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Comment by 2bitencryption | Hacker News Reader

This type of article (or press release, or whatever you want to call it) is exactly what makes the future so interesting.

The cat is out of the bag, the genie is out of the bottle, the confetti has left the cannon[0].

It's tempting to see a world dominated by Google Bard, ChatGPT, Bing Search, etc. And no doubt, they will be huge players, with services that are far more powerful than anything that can be run on the edge.

But. BUT. The things that we can do on the edge are incredible now. Just imagine a year from now, or two. These earth-shattering models, which seem to be upending a whole industry, will soon have equivalents that run on the edge. Without services spying on your data. Without censorship on what the model can/cannot say. Because it's all local.

When was the last time this happened? There will be players who publish weights for models that are free to use. The moment that torrent magnet link is published, it's out in the wild. And smart people will package them as "one click installers" for people who aren't tech-savvy. This is already happening.

So every time you're amazed by something chat-gpt4 says, remember that soon this will be in your pocket.

[0] the "confetti" idiom brought to you by chat-gpt4.

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Google: "confetti has left the cannon"

> No results found for "confetti has left the cannon".

I'm amazed that a "stochastic parrot" can come up with such a beautiful idiom.

Out of distribution generations are a thing.

I understand that all of this is based on some fundamental mathematics, a couple of algorithms here, and some statistical analysis there. And I find it absolutely amazing that we can use all of that knowledge and encode it into something that resembles intelligence. This makes me think if our brains and the concept of intelligence are really as special and mysterious as we assume.

That name aged like milk. First of all, what you said. And second - a parrot can make more parrots without human help, language models can't make GPU chips. Insulting for both LLMs and parrots.

> language models can't make GPU chips

Not yet. I'm sure somebody is taking a hard look already.

Language models exist not in the physical world but digital. A more apt comparison would be if LLMs can compile new (or slightly modified) LLMs, and I believe the answer will soon be (if not already), a resounding yes.

I agree, LLMs can generate the source code for their network, can generate the training data, supervise a model being trained and explain step by step what they do. That's why I specifically said they can't make GPUs, if they could, they would be self replicators. We'd have to consider them alive.

For the curious, here was the conversation I had:

ME: What's another saying similar to: "The cat is out of the bag" or "The genie is out of the bottle"?

chatgpt: Another similar saying is "the toothpaste is out of the tube." Like the other two expressions, this phrase conveys the idea that something has been revealed or unleashed, and it cannot be undone or put back into its original state.

ME: Can you invent a new phrase that has the same meaning, which is completely new?

chatgpt: Certainly! Here's a new phrase with a similar meaning: "The confetti has left the cannon." This phrase suggests that once something is out in the open or has happened, it's impossible to reverse or contain it, much like confetti that has been shot out of a cannon during a celebration.

ME: What's the name for this kind of expression? Aphorism? Fable?

chatgpt: This kind of expression is known as an idiom. Idioms are phrases or expressions with a figurative meaning that is different from their literal meaning. They are commonly used in everyday language to convey a specific message or sentiment in a more colorful or memorable way.

I don’t understand why people aren’t more impressed with it clearly understanding and then even inventing idioms. That shows some real intelligence.

It’s because they’re confused in thinking human intelligence isn’t learned stochastic expectation.

That's how the brain evolved, after all: take incoming input, process according to previous patterns, output a reaction to peripheral nervous system that will enhance survival. The evolutionary purpose of a cortex is to store a statistical model of an animal's environment based on past stimulus. Human intelligence is just a complicated permutation of these initial systemic parameters.

That’s why people have to take courses in reasoning and logic to be able to successfully apply it. It’s not like reasoning and logic are natural human skills. They’re learned through reinforcement learning by repeatedly exposing our brain to patterns again and again until we can train our stochastic expectation juice to not hallucinate an answer.

And it takes ~20 years to train a new brain so it can coherently answer questions about a wide variety of topics. Even worse, you can't even copy-paste it once you're done!

It arguably needs much less training data though.

