In discussions, transformative AI is often seen as a certain point in time when an AI system achieves a certain level of performance or capabilities. For example, some people believe(d) that transformative AI will occur when an AI system can pass the Turing test, or when an AI system can...
I was wondering how in more optimistic future scenarios people would be fed or get money to buy food et cetera. A common topic in discussions between pro and anti doomers is how the AI will kill us. It is a fair point, killing 8 billion people seems difficult at...
In discussions, transformative AI is often seen as a certain point in time when an AI system achieves a certain level of performance or capabilities. For example, some people believe(d) that transformative AI will occur when an AI system can pass the Turing test, or when an AI system can create art or music that is indistinguishable from human-created art or music (yep, a few years ago people said that). More modern definitions have a higher bar (e.g. do all human jobs at low cost.)
However, it is also possible to see transformative AI as a process rather than a specific point in time. In this view, transformative AI is not a single... (read 1318 more words →)
Fine-tuning, whether using RL or not, is the proverbial “cherry on the cake” and the pre-trained model captures more than 99.9% of the intelligence of the model.
I am still amazed by the strength of general models. There is the no-free lunch theorem that people use to point out that we will probably have specialized AI's because they will be better. Current practice seems to contradict this.
AI will probably displace a lot of cognitive workers in the near future. And physical labor might take a while to get below 25$/hr.
I agree that AI might not replace swim instructors by 2030. It is the cognitive work where the big leaps will be.
An interesting development is the development of synthetic data. This is also a sort of algorithmic improvement, because the data is generated by algorithms. For example in the verify step by step paper there is a combination of synthetic data and human labelling.
At first this seemed counter intuitive to me. The current model is being used to create data for the next model. Feels like bootstrapping. But it starts to make sense now. Better prompting (like CoT or ToT) is a method to get better data or a second model that is trained to pick the best answers from a thousand and that will get you data good enough to improve the model.
Demis Hassabis said in his interview with Lex Fridman that they used synthetic data when developing AlphaFold. They had some output of AlphaFold that they had great confidence in. Then they fed the output as input and the model improved (this gives your more data with great confidence, repeat).
Specific Resources (Access to a DGX data center): Even if an AI had access to such resources, it would still need to understand how to use them effectively, which would require capabilities beyond what GPT-4 or a hypothetical GPT-5 have.
To my knowledge resource management in data centers is done by AI's. It is the humans who cannot do this. The AI already can.
Algorithmic improvement has more FOOM potential. Hardware always has a lag.
Hanson's chance on extinction is close to a 100%. He just thinks it's slower. He is optimistic about something that most would call a dystopia (a very interesting technological race that will conquer the stars before the grabby aliens do). A discussion between Yudkowsky and Hanson is about are we dying fast or slow. It is not really a doomer vs non-doomer debate from my perspective (still a very interesting debate btw, both have good arguments).
I do appreciate the Hanson perspective. It is well thought out and coherent. I just would not call it optimistic (because of the extinction). I have no ready example of a non-extinction perspective coherent view on the future. Does anybody have a good example of a coherent non-extinction view?
If I understand you correctly you mean this transfer between machine learning and human learning. Which is an interesting topic.
When a few years ago I learned about word2vec I was quite impressed. It felt a lot like how humans store information according to cognitive psychology. In cognitive psychology, a latent space or a word vector would be named as a semantic representation. Semantic representations are mental representations of the meaning of words or concepts. They are thought to be stored in the brain as distributed representations, meaning that they are not represented by a single unit of activation, but rather by a pattern of activation across many units.
That was sort my "o... (read more)
I am in education (level about high school/AP macro economics)
possible implications:
The last is the most important I think. What is the place of education in todays world. What should a kid of fifteen years old learn to be prepared for what is coming? I don't know because I don't know what is coming.
One thing I do know. Learning from a machine is a... (read more)
Your model has some uncertainty, but you know the statistical distributions. For example, with probability 80% the world is in state X, with probability 20% it is in state Y.
Nice way of putting it.
I was wondering how in more optimistic future scenarios people would be fed or get money to buy food et cetera. A common topic in discussions between pro and anti doomers is how the AI will kill us. It is a fair point, killing 8 billion people seems difficult at first glance. But what if the AI does not kill us, how will we eat? Our current agro-industrial food supply is very fragile. And feeding 8 billion people takes a lot of resources. So in the utopian view that question needs answering. There are several proposals.
Universal basic income
The AI would do most or all of the work so there is not much... (read 1463 more words →)
This emphasis on generality makes deployment of future models a lot easier. We first build a gpt4 ecosystem. When gpt5 comes out it will be easy to implement (e.g. autogpt can run just as easy on gpt4 as on gpt5). The adaptions that are necessary are very small and thus very fast deployment of future models is to be expected.