I see Tristan Harris has moved on to scaring everyone about AI after scaring everyone about social media. Shows where the consulting fees are now
He's not particularly wrong tbf. Social media is brain poison
I don't disagree but he tends to ignore his role in it!
Up to now, unless they've made a huge breakthrough recently, it's been at the level of 'visualise eating an apple', then 'visualise eating an orange', repeated a few times in the scanner and followed by detailed analysis of the data (where the AI comes in, presumably). Then 'visualise either eating an apple or eating an orange (in the scanner) and we'll try to guess which one you're thinking about'.
the most impressive for me, and I will keep banging on about it because if this tech is not for such uses then what is it- the paraplegic who they had thinking about writing, how you'd form each stroke for each letter etc and then used machine learning to turn the data into 95% accurate words on a computer screen.
Yeah, that’s brilliant. It’s what I mean when I say that focusing on unrealistic utopias (and dystopias) can get in the way of actually doing useful things with the tech here and now. Hype is double-edged; when people don’t get their promised brain reading general AI, they might suddenly declare the whole thing a joke that will never happen and make funding for realistic gains a whole lot more difficult to obtain.
royalsociety.org
I agree. That is impressive. And certainly I can see how AI is going to bring this ability into focus. Already has.
Machine learning and neural nets are a form of AI. I skimmed the article in your first link, but it's quite long so didn't get the author's definition of AI, and why NN/LLMs aren't it.
There is a definitional problem here tbf. There isn't one agreed definition of AI.
This is why I was saying earlier the issue of intelligence and consciousness is secondary right now, more pressing is the undeniable effects and powers of the machines
I half-agree. I think they're linked.
That's true. And the definition of AI seems to be one of those moving targets. Every time something is achieved that previously would have been thought of as "AI", the goalposts are moved and we hear things like, "It's just predicting the next word".
Good place for a link to The Chinese Room Argument
NoXion mentioned Blindsight earlier, theres a detailed look at the chinese room argument in it.
That’s a good article but I feel that it ignores the last 30 years of sociocultural psychology. One thing we now understand is that knowledge, understanding and meaning is not really contained in individual heads, but distributed across a society. Humans do not just see an object, they also name it and represent it as having meaning, and this representation implies a model of the world. It is by having these social representations that humans can master their bewildering social environments and communicate with each other — to arrange a meal together, for example, we need to have common assumptions about what “meals” are, where they take place, at what time and so on.
But that is what it did. It referred to news articles that didn't exist and it couldn't have seen in training.
I thought you might have an interesting response to that
If you ask it for references and it generates some text that look like references then it has done its job correctly. It's not copying from the training data to the output. It's generating text that is likely to look like what the user wanted, based on the patterns in the training data.
It's a fundamental feature - feature, not bug. It learns through its game of 'predict the next word', and that involves introducing an incentive to get that prediction right through positive and negative feedback - yes, that was right, or no that was wrong. In order to improve, it needs to try to get the prediction right each time. But if it doesn't have enough information to make a good prediction, it doesn't know that it doesn't have enough information to make a good prediction. It doesn't know it's bullshitting when it bullshits just as it doesn't know it is telling the truth when it tells the truth. It doesn't know anything.
To extend my above thought, I would say that the debates around the Chinese Room kind of miss the point about what language actually is. They mobilise an underlying model of language as a cognitive structure, waiting to be activated — something intensely individual. But mostly, language is not really contained in the person, it’s part of the culture. One person with a cognitive structure of English isn’t going to be able to do much with it. Language is a two-way (at least) relational process that takes place in a context. The words are just symbols that mediate meaning between two people, all within that crucial context. If you look at it like that then the question of whether the individual or system in the Chinese Room “understands” language suddenly reveals itself as the wrong question. What is the context and what meaning is being transferred? Those are the irreducible elements of a language.I thought you might have an interesting response to that
I don't know much about philosophy or psychology but I think the focus on whether there's an understanding of language is missing the point a bit. I'd say programs like Good Predictive Text (tm) simulate an understanding of language but don't actually have it. Some would say they do have it and some would say those are the same thing.
I've always had problems with this. This is Searle's original formulation of the argument.
It is formulated more awkwardly there. But you could imagine that the rules he is following are an algorithm that has been created by decoding what is inside the neural net after the training process.
Even then you've just moved the question and the proper question should be whether the neural net is thinking. The room is just a clumsy interface and a red herring.