What we shouldn't is anthropomorphise it too much. While LLMs can express themselves and interact with us in natural language, their minds are very different from ours - they never learned by having an embodied self, and they can't continuously learn and adapt the way we do - once the conversation is over, it's like it never existed unless it's captured for a future training cycle.

Right now, their ability to learn is severely limited. And, yet, they outcompete us quite easily in a lot of different tasks.

Agreed. There are a hundred different kinds of information processing that go into a human-like mind, and we've kinda-sorta built one piece. And there are a lot of pieces that it would neither be sane nor useful to build (eg. internalized emotions), so we might not see an AI with all the pieces for a very long time ("never" is probably too much to hope for).

It's amusing that our first contact with a completely alien intelligence is with one of our own making.

How? Organisms with brains process every second of their life, is that not training data on a level comparable with current AI models?

From a pure data amount point of view yes, but relatively little of that would seem to be relevant for our intellectual capacities. If GPT was a robot moving autonomously around in the world with full visual, auditory and tactile apparatus, it may be a bit different.

Hm, not sure how most of that data would be irrelevant, could you clarify? I think all of that data as well as interacting with the environment creates the level of knowledge and intelligence we have today.

This seems backward to me. Wouldn’t you be less impressed by ChatGPT if you thought that thought that human intelligence worked the same way as LLMs?

If humans have some special sauce different from the computer, then it’s crazy that ChatGPT can emulate human writing so well. If humans are also just statistical models, then of course you can throw a big training set at some GPUs and it’ll do the same thing. Why should we be surprised or impressed by idioms?

Well, I don’t they work exactly like the human mind, because we are likely a more complex ensemble model. But a) it’s impressive because it’s reaching a goal of computing for generations and as a computer scientist I’m excited, b) it’s corpus of learnings is the amazing collection of everything that we built with the modern internet, so it’s breadth and depth is astounding. The act of creating what we have naturally is itself exciting though.

I suppose it depends on the extent to which you're impressed by the concept of LLMs, I can see it both ways.

To explain the other way to your thinking: human intelligence is the same; holy crap they cracked robotic 'human' intelligence, it works exactly the same way.

I'm definitely impressed when I see things like this. This is much more impressive than writing a 5 paragraph nonsense about a 2 sentence prompt.

Is it?

There are many mentions of confetti cannons on the web, along with explanations of how they work (saying something like confetti shoots out of the cannon). Chat-GPT just picked a random thing (confetti) and completed the pattern "X out of Y" with the thing confetti comes out of.

It's easy. The cereal is out of the box. The helium is out of the balloon. The snow is out of the globe.

And it's exactly the one thing LLMs are trained to do: reproduce patterns of words. They are (perhaps) already better than humans at that one specific skill (another win for AI research) but I don't think it's a sign of general intelligence.

"The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence."

https://en.wikipedia.org/wiki/AI_effect

Better that than the opposite effect, to assume that because a system solves a single problem very well, it is intelligent.

Is Stockfish intelligent?

Is a system with A* pathfinding intelligent?

I would define intelligence as the ability to solve a wide variety of novel problems. A system built to be excellent at a single task may be better than humans at that task but still lack "intelligence".

We still don't know what that even is exactly, but historically people consistently underestimate how difficult it is.

Not even knowing how to approach it, researchers work on solving single specialized problems instead and make little progress on whatever "intelligence" is.

(If you'd prefer a different definition of intelligence under which Stockfish and GPT are intelligent, then what would you call the ability to solve a wide variety of novel problems? Feel free to substitute that word for "intelligence" above if you'd like to understand what I'm saying.)

> Is Stockfish intelligent?

> Is a system with A pathfinding intelligent?*

I'm not sure if we should get stuck on definitions of intelligence.

The fact is that these tools are useful, as are the currently existing AI's. The latter can also pass for humans, in many ways, while the algorithms you mentioned can only pass for humans in very narrow domains. Both can exceed human performance in some ways.

Eventually, AI's may be indistinguishable from human or convince humans that they should be treated differently from "mere" programs and algorithms, and at that point we will have entered a new era, call it what you will.

We'll have to "get stuck on" definitions of intelligence if we want to talk precisely about what LLMs are capable of.

