I’m all for courses like this but they become outdated very quickly. This area is moving fast
Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
371–380 of 652 posts
Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
#372Earlier quoted context omitted.
> And if there are facts or code involved, both require manual confirmation. The hidden assumption here seems to be that the model needs to be perfect before it has utility.
Also hidden assumption, or perhaps lack of clear perception of reality, that most jobs on the market are strongly dependent on factual correctness. Also assumption that this is any different than human relationship with empirical truth is.
Clearly generative AI can currently only be used when verification is easy. A good example is software. Not sure why you think that I claimed otherwise.
Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
#373Earlier quoted context omitted.
this is the question that the greeks wrestled with over 2000 years ago. at the time there were the sophists (modern llm equivalents) that could speak persuasively like a politician. over time this question has been debated by philosophers, scientists, and anyone who wanted to have better cognition in general.
So how can you claim what an LLM is doing if we cannot define it regardless?
I think the third worst part of the GenAI hype era is that every other CS grad now thinks not only is a humanities/liberal arts degree meaningless but now also they're pretty sure they have a handle on the human condition and neurology enough to make judgment calls on what's sentient. If people with those backgrounds ever attempted to broach software development topics they'd be met with disgust by the same people.
Somehow it always seems to end up at eugenics and white supremacy for those people.
Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
#374I wonder if the authors can explain the aparent inconsistency between what we now know about R1 and their statement “They don’t engage in logical reasoning” from the first lesson. My simple-minded view of logical reasoning by LLMs is that the hard question (say a math puzzle) has a verifiable answer that is hard to produce and is easy to verify, yet within the realm of knowledge of humans or the LLM itself, so the “thought” stream allows the LLM to increase its confidence by a self-discovered process that resembles human reasoning, before starting to write the answer stream. Much of the thought process that these LLMs use looks like conventional reasoning and logic, or more generally higher level algorithms to gain confidence in an answer, and other parts are not possible for humans to understand (yet?) despite the best efforts by DeepSeek. When combined with tools for the boring parts, these “reasoning” approaches can start to resemble human research processes as per the Deep Research by OpenAI.
Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
#375“ When we write, we share the way that we think. When we read, we get a glimpse of another mind. But when an LLM is the author, there is no mind there for a reader to glimpse.” — I dunno, I feel like reading is more a glimpse into how I think than how the author thinks…a generated story can be just as moving as one from a human, I think.
Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
#376That's an interesting Altman quote on the site. LLMs cannot be compared to electricity and the Internet. People wanted those. LLMs were an impressive parlor trick at first but disappointing later. Many stopped using them altogether. Now there is a president who fuels the hype, shakes down rich countries for "AI" investments. The Saudi prince who lost money on Twitter is in for the new grift and praises Musk on Tucker…
In Similarweb's list of top websites chatgpt.com is now at no 6 above x/twitter and yahoo
US iPhone apps the top two are deepseek and chatgpt
That doesn't really say people have stopped using LLMs
Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
#377Earlier quoted context omitted.
It seems like an arbitrary distinction. If an LLM can accomplish a task that we’d all agree requires reasoning for a human to do, we can’t call that reasoning just because the mechanics are a bit different?
Yes because it isn't an arbitrary distinction. My good old TI-83 can do calculations that I can't even do in my head but unlike me it isn't reasoning about them, that's actually why it's able to do them so fast, and it has some pretty big implications about what it can't do. If you want to understand where a systems limitations are you need to understand not just what it does but how it does it, I feel like we need t…
An LLM’s mechanics are algorithmically much closer to the human brain (which the LLM is modeled on) than a TI-83, a CPU, or any other Turing machine. Which is why, like the brain, it can solve problems that no individual Turing machine can.
Are you sure you aren’t just defining reasoning as something only a human can do?
Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
#378Not sure why everyone rates this. It’s full of very confidently made statements like “the AI has no ground truth” (obviously it does, it has ingested every paper ever), it “can’t reason logically” which seems like a stretch if you ever read the CoT of a frontier reasoning model and “can’t explain how they arrived at conclusions” where - I mean just try it yourself with o1, go as deep as you like asking how it arrived…
Training on all papers does not mean the model believes or knows the truth. It is just a machine that spits out words.
>Training on all papers does not mean the model believes or knows the truth.
It is just a machine that spits out words.
Sounds like humans at school. Cram the material. Take the test. Eject the data.
Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
#379Earlier quoted context omitted.
this is the question that the greeks wrestled with over 2000 years ago. at the time there were the sophists (modern llm equivalents) that could speak persuasively like a politician. over time this question has been debated by philosophers, scientists, and anyone who wanted to have better cognition in general.
So how can you claim what an LLM is doing if we cannot define it regardless?
math arose firstly as a language and formalism in which statements could be made with no room for doubt. the sciences took it further and said that not only should the statements be free of doubt, but also that they should be testable in the real world via well defined actions which anyone could carry out. all of this has given us the gadgets we use today.
llm, meanwhile, is putting out plausible tokens which is consistent with its training set.
Re: Modern-Day Oracles or Bullshit Machines? How to thrive in a ChatGPT world
#380There is a bit of very important content missing from the explanation of the autocomplete analogy. The combination of encoding / tokenization of meanings and ideas, related concepts, and mapping these relationships in vector space makes LLMs not so much glorified text prediction engines as browsers/oracles of the sum total of cultural-linguistic knowledge as captured in the training corpus. Understanding how the impl…
What they capture is not knowledge, it's word relationships. And that can indeed be powerful, useful and valuable. They're a tool I'm grateful to have in my armoury. I can use it as a torch to shine light into areas of human knowledge which would otherwise be prohibitively difficult to access. But they're information retrieval machines, not knowledge engines.
I’d argue that they extract knowledge from the training corpus in the same way that knowledge can be encapsulated in a book… it’s just words, after all.
Tokenization goes well beyond words and punctuation. Knowledge and relationships between concepts, reactions, emotions, values, attitudes, and actions all get included in the vector space.
But, it also can come to wrong conclusions, of course.
Ultimately they are information extraction engines that are controlled by semantic search.
They aren’t smart.
But it turns out that in the same way that an infinitely sized and detailed choose-your-own-adventure book at 120 pages per second could be indistinguishable from a simulation of reality, the free traversal of the entirety of the wealth of human culture and knowledge is similarly difficult to distinguish from intelligence.
In the end it may boil down to the simulation vs reality argument.