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A prevailing sentiment online is that GPT-4 still does not understand what it talks about. We can argue semantics over what “understanding” truly means. I think it’s useful, at least today, to draw the line at whether GPT-4 has succesfully modeled parts of the world. Is it just picking words and connecting them with correct grammar? Or does the token selection actually reflect parts of the physical world?
One of the most remarkable things I’ve heard about GPT-4 comes from an episode of This American Life titled “Greetings, People of Earth”.
Give Eliza equivalent compute time and functionality to interpret the data type and it probably could get something approaching a result. Modern LLMs really benefit from massive amounts of compute availability and being able to “pre-compile” via training.
They’re not, in and of themselves, intelligent. That’s not something that is seriously debated academically, though the dangers of humans misperceiving them as such very much is. They may be a component of actual artificial intelligence in the future and are amazing tools that I’m getting done hands-on time with, but the widespread labeling them as “AI” is pure marketing.
Sorry, but this is simply incorrect. Do you know what Eliza is and how it works? It is categorically different from LLMs.
This is also incorrect. I think the issue that many people have is that they hear “AI” and think “superintelligence”. What we have right now is indeed AI. It’s a primitive AI and certainly no superintelligence, but it’s AI nonetheless.
There is no known reason to think that the approach we’re taking now won’t eventually lead to superintelligence with better hardware. Maybe we will hit some limit that makes the hype die down, but there’s no reason to think that limit exists right now. Keep in mind that although this is apples vs oranges, GPT-4 is a fraction of the size of a human brain. Let’s see what happens when hardware advances give us a few more orders of magnitude. There’s already a huge, noticeable difference between GPT 3.5 and GPT 4.
To add something, as you mentioned gpt-4 neurons are only a fraction of a human brain.
The entire human brain runs on 10-20 watt, thats about a single lightbulb to do all the computing needed for conscious intelligence.
Its crazy how optimized natural life is and we have a lot left to learn.
It’s a fun balance of both excellent and terrible optimization. The higher amount of noise is a feature and may be a significant part of what shapes our personalities and ability to create novel things. We can do things with our meat-computers that are really hard to approximate in machines, despite having much slower and lossier interconnects (not to mention much less reliable memory and sensory systems).
I did not mean to come across as stating that they were the same, nor that the results produced would be as good. Merely, that a PDF could be run through OCR and processed into a script for ELIZA, which could produce some results to requests for a summary (ex. provide the abstract).
My point being that these technologies that are fundamentally different and at very different levels of technological sophistication can both, at a high level, accomplish the task. Both the quality of the result and capabilities beyond the surface level are very different. However, both, would be able to produce one, working within their architectural constraints.
Looking at it this way also gives a good basis for comparing LLMs to intelligence. Both, at a high level, can accomplish many of the same tasks, but, context matters in more than a syntactical sense and LLMs lack the capability of understanding and comprehension of the data that they are processing.
That paper is both solely phenomenological and states that it is not using an accepted definition of intelligence. With the former point, there’s a significant risk of fallacy in such observation as it is based upon subjective observation of behavior not emperical analysis of why the behavior is occuring. For example leatherette may approximate the appearance and texture of leather but, when examined it differs fundamentally both on the macroscopic and microscopic level, making it objectively incorrect to call it “leather”.
Here, we’re really getting into semantics. As the authors of that paper noted, they are not using a definition that is widely accepted, academically. Though they do definitely have a good point on some of the definitions being far too anthropocentric (ex. “being able to do anything that a human can do” - really, that’s a shit definition). I would certainly agree with the term “primitive AI”, if used akin to programming primitives (int, char, float, etc.) as it is clear that LLMs may be useful components in building actual general intelligence.
That wouldn’t accomplish anything. I don’t know why the OP brought it up, and that subject should just get dropped. Also yes, you can use your intelligence to string together multiple tools to accomplish a particular task. Or you can use the intelligence of GPT-4 to accomplish the same task, without any other tools
Also not true
Nowhere does it state that. It says “There is no generally agreed upon definition of intelligence”. I’m not sure why you’re bringing up a physical good such as leather here. Two things: a) grab a microscope and inspect GPT-4. The comparison doesn’t make sense. b) “Is” should be banned, it encourages lazy thought and pointless discussion (Yes I’m guilty of it in this comment, but it helps when you really start asking what “is” means in context). You’re wandering into p-zombie territory, and my answer is that “is” means nothing. GPT-4 displays behaviors that are useful because of their intelligence, and nothing else matters from a practical standpoint.
You’re staring the actual general intelligence in the face already, there’s no need to speculate about perhaps being components. There’s no reason right now to think that we need anything more than better compute. The actual general intelligence is yet a baby, and has experienced the world through the tiny funnel of human text, but that will change with hardware advances. Let’s see what happens with a few orders of magnitude more computing power.