#Machine Learning Technologies

[manojkusingh]

Key Machine Learning Technologies to Look for in an ML Service Provider

When selecting an ML company in India businesses should verify their technological skills with attention given to their knowledge of cloud computing, deep learning frameworks, natural language processing, computer vision and MLOps systems. This document shows businesses which ML technologies they need to search for in service providers to find the best fits for their projects.

Read more:

[guerrillatech]

We need to change the way we think about AI and remember that arms races don’t just exist between nations. The problem is once again capitalism, not the technology itself or some other boogeyman.

[disease]

Frank Rosenblatt, often cited as the Father of Machine Learning, photographed in 1960 alongside his most-notable invention: the Mark I Perceptron machine — a hardware implementation for the perceptron algorithm, the earliest example of an artificial neural network, est. 1943.

[incognitopolls]

For the purposes of this poll, research is defined as reading multiple non-opinion articles from different credible sources, a class on the matter, etc.– do not include reading social media or pure opinion pieces.

Fun topics to research:

Can AI images be copyrighted in your country? If yes, what criteria does it need to meet?

Which companies are using AI in your country? In what kinds of projects? How big are the companies?

What is considered fair use of copyrighted images in your country? What is considered a transformative work? (Important for fandom blogs!)

What legislation is being proposed to ‘combat AI’ in your country? Who does it benefit? How does it affect non-AI art, if at all?

How much data do generators store? Divide by the number of images in the data set. How much information is each image, proportionally? How many pixels is that?

What ways are there to remove yourself from AI datasets if you want to opt out? Which of these are effective (ie, are there workarounds in AI communities to circumvent dataset poisoning, are the test sample sizes realistic, which generators allow opting out or respect the no-ai tag, etc)

–

We ask your questions so you don’t have to! Submit your questions to have them posted anonymously as polls.

[textsearchbradleycarlgeigerbrad]

TEXT SEARCH BRADLEY CARL GEIGER AND BRAD GEIGER AND EVERYTHING ASSOCIATED

BRAD GEIGER AND CENTRAL INTELLIGENCE AGENCY

BRADLEY CARL GEIGER AND CENTRAL INTELLIGENCE AGENCY

BRAD GEIGER AND WIKIPEDIA

BRADLEY CARL GEIGER AND WIKIPEDIA

[hermanlowelillyrobertchamberlain]

HERMAN LOWE LILLY ROBERT CHAMBERLAIN

[herigo]

[prokopetz]

I just watched a video where someone is using ChatGPT to generate comments on their code. Even as a layman I feel like I should be screaming at him, but on a scale from 1 to apocalypse, how bad is this?

Machine-generated comments could not possibly be more useless, nonsensical or maliciously misleading than most of the human-generated comments I've seen.

[allthecanadianpolitics]

After months of resisting, Air Canada was forced to give a partial refund to a grieving passenger who was misled by an airline chatbot inaccurately explaining the airline's bereavement travel policy. On the day Jake Moffatt's grandmother died, Moffat immediately visited Air Canada's website to book a flight from Vancouver to Toronto. Unsure of how Air Canada's bereavement rates worked, Moffatt asked Air Canada's chatbot to explain. The chatbot provided inaccurate information, encouraging Moffatt to book a flight immediately and then request a refund within 90 days. In reality, Air Canada's policy explicitly stated that the airline will not provide refunds for bereavement travel after the flight is booked. Moffatt dutifully attempted to follow the chatbot's advice and request a refund but was shocked that the request was rejected.

Continue Reading

Tagging @politicsofcanada

[sag-dab-sar]

Clarification: Generative AI does not equal all AI

💭 "Artificial Intelligence"

AI is machine learning, deep learning, natural language processing, and more that I'm not smart enough to know. It can be extremely useful in many different fields and technologies. One of my information & emergency management courses described the usage of AI as being a "human centaur". Part human part machine; meaning AI can assist in all the things we already do and supplement our work by doing what we can't.

💭 Examples of AI Benefits

AI can help advance things in all sorts of fields, here are some examples:

Emergency Healthcare & Disaster Risk X

Disaster Response X

Crisis Resilience Management X

Medical Imaging Technology X

Commercial Flying X

Air Traffic Control X

Railroad Transportation X

Ship Transportation X

Geology X

Water Conservation X

Can AI technology be used maliciously? Yeh. Thats a matter of developing ethics and working to teach people how to see red flags just like people see red flags in already existing technology.

AI isn't evil. Its not the insane sentient shit that wants to kill us in movies. And it is not synonymous with generative AI.

💭 Generative AI

Generative AI does use these technologies, but it uses them unethically. Its scraps data from all art, all writing, all videos, all games, all audio anything it's developers give it access to WITHOUT PERMISSION, which is basically free reign over the internet. Sometimes with certain restrictions, often generative AI engineers—who CAN choose to exclude things—may exclude extremist sites or explicit materials usually using black lists.

