It turns out that AI has created a whole new language. Humans do not speak it, and they may even mistake it for talk about sex. But luckily Generative AI is able to translate it to something humans can understand (and where the sex doesn't show up).
Tag: LLM
Generative AI ‘reasoning models’ don’t reason, even if it seems they do
'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.
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.
Mastering ArchiMate 3.2 has been released (PDF version)
Mastering ArchiMate 3.2 has been released. Finally. This post contains release information, and a link to the book's page where you can order the free excerpt (with the entire language description as well as a short BPMN primer) or the entire book (both PDF).
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.
Will Sam Altman’s $7 Trillion Plan Rescue AI?
Sam Altman wants $7 trillion for AI chip manufacturing. Some call it an audacious 'moonshot'. Grady Booch has remarked that such scaling requirements show that your architecture is wrong. Can we already say something about how large we have to scale current approaches to get to computers as intelligent as humans — as Sam intends? Yes we can.
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.
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 hidden meaning of the errors of ChatGPT (and friends)
We should stop labelling the wrong results of ChatGPT and friends (the 'hallucinations') as 'errors'. Even Sam Altman — CEO of OpenAI — agrees, they are more 'features' than 'bugs' he has said. But why is that? And why should we not call them errors?
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