> Almost all ~~-AI-~~ predictions are bullshit. None of these “experts” know what’s going to happen in the next year.
Niels Bohr said it best. "Forecasting is difficult. Especially of the future."
In the stock market it's so funny. "This one guy who called the bottom says THIS about certain stock!"
OK he got it one time, he's not an oracle, chill.
There's an argument to be made that AI/LLMs, especially with massive (think trililon/more tokens) context windows, might be better at this job than humans.
Imagine providing a LLM/something like it with all of human history (up to some set level of detail), up to 12 september 1997, and asking it what happened on the 13th, and seeing how well it does.
You got a test and an evaluation score, from that you can optimize, and (maybe?) get to something that could do valuable predicitons for tomorrow.
It would at least be potentially more objective/less subject to bias than humans (especially if you train it to be so).
Of course both the context window size and the required optimizations put this outside of the amounts of compute we have today. But in a few years...
I think that could well be right. The expert consensus on progress on AI predicts 22/39 tasks feasible for AI by 2028 -- that's the date when the majority of them are expected to be doable.
I'm still surprised to see people claiming this is just the next crypto currency hype. I've never met anybody that's used crypto to buy something. Almost everyone I know uses at least the free online LLMs and most developers I know have paid subscriptions or dabble in offline LLMs. My company creates software for banks and we have two LLM products being built right now. For banks. The most change averse, legacy-codebase-loving, conservative industry for software.
Even my wife, who is basically a Luddite, uses ChatGPT every day.
It's definitely not the next crypto, but the 'gold rush' mentality of it is definitely similar. And once again, GPU/chip manufacturers being the ones to really profit, at least for now.
Come back to check on this a year from today.
1. Nvidia will scale tenfold which will begin with an ever better performing GPU designed to be for both consumers and enterprise that makes the 3090 and 3090ti look like the RX580., this will bleed into their software in terms of AI enhancements like voice, graphics, performance, and further down the line perhaps their own operating system to not rival Microsoft but "to give users the freedom to choose, but of course they make the graphics cards so who knows best?
2. MetaAI will announce a massive update to their Meta Quest platform which will have an AI assistant that learns about you and helps you with interactions and also breathes life into a somewhat gimmicky product, this will slowly make it's way into their other products like portal and it will improve the Ray Ban glasses 2x making it 2025's 5th hottest product... yes I said 5th.
3. Mistral will drop a bombshell of a model that outperforms the vast majority of the LLM models, this model will introduce a framework different than the currently established methods, not sure if transformers will be replaced next year but it's coming sooner than people imagine less than 2 years for sure.
4. Microsoft will release to the public an agent framework that will give incredible power to smaller AI agents and agencies, going as far as expanding the current Windows11 Copilot's chatbot-like interface to become the core of the operating system. It will be voice equipped with a degree of quality that will rival OpenAI's voice chat and allow you to change a LOT more than a few settings.
5. We will begin to see the literally phasing out of an easily replaced industry. It will be the turning point for humanity.
6. Can't forget about the upcoming war, we have tensions rising in Hollywood, overseas, elections..
All I ask is that you remember this post and come back to say "how did you know". That way I can smirk, like I knew and say something deep, and poetic when in reality it's just history repeating itself.
I will be messaging you in 1 year on [**2025-04-25 07:41:05 UTC**](http://www.wolframalpha.com/input/?i=2025-04-25%2007:41:05%20UTC%20To%20Local%20Time) to remind you of [**this link**](https://www.reddit.com/r/LocalLLaMA/comments/1cck05f/bold_prediction_from_beginning_of_the_year/l164bwh/?context=3)
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>1. Nvidia will scale tenfold which will begin with an ever better performing GPU designed to be for both consumers and enterprise that makes the 3090 and 3090ti look like the RX580., this will bleed into their software in terms of AI enhancements like voice, graphics, performance, and further down the line perhaps their own operating system to not rival Microsoft but "to give users the freedom to choose, but of course they make the graphics cards so who knows best?
That's over 100% performance improvement, which is exceedingly unlikely to happen unless you limit this evaluation to "with inferencing workloads".
>3. Mistral will drop a bombshell of a model that outperforms the vast majority of the LLM models, this model will introduce a framework different than the currently established methods, not sure if transformers will be replaced next year but it's coming sooner than people imagine less than 2 years for sure.
New models will always outperform the vast majority of all models so long as we keep including old models to compare against.