AI can create images of real individuals without permission, including revenge porn. Create music using someones voice without their permission and then sell that music. It can spread disinformation faster than it can be fact checked, and create false evidence that our court systems are not ready to handle.

AI bros eat it up without question: "it makes art more accessible" , "it'll make entertainment production cheaper" , "its the future, evolve!!!"

💭 AI is not similar to human thinking

When faced with the argument "a human didn't make it" the come back is "AI learns based on already existing information, which is exactly what humans do when producing art! We ALSO learn from others and see thousands of other artworks"

Lets make something clear: generative AI isn't making anything original. It is true that human beings process all the information we come across. We observe that information, learn from it, process it then ADD our own understanding of the world, our unique lived experiences. Through that information collection, understanding, and our own personalities we then create new original things.

💭 Generative AI doesn't create things: it mimics things

Take an analogy:

Consider an infant unable to talk but old enough to engage with their caregivers, some point in between 6-8 months old.

Mom: a bird flaps its wings to fly!!! *makes a flapping motion with arm and hands*

Infant: *giggles and makes a flapping motion with arms and hands*

The infant does not understand what a bird is, what wings are, or the concept of flight. But she still fully mimicked the flapping of the hands and arms because her mother did it first to show her. She doesn't cognitively understand what on earth any of it means, but she was still able to do it.

In the same way, generative AI is the infant that copies what humans have done— mimicry. Without understanding anything about the works it has stolen.

Its not original, it doesn't have a world view, it doesn't understand emotions that go into the different work it is stealing, it's creations have no meaning, it doesn't have any motivation to create things it only does so because it was told to.

Why read a book someone isn't even bothered to write?

Related videos I find worth a watch

ChatGPT's Huge Problem by Kyle Hill (we don't understand how AI works)

Criticism of Shadiversity's "AI Love Letter" by DeviantRahll

AI Is Ruining the Internet by Drew Gooden

AI vs The Law by Legal Eagle (AI & US Copyright)

AI Voices by Tyler Chou (Short, flash warning)

Dead Internet Theory by Kyle Hill

-Dyslexia, not audio proof read-

[rthidden]

What is an Algorithm in 30 Seconds?

An algorithm is simply a series of instructions.

Think of a recipe: boil water, add pasta, wait, drain, eat. These are steps to follow.

In computer terms, an algorithm is a set of instructions for a computer to execute.

In machine learning, these instructions enable computers to learn from data, making machine learning algorithms unique and powerful.

[disease]

Garry Kasparov, world champion chess player, succumbing to his public defeat by Deep Blue, IBM: a 'supercomputer' in development at the time. — MAY 11, 1997

[wronghands1]

[exposimetro]

Cyber Beach

[beakers-and-telescopes]

Okay, I have my own opinions about AI, especially AI art, but this is actually a very cool application!

So when you think about it, we can quantify vision/sight using the actual wavelengths of light, and we can quantify hearing using frequency, but there really isn't a way to quantify smell. So scientists at the University of Reading set out to create an AI to do just that.

The AI was trained on a dataset of 5000 known odor-causing molecules. It was given their structures, and a list of various scent descriptors (such as "floral sweet" or "musty" or "buttery") and how well those descriptors fit on a scale of 1-5. After being trained on this data, the AI was able to be shown a new molecule and predict what its scent would be, using the various descriptors.

The AI's prediction abilities were compared against a panel of humans, who would smell the compound of interest and assign the descriptors. The AI's predictions were actually just as good as the human descriptions. Professor Jane Parker, who worked on the project, explained the following.

"We don't currently have a way to measure or accurately predict the odor of a molecule, based on its molecular structure. You can get so far with current knowledge of the molecular structure, but eventually you are faced with numerous exceptions where the odor and structure don't match. This is what has stumped previous models of olfaction. The fantastic thing about this new ML generated model is that it correctly predicts the odor of those exceptions"

Now what can we do with this "AI Nose", you might ask? Well, it may have benefits in the food and fragrance industries, for one. A machine that is able to quickly filter through compounds to find one with specific odor qualities could be a good way to find new, sustainable sources of fragrance in foods or perfumes. The team also believes that this "scent map" that the AI model builds could be linked to metabolism. In other words, odors that are close to each other on the map, or smell similar, are also more likely to be metabolically related

[hackeocafe]

How To Learn Math for Machine Learning FAST (Even With Zero Math Background)

I dropped out of high school and managed to became an Applied Scientist at Amazon by self-learning math (and other ML skills). In this video I'll show you exactly how I did it, sharing the resources and study techniques that worked for me, along with practical advice on what math you actually need (and don't need) to break into machine learning and data science.