Not necessarily, as we can just evaluate them on their performance as we give them ever greater challenges.

To do this we do not need to consider whether they're intelligent at all.

To continue with some human brain powered pedantry for fun I’d argue that “the snow is out of the globe” is not a valid idiom to suit the “X out of Y” pattern.

We have multiple example pairings of X and Y but the common components are that putting X back in Y would be impossible or extremely difficult, and also that X is in some way meant to no longer be contained inside Y as part of either a desired outcome, it’s normal function or the natural expected and thus inevitable result. Cats want to escape, helium leaks, confetti is expelled to have the desired effect, and toothpaste is squeezed out to use it…

For the snow to come out of a snow globe you have to smash it which is not normal usage, not normally meant to happen, and shouldn’t happen by itself. Making the idiom “The snow is out of the globe” not a proper member of this “family” of idioms. (Also I’m not sure if there’s an agreed upon collective noun for idioms)

The issue I see here is you are doing a worse job at this than ChatGTP. Creating idioms is hard, that is why we left most of them to Shakespeare.

- I regularly return cereal to its box.

- "helium" and "balloon" have a more awkward rhythm than "confetti" and "cannon". It also loses the connotations of sudden, explosive and exciting change.

- Snow & globe I'm not even sure what that means in practice. It has poor prospects as an idiom. Is the snow even known for leaving globes?

> "helium" and "balloon" have a more awkward rhythm than "confetti" and "cannon". It also loses the connotations of sudden, explosive and exciting change.

Not only that, but "the confetti has left the cannon" is an alliteration, which makes the phrase even more poetic.

I actually agree, it's a very good phrase.

But I do think it's cherry picking the most impressive example. I repeated the dialog (and some variations), each time asking for a completely new idiom, and ChatGPT responded with several phrases that aren't new at all:

"The toothpaste is out of the tube"

"The lid has been lifted"

"The secret has been spilled"

"The arrow has left the bow"

> But I do think it's cherry picking the most impressive example.

Yes, but don't we cherry pick from what humans have said, too? I'm sure there have been many dumb and obvious proverbs that didn't survive.

> ChatGPT responded with several phrases that aren't new at all

Are you using the GPT-4 version of ChatGPT? That's what GP used.

it also attests to the ramifications, the magnificent difficulty of cleaning confetti, you will keep occasionally finding confetti around the house for months or years

In a way, this comment perfectly encapsulates why the argument "machines will never replicate human behavior" is so ridiculous. Instead of engaging with the discussion and topic, you chose a position, and then tried to justify it without really thinking about why one example works and the other one doesn't. In doing so you're literally showing that for certain topics, machines are already more capable than some humans.

I didn't say "machines will never replicate human behavior" so I don't think you're engaging with what I said.

Sure, you didn't say that. You merely said:

> And it's exactly the one thing LLMs are trained to do: reproduce patterns of words. They are (perhaps) already better than humans at that one specific skill (another win for AI research) but I don't think it's a sign of general intelligence.

while completely missing why the machine did a better job.

Like you, I thought these pieces of software and data were little more than statistics-based text generators. But it turns out that this is a Category Mistake.

There was an argument made by Raphaël Millière in a recent Mindscape Podcast [1] with Sean Carroll that finally landed for me. He used the example that human beings are driven to eat and reproduce, so by that argument all humans are just eating and reproducing machines. "Ah! But we developed other capabilities along the way to allow us to be good at that!" And that's the point.

GPT-4, for example, is very very good at producing pleasing and useful output for a given input. It uses a simulated neural net to do that. Why would one assume that on the way toward becoming excellent at that task that a neural net wouldn't also acquire other abilities that we associate with reasoning or cognition? When we test GPT-4 for these things (like Theory of Mind) we actually find them.

"Ah hah!" you say, "Humans are set up to learn from the get go, and machines must be trained from scratch." However if you consider the entirety of our genetic legacy together with our childhoods, those are our equivalent "training" from scratch.

I don't think it can be easily dismissed that we're seeing something significant here. It's not human-level intelligence yet. Part of the reason for that is that human brains are vastly more complex than any LLM at the moment (100s of trillions of "parameters" in LLM-speak, along with other advantages). But we're seeing the emergence of something important.

