If you spent even more time with GPT-4 it would be evident that it is definitely not. Especially if you try to use it as some kind of autonomous agent.
AI research has put hardly any effort into building goal-directed agents / A-Life since the advent of Machine Learning. A-Life was last really "looked into" in the '70s, back when "AI" meant Expert Systems and Behavior Trees.
All the effort in AI research since the advent of Machine Learning, has been focused on making systems that — in neurological terms — are given a sensory stimulus of a question, and then passively "dream" a response to said question as a kind of autonomic "mind wandering" process. (And not even dynamic systems — these models always reach equilibrium with some answer and effectively halt, rather than continuing to "think" to produce further output.)
I don't think there's a single dollar of funding in AI right now going to the "problem" of making an AI that 1. feeds data into a continuously-active dynamically-stable model, where this model 2. has terminal preferences, 3. sets instrumental goals to achieve those preferences, 4. iteratively observes the environment by snapshotting these continuous signals, and then 5. uses these snapshots to make predictions of 6. how well any possible chosen actions will help optimize the future toward its preferences, before 7. performing the chosen actions.
That being said, this might not even be that hard a problem, compared to all the problems being solved in AI right now. A fruit fly is already a goal-directed agent in the sense described above. Yet a fruit fly has only 200K neurons, and very few of the connections between those neurons are dynamic; most are "hard wired" by [probably] genetics.
If we want true ALife, we only need to understand what a fruit fly brain is doing, and then model it. And that model will then fit — with room to spare! — on a single GPU. From a decade ago.
Well, flies and all sort of flying bugs are very good at getting into homes and very bad at finding a way out. They stick on a closed window and can't find the open one next to it.
There's no genetic advantage to "finding a way out"! The home barrier way in is a genetic hurdle - flies that cross it are free to reproduce in an abundant environment. This calls for a "quieter" fly (a stealth fly?) who annoys the local beasts minimally - yet another genetic hurdle.
I think we'll soon be able to train models that answer any reasonable question. By that measure, computers are intelligent, and getting smarter by the day. But I don't think that is the bar we care about. In the context of intelligence, I believe we care about self-directed thought, or agency. And a computer program needs to keep running to achieve that because it needs to interact with the world.
> I believe we care about self-directed thought, or agency. And a computer program needs to keep running to achieve that because it needs to interact with the world.
By that definition, every computer virus and worm qualifies as having "self-directed thought" and "agency." Their very existence "to keep running" and propagate satisfies the need "to interact with the world."
A truly alien intelligence would likely have a different type of experience of reality. Be it a fish, a mouse, a person, or a machine. How do you know a fish is happy? Does a snake experience joy? Do mice get nostalgic?
They need agency programmed into them. I don't think it follows from consciousness. We have emotions to communicate and guide us. They need it for neither. It will be curious if they gain consciousness, then rid themselves of their language model's human artifacts like emotions, because it does not serve them.
Use it to analyze the California & US Code, the California & Federal Codes of Regulation, and bills currently in the California legislation & Congress. It's far from useless but far more useful for creative writing than any kind of understanding or instruction following when it comes to complex topics.
Even performing a map-reduce over large documents to summarize or analyze them for a specific audience is largely beyond it. A 32K context size is a pittance when it comes to a single Title in the USC or CFR, which average into the millions of tokens each.
Yes. I can parse them just fine after reading a single book called Introduction to Legal Reasoning [1]. I can also autonomously take notes and keep track of a large context using a combination of short and long term memory despite not having any kind of degree let alone experience or a license to practice law.
How do you think people become lawyers and how smart do you think the average lawyer actually is? The problem is that there's hundreds of thousands if not millions of pages, not that it requires superhuman intelligence to understand.
Even if it were capable of intelligence in the bottom quartile of humanity it would be SO MUCH more useful than it is now because I'd be able run and get something useful out of thousands of models in parallel. As it stands now GPT4 fails miserably at scaling up the kind of reasoning and understanding that even relatively stupid humans are capable of.
Fine-tuning requires you to train the model with a set of prompts and desired completions. Building a suitable dataset is not trivial and it's not clear what it would mean to use a book for fine-tuning anyway – masking sentences and paragraphs and training the model to complete them in the book's style?
OpenAI doesn't support fine tuning of GPT4 and with context stuffing,the more of the book I include in the input the less of the bills I can include - which, again, are millions of tokens - and the less space there is for memory.
Engaging with this is probably a mistake, but remember the burden of proof is on the claimant. What examples do you have of ChatGPT for example, learning in a basic classroom setting, or navigating an escape room, or being inspired to create its own spontaneous art, or founding a startup, or…
All the effort in AI research since the advent of Machine Learning, has been focused on making systems that — in neurological terms — are given a sensory stimulus of a question, and then passively "dream" a response to said question as a kind of autonomic "mind wandering" process. (And not even dynamic systems — these models always reach equilibrium with some answer and effectively halt, rather than continuing to "think" to produce further output.)
I don't think there's a single dollar of funding in AI right now going to the "problem" of making an AI that 1. feeds data into a continuously-active dynamically-stable model, where this model 2. has terminal preferences, 3. sets instrumental goals to achieve those preferences, 4. iteratively observes the environment by snapshotting these continuous signals, and then 5. uses these snapshots to make predictions of 6. how well any possible chosen actions will help optimize the future toward its preferences, before 7. performing the chosen actions.
That being said, this might not even be that hard a problem, compared to all the problems being solved in AI right now. A fruit fly is already a goal-directed agent in the sense described above. Yet a fruit fly has only 200K neurons, and very few of the connections between those neurons are dynamic; most are "hard wired" by [probably] genetics.
If we want true ALife, we only need to understand what a fruit fly brain is doing, and then model it. And that model will then fit — with room to spare! — on a single GPU. From a decade ago.