Earlier quoted context omitted.
When someone says "AIs aren't really thinking" because AIs don't think like people do, what I hear is "Airplanes aren't really flying" because airplanes don't fly like birds do.
If I shake some dice in a cup are they thinking about what number they’ll reveal when I throw them?
Bag of words, have mercy on us
291–300 of 362 posts
Re: Bag of words, have mercy on us
#292Earlier quoted context omitted.
No, but watching a novelist at work is boring, and yet people like books that are written by humans because they speak to the condition of the human who wrote it. Let us not forget the old saw from SICP, “Programs must be written for people to read, and only incidentally for machines to execute.” I feel a number of people in the industry today fail to live by that maxim.
That old saw is patently false.
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Re: Bag of words, have mercy on us
#293Earlier quoted context omitted.
Are you a stream of words or are your words the “simplistic” projection of your abstract thoughts? I don’t at all discount the importance of language in so many things, but the question that matters is whether statistical models of language can ever “learn” abstract thought, or become part of a system which uses them as a tool. My personal assessment is that LLMs can do neither.
I am a stream of words - I have even ran out of tokens while speaking before :) But raising kids, I can clearly see that intelligence isn't just solved by LLMs
> But raising kids, I can clearly see that intelligence isn't just solved by LLMs
Funny, I have the opposite experience. Like early LLMs kids tend to give specific answers to the questions they don't understand or don't really know or remember the answer to. Kids also loop (give the same reply repeatedly to different prompts), enter highly emotional states where their output is garbled (everyone loves that one), etc. And it seems impossible to correct these until they just get smarter as their brain grows.
What's even more funny is that adults tend to do all these things as well, just less often.
Re: Bag of words, have mercy on us
#294In this thread: 99% of posters using their own personal definition of "thinking" without explaining it; 0.99% of posters complaining that it all depends on what that definition is; not enough posts yet for that 0.01% response to occur...
There's no definition of thinking that isn't a purely internal phenomenon, which means that there's no way to point a diagnostic device at someone and determine whether they're thinking. The only way to determine whether something is conscious/thinking is through some sort of inference, which is why Turing landed on the Turing Test that he did. Problem is, technology over the past 5 years pretty easily passes variations of the Turing Test, and exposed a lot of its limits as well.
So the next definition of detecting "thinking" will have to be externally observable and inferrable like a Turing Test, but get into the other things that we consider part of consciousness/thinking.
Often this is some combination of introspection (understanding internal states), perception (understanding external objects), and synthesis of the two into testable hypotheses in some sort of feedback loop between the internal representation of the world and the external feedback from the world.
Right now, a chatbot can say all sorts of things about itself and about the world, but none of that is based on real-time, factual information. Whereas an animal can't speak, but they clearly process information and consider it when determining their future and current actions.
Re: Bag of words, have mercy on us
#295Every day I see people treat gen AI like a thinking human, Dijkstra's attitudes about anthropomorphizing computers is vindicated even more. That said, I think the author's use of "bag of words" here is a mistake. Not only does it have a real meaning in a similar area as LLMs, but I don't think the metaphor explains anything. Gen AI tricks laypeople into treating its token inferences as "thinking" because it is traine…
For me, the problem is in the "chat" mechanic that OpenAI and others use to present the product. It lends itself to strong antropomorphizing. If instead of a chat interface we simply had a "complete the phrase" interface, people would understand the tool better for what it is.
I agree 100%. Most people haven't actually interacted directly with an LLM before. Most people's experience with LLMs is ChatGPT, Claude, Grok, or any of the other tools that automatically handle context, memory, personality, temperature, and are deliberately engineered to have the tool communicate like a human. There is a ton of very deterministic programming that happens between you and the LLM itself to create this experience, and much of the time when people are talking about the ineffable intelligence of chatbots, it's because of the illusion created by this scaffolding.
Re: Bag of words, have mercy on us
#296Earlier quoted context omitted.
I looked up the Libet experiment: "Implications The experiment raised significant questions about free will and determinism. While it suggested that unconscious brain activity precedes conscious decision-making, Libet argued that this does not negate free will, as individuals can still choose to suppress actions initiated by unconscious processes."