[1] https://www.youtube.com/watch?v=aUJOcVPdDvg

That emergence is precisely what I'm looking for evidence of.

Human beings evolved to eat and reproduce and yet here we are, building computers and inventing complex mathematical models of language and debating whether they're intelligent.

We're so far from the environment we evolved to solve that we've clearly demonstrated the ability to adapt.

ChatGPT doing well at a language task isn't demonstrating that same ability to adapt because that's the task it was designed and trained to do. ChatGPT doing something completely different would be the impressive example.

In short: I don't categorically reject the possibility that LLMs might become capable of more than being "statistics-based text generators", I simply require evidence.

Another poster shared a link to this paper last week for Theory of Mind: https://arxiv.org/abs/2302.02083

We're seeing those other capabilities emerge; like being able to play chess though it's not been trained to do so. That is, these LLMs are displaying emergent abilities associated with reasoning.

These LLMs aren't R. Daneel Olivaw or R2D2 (which is what I think of when I think of the original term for AI, and what we took to calling AGI). We're closer to seeing the just-the-facts AIs we encounter in Blindsight. Intelligence without awareness.

Funny that we still have to use science fiction to make our comparisons because our philosophy of intelligence, mind, and consciousness are insufficient to speak on the matter clearly.

This paper from late last year shows that LLMs are not "just" stochastic parrots, but they actually build an internal model of the "world" that is not programmed in, just from trying to predict the next token.

https://ar5iv.labs.arxiv.org/html/2210.13382

PS: More research has been done since that confirmed and strengthened the conclusion.

Is it so impressive that the “fart left the butt” ? :)

That is impressive, especially given that neither Google nor DuckDuckGo find any previous trace of that idiom.

did you search in different languages?

Not exactly sure why it would be surprising that it can come up with a convincing idiom when it can produce remarkably good _poetry_

I think the thrilling part is that it's a somewhat atomic concept that can somewhat convincingly be proven to not exist in the training data. While poetry is more impressive if it's as original it's harder to show that it's not just stitched together from the training data.

I just asked GPT-4 to come up with more such "provably original idioms":

""" Here are a few more examples of idioms with meaningful and provable atomic originality:

"The kite has touched the stars" - This phrase could mean that someone has achieved a seemingly impossible goal or reached a level of success that was thought to be unattainable.

"The paint has mingled on the canvas" - This idiom might convey the idea that once certain decisions are made or actions taken, the resulting outcome can't be easily separated or undone, similar to colors of paint that have blended together on a canvas.

"The clock has chimed in reverse" - This expression could be used to describe a situation where something unexpected and unusual has occurred, akin to the unlikely event of a clock chiming in reverse order.

"The flower has danced in the wind" - This phrase could signify that someone or something has gracefully and nimbly adapted to changing circumstances, just as a flower might sway and move in response to the wind. """

Yeah but can it beatbox?

Conspiracy theory: this idiom used to be widespread on the internet, but a shadowy cabal of AI maximalists erased millions of such expressions from search engines so GPT could appear to re-invent them.

It is a good expression though -- evocative but not gross or violent. You could imagine many less successful analogies to something ejecting something else.

That is not a conspiracy theory... it is alternate reality fan fiction.

While making up "what ifs" can be fun, it doesn't merit either of the words "conspiracy" or "theory".

That’s brilliant. Though personally I prefer "The SARS-COV-2 virus has left the lab"

Could be lifted from another language. Is nice though.

Try looking up "pinkie pie party cannon"

Not exactly the same, but from:

https://www.instagram.com/p/CQdBiVyh5C2/?hl=en

Now that the cat is out of the bag, or, should I say the confetti is out of the… can?

Is it strange that I think it's more likely that LLMs generalize and synthesize new things out of old patterns instead of just copying a singular example.

It’s plausible that the above quote made it a closer association for GPT to come up with.

Now Google has one search result pointing to your comment :)

Serious question: is it typical to describe client-side computing as "on the edge"?