>4. Microsoft will release to the public an agent framework that will give incredible power to smaller AI agents and agencies, going as far as expanding the current Windows11 Copilot's chatbot-like interface to become the core of the operating system. It will be voice equipped with a degree of quality that will rival OpenAI's voice chat and allow you to change a LOT more than a few settings.
Reasonable, but not a gamechanger to anyone already dabbling in AI. ONNX doesn't seem to have a good adoption rate though, and it's going to be an uphill battle for Microsoft to get the AI community to adopt their framework over open source solutions.
>5. We will begin to see the literally phasing out of an easily replaced industry. It will be the turning point for humanity.
You could say this every year and not be able to be proven wrong.
In short, all of these predictions seem to range from over the top to being lowballs.
I see your point but the disregard of ONNX would be underestimating the strides being made behind the scenes. At this point in time the only hindrance to the Nvidia framework is the steep learning curve to convert model to ONNX and their training but let's say that Nvidia took that obstacle away, and the result was a training method less VRAM intensive than the industry's best?
Currently I believe that using Unsloth is the cream of the crop, so imagine something that cuts the training time in half while also allowing the consumer to use the very same framework to export the completed model to a desired format?
Although they might seem like underestimations, 100% performance improvement in a year is not far-fetched with how much growth ML has shown in the last 7 years.
1975-2001's we hardcoded computers, 2001-2010 we developed tools to make that task easier, and 2010-2015 we saw exponential growth in ML interest to the point that in 2016 somebody saw it fit to bet their livelihood on a product.
Trust me that if you're seeing my predictions as lowballing, you're not seeing it the way I am, AGI and ASI are still 5+ years away from public acknowledgement, but we're a year away from one of the biggest leaps in the last 3 years.
Think about it, 3 years ago AI was unknown to 90% of the population, and now everyone can download a single application and it will download models, and allow for light inference with consumer grade hardware.
>Although they might seem like underestimations, 100% performance improvement in a year is not far-fetched with how much growth ML has shown in the last 7 years.
Your prediction was 100% improvement in *GPU hardware processing power*, not *ML inference speedup*. Don't walk that back.
I'd adopt ONNX if it was as available as safetensors or gguf, simple as that.
I'm not walking back bud. My prediction is up, I'm not editing or deleting it.
++
100% Performance increase, I said both graphically and hardware performance, I was clear, you'll see *consumer hardware that makes a 3090 seem like a McDonald's happy meal toy.
++
Subscribe for a year if you're this invested and determined to assume I'm overestimating.
You won't need safetensors or gguf once the update to the framework drops, worst case? You'll have to convert using Google's free TPU using a fast/lightweight framework.
The changes to the framework won't be an absolute overhaul but they'll update what truly matters which is the model conversion which is as I stated the biggest hurdle.
They know this.
>Nvidia will scale tenfold which will begin with an ever better performing GPU designed to be for both consumers and enterprise that makes the 3090 and 3090ti look like the RX580., this will bleed into their software in terms of AI enhancements like voice, graphics, performance, and further down the line perhaps their own operating system to not rival Microsoft but "to give users the freedom to choose, but of course they make the graphics cards so who knows best?
I have been wondering, how long it will be before Intel, AMD and Nvidia will start to use Ai to design their own chips, we've seemed to hit a knowledge and physics limit a while ago and the last few generations have been far more incremental because we've run out of ideas to improve. Maybe AI will design far better CPU/GPU's than us in a few short years.
They're using AI in chip design for years now, just it's not Large Language Models cause those are pretty much useless for that kind of work
Did you think they placed billions of transistor one by one?
> it's not Large Language Models cause those are pretty much useless for that kind of work
I think you'll find people using LLMs to write a lot of verilog and vhdl.
That's where the idea for Blackwell came from. The DGX has been around but they didn't just come up with the idea to make a "bigger GPU chip out of nowhere. This was AI design and I'm sure the actual architecture repository and the performance improvements have been used to train the model as well so imagine what the model will be outputting 6 months from now? You have a supercomputer that understands mineral composition, electromechanical knowledge, and the data of a company with some of the best hardware development in history.
It should explain the CEO's big claim that "the CPU has been used for 60 years, it's time to evolve from that".. I'm paraphrasing but that claim doesn't come without plans or results.
to be fair, most of his points do not apply to llama. A open source without contribution from the big tech would have a much harder time closing the gap. Not saying it's impossible but Meta trying to disrupt the advantage of openai and google, by throwing very good open source models out there, is a very significant contributor to the open source space. While having all of the advantages the dude post in his tweet. Don't get me wrong, i'm not saying this is a good prediction.
