Earlier quoted context omitted.
Why do people call is "Artificial Intelligence" when it could be called "Statistical Model for Choosing Data"? "Intelligence" implies "thinking" for most people, just as "Learning" in machine learning implies "understanding" for most people. The algorithms created neither 'think' nor 'understand' and until you understand that, it may be difficult to accurately judge the value of the results produced by these systems.
How do you feel about Business Intelligence as a term?
Using secondary school maths to demystify AI
171–180 of 264 posts
Re: Using secondary school maths to demystify AI
#172Earlier quoted context omitted.
Usually it is the work of the one claiming something to prove it. So if you believe that AI does "think" you are expected to show me that it really does. Claiming it "thinks - prove otherwise" is just bad form and also opens the discussion up for moving the goalposts just as you did with your brain emulation statement. Or you could just not accept any argument made or circumvent it by stating the one trying to dispro…
Then prove to me you are thinking, lest we assume you are a philosophical zombie and need no rights or protections. Sometimes, because of the consequences of otherwise, the order gets reversed
Whatever you meant to say with "Sometimes, because of the consequences of otherwise, the order gets reversed" eludes me as well.
Re: Using secondary school maths to demystify AI
#173[stub for offtopicness] (in this case, thinkiness)
Wouldn't 'thinking' need to be updating the model of reality (LLM is not yet that, just words) - at every step doing again all that extensive calculations as when/to creating/approximating that/better model ( learning ) ? Expecting machines to think is.. like magical thinking (but they are good at calculations indeed). I wish we didn't use the word intelligence in context of LLMs - shortly there is Essence and the re…
An LLM could be thinking in one of two ways. Either between adding each individual token, or collectively across multiple tokens. At the individual token level the physical mechanism doesn’t seem to fit the definition being essentially reflexive action, but across multiple tokens that’s a little more questionable especially as multiple approaches are used.
Re: Using secondary school maths to demystify AI
#174Earlier quoted context omitted.
I would say a different problem: There's many definitions of "thinking". AI and brains can do some, AI and brains definitely provably cannot do others, some others are untestable at present, and nobody really knows enough about what human brains do to be able to tell if or when some existing or future AI can do whatever is needed for the stuff we find special about ourselves. A lot of people use different definitions…
What's a definition of thinking that brains definitely provably can't do?
There are people who insist that the halting problem "proves" that machines will never be able to think. That this means they don't understand the difference between writing down (or generating a proof of) the halting problem and the implications of the halting problem, does not stop them from using it.
Re: Using secondary school maths to demystify AI
#175Earlier quoted context omitted.
I can make a claim much stronger than "you could probably" The counterclaim here is that the brain may not obey physical laws that can be described by mathematics. This is a "5G causes covid" level claim. The overwhelming burden of proof is on you.
There are some quantum effects in the brain (for some people, that's a possible source of consciousness). We can simulate quantum effects, but here comes the tricky part: even if our simulation matches the probability, say 70/30 of something happening, what guarantees that our simulation would take the same path as the object being simulated?
Re: Using secondary school maths to demystify AI
#176Earlier quoted context omitted.
> Concepts like > finite context windows like a human has > or the fact that the model is "frozen" and stateless, much like a human adult. Models get updated at a slower frequency than humans. AI systems have access to fetch new information and store it for context. > or the idea that you can transfer conversations between models are trivial because computers are better-organized than humanity.
> much like a human adult. I do hope you're able to remember what you had for lunch without incessantly repeating it to keep it in your context window
One of my earliest memories is of painting a ceramic mug when I was about 3 years old. The only reason I remember it is because every now and then I think about what my earliest memory is, and then I refresh my memory of it. I used to remember a few other things from when I was slightly older, but no longer do, because I haven't had reasons to think of them.
I don't think humans have specific black and white differences between types of knowledge that way LLMs do, but there is definitely a lot of behavior that is similar to context window vs training data (and a gradient in between). We remember recent things a lot better than less recent things. The quantity of stuff we can remember in our "working memory" is approximately finite. If you try to hold a complex thought in your mind, you can probably do that indefinitely, but if you then try to hold a second equally complex thought as well, you'll often lose the details of the first thought and need to reread or rederive those details.
Re: Using secondary school maths to demystify AI
#177Earlier quoted context omitted.
I feel like these conversations really miss the mark: whether an LLM thinks or not is not a relevant question. It is a bit like asking “what color is an Xray?” or “what does the number 7 taste like?” The reason I say this is because an LLM is not a complete self-contained thing if you want to compare it to a human being. It is a building block. Your brain thinks. Your prefrontal cortex however is not a complete syste…
Why do people call is "Artificial Intelligence" when it could be called "Statistical Model for Choosing Data"? "Intelligence" implies "thinking" for most people, just as "Learning" in machine learning implies "understanding" for most people. The algorithms created neither 'think' nor 'understand' and until you understand that, it may be difficult to accurately judge the value of the results produced by these systems.
Re: Using secondary school maths to demystify AI
#178Earlier quoted context omitted.
Wouldn't 'thinking' need to be updating the model of reality (LLM is not yet that, just words) - at every step doing again all that extensive calculations as when/to creating/approximating that/better model ( learning ) ? Expecting machines to think is.. like magical thinking (but they are good at calculations indeed). I wish we didn't use the word intelligence in context of LLMs - shortly there is Essence and the re…
Thinking is different than forming long term memories. An LLM could be thinking in one of two ways. Either between adding each individual token, or collectively across multiple tokens. At the individual token level the physical mechanism doesn’t seem to fit the definition being essentially reflexive action, but across multiple tokens that’s a little more questionable especially as multiple approaches are used.
> across multiple tokens
- but how many ? how many of them happen in sole person life ? How many in some calculation ? Does it matter, if a calculation doesn't reflect it but stay all the same ? (conversation with.. a radio - would it have any sense ?)
Re: Using secondary school maths to demystify AI
#179Earlier quoted context omitted.
You can simulate a human brain on pen and paper too.
The simulation isn't an operating brain. It's a description of one. What it "means" is imposed by us, what it actually is, is a shitload of graphite marks on paper or relays flipping around or rocks on sand or (pick your medium). An arbitrarily-perfect simulation of a burning candle will never, ever melt wax. An LLM is always a description. An LLM operating on a computer is identical to a description of it operating…
Build a simulation of creatures that evolve from simple structures (think RNA, DNA).
Now, if in this simulation, after many many iterations, the creatures start talking about consciousness, what does that tell us?
Re: Using secondary school maths to demystify AI
#180Earlier quoted context omitted.
> much like a human adult. I do hope you're able to remember what you had for lunch without incessantly repeating it to keep it in your context window
A lot of people genuinely can't remember what they did an hour ago, but to be very clear you're implying that an LLM can't "remember" something from an hour, or three hours ago, when it's the opposite. I can restart a conversation with an LLM 15 days later and the state is exactly as it was. Can't do that with a human. The idea that humans have a longer, more stable context window than LLM's, CAN or is even LIKELY to…