'Reasoning models' such as GPT4-o3 have become a well known member of the Generative AI family. But look inside and while they add a certain depth, at the same time they add nothing at all. Not 'reasoning' anyway. Just another 'level of indirection' when approximating. Sometimes powerful. Always costly.
Tag: artificial intelligence
Let’s call GPT and Friends: ‘Wide AI’ (and not ‘AGI’)
GPT-3o has done very well on the ARC-AGI-PUB benchmark. Sam Altman has also claimed OpenAI is confident that it can build Artificial General Intelligence (AGI). But that may be based on confusions around 'learning'. On the difference between narrow, general and (introducing) 'wide' AI.
Generative AI doesn’t copy art, it ‘clones’ the artisans — cheaply
The early machines at the beginning of the Industrial Revolution produced 'cheap' (in both meanings) products and it was the introduction of that 'cheap' category that was actually disruptive. In the same way, where 'cheap' is acceptable (and no: that isn't coding), GenAI may disrupt today. But there is a difference. Early machines were separate inventions creating a comparable product. GenAI is trained on the output of humans, their skill is 'cloned' and it is this 'cloned skill' that produces the 'comparable product'. GenAI is not 'copying art', it is 'cloning the artisan'. And our intellectual rights haven't yet caught up.
When ChatGPT summarises, it actually does nothing of the kind.
One of the use cases I thought was reasonable to expect from ChatGPT and Friends (LLMs) was summarising. It turns out I was wrong. What ChatGPT isn't summarising at all, it only looks like it. What it does is something else and that something else only becomes summarising in very specific circumstances.
Microsoft lays a limitation of ChatGPT and friends bare
Microsoft researchers published a very informative paper on their pretty smart way to let GenAI do 'bad' things (i.e. 'jailbreaking'). They actually set two aspects of the fundamental operation of these models against each other.
Ain’t No Lie — The unsolvable(?) prejudice problem in ChatGPT and friends
Thanks to Gary Marcus, I found out about this research paper. And boy, is this is both a clear illustration of a fundamental flaw at the heart of Generative AI, as well as uncovering a doubly problematic and potentially unsolvable problem: fine-tuning of LLMs may often only hide harmful behaviour, not remove it.
The Department of “Engineering The Hell Out Of AI”
ChatGPT has acquired the functionality of recognising an arithmetic question and reacting to it with on-the-fly creating python code, executing it, and using it to generate the response. Gemini's contains an interesting trick Google plays to improve benchmark results. These (inspired) engineering tricks lead to an interesting conclusion about the state of LLMs.
Memorisation: the deep problem of Midjourney, ChatGPT, and friends
If we ask GPT to get us "that poem that compares the loved one to a summer's day" we want it to produce the actual Shakespeare Sonnet 18, not some confabulation. And it does. It has memorised this part of the training data. This is both sought-after and problematic and provides a fundamental limit for the reliability of these models.
What makes Ilya Sutskever believe that superhuman AI is a natural extension of Large Language Models?
I came across a 2 minute video where Ilya Sutskever — OpenAI's chief scientist — explains why he thinks current 'token-prediction' large language models will be able to become superhuman intelligences. How? Just ask them to act like one.
The Truth about ChatGPT and Friends — understand what it really does and what that means
On 10 October I gave an (enthusiastically received) explainer talk at the EABPM Conference Europe 2023, making clear what ChatGPT and friends actually do — addressing the technology in a non-technical but correct way — and what that means. That presentation fills the gap between the tech and the results. At the end you will understand what these models really do in a practical sense (so not the technical how) when they handle language, see not only how impressive they are, but also how the errors come to be (with a practical example), and what that means what we may expect from this technology in the future.
