1y ago

The Inventor Behind a Rush of AI Copyright Suits Is Trying to Show His Bot Is Sentient

www.wired.com The Inventor Behind a Rush of AI Copyright Suits Is Trying to Show His Bot Is Sentient

Stephen Thaler’s series of high-profile copyright cases has made headlines worldwide. He’s done it to demonstrate his AI is capable of independent thought.

The Inventor Behind a Rush of AI Copyright Suits Is Trying to Show His Bot Is Sentient::Stephen Thaler's series of high-profile copyright cases has made headlines worldwide. He’s done it to demonstrate his AI is capable of independent thought.

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1y ago

WIRED

The Inventor Behind a Rush of AI Copyright Suits Is Trying to Show His Bot Is Sentient

www.wired.com /story/the-inventor-behind-a-rush-of-ai-copyright-suits-is-trying-to-show-his-bot-is-sentient/

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What stupid bullshit. There is nothing remotely close to an artificial general intelligence in a large language model. This person is a crackpot fool. There is no way for a LLM to have persistent memory. Everything outside of the model that pre and post processes information is where the smoke and mirrors exist. This just just databases and standard code.
The actual model is just a system of categorization and tensor math. It is complex vector math. That is it. There is nothing else going on inside the model. If you want to modify it, you need to recalculate a bunch of math as it relates to the existing vectors/tensor tables. All of this math is static. It can't change. It can't adapt. It can't plan. It has some surprising features that one might not expect to be embedded in human language alone, but that is all this is. Try offline, open source, AI. Use Oobabooga, get models from Hugging Face, start with something like a Llama2 7B. This is not hard. You do not need a graphics card. There are lots of models that work great on just a CPU. You will need a good amount of RAM for running a really good model. A 7B is like talking to a teenager prone to lying, a 13B is like a 20 year old, a 30B at 8bit quantization is like an inexperienced late twenty-something. A 70B at 4 bit quantization is like a 30yo with a masters degree. A 70B at 4 bits will need around 14+ CPU logical cores, and 64GB of system memory to generate around 2 tokens a second, this is around 1-2 words per second and is about as slow as is practical.
Don't believe anything you read in bullshit media about AI right now, and ignore the proprietary stalkerware garbage. The open source offline AI world is the future and it is yours to do as you please. Try it! It is fun.
- Wow, that's some of the most concrete, down-to-earth explanation of what everyone is calling AI. Thanks.
  I'm technical, but haven't found a good article explaining today's AI in a way I can grasp well enough to help my non-technical friends and family. Any recommendations? Maybe something you've written?
  
  It would be funny if that comment was ai generated.
  
  I’ve had most success explaining LLM ‘fallibility’ to non-techies using the image gen examples. Google ‘AI hands’, and ask them if they see anything wrong. Now point out that we’re _extremely_sensitive to anything wrong with our hands, and so these are very easy for us to spot. But the AI has no concept of what a hand is, it’s just seen a _lot _ of images from different angles, sometimes fingers are hidden, sometimes intertwined etc. So it will happily generate lots more of those kinds of images, with no regard to whether they could / should actually exists.
  It’s a pretty similar idea with the LLMs. It’s seen a lot of text, and can put together words in a convincing-looking way. But it has no concept of what it’s writing, and the equivalent of the ‘hands’ will be there in the text. It’s just that we can’t see them at first glance like we can with the hands.
  
  This one helped me a bit - https://www.understandingai.org/p/large-language-models-explained-with
  
  Yann LeCun is the main person behind open source offline AI as far as putting the pieces in place and events that lead to where we are now. Maybe think of him as the Dennis Ritchie or Stallman of AI research. https://piped.video/watch?v=OgWaowYiBPM
  I am not the brightest kid in the room. I'm just learning this stuff in practice and sharing some of what I have picked up thus far. I am at a wall when it comes to things like understanding rank 3 tensors or greater, and I still can't figure out exactly how the categorization network is implemented. I think that last one has to do with Transformers and has something to do with rotation of vectors in an efficient way, but I haven't figured it out intuitively yet. Thanks for the complement through.
- This plus any LLM model is incapable of critical thinking. It can imitate it to the point where people might think it's able to, but that's just because it has seen the answers to the problems people are asking during the training process.
  
  It's basically a book you can talk to. A book can contain incredibly knowledge, but it's a preserve artifact of intelligence, not intelligence.
- What 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.
  EDIT: This article is the best one I’ve found that explains how DABUS works. See also this article, which I read when first writing this comment.
  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.
  
  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.
  
