I had a professor in college that said when an AI problem is solved, it is no longer AI.
Computers do all sorts of things today that 30 years ago were the stuff of science fiction. Back then many of those things were considered to be in the realm of AI. Now they’re just tools we use without thinking about them.
I’m sitting here using gesture typing on my phone to enter these words. The computer is analyzing my motions and predicting what words I want to type based on a statistical likelihood of what comes next from the group of possible words that my gesture could be. This would have been the realm of AI once, but now it’s just the keyboard app on my phone.
The approach of LLMs without some sort of symbolic reasoning layer aren’t actually able to hold a model of what their context is and their relationships. They predict the next token, but fall apart when you change the numbers in a problem or add some negation to the prompt.
Awesome for protein research, summarization, speech recognition, speech generation, deep fakes, spam creation, RAG document summary, brainstorming, content classification, etc. I don’t even think we’ve found all the patterns they’d be great at predicting.
There are tons of great uses, but just throwing more data, memory, compute, and power at transformers is likely to hit a wall without new models. All the AGI hype is a bit overblown. That’s not from me that’s Noam Chomsky https://youtu.be/axuGfh4UR9Q?t=9271.
I had a professor in college that said when an AI problem is solved, it is no longer AI.
Computers do all sorts of things today that 30 years ago were the stuff of science fiction. Back then many of those things were considered to be in the realm of AI. Now they’re just tools we use without thinking about them.
I’m sitting here using gesture typing on my phone to enter these words. The computer is analyzing my motions and predicting what words I want to type based on a statistical likelihood of what comes next from the group of possible words that my gesture could be. This would have been the realm of AI once, but now it’s just the keyboard app on my phone.
The approach of LLMs without some sort of symbolic reasoning layer aren’t actually able to hold a model of what their context is and their relationships. They predict the next token, but fall apart when you change the numbers in a problem or add some negation to the prompt.
Awesome for protein research, summarization, speech recognition, speech generation, deep fakes, spam creation, RAG document summary, brainstorming, content classification, etc. I don’t even think we’ve found all the patterns they’d be great at predicting.
There are tons of great uses, but just throwing more data, memory, compute, and power at transformers is likely to hit a wall without new models. All the AGI hype is a bit overblown. That’s not from me that’s Noam Chomsky https://youtu.be/axuGfh4UR9Q?t=9271.