Plus the marketing writes itself
Don’t miss DABUS!
Comment on The Inventor Behind a Rush of AI Copyright Suits Is Trying to Show His Bot Is Sentient
hedgehog@ttrpg.network 1 year agoWhat stupid bullshit. There is nothing remotely close to an artificial general intelligence in a large language model.
Correct, but I haven’t seen anything suggesting that DABUS is an LLM. My understanding is that it’s basically made up of two components:
- An array of neural networks
- A supervisor component (that its creator calls a “thalamobot”) that manages those networks, notices when they’ve come up with something worth exploring further. The supervisor component can direct the neural networks as well as trigger other algorithms.
Other than using machine vision and machine hearing (“acoustic processing algorithms”) to supervise the neural networks, I haven’t found any description of how the thalamobot functions. Machine vision / hearing could leverage ML but might not, and either way I’d be more interested in how it determines what to prioritize / additional algorithms to trigger rather than how it integrates with the supervised system.
This person is a crackpot fool.
As far as I can tell, probably, but not necessarily.
There is no way for a LLM to have persistent memory. Everything outside of the model that pre and post processes infor is where the smoke and mirrors exist. This just just databases and standard code.
Ignoring Thaler’s claims, theoretically a supervisor could be used in conjunction with an LLM to “learn” by re-training or fine-tuning the model. That’s expensive and doesn’t provide a ton of value, though.
That said, a database / external process for retaining and injecting context into an LLM isn’t smoke and mirrors when it comes to persistent memory; the main difference compared to re-training is that the LLM itself doesn’t change. There are other limitations, too. But if I have an LLM that can handle an 8k token context where the first 4k is used (including during training) to inject summaries of situational context and of topics/concepts that are currently relevant, and the last 4k are used like traditional context, then that gives you a lot of what persistent memory would. Combine that with the ability for the system to retrain as needed to assimilate new knowledge bases and you’re all the way there.
That’s still not an AGI or even an attempt at one, of course.
OrteilGenou@lemmy.world 1 year ago
j4k3@lemmy.world 1 year ago
Just talking hypothetically, I think it may be possible to actually make an AGI with an LLM base with a threaded interpreted language like Forth. If it was integrated into the model, it might be able to add network layers like a LoRA in real time or let’s say average prompt to response time. The nature of Forth makes it possible to negate issues with code syntax as a single token or two could trigger a Forth program of any complexity. I can imagine a scenario where Forth is fully integrated and able to modify the network with more than just LoRAs and embeddings, but I’m no expert; just a hobbyist. I fully expect any major breakthrough will be from white paper research, and not someone that is using hype media nonsense and grandstanding for a spotlight. It will not involve external code.
Tacking systems together with databases is not what I would call a human-brain analog or AGI. I expect a plastic network with self modifying behavior in near real time along with the ability to expand at or arbitrarily alter any layer. It would also require a self test mechanism and bookmarking system to roll back any unstable or unexpected behavior using self generated tests.
hedgehog@ttrpg.network 1 year ago
Agreed, and either of those are more than a system with persistent memory.
I think it would be wise for such a system to have a rollback mechanism, but I don’t think it’s necessary for it to qualify as a human brain analog or AGI - I don’t have the ability to roll back my brain to the way it was yesterday, for example, and neither does anyone I’ve ever heard of.
I don’t think this is realistic or necessary, either. If I want to learn a new, non-trivial skill, I have to practice it, generally over a period of days or longer. I would expect the same from an AI.
Sleeping after practicing / studying often helps to learn a concept or skill. It seems to me that this is analogous to a re-training / fine-tuning process that isn’t necessarily part of the same system.
It’s unclear to me why you say this. External, traditional code is necessary to link multiple AI systems together, like a supervisor and a chatbot model, right? (Maybe I’m missing how this is different from invoking a language from within the LLM itself - I’m not familiar with Forth, after all.) And given that human neurology is basically comprised of multiple “systems” - left brain, right brain, frontal node, our five senses, etc. - why wouldn’t we expect the same to be true for more sophisticated AIs? I personally expect there to be breakthroughs if and when an AI that is trained on multi-modal data (sight + sound + touch + smell + taste + feedback from your body + anything else of relevance) is built (e.g., by wiring up people with sensors to pull down that data), and I believe that models capable of interacting with that kind of training data would comprise multiple systems.
At minimum you currently need an external system wrapped around the LLM to emulate “thinking,” which my understanding is something ChatGPT already does (or did) to an extent. I think this is currently just a “check your work” kind of loop but a more sophisticated supervisor / AI consciousness could be much more capable.
That said, I would expect an AGI to be able to leverage databases in the course of its work, much the same way that Bing can surf the web now or ChatGPT can integrate with Wolfram — separate from its own ability to remember, learn, and evolve.
j4k3@lemmy.world 1 year ago
I think the fundamental difference in our perspectives is that I want to see neural expansion capabilities that are not limited by a static state and dedicated compilation. I think this is the only way to achieve a real AGI. If the neural network is static, ultimately you have a state machine with a deterministic output. It can be ultra complex for sure, but it is still deterministic. I expect an AGI to have expansion in any direction at all times according to circumstances and needs; aka adaptability beyond any preprogrammed algorithms.
Forth is very old, and from an era when most compute hardware was tailor made. It was originally created as a way to get professional astronomy observatories online much more quickly. The fundamental concept with Forth is to create the simplest looping interpreter on any given system using assembly or any supported API. The interpreter can then build on the Forth dictionary of words. Words are the fundamental building block of Forth. They can be anything from a pointer to a variable, or a function, to an entire operating system and GUI. Anything can be assigned to a word and a word can be any combination of data, types, and other words. The syntax is extremely simple. It is a stack based language that is very close to the bare metal. It is so simple and small, that there are versions of Forth that run on tiny old 8 bit AVRs and other microcontrollers.
Anyways, the idea of a threaded interpreter like Forth, could be made to compile tensor layers. The API for the network would be part of the Forth dictionary. Another key aspect to Forth is that the syntax to create new words is so simple that a word can be made that creates the required formatting. This could make it possible for a model to provide any arbitrary data for incorporation/modification and allow Forth to attempt to add it into the network in real time. It could also be used to modify specific tensor weights when a bad output is indicated by the user and a correction is provided.
If we put aside text formatting, settings, and user interface elements, the main reason a LLM needs external code for interfacing is because of the propensity for errors due to syntax complexity with languages like Python or C. No models can generate reliable complex code suitable for their own execution internally without intervention. Forth is so flexible that a dictionary could even be a tensor table of weights, like words could be the values. Forth is probably the most anti-standards, anti-syntax, language ever created.
Conceptually, the interpreter is like a compiler, command line, task scheduler, and init/process manager all built into one ultra simple system. Words are built from the registers, flags, and interrupts, up to anything of arbitrary complexity. A model does not need this low level interface with compute hardware, but this is not my point. Models are built on tensors and tokens. Forth can be made to speak these natively and in near real time as prompted internally and without compilation; a true learning machine. Most Forth implementations also have an internal bookmarking system that allows the dictionary to roll back to a known good state when encountering errors in newly created words.
A word of warning, full implementations like ANS Forth or G-Forth are intimidating at first glance. It is far better to look at something like Flash Forth for microcontrollers to see the raw power of the basic system without the giant dictionaries present in modern desktop implementations.
The key book on the concepts behind Forth and threaded interpretive languages is here: archive.org/…/R.G.LoeligerThreadedInterpretiveLan…