Recent AI failures are cracks in the magic
Recent AI failures are cracks in the magic
Trouble in trillions-land
There's magic?
Only if you believe in it. Many CEOs do. They're very good in magical thinking.
Magic as in street magician, not magic as in wizard. Lots of the things that people claim AI can do are like a magic show, it's amazing if you look at it from the right angle, and with the right skill you can hide the strings holding it up, but if you try to use it in the real world it falls apart.
Sam Altman will make a big pile of investor money disappear before your very eyes.
If you're a thechbro, this is the new magic shit, man! To the moooooon!
The masses have been treating it like actual magic since the early stages and are only slowly warming up to the idea it‘s calculations. Calculations of things that are often more than the sum of it‘s parts as people start to realize. Well some people anyway.
If only.
Yea, try talking to chatgpt about things that you really know in detail about. It will fail to show you the hidden, niche things (unless you mention them yourself), it will make lots of stuff up that you would not pick up on otherwise (and once you point it out, the bloody thing will "I knew that" you, sometimes even if you are wrong) and it is very shallow in its details. Sometimes, it just repeats your question back to you as a well-written essay. And that's fine...it is still a miracle that it is able to be as reliable and entertaining as some random bullshitter you talk to in a bar, it's good for brainstorming too.
It's like watching mainstream media news talk about something you know about.
Oh good comparison
Haha, definitely, it's infuriating and scary. But it also depends on what you are watching for. If you are watching TV, you do it for convenience or entertainment. LLMs have the potential to be much more than that, but unless a very open and accessible ecosystem is created for them, they are going to be whatever our tech overlords decide they want them to be in their boardrooms to milk us.
I really only use for "oh damn, I known there's a great one-liner to do that in Python" sort of thing. It's usually right and of it isn't it'll be immediacy obvious and you can move on with your day. For anything more complex the gas lighting and subtle errors make it unusable.
Oh yes, it's great for that. My google-fu was never good enough to "find the name of this thing that does this, but only when in this circumstance"
ChatGPT is great for helping with specific problems. Google search for example gives fairly general answers, or may have information that doesn't apply to your specific situation. But if you give ChatGPT a very specific description of the issue you're running into it will generally give some very useful recommendations. And it's an iterative process, you just need to treat it like a conversation.
It's also a decent writer's room brainstorm kind of tool, although it can't really get beyond the initial pitch as it's pretty terrible at staying consistent when trying to clean up ideas.
I find it incredibly helpful for breaking into new things.
I want to learn terraform today, no guide/video/docs site can do it as well as having a teacher available at any time for Q&A.
Aside from that, it's pretty good for general Q&A on documented topics, and great when provided context (ie. A full 200MB export of documentation from a tool or system).
But the moment I try and dig deeper I to something I'm an expert in, it just breaks down.
That's why I've found it somewhat dangerous to use to jump into new things. It doesn't care about bes practices and will just help you enough to let you shoot yourself in the foot.
Just wait for MeanGirlsGPT
Good. It's dangerous to view AI as magic. I've had to debate way too many people who think they LLMs are actually intelligent. It's dangerous to overestimate their capabilities lest we use them for tasks they can't perform safely. It's very powerful but the fact that it's totally non deterministic and unpredictable means we need to very carefully design systems that rely on LLMs with heavy guards rails.
Conversely, there are way too many people who think that humans are magic and that it's impossible for AI to ever do .
I've long believed that there's a smooth spectrum between not-intelligent and human-intelligent. It's not a binary yes/no sort of thing. There's basic inert rocks at one end, and humans at the other, and everything else gets scattered at various points in between. So I think it's fine to discuss where exactly on that scale LLMs fall, and accept the possibility that they're moving in our direction.
It's not linear either. Brains are crazy complex and have sub cortexes that are more specialized to specific tasks. I really don't think that LLMs alone can possibly demonstrate advanced intelligence, but I do think it could be a very important cortex for one. There's also different types of intelligence. LLMs are very knowledgeable and have great recall but lack reasoning or worldview.
I find the people who think they are actually an AI are generally the people opposed to them.
People who use them as the tools they are know how limited they are.
Not being combative or even disagreeing with you - purely out of curiosity, what do you think are the necessary and sufficient conditions of intelligence?
A worldview simulation it can use as a scratch pad for reasoning. I view reasoning as a set of simulated actions to convert a worldview from state a to state b.
It depends on how you define intelligence though. Normally people define it as human like, and I think there are 3 primary sub types of intelligence needed for cognizance, being reasoning, awareness, and knowledge. I think the current Gen is figuring out the knowledge type, but it needs to be combined with the other two to be complete.
I think it's a big mistake to think that because the most basic LLMs are just autocompletes, or that because LLMs can hallucinate, that what big LLMs do doesn't constitute "thinking". No, GPT4 isn't conscious, but it very clearly "thinks".