It's been repeated a huge number of time since, and widely debated. When Libet first did the experiment it was only like 200ms before the mind become consciously aware of the decision. More recent studies have shown they can predict actions up to 7-10 seconds before the subject is aware of having made a decision. It's pretty hard to argue that you're really "free" to make a different decision if your body knew which…
"I conducted an experiment where I instructed experienced drivers to follow a path in a parking lot laid out with traffic cones, and found that we were able to predict the trajectory of the car with greater than 60% accuracy. Therefore drivers do not have free will to just dodge the cones and drive arbitrarily from the start to the finish."
Clearly, that conclusion would be patently absurd to draw from that experiment. There are so many expectation and observation effects that go into the very setup from the beginning. Humans generally follow directions, particularly when a guy in a labcoat is giving them.
> At some point, when they felt the urge to do so, they were to freely decide between one of two buttons, operated by the left and right index fingers, and press it immediately. [0]
Wow. TWO whole choices to choose from! Human minds tend to pre-think their choice between one of two fingers to wiggle, therefore free will doesn't exist.
> It's pretty hard to argue that you're really "free" to make a different decision if your body knew which you would choose 7 seconds before you became aware of it.
To really spell it out since the analogy/satire may be lost: You're free to refrain from pressing either button during the prompt. You're free to press both buttons at the same time. You're free to mash them rapidly and randomly throughout the whole experiment. You're free to walk into the fMRI room with a bag full of steel BB's and cause days of downtime and thousands of dollars in damage. Folks generally don't do those things because of conditioning.
[0] - http://behavioralhealth2000.com/wp-content/uploads/2017/10/U...
Re: Bag of words, have mercy on us
#297Earlier quoted context omitted.
When someone says "AIs aren't really thinking" because AIs don't think like people do, what I hear is "Airplanes aren't really flying" because airplanes don't fly like birds do.
This really shows how imprecise a term 'thinking' is here. In this sense any predictive probabilistic blackbox model could be termed 'thinking'. Particularly when juxtaposed against something as concrete as flight that we have modelled extremely accurately.
Yes, to a degree. A very low degree at that.
Re: Bag of words, have mercy on us
#298Earlier quoted context omitted.
All three appear to be technically correct, but are (normally) only incidental to the operation of neurons as neurons. We know this because we can test what aspects of neurons actually lead to practical real world effects. Neurophysiology is not a particularly obscure or occult field, so there are many many papers and textbooks on the topic.(And there's a large subset you can test on yourself, besides, though I would…
> We know this because we can test what aspects of neurons actually lead to practical real world effects. Electric current is also quantum phenomena, but it is also very averaged in most circumstances that lead to practical real world effects. What is wonderful here is that contemporary electronics wizardry that allowed us to have machines that mimic some of thinking, also is very concerned of the quantum-level elect…
On reread, if your actual argument is that SNN are surprisingly sophisticated and powerful, and we might be underestimating how complex the brain's circuits really are, then maybe we're in violent agreement.
Re: Bag of words, have mercy on us
#299Every day I see people treat gen AI like a thinking human, Dijkstra's attitudes about anthropomorphizing computers is vindicated even more. That said, I think the author's use of "bag of words" here is a mistake. Not only does it have a real meaning in a similar area as LLMs, but I don't think the metaphor explains anything. Gen AI tricks laypeople into treating its token inferences as "thinking" because it is traine…
For me, the problem is in the "chat" mechanic that OpenAI and others use to present the product. It lends itself to strong antropomorphizing. If instead of a chat interface we simply had a "complete the phrase" interface, people would understand the tool better for what it is.
But people aren't using ChatGPT for completing phrases. They're using it to get their tasks done, or get their questions answered.
The fact that pretraining of ChatGPT is done with a "completing the phrase" task has no bearing on how people actually end up using it.
Re: Bag of words, have mercy on us
#300Earlier quoted context omitted.
People can claim whatever they like. That doesn't mean it's a good or reasonable hypothesis (especially for one that is essentially unfalsifible like predictive coding).
I'm not trying to advance a testable hypothesis. If you think the unfalsifiability of my claim is a problem, you haven't understood what I'm trying to do. My claim is that the two concepts are indistinguishable, thus equivalent. The unfalsifiability is what makes it a natural equivalence, the same as in the other examples I gave.
IMHO, you should. The opponent does not have an alternative definition of thinking that would have a prediction power matching the token prediction. Whatever they are thinking thinking is is a strictly worse scientific theory.