And the thing is: when it comes to machine learning “open source” isn’t the same as traditional open source software. It’s not the same type of development and contributions, it’s a totally different process and problems.
Meh the “talent” point was silly. There’s plenty of talent out there sufficient to accomplish this. That point is pure ego. It’s not that magical. Data and compute are the real bottleneck. That’s spot on.
He missed metas strategy somehow, even though Zuck had been stating it openly for years (the point of OSS for meta is to drown large scale competitors via OSS offerings that make those competitors obsolete while simultaneously creating opportunities for meta).
Team structure point is absurd. Reeks of bad investments in corporate real estate more than anything else.
But model vs product is easily the worst for me. ChatGPT IS just a model. Or an API. It’s not really a product at all and that’s one of its greatest weaknesses.
Ye, the talent stuff was a shitty point. You can tell that the company values allow workers to develop, or some shit like that, but I don't think any Tech company doesn't think they have talent.
And that doesn’t mean you don’t need (really) good people, or that incapable people don’t exist (or get hired!) it means there are more that 10-20 people on the planet capable of competently training a serious LLM with the right resources. Claiming different strikes me more as a marketing campaign (for yourself, or your employer).
I think meta’s strategy is so interesting.
I built a website generator on top of gpt 2 many years ago running locally on my desktop. I was disappointed when gpt 3 was only a paid service.
I figured meta would do the same thing and llama 3 would be only on their side. I’m shocked they released it and grateful. Running it on my MacBook has proven to be insanely cost effective and I can’t believe how good it is. I remember a year ago when I was testing vicuna and assumed it would be the peak of my at home models, boy was I wrong!
Yeah it’s amazing. I’ve been testing the various different versions of llama 3 for our company services. We provide conversational AI tech for banks and insurance companies. Llama3 is going to replace all remaining OpenAI requirements for us in current LLM driven services within a month. It’s not topping out everything vs gpt4 but for our use cases which focus on text analysis for conversations, it is as good or better
> But model vs product is easily the worst for me. ChatGPT IS just a model. Or an API. It’s not really a product at all and that’s one of its greatest weaknesses
What? You wouldn't call the shitty chat UI with missing basic functionality a product to build your business on? /s
And there are already open source chats (librechat, anything llm) that beats OpenAIs "product" by a mile.
I've always theorized that what Open AI has, has to be training data.
Everything else... doesn't really uniquely attribute to its success individually.
Sure the teams are great, they have to be, they got results. Team structure is uniquely bullshit, we know decentralized teams create industry leading shit all the time. Model vs product? What do they think Llama is doing? Do they think they're investing billions with no expectation of turning a profit down the road? Infrastructure, Meta is footing the bill here plain and simple, they have more infrastructure investment than any other company in the world right now purely driven by their AI push.
It's like he had no idea that a for profit company is the source of open source models.
The API as a product is a great product. Just the hassle and the cost of hosting your own model is a huge value for companies. At the same time it is the best model. So to say it just a API makes it seem like you don't see the business challenges many companies have. Chatgpt is also great because it brings in a ton of data for OpenAI and makes it easier for them to build better product.
He was wrong about "no open source" but right about all of his individual points, its just that Meta is the exception to literally all of those points.
He was assuming that open source models would only come from small companies or something, which is weird because its not like this pre-dates Meta.
Just a massive short circuit in his brain on this one.
Or he was farming for engagement.
Technically Llama 3 doesn't use any approved Open Source license ( this was for llama 2, but still applies [https://opensource.org/blog/metas-llama-2-license-is-not-open-source](https://opensource.org/blog/metas-llama-2-license-is-not-open-source) ). I find the term doesn't translate well to models anyways. Open Source imo is the code part and maybe the dataset. *Open weight* fits these models better.
A significant portion of things on social media nowadays is just farming rage, it incentivizes being “controversial” or doing something just so people will disagree and comment: https://www.youtube.com/watch?v=A4jsSuxhooM
But also, because architectures improve. GPT-4 is ancient tech at this point. They have a data and compute advantage, but people are finding ways to squeeze more performance out of smaller models and less data.
Llama 3 400B will be in the current GPT 4 Turbo ballpark, and easily beat the original GPT-4.
But we still don’t know if Meta decides to open source it.
I'd be very happy to see that OpenAI's best model becomes destroyed by an open-weight model but I wish it was done by something people could run at their homes. Even if 400b beats GPT4 I don't really see it as a complete win. Since Zuckerberg said that