Wow, Joscha Bach and Stephen Wolfram on one stage. Is there more of this discussion?

By YouSum Live 00:00:00 Science and AI limitations in predicting complex systems. 00:00:30 AI struggles with extrapolation beyond trained data. 00:01:41 Language simplicity aids AI success in text analysis. 00:02:07 AI's limitations in creativity and originality. 00:06:53 Computational exploration of vast possibilities by humans. 00:09:23 Computational language as a tool for formalizing the world. 00:14:52 The importance of computational thinking and automation in work. 00:15:20 Leveraging AI as an interface for computational tasks. 00:16:48 Training AI models for specific computational tasks. 00:17:45 Weak form of computation in llms. 00:17:50 Challenges in guiding proofs using llms. 00:18:00 Limitations of llms in mathematical proofs. 00:18:43 Llms excel in making homework but struggle at edge of human knowledge. 00:19:02 Llms prone to errors in math without guidance. 00:19:37 OpenAI's focus on long-form reasoning surpassing human capabilities. 00:20:20 Building systems to extend human capabilities. 00:20:36 Exploring the fundamental workings of machine learning. 00:21:26 Balancing computational capabilities with human needs. 00:22:11 Challenges in developing effective AI tutoring systems. 00:22:28 Goal for llms to understand and assist human learning. 00:23:00 Conceptualizing beyond human intelligence and AI capabilities. By YouSum Live

My AI predicted the text “so to speak”. Only joking, I love Stephen’s videos. He’s a true genius.

thanks

Anyone know where to find the full talk? Thank you

Wolfram is our modern day genius.

So, there is a plugin for ChatGPT so it can access Wolfram resources. How about an interface to Wolfram resources that can be used by any language model?

Semantic space has a shape. It's a model, so of course it has a similar shape to what is being modeled. I like the idea that only that which is simple or computationally reducible can be modeled sufficiently in current scale foundation models. Rigorous agentic behavior is necessary to deal with computationally difficult activation pathways.

I liked the part where he said "computational"

Computationally speaking of course.

Thank you Stephen for being a scientist and a man of truth!

❤❤❤❤❤❤

Dream : Wolphram and Tegmark takking to each other.

Also it's pretty discrediting to LLMs to say they are only good because language has (easy) grammar. A lot of tests on LLMs show that they have a (though limited, incomplete) world model. It understands basic mathematics, and some basic things about our world.

It’s encoding not compression. The difference is subtle but important for technical rigour and to explain the decoding which holds the generative capacity. Decompression wouldn’t be considered correct either it’s called decoding.

17:00

Before we can expect an AI to accurately predict meaningful events, it probably needs to be able to accurately describe the present, and prior events. A graph structure is probably a good way to represent the present and the past.

😊

Would be helpful if he could produce a simple example in which LLM plus his calculation engine is better than LLM alone.

Map your perception onto the Transformers Perception

What about protein folding. AI was wildly successful.

There is no specificity regarding the metrics of measuring computational intelligence and representing it.

I cannot challenge Stephen but Max os saying it can give a symbolic equation like sine theta.

2:44 Textadistics?

Sometimes I wonder why people talk about stuff so clearly and still miss the point 😂😂

I understood NKS is based on Emergence. Sabine is supporting now. Is Stephen Wolphram nks based on emergence? Say yes or no Yes. ChatGPT I understood you after the movie Automata

LMs in Science? Do LMs behave like scientists? After chatting with ChatGPT for some time I think it behaves like a lazy student during an oral exam. The student is brilliant in using the language, maybe because he already read some books in his life but he did not prepare for this particular exam. So, when asked by the teacher he produces a good sounding answer, the best he can produce, some kind of small talk inspired by a mix of everything he read in his life. The question is, are we (humans, scientists) all and always behaving like a lazy student? Does the ability to create or find new knowledge emerge from the size of a LM?

“If it has linear activation functions it will predict a linear continuation of a sine wave”, really?! Not sure about that one 😅

Hype vs Reality

"have you know maybe 50,000 words in typical languages” THERES 170,000 words in English

I love him but his conceit is exhausting

ai will become more useful over time to help people drink water.

Ok. So. That seems wrong. A sine wave is super easy to define and anyone can plot the next points based on the previous points whereas for a sentence the prediction, yes maybe easier than we thought, but it’s much harder to predict the next word. Anyway. I feel that was a poor example.

My opinion is that Stephen Wolfram is struggling to understand modern transformer based AI and I'm not sure why because he's actually described them in the past. Whilst its technically true an LLM such as chat GPT simply produces a next word based on statistics, its disingenuous to say that's all its doing because with that statement there is no implication of the profound understanding inherent in the model of every word prior, in the context of its training leading to that choice. Stephen's downplaying of neural networks leaves me cold. Sorry, your attempt at creating AI using graphs and tokens didn't work out, Stephen.

he lying out of teeth in some areas

This guy is overrated, he is saying much but concludes very little.

If ChatGPT writing code makes it boilerplate, then so is nearly all code. It outdesigned humans for ML reward heuristics (Eureka paper), for example. He's much more pessimistic on AI than I imagined. Disappointing.

Never heard of this guy, but he comes across as someone whose qualities do not include humility and curiosity. His idea of the genesis of human language seems rather unsophisticated. People 200000 years ago were certainly much more concerned with animals and plants they could eat than they were with rocks. That he mentions rocks first tells me that he has not really spent a lot of time thinking about how human language could have evolved.

he is too biased to say AI is dumb. this is not a good spirit