at each other, they’re pleased to see that the gods agreed with them reclaiming their Tongues once they understood the point that was being made.

Given that the skulks were the first to invent a written variant of their language, they were among the easiest to rebuild once they could understand the markings and managed to break it down. As such, it’s considered to be the tongue closest to its Old Form before the Connection - something they take great pride in. This means that much like the Sky Kingdoms, the language isn’t exclusive to any rank or position but rather, an inherent part of living within the skulks. 

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Part 1: Phonology and Phonetics

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Kujhikoslan has 19 official consonant sounds;

The squiggly lines are where the pronunciation of the sound isn’t quite set in stone. The skulks towards the west use /ʤ/ instead of /ʧ/.

And there are 5 vowel sounds:

Phonetic Summary

Onset: m n ny p b t d k g s x c tc jh h r y l

General Clusters: nh ns nc mc ts

Coda Clusters: mk nk pk lk

R Clusters: br dr gr nr jhr

L Clusters: nl ml kl tl pl

Nucleus: a e i o u

Nucleus Clusters (a): ai aia 

Nucleus Clusters  (i): ia ii 

Nucleus Clusters (o): oa

Coda: s n l k

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Part 2: Word Order

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SVO Primary - The cat jumps over the tree

SOV Secondary - The cat, over the log jumps

Demonstrative - Noun | This goat

Numeral - Noun | One goat

Possessive - Noun| Your goat

Noun - Adjective | Goat big

Noun - Genitives | The sandwich of the goat (not, the goat’s sandwich)

Noun - Relative Clauses | The goat, who ate the sandwich is thick

Verb - Auxiliary | Go must (I go must | I must go)

Verb - Subordinate Verb | Went to buy (vs. to buy went)

Adjective - Adverb | Big really (really big)

Yes/No Particles - Final (End of a sentence)

Question Words - Final (End of a sentence)

Proper Noun - Common Noun | state Kansas instead of Kansas State

Modifier Order: Quantity - Opinion - Age - Size - Origin - Color - Material - Purpose + Noun

Modifier Example:Two pretty old large Dutch white cotton goats.

Compounds:

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Part 3: Animacy Based Noun Classes

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Kujhikoslan has simply tri-class animacy system. The hierarchy is very basically animate, semi-animate and inanimate objects.

The Animate Class is usually fairly obvious - it’s moving, breathing things like fellow creatures and most plants that grow rapidly such as flowers. The Inanimate Class is equally obvious consisting mostly of things such as trees, rocks, mud and certain creatures that a skulk can be stubborn about.

The Semi Animate Class is for things granted a degree of life from their religious beliefs.

Animate Examples (-(r)a) - Jhiko (fox), Kis (cat), Tciri (a partner bird)

Semi Animate Examples (-(m)e) - Ansel (wind), 

Inanimate Examples (-(h)a)) - Komok (rock), Hiyil (river)

When put into practice, the animacy system applies to and affects numerals and adjectives. 

Ainra kis  - one cat Arume ansel - blue wind Ainhal hiyil - one river

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Part 4: Grammatical Number

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Much like Adovusala, the skulks of strelles have a rather simple grammatical number system. Only a singular/plural divide leaving the singular bare - jhiiko (fox) - and adding the prefix ku- to make it plural - kujhiko (foxes). 

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Part 5: Tense and Aspect

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There are four tenses and aspects in Kujhikoslan the same as Adovusala, there are also four tenses in this language. The four aspects are the perfective, habitual, continuous and pausative aspects - the four tenses being distant past, past, present and future tenses.

Here’s the example word;

In the second and third persons, the animacy hierarchy triggers agreement on the verbs.

Asla - to speak (animate) Aslamri - to speak (semi animate) Aslaso - to speak (inanimate)

These agreements are meant to emphasize what’s happening - generally speaking, rocks don’t speak and ergo, the agreement is meant to confirm that yes, this improbable interpretation is indeed what this sentence means.

Hel kis aslar ir hel ansel - the cat speaks to the wind

In this situation, the cat is the one doing the speaking and ergo, the word used is aslar. 

Hel anser aslamrir ir hel kis - the wind speaks to the cat

Now here, it’s the wind speaking to a cat. In this case, it’s assumed that the wind is actually a spirit of a sort, whispering to the cat rather than the actual wind itself.

Hel komok aslasor ir hel kis - the rock speaks to the cat

Same situation as the previous sentence except even more unbelievable to the listener - rocks rarely speak if ever, so a speaking rock is typically interpreted as a trapped spirit or even a demon. 

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Part 6: Pronouns

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Now unlike the language of the packs, Kujhikoslan doesn’t have a system of honorifics making their pronouns much simpler than Adovusala;

The possessives unfortunately get a little bit more complicated than that as the skulks differentiate between alienable and inalienable objects.

Alienable Nouns are things you have a choice over owning and can be divorced from you such as a food or an adornment. Meanwhile, Inalienable nouns are things that can’t be divorced from the person such as a blood relations and your species.