I thought running something on the edge referred to running it in close network proximity to the user, rather than users having control and running things themselves.

Yes, "edge computing" can refer to both computing done as close to the user as possible geographically, or even on the device itself. If someone says "I wanna do edge computing" it's not clear enough to know if they just want to have servers they control as close to the user as possible, or do the computing on the device itself. I think Apple would say "edge computing" is on the actual device while CloudFlare would say "edge computing" is on their infrastructure, but distributed to be physically closer to the end user.

I guess I've been out of the loop for a bit and didn't realize that "edge computing" became a term since cloud computing took off.

It is kind of cyclical then is not?

By that I mean computers used to be shared and to log into it through a terminal.

Then the PC came around.

Then about 15 years ago Cloud computing became the rage (really an extension or more sophisticated system than the first time shared computers)

Now we're back to local computing. I even see more self hosting and moving away from cloud due to costs.

All that rant is to say is it's interesting.

Side note, getting this AI to be localized as much as possible I imagine will be really useful in the medical industry because it helps alleviate HIPAA requirements.

> It is kind of cyclical then is not?

> By that I mean computers used to be shared and to log into it through a terminal.

> Then the PC came around.

> Then about 15 years ago Cloud computing became the rage (really an extension or more sophisticated system than the first time shared computers)

There's a really neat article called "The Eternal Mainframe"[1] that you might be interested. It explores this idea in greater depth.

---

1. http://www.winestockwebdesign.com/Essays/Eternal_Mainframe.h...

Thanks, that was an interesting read!

I wonder if the author's perspective has changed with regards to freedom to compute.

Social Media is often used as an example of privacy invasion though I've failed to see why concerns over Facebook handling your private data is worrying when they don't have a product you need to have.

Email on the other hand, is pretty much a necessity today so privacy concerns are vital there imo. Of course you can host your own server whereas you can't host your own Facebook.

I believe this has been extended to mean "on device", which is interesting. See Gerganov's article on Github [0]. I wrote about this here [1] where I made a contrast between the core and the edge. I think the term maps well to this meaning.

What I find more interesting is that in the classic "close network proximity", some parts of the world may not have benefited as much from that trend since the closest nodes of a global delivery network could be several countries away.

[0] https://github.com/ggerganov/llama.cpp/discussions/205

[1] https://medium.com/sort-of-like-a-tech-diary/consumer-ai-is-...

> I believe this has been extended to mean "on device", which is interesting.

I don't like the connotations this carries. This is almost openly talking about reaching all the way into peoples' hardware to run your software, for your benefit, on them, without their knowledge, consent or control...

I see. Hadn't considered this. Yes, I see how that might be a concern.

What I think is important in this AI Spring is that we make it possible for people to run their own models on their own hardware too, without having to submit anything to a large, centralised model for inference.

> Serious question: is it typical to describe client-side computing as “on the edge”?

Somewhat; its consistent with, e.g., Google’s “Edge TPU” designation for its client-side neural processors.

> I thought running something on the edge referred to running it in close network proximity to the user

Typically, but on the client device is the limit-case of “close network proximity to the user”, so the use is consistent.

“Edge computing” arguably implies there’s a network you are connected t, that you’re on the edge of, so I wouldn’t apply the term to applications that can function completely offline. With edge computing there’s usually still a notion of having some sort of internet integration, like IoT devices.

I've used "edge" in this context for around 15 years[1], and I've always intended it to mean "at the edge of the network", which can include being on the other side of the world to a user.

[1] from https://www.oblomovka.com/wp/2007/08/ at least

Because of the ambiguity of the term "on the edge" that is used to refer to both close network proximity and the device closest to the user, as evidenced by this thread, I would suggest to use a new term, at least in the context of A.I. The AI running on the device closest to the user should be called a "terminator".

> Without services spying on your data. Without censorship on what the model can/cannot say. Because it's all local...

Wouldn't that be nice? It would also be contrary to all experience of the outcomes and pulls of corporations in modern society. The "local" LLMs will be on the fringe more than at the edge, because the ones that work the best and attract the most money will be the ones controlled by walled-garden "ecosystems."

