Robot Brains: Neurosymbolics

Thoughtful piece that outlines what is necessary for Building Future Intelligence.  Not sure that current results are yet close to this.  Year old.  Technical.   

https://youtu.be/fCoavgGZ64Y

Neurosymbolic Models

86,914 views  Sep 21, 2021  Season One | The Robot Brains Podcast

On the last episode of Season One, our guest is Ilya Sutskever. Ilya is the Co-Founder and Chief Scientist of OpenAI. As a PhD student at Toronto, Ilya was one of the authors on the 2012 AlexNet paper that completely changed the field of AI, resulting in the widespread adoption of deep learning and the avalanche of AI breakthroughs we’ve seen the past 10 years. 

After the AlexNet breakthrough in computer vision, at Google, among many other breakthroughs, Ilya showed that neural networks are unexpectedly great at machine translation, at least at the time it was unexpected, now it’s long become the norm to use neural nets for machine translation. Late 2015 Ilya left Google to co-found OpenAI, where he is Chief Scientist. Some of his breakthroughs include GPT, CLIP, DallE, Codex. Ilya’s academic work, less than 10 years out of his PhD, has ben cited over 250,000 times, reflecting his absolutely mind-blowing influence on the field. 

What's in this episode:

00:00:00 Introductions

00:03:00 Why take a closer look at neural net works originally?

00:08:25 What was going through Ilya's mind during the AlexNet discovery?

00:18:25 Ilya's early years 

00:21:19 How Ilya stayed motivated 

00:29:07 Sam Altman and the beginning of OpenAI

00:36:22 LSTM models and reinforcement learning 

00:56:06 How will our productivity change?

01:00:22 Instruction-following models

01:12:13 Ilya's vision of the future of work

01:16:14 Ilya's advice to be productive 

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AI is Teaching us New, Surprising Things About the Human Mind

 ACM TECHNEWS

AI is Teaching Us New, Surprising Things About the Human Mind

By The Wall Street Journal, April 5, 2023

A researcher demonstrates the brain-scanning magnetoencephalography device at New York University.

Artificial intelligence is helping scientists decode how neurons in our brains communicate, and to explore the nature of cognition.

Scientists are gaining new insights into the human mind though artificial intelligence (AI), including the mechanism of communication between neurons, and the roots of cognition.

The University of California, Berkeley's Celeste Kidd and colleagues used a clustering model to find people's opinions tend to diverge about even the most fundamental properties of things. Researchers led by Princeton University's Tatiana Engel used artificial neurons to interpret hundreds of neurons' electrical impulses in animals' brains simultaneously, then trained them to perform identical tasks.

These networks self-organize into reasonable approximations of those in animals, indicating dynamic electrical activity forms the substance of thought, according to Engel.

From The Wall Street Journal

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Self-Taught AI

 Like the linking to how the brain works, though brain and AI are still very different.

Self-Taught AI Shows Similarities to How the Brain Works

By Quanta Magazine, August 11, 2022

For a decade now, many of the most impressive artificial intelligence systems have been taught using a huge inventory of labeled data. An image might be labeled "tabby cat" or "tiger cat," for example, to "train" an artificial neural network to correctly distinguish a tabby from a tiger. The strategy has been both spectacularly successful and woefully deficient.

Such "supervised" training requires data laboriously labeled by humans, and the neural networks often take shortcuts, learning to associate the labels with minimal and sometimes superficial information. For example, a neural network might use the presence of grass to recognize a photo of a cow, because cows are typically photographed in fields.

"We are raising a generation of algorithms that are like undergrads [who] didn't come to class the whole semester and then the night before the final, they're cramming," said Alexei Efros, a computer scientist at the University of California, Berkeley. "They don't really learn the material, but they do well on the test." ... 

In recent work, computational models of the mammalian visual and auditory systems built using self-supervised learning models have shown a closer correspondence to brain function than their supervised-learning counterparts. ...

From Quanta Magazine