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Part 7: Articles and Demonstratives

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The articles and demonstratives of Kujhikoslan are very simple with definite articles and indefinite articles for a specific thing and a broad thing. Unlike other languages however, the articles are divided up into the three animacy classes;

Hel is the definite articles are;

Ma anser - the wind Ke kommok - the rock Hel bero - the dog

And the indefinite articles are;

Ya bero - a dog Lu neco - an egg El kommok - a rock

The demonstratives have been left alone in the distinction with only a near/far difference;

Yel bero - this dog Sul bero - that dog

[tagxdata22]

MLOps and ML Data pipeline: Key Takeaways

If you have ever worked with a Machine Learning (ML) model in a production environment, you might have heard of MLOps. The term explains the concept of optimizing the ML lifecycle by bridging the gap between design, model development, and operation processes.

As more teams attempt to create AI solutions for actual use cases, MLOps is now more than just a theoretical idea; it is a hotly debated area of machine learning that is becoming increasingly important. If done correctly, it speeds up the development and deployment of ML solutions for teams all over the world.

MLOps is frequently referred to as DevOps for Machine Learning while reading about the word. Because of this, going back to its roots and drawing comparisons between it and DevOps is the best way to comprehend the MLOps concept.

MLOps vs DevOps

DevOps is an iterative approach to shipping software applications into production. MLOps borrows the same principles to take machine learning models to production. Either Devops or MLOps, the eventual objective is higher quality and control of software applications/ML models.

What is MLOps?

Machine Learning Operations is referred to as MLOps. Therefore, the function of MLOps is to act as a communication link between the operations team overseeing the project and the data scientists who deal with machine learning data.

The key MLOps principles are:

Versioning – keeping track of the versions of data, ML model, code around it, etc.;

Testing – testing and validating an ML model to check whether it is working in the development environment;

Automation – trying to automate as many ML lifecycle processes as possible;

Reproducibility – we want to get identical results given the same input;

Deployment – deploying the model into production;

Monitoring – checking the model’s performance on real-world data.

What are the benefits of MLOps?

The primary benefits of MLOps are efficiency, scalability, and risk reduction. 

Efficiency: MLOps allows data teams to achieve faster model development, deliver higher quality ML models, and faster deployment and production. 

Scalability: Thousands of models may be supervised, controlled, managed, and monitored for continuous integration, continuous delivery, and continuous deployment thanks to MLOps’ extensive scalability and management capabilities. MLOps, in particular, makes ML pipelines reproducible, enables closer coordination between data teams, lessens friction between DevOps and IT, and speeds up release velocity.

Risk reduction: Machine learning models often need regulatory scrutiny and drift-check, and MLOps enables greater transparency and faster response to such requests and ensures greater compliance with an organization’s or industry’s policies.

Data pipeline for ML operations

One significant difference between DevOps and MLOps is that ML services require data–and lots of it. In order to be suitable for ML model training, most data has to be cleaned, verified, and tagged. Much of this can be done in a stepwise fashion, as a data pipeline, where unclean data enters the pipeline, and then the training, validating, and testing data exits the pipeline.

The data pipeline of a project involves several key steps:

Data collection: 

Whether you source your data in-house, open-source, or from a third-party data provider, it’s important to set up a process where you can continuously collect data, as needed. You’ll not only need a lot of data at the start of the ML development lifecycle but also for retraining purposes at the end. Having a consistent, reliable source for new data is paramount to success.

Data cleansing: 

This involves removing any unwanted or irrelevant data or cleaning up messy data. In some cases, it may be as simple as converting data into the format you need, such as a CSV file. Some steps of this may be automatable.

Data annotation: 

Labeling your data is one of the most time-consuming, difficult, but crucial, phases of the ML lifecycle. Companies that try to take this step internally frequently struggle with resources and take too long. Other approaches give a wider range of annotators the chance to participate, such as hiring freelancers or crowdsourcing. Many businesses decide to collaborate with external data providers, who can give access to vast annotator communities, platforms, and tools for any annotating need. Depending on your use case and your need for quality, some steps in the annotation process may potentially be automated.

After the data has been cleaned, validated, and tagged, you can begin training the ML model to categorize, predict, or infer whatever it is that you want the model to do. Training, validation, and hold-out testing datasets are created out of the tagged data. The model architecture and hyperparameters are optimized many times using the training and validation data. Once that is finished, you test the algorithm on the hold-out test data one last time to check if it performs enough on the fresh data you need to release.

Setting up a continuous data pipeline is an important step in MLOps implementation. It’s helpful to think of it as a loop, because you’ll often realize you need additional data later in the build process, and you don’t want to have to start from scratch to find it and prepare it.

Conclusion

MLOps help ensure that deployed models are well maintained, performing as expected, and not having any adverse effects on the business. This role is crucial in protecting the business from risks due to models that drift over time, or that are deployed but unmaintained or unmonitored.

TagX is involved in delivering Data for each step of ML operations. At TagX, we provide high-quality annotated training data to power the world’s most innovative machine learning and business solutions. We can help your organization with data collection, Data cleaning,  data annotation, and synthetic data to train your Machine learning models.