Artificial Intelligence

[Brian Peacock]

I for one welcome our new finger-crushing overlords etc etc...

[pErvinalia]

Check mate, kid!

[JimC]

Combat chess, the new frontier!

[Tero]

AI targeting homeless to eradicate.
https://www.theguardian.com/technology/ ... -detection

[pErvinalia]

New algorithm discovers language just by watching videos

Mark Hamilton, an MIT PhD student in electrical engineering and computer science and affiliate of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), wants to use machines to understand how animals communicate. To do that, he set out first to create a system that can learn human language “from scratch.”

“Funny enough, the key moment of inspiration came from the movie ‘March of the Penguins.’ There’s a scene where a penguin falls while crossing the ice, and lets out a little belabored groan while getting up. When you watch it, it’s almost obvious that this groan is standing in for a four letter word. This was the moment where we thought, maybe we need to use audio and video to learn language,” says Hamilton. “Is there a way we could let an algorithm watch TV all day and from this figure out what we're talking about?”

“Our model, ‘DenseAV,’ aims to learn language by predicting what it’s seeing from what it’s hearing, and vice-versa. For example, if you hear the sound of someone saying ‘bake the cake at 350’ chances are you might be seeing a cake or an oven. To succeed at this audio-video matching game across millions of videos, the model has to learn what people are talking about,” says Hamilton.

Once they trained DenseAV on this matching game, Hamilton and his colleagues looked at which pixels the model looked for when it heard a sound. For example, when someone says “dog,” the algorithm immediately starts looking for dogs in the video stream. By seeing which pixels are selected by the algorithm, one can discover what the algorithm thinks a word means.

Interestingly, a similar search process happens when DenseAV listens to a dog barking: It searches for a dog in the video stream. “This piqued our interest. We wanted to see if the algorithm knew the difference between the word ‘dog’ and a dog’s bark,” says Hamilton. The team explored this by giving the DenseAV a “two-sided brain.” Interestingly, they found one side of DenseAV’s brain naturally focused on language, like the word “dog,” and the other side focused on sounds like barking. This showed that DenseAV not only learned the meaning of words and the locations of sounds, but also learned to distinguish between these types of cross-modal connections, all without human intervention or any knowledge of written language.

One branch of applications is learning from the massive amount of video published to the internet each day: “We want systems that can learn from massive amounts of video content, such as instructional videos,” says Hamilton. “Another exciting application is understanding new languages, like dolphin or whale communication, which don’t have a written form of communication. Our hope is that DenseAV can help us understand these languages that have evaded human translation efforts since the beginning. Finally, we hope that this method can be used to discover patterns between other pairs of signals, like the seismic sounds the earth makes and its geology.”

[JimC]

Clever!

[Tero]

https://www.oneusefulthing.org/p/someth ... strawberry

From Co-Intelligence to…
Using o1-preview means confronting a paradigm change in AI. Planning is a form of agency, where the AI arrives at conclusions about how to solve a problem on its own, without our help. You can see from the video above that the AI does so much thinking and heavy lifting, churning out complete results, that my role as a human partner feels diminished. It just does its thing and hands me an answer. Sure, I can sift through its pages of reasoning to spot mistakes, but I no longer feel as connected to the AI output, or that I am playing as large a role in shaping where the solution is going. This isn’t necessarily bad, but it is different.

[Sean Hayden]

--ask it for a book on how best To Serve The Rich.

[Svartalf]

meh, the rich are not good eating, they are either too fatty, or totally gamey from excessive working out

[Tero]

I can imagine some good cuts from a roasted Elon Musk. Trump.....blaah.

[Brian Peacock]

Video includes a good layman's explanation of diffusion and LLM algorithm training.

[macdoc]

Big gains from Ai assisted nursing

https://www.cbc.ca/news/health/ai-health-care-1.7322671

[Sean Hayden]

I like the idea of AI for people forced to be in solitary confinement for the majority of their time in jail.

[Svartalf]

well, there might be rewards for proving the AI failing Turing's test.

[Tero]

ChatGPT Has No Place in the Classroom

By Emily
Their answer to the third question (What makes ChatGPT different?) is also worse than useless. They say that generative AI (including ChatGPT) is different because it can create content. What it actually does is extrude synthetic text that mimics the form and style of something a person might write, but without any accountability for the actual content of that text (what it means). Oh, and the text isn't fully random but will reproduce biases in the training data. Training data which OpenAI still is not open about.

The cognitive dissonance here never ceases to amaze me. The people creating this guide (and the tech it's based on) must surely know that their product is designed to make shit up. This is why they say what differentiates ChatGPT is that it can create "content" ... but don't say anything about the value of that content. And yet, and yet: they would have us believe that it is somehow worth educators’ time to go wading through the synthetic text extruded from this thing to check every last bit and make sure it's accurate. Again we can tell there are no actual, honest user studies underlying these recommendations.

https://buttondown.com/maiht3k/archive/ ... classroom/