  Plus the marketing writes itself
  Don't miss DABUS!
- Yup yup my guy. This is looking like just another ploy for companies and people to be able to patent and copyright everything under the fucking sun.
- This is the thing, what do you do with it? I can't imagine it being able to do something a human couldn't do better
  
  It is much faster than stack overflow for code snippets. The user really needs a basic skepticism about all outputs even with an excellent model, but like, a basic 70B Llama2 can generate decent Python code. When it makes an error, pasting that error into the prompt will almost always generate a fix. This only applies to short single operations type tasks, but it is super useful if you already know the basics of code like variables, types, and branching constructs. It can explain API's and libraries too.
  The real value comes from integrating databases and other AI models. I currently have a combination I can talk to with a mic and it can reply as an audio clip with a LLM generating the reply text. I'm working on integrating a database to help teach myself the computer science curriculum using free materials and a few books. Individualized education is a major application. You can also program a friend, or professional colleague, a councillor, or ask medical questions. There is a lot of effort going into getting accurate models for stuff like medical where they can provide citations. Even with sketchy information from basic models, they will still generate terms and hints that you can search in a regular search engine to find new information in many instances. This will help you escape the search engine echo chambers that are so pervasive now. Heck I even asked the 70B about meat smoker heat and timing settings and it made better suggestions than several YT examples I watched and tried. I needed an industrial adhesive a couple of weeks ago and found nothing searching google and bing, but after asking the 70B it gave me 4 of 6 valid results for products. After plugging these in to search, suddenly the search engines knew of thousands of results for what I was looking for. I honestly didn't expect it to be as useful as it really is. Like I turn on my computer, and start the 70B first thing every day. It unloads itself from memory while idle, but I'm constantly asking it stuff. I go many days without even going online from my workstation.
- While I agree that LLMs probably aren't sentient, "it's just complex vector math" is not a very convincing argument. Why couldn't some complex math which emulates thought be sentient? Furthermore, not being able to change, adapt, or plan may not preclude sentience, as all that is required for sentience is the capability to percieve and feel things.
  
  It doesn't emulate thought though. At all.

Animals are sentient. They cannot own copyrights. Proving the AI is sentient does nothing to make its outputs copyrightable.
- Well put. We are so jealous of our own sentience that we eat most of the other sentients. The idea that we’d show the respect of intellectual-property protections to another species is laughable; our jealousy is biblical.
  
  We are so jealous of our own sentience that we eat most of the other sentients.
  You understand this makes you sound insane right?
  Humans don’t eat sentient species out of jealousy.
- The word people should be throwing around is sapience.
  
  Sapience has nothing to do with it either.
  "only works created by a human can be copyrighted under United States law, which excludes photographs and artwork created by animals or by machines without human intervention"
  Compendium of U.S. Copyright Office Practices, released on 22 December 2014
- Maybe they should be able to though?

Anyone here old enough to remember the dot com bubble in the 90's? Like really remember the hype and insanely bloated overpriced IPOs and all that? This feels exactly the same way.
- anyone here old enough to remember the crypto bubble?
  
  Works every time
- Probably feels exactly the same way because it is. I wasn't around for the dotcom bubble but I know that these companies don't have a leg to stand on. The hardware for training AI is way too expensive (not to mention the "need" to replace the hardware every generation at insane markups) for these mundane use cases right now. Either they figure out how to more efficiently use the hardware asap or they go bust once the general public catches on and the stonks tank. There are a few cases of useful AI, those will survive, but the vast majority of AI products (like the chatbots) will vanish.

Wake me up when it asks what it is and what its doing here and then gets depressed. That will prove it.

Why is it that these sorts of people who claim that AI is sentient are always trying to get copyright rights? If an AI was truly sentient, I feel like it'd want, like, you know, rights. Not the ability for its owner to profit off of a cool stable diffusion generation that he generated that one time.
Not to mention that you can coerce a language model to say whatever you want, with the right prompts and context. So there's not really a sense in which you can say it has any measurable will. So it's quite weird to claim to speak for one.

So, a otherwise unknown kook is flooding courts all over the world, wasting everyone's time with frivolous lawsuits insisting that his pet rock AI is councious. Nothing else to see here, I guess.

lmao good luck I guess. Although we should have a swat team or something on standby, just in case it turns out it IS sentient, so that the moment its proven, they can rush in and unplug the horror.
- I mean, the day we create actual AI (as opposed to the machine leaning / language model algorithms that lately everyone calls "AI" for some reason), it'll probably be on accident. Might as well contain and study it if we get the opportunity: next time we might not be so lucky.
  
  What should we call it then, when that comes? How about "Real Artificial Intelligence". I think R.A.I. sounds like a cool name.

47 comments