I really hope it's different. I really hope there are local models. Actual personal assistants actually designed to assist their users and not the people that provide the access.

I for one dream of a future without maps. I want to walk through a distant forest to find an ancient, unconnected ESP-32 in the bark of a tree containing a tiny specialized AI that can only tell me about things relevant to the area, how far to walk upstream to the nearest town. And only if I can find it and scan an RFID tag to wake it up.

A beautiful dream!

> I like to think (right now please!) of a cybernetic forest filled with pines and electronics where deer stroll peacefully past computers as if they were flowers with spinning blossoms.

I'd go one step further if it is not happening yet: smaller companies should really pool their resources to train open LLMs. Say, form a consortium and work with the open source community to build ChatGPT-equivalent. Companies will be crazy to assume that they can hand their future to the APIs offered by a handful of companies during this monumental technological paradigm shift in history.

That is, a real OpenAI with a open government body.

Yes, yes, and yes. I'm waiting for an actually open AI that can run on the edge, purely on commodity hardware like our laptops and phones - it's inevitable.

I imagine this "cat out of the bag" situation, the democratization and commodification of powerful technology accessible and affordable to the public, is similar to what's happening with single-board computers and microcontrollers like Raspberry Pi, Arduino, ESP32.

It might be similar to what happened with mobile phones, but there the power was quite restricted. The (mostly) duopoly of iOS and Android, with devices and apps locked down in various ways. Sure we can "jail break" and "root" our phone, but that's not for the general public.

Maybe solar energy production is going through a similar process, with panels and batteries becoming more efficient and affordable every year.

Certainly, it reminds one of the history of personal computers, the way such a powerful general-purpose tool became ubiquitous and local.

After using ChatGPT 4 extensively for a few days, I think we're probably only a few years away from the first generation of truly conversational assistants ala Jarvis in Iron Man. Between LangChain and existing voice recognition software, we've already 95% of the way there, it just needs to be packaged up into a UI/UX that makes sense.

These local models are absolutely critical for that to happen though. I'm hitting daily situations where I have to reconsider my use of ChatGPT because what I'm asking would leak very private personal information or somehow trip its morality filter.

Just swapped in a 2TB nvme for a separate /home mount and reinstalled Arch just so I could have enough disk space to test a dozen models locally. I'm so ready!

I pray to the AI gods that OpenAI will fail at calibrating the censorship layer and will continue to overcensor, which in turn will hopefully lead to many usecases requiring local LLMs, which in turn would increase the incentive to build them.

Only a few years? Nobody can predict accurately in years anymore. Feels more like "only a few months" away.

I'm not so confident.

The speed of recent advances may be due to picking low-hanging fruit.

Soon there may not be much low-hanging fruit left.

True, though perhaps maybe "low hanging fruit is all you need?" :P

GPT-4 training was finished last summer. Karpathy is working on a JARVIS there. I think they already have something quite good for internal use, maybe trying to make it "safe" before releasing it.

My guess: we will have a competent JARVIS(minus the holograms) this year.

> the democratization and commodification of powerful technology accessible and affordable to the public

Empowering everyone equally, very low barrier of entry. The spread of AI will make some skills become less important and other skills more important. There will be painful social changes. Memorising stuff, being exact, being careful, diligent - all these things can be done by LLM. But also creative recombination of ideas. For now we still hold supremacy in long form content, complex software projects and detecting inconsistencies, but they could be emergent qualities in GPT-5.

If you thought crypto miners drove up the prices of GPUs, just wait until a gaming card can host a person who can get a job.

Integer-quantized LLaMA can fit on a 4090, and isn't quite human-level. If a fully aligned, reliably non-hallucinary turingrade LLM can run on the next generation 5 series GPUs then expect their market price to quadruple or more.

Yes, this is true. But, I worry about how long it will take for the utility of "GPT-4" on my phone to be close enough to whatever is only possible through models running on large cloud platforms to make that choice relatively drawback free.

Is the curve of what this class of algorithms can provide sigmoid? If so, then yeah, eventually researchers should be able to democratize it sufficiently that the choice to use versions that can run on private hardware rational. But if the utility increases linearly or better over time/scale, the future will belong to whoever owns the biggest datacenters.

