It's interesting that the current AI revolution began with transformers that came out of Google who was trying to apply AI to search, and now that seems to be the an unpopular use for it. I think it's one of the better use cases for it. It's great at compressing knowledge and if it can search the web and summarize things I don't see much of a downside. As far as making a profit, I don't see why they wouldn't charge a small amount for it, and that way they're not shoving everyone to use it.
I should rephrase on the first one. We only have one source of reference for what thinking is and where it happens.
On the second one, yes there can be multiple ways that consciousness can manifest in wildly different systems, but what theory do we have that would validate these other different kinds of consciousness? Without that theory for validation the safer bet is to stick with what we already have a reference to, unless the claim is that consciousness is such an easy algorithm it can easily be stumbled upon by all kinds of systems, i.e. a bunch of matrix multiplications that doesn't even run in real-time (it executes prompts and then stops). I think that is not likely in my opinion.
We evolved to handle light as a sensory stream of data, whether a few are born without part of the system (usually the sensors and not the processing system) is something else. My point is more that we don't really know. The exceptions are not disproving how biological systems evolved.
I would include processing real-time sensory data, i.e. light, building a real-time model of a self and the world, a unified way to perceive, react, and learn from the sensory data. This would be for consciousness in general. Once we're talking about human-level consciousness where language is involved, the list is longer, with theory of mind, ability to construct models of others fairly quickly, and ability to navigate social situations.
Why those properties though? What's so special about them? I just have a longer list of things it would need to satisfy before I can say faking doesn't matter.
Right, but we don't know how much that is necessary for a conscious system. Maybe processing light has certain properties necessary for consciousness. If we don't know this then for me the safe bet is to say it is not conscious unless the system is processing at least a similar stream of information that maps to light waves.
That's the question, it's the Chinese room or philosophical zombie question. How can we tell them apart... but we're not even there yet.
If you put an LLM on your phone and talk to it, it's obvious it's not human. It doesn't have the same social context, it doesn't respond as quickly, it clearly won't form a theory of m ind connection like two humans socializing. So then why would we jump to the conclusion there is something there when the only example we have are humans (that use language and are conscious)?
It is a different signal conversion, but the question is whether that difference is meaningful or not. Light waves are much different than tokens. I think it affects the overall system of our brains much more than we currently know. Having access to so much data has probably shaped biological brains at its "core" (initial evolution stages).
The starting point is the consciousness we already know. That is ourselves, which most people then extend to others, however it's not hard to then extended to at the very least mammals. I then extend this to most animals (whether it's 100% is irrelevant for this point). That extension from my consciousness to all animals is a small logical leap based on biology and our shared tree of life... however it doesn't mean other consciousnesses are not possible. It's more so that they would require a lot more evidence and a proper theory of C. in order to validate. Without that theory we can only point at similar C., of which LLMs are not displaying. They are showing a different kind of "cognition" if we can even call it that (I do think it's debatable).
So it's more a hueristic. We know one kind of C., therefore that's what we should look for. Statistically it's probable that there are many more ways to show similarities with conscious behavior which are NOT conscious, then it is for a divergent behavior to happen to be conscious. In other words, there are more ways to fake similar behavior than to land on an actual conscious behavior that is somehow much different than what we know.
I just don't know if "similar" is good enough. We know what thinking is based on biological brains, so if we are to conclude it happens in machines, the experiment should have identical results, or it's more probable that it's something completely different.
The way around this is to have a predictive theory of how thoughts work, and if that is proven true, then use that to analyze the LLMs. So far we have no such theory so I think the only tool is to look at the exact behavior match.
There's probably an analog to it, but I really doubt it's the same or even close to it. Just think about how we read, it's not tokens it's visual representations. I think LLMs are tapping into some larger knowledge structure that we also use.
There's probably an analog to it, like messing with neuron signals might get the patient to show weird behavior, but I don't think it would be 1:1. For example you could mess with a person's ability to speak language but finding a neuron and having the person have a completely different but coherent response is probably not how the brain works.
It would be hard to perform the same test on biological brains, but whatever equivalent could be done I'm sure it would be 99.999% if not 100%. At the very least this is not thinking as we know it in biological brains. It becomes a semantic issue at that point.