It's started to feel to me like current AIs are reasonable recreations of parts of our minds. It's like they're our ability to visualize, to verbalize, and to an extent, to reason (at least the way we intuitively reason, not formally), but separared from the "rest" of our thought processes.
Depends on how you define thinking. I agree, LLMs could be a component of thinking, specifically knowledge and recall.
Yes, as Linus Torvalds said humans are also thinking like autocomplete systems.
Those recent failures only come across as cracks for people who see AI as magic in the first place. What they're really cracks in is people's misperceptions about what AI can do.
Recent AI advances are still amazing and world-changing. People have been spoiled by science fiction, though, and are disappointed that it's not the person-in-a-robot-body kind of AI that they imagined they were being promised. Turns out we don't need to jump straight to that level to still get dramatic changes to society and the economy out of it.
I get strong "everything is amazing and nobody is happy" vibes from this sort of thing.
Also interesting is that most people don't understand the advances it makes possible so when they hear people saying it's amazing and then try it of course they're going to think it's not lived upto hype.
The big things are going to completely change things like how we use computers especially being able to describe how you want it to lay out ui and create custom tools on the fly.
Exactly, it's the people who know that are amazed by the subtle intricacies of AI and the implications of it. It's the people that don't know saying, "I asked it to write a horror story about a killer clown, and it ended up sounding like Stephen King. What a rip off machine."
Here is an alternative Piped link(s):
everything is amazing and nobody is happy
Piped is a privacy-respecting open-source alternative frontend to YouTube.
I'm open-source; check me out at GitHub.
I hope it collapses in a fire and we can just keep our foss local models with incremental improvements, that way both techbros and artbros eat shit
There are quite a lot of AI-sceptics in this thread. If you compare the situation to 10 years ago, isn't it insane how far we've come since then?
Image generation, video generation, self-driving cars (Level 4 so the driver doesn't need to pay attention at all times), capable text comprehension and generation. Whether it is used for translation, help with writing reports or coding. And to top it all off, we have open source models that are at least in a similar ballpark as the closed ones and those models can be run on consumer hardware.
Obviously AI is not a solved problem yet and there are lots of shortcomings (especially with LLMs and logic where they completely fail for even simple problems) but the progress is astonishing.
I think a big obstacle to meaningfully using AI is going to be public perception. Understanding the difference between CHAT-GPT and open source models means that people like us will probably continue to find ways of using AI as it continues to improve, but what I keep seeing is botched applications, where neither the consumers nor the investors who are pushing AI really understand what it is or what it's useful for. It's like trying to dig a grave with a fork - people are going to throw away the fork and say it's useless, not realising that that's not how it's meant to be used.
I'm concerned about the way the hype behaves because I wouldn't be surprised if people got so sick of hearing about AI at all, let alone broken AI nonsense, that it hastens the next AI winter. I worry that legitimate development may be held back by all the nonsense.
Fair point. I personally think that AI lives up to enough parts of the hype so that there won't be another AI winter but who knows. Some will obviously get disillusioned but not enough.
Lol. It doesn't do video generation. It just takes existing video and makes it look weird. Image generation is about the same: they just take existing works and smash them together, often in an incoherent way. Half the text generation shit is just fine by underpaid people in Kenya Ave and similar places.
There are a few areas where llm could be useful, things like trawling large data sets, etc, but every bit of the stuff that is being hyped as "AI" is just spam generators.
That's totally not how it works. Not only nobody has the need for such tools, but the technology got there much before the current state of AI
Confidently incorrect.
As I often mention when this subject pops up: while the current statistics-based generative models might see some application, I believe that they'll be eventually replaced by better models that are actually aware of what they're generating, instead of simply reproducing patterns. With the current models being seen as "that cute 20s toy".
In text generation (currently dominated by LLMs), for example, this means that the main "bulk" of the model would do three things:
- convert input tokens into sememes (units of meaning)
- perform logic operations with the sememes
- convert sememes back into tokens for the output
Because, as it stands, LLMs are only chaining tokens. They might do this in an incredibly complex way, but that's it. That's obvious when you look at what LLM-fuelled bots output as "hallucination" - they aren't the result of some internal error, they're simply an undesired product of a model that sometimes outputs desirable stuff too.
Sub "tokens" and "sememes" with "pixels" and "objects" and this probably holds true for image generating models, too. Probably.
Now, am I some sort of genius for noticing this? Probably not; I'm just some nobody with a chimp avatar, rambling in the Fediverse. Odds are that people behind those tech giants already noticed the same ages ago, and at least some of them reached the same conclusion - that better gen models need more awareness. If they are not doing this already, it means that this shit would be painfully expensive to implement, so the "better models" that I mentioned at the start will probably not appear too soon.
Most cracks will stay there; Google will hide them with an obnoxious band-aid, OpenAI will leave them in plain daylight, but the magic trick will still not be perfect, at least in the foreseeable future.