This is a shocking turn of events given there's no edge equivalent of the previous most powerful information tools (web-scale search). It does seem like it will still be a challenge to continuously collect, validate, and train on fresh information. Large orgs like Google/YouTube/TikTok/Microsoft still seem to have a huge advantage there.

> And smart people will package them as "one click installers" for people who aren't tech-savvy. This is already happening.

Any projects I can follow? Because I haven't seen any one click installers yet that didn't begin with "first install a package manager on the command line"

Not an llm but this 1 click installer for stable diffusion is literally a 1 click installer. Impressively works.

https://github.com/cmdr2/stable-diffusion-ui

> In the terminal, run ./start.sh (or bash start.sh)

smh.

well, very close! its interesting that they just got Mac M1/M2 support at all, 2 weeks ago.

for SD I've been using DiffusionBee since maybe October last year.

I expect LLMs to have something in a few weeks, just want to know about it so I can tell other people that need it that way.

I was mostly referring to this project, which has some 1-click installers: https://github.com/oobabooga/text-generation-webui#alternati...

Though I have not tried those 1-click installers, instead I have been manually running it.

That project is based on the concept of this Stable Diffusion project: https://github.com/AUTOMATIC1111/stable-diffusion-webui

Which is a few months ahead (because the Stable Diffusion tech happened a few months earlier) and is definitely at a point where anyone can easily run it, locally or on a hosted environment.

I expect this "text-generation-webui" (or something like it) will be just as easy to use in the near, near future.

Here's alpaca running in electron. Not exactly one click but close.

https://github.com/ItsPi3141/alpaca-electron

>So every time you're amazed by something chat-gpt4 says, remember that soon this will be in your pocket.

I want to believe you, but I'm ignorant of the hardware requirements for these things. How soon do you think we'd be able to run something reasonably gpt4-like on, say, a 4090?

I feel like no less than 10 years if the singularity doesn't kick in before that. Hardware and energy isn't progressing as fast as we'd like, and that is the main bottleneck. As in, imagine a world where we actually had the same computing power required to train (not run) GPT-4 in 1s in a phone? That kind of world is way beyond AGI and the cure of cancer IMO. Which is great, because it gives us a very objective goal to achieve these things. Sadly, I don't think we're nowhere near that. What was even the total energy consumption of GPT-4 training? Very hard to imagine computers will get that much better anytime soon. IIRC we have some kind of data that the a smartphone today has the power of the best supercomputer... of 30 years ago, right? Don't remember the source, sadly.

I don't think 10 years for training such large models on phones is entirely feasible, if only because phones are mainly concerned with power drain and ergonomics before anything else. Nobody is going to buy an iPhone 15 if it's the size of a brick and lasts 2 hours just because you're training&running models on it.

The focus instead should be on running expansive models locally on your desktop system at home. This is a better focus in two ways: one the power issue is a non-concern (and AMD has already stated power usage will reach 500-700W average by 2025), two once you have it running locally the avenues of use open up such as being able to access your local model through other devices without the heavy burden on those devices.

Your comment makes me wonder if it's not a coincidence that as we seem to be the hitting a limit in hardware power that human level intelligence begins to emerge.

Such hardware problems might be overcome with new computer architectures and/or substrates, like DNA computing, quantum computing, etc... Current AI's could help us overcome such limits.

It's literally already here: https://twitter.com/thiteanish/status/1635678053853536256

llama-7b is nowhere even close to GPT-4. Not even llama-65b is.

We'll see if fine tuning can improve this, but e.g. alpaca-30b is still inferior to ChatGPT-3.5.

The cat is out of the bag,The genie is out of the bottle,The confetti has left the cannon,The ship has sailed,The horse has bolted,The toothpaste is out of the tube,The beans have been spilled,The train has left the station,The die is cast,The bell has been run.

The cookie has crumbled.

The mirror has shattered.

The poop has hit the propeller.

Pandora's box has opened.

An LLM running locally providing type-ahead completions seems inevitable.

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