It's comparing apples to oranges. I'm just saying it's pattern matching at scale, not that the scale is proof of anything. And yeah spiders are conscious or at least think and they have tiny brains, but that's just more proof there is a much simpler path to intelligence than what LLMs are doing because they need so much data and compute power.
On the side note, it's a different subject matter but life consumes other life, this happens on every level from bacteria to mammals. I also don't get the fixation on preventing suffering so much (a little I get it). Life entails suffering, this gets philosophical but aiming at a life with no suffering at all amounts to not being alive. Farm animals are necessary for humans to grow into a civilization size species. We could go back before farming but that would mean billions of people will not exist. I guess pick which evil you want, less humans or animals being domesticated and consumed. (disclaimer, I still think mass farming is really bad and should be solved by responsible consumption and less population).
Pretty fun. Didn't expect to be participating in some kind of meta-level prisoner's dilemma with the presentation itself hehe. I had to adjust my expectations to predict a total of 3/4. I still want to know what the overall research results tell us about LLMs. It sounds like they don't do tit-for-tat, and try to defect more than humans.
Nice, is this going to allow a practical use for video games? I know you can do network calls with any model but the latency limits the use cases. If it can run locally it opens up all kinds of use cases like behavior AI for NPCs. Will need to take a look.
This type of research should put the "do LLMs think" to rest, not to mention the ludicrous claim of consciousness cough Dawkins cough. It is quite clearly a pattern matching algorithm at scale.
It's still narrow AI but massively scaled to look like general AI.
Good summary, thanks. Sounds like it's all still pretty early, even for graphics but much more for parallel compute, which seems the direction the hardware is going in. I'm sure there is a CUDA equivalent for CPU but I would guess it's all the way back in the 70s or earlier. Hopefully open standards gets some traction.
Right, that makes sense. There's a ton of overhead in the setup though, I'm assuming that's because everything runs on the CPU by default. Is this then an OS design choice? Thanks for your input.
Nice breakdown. I've been wondering why it's so complicated to program on the GPU compared to the CPU. Is it a language paradigm issue or maybe a hardware design limitation? I always think of this when looking at the graphic APIs, espeically Vulkan.
I thought this was about evolving a game engine as in a genetic algorithm, but it's about gradually introducing ECS into a game engine that didn't have it before. Still pretty good, but now I want to see someone evolve a game engine.
The dev has a co-founder and a publisher, and operating outside the US, so taxes. 30% is bad but would he make it any other way is the question. You can always publish it in multiple stores, I'm assuming.
That's right. The pushback on this is how art went digital but we can clearly see the leap from traditional media to digital is a lot smaller in comparison to the AI leap that is automating almost every aspect of it aside from an initial idea.
Interesting, maybe I should had gone into hardware (assuming it's embedded software not literal hardware). I've always said unit tests is like pouring concrete on your code. It will solidify and keep you from making changes. Which is ironic that the motivation is about changing things more as the article suggests:
> quality improvements are perceived as worthwhile if and only if they accelerate the rate at which the program is changed
Every company I've worked for shoots themselves in the foot doing this. Tests should mostly happen when the code is complete, and nowadays almost no company commits to this kind of workflow. There are ways to develop an app such that you can layer completed code on top of completed code adding tests as you go, but this has implications on the size and types of teams which is above the pay grade of an engineer.
To me it's one of the biggest frustrations with the software industry and how every job is always a mess and constantly putting out fires when it could easily be a smooth and predictable process like a lot of other engineering fields.
Are they begging to be shut down? I don't get why the warning would come from the place creating the risk. It's like a nuclear power plant warning everyone of a meltdown, like what are we supposed to do, you control it!
The problem is this money goes through an incompetent government that can't even build a train anymore. Maybe 30 years ago the story would be different but that's why more taxes is never a good idea. The functioning governments will always decay into incompetence and if they have billions of dollars more to waste, they will.
People incorrectly think this is defending billionaires, but it's actually them defending government. We really should rethink how this wealth flows down, and it should be a more direct line. Maybe instead of a tax it's some kind of obligation to fund non-profit orgs of their choosing.