And some might say "use MOAR processing power!", or "input MOAR training data!", in the hopes that the current approach will "magically" fix itself. For those, imagine yourself trying to drain the Atlantic with a bucket: does it really matter if you use more buckets, or larger buckets? Brute-forcing problems only go so far.
Just my two cents.
I agree 100%, and I think Zuckerberg's attempt at a massive 340,000 of Nvidia’s H100 GPUs AI based on LLM with the aim to create a generel AI sounds stupid. Unless there's a lot more to their attempt, it's doomed to fail.
I suppose the idea is something about achieving critical mass, but it's pretty obvious, that that is far from the only factor missing to achieve general AI.
I still think it's impressive what they can do with LLM. And it seems to be a pretty huge step forward. But It's taken about 40 years from we had decent "pattern recognition" to get here, the next step could be another 40 years?
I think that Zuckerberg's attempt is a mix of publicity stunt and "I want [you] to believe!". Trying to reach AGI through a large enough LLM sounds silly, on the same level as "ants build, right? If we gather enough ants, they'll build a skyscraper! Chrust me."
In fact I wonder if the opposite direction wouldn't be a bit more feasible - start with some extremely primitive AGI, then "teach" it Language (as a skill) and a language (like Mandarin or English or whatever).
I'm not sure on how many years it'll take for an AGI to pop up. 100 years perhaps, but I'm just guessing.
I don't know much about LLMs but latent diffusion models already have "meaning" encoded into the model. The whole concept of the u-net is that as it reduces the spacial resolution of the image, it increases the semantic resolution by adding extra dimensions of information. It came from medical image analysis where the idea of labelling something as a tumor would be really useful.
This is why you get body dysmorphic results on earlier (and even current) models. It's identified something as a human limb, but isn't quite sure on where the hand is, so it adds one on to what we know is a leg.
There was an interesting paper published just recently titled Generative Models: What do they know? Do they know things? Let's find out! (a lot of fun names and titles in the AI field these days :) ) That does a lot of work in actually analyzing what an AI image generator "knows" about what they're depicting. They seem to have an awareness of three dimensional space, of light and shadow and reflectivity, lots of things you wouldn't necessarily expect from something trained just on 2-D images tagged with a few short descriptive sentences. This article from a few months ago also delved into this, it showed that when you ask a generative AI to create a picture of a physical object the first thing the AI does is come up with the three-dimensional shape of the scene before it starts figuring out what it looks like. Quite interesting stuff.
That's perhaps why image generators are comparatively better than text generators. But there's still something off, by your example it seems that the model cannot reliably use clues like position to understand "this is a «leg»". And I don't know much about image generators but I think that they're still statistics- and probability-based.
That's a huge oversimplification of the way LLMs work. They're not statistical in the way a Markov chain is. They use neural networks, which are a decent analogy for the human brain. The way the synapses between neurons are wired is obviously different, and the way the neurons are triggered and the types of signals they can send to other neurons is obviously different. But overall, similar capabilities can in theory be achieved with either method. If you're going to call neural networks statistics based, you might as well call the human brain statistics based as well.
That’s a huge oversimplification of the way LLMs work.
I'm sticking to what matters for the sake of the argument. Anyone who wants to inform themself further has a plethora of online resources to do so.
They’re not statistical in the way a Markov chain is.
Implied: "you're suggesting that they work like Markov chains, they don't."
In no moment I mentioned or even implied Markov chains. My usage of the verb "to chain" is clearly vaguer within that context; please do not assume words onto my mouth.
They use neural networks, which are a decent analogy for the human brain. The way the synapses between neurons are wired is obviously different, and the way the neurons are triggered and the types of signals they can send to other neurons is obviously different. But overall, similar capabilities can in theory be achieved with either method.
I don't disagree with the conclusion (i.e. I believe that neural networks can achieve human-like capabilities), but the argument itself is such a fallacious babble (false equivalence) that I'm not bothering further with your comment.
And it's also an "ackshyually" given this context dammit. I'm not talking about the bloody neural network, but how it is used.
I found this graph very clear
Well, natural language processing is placed in the trough of disillusionment and projected to stay there for years. ChatGPT was released in November 2022...
Trying to make real and good use of AI generative models are cracks in the magic.
It's pretty useful if you know exactly what you want and how to work within it's limitations.
Coworkers around me already use ChatGPT to generate code snippets for Python, Excel VBA, etc. to good success.
Right, it's a tool with quirks, techniques and skills to use just like any other tool. ChatGPT has definitely saved me time and on at least one occasion, kept me from missing a deadline that I probably would have missed if I went about it "the old way" lmao
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(Also that page has a script I had to override just to copy and paste that)
It's well worth reading the longer newsletter the above link quotes: https://www.wheresyoured.at/sam-altman-fried/
I kinda agree we are probably cresting the peak of the hype cycle right now.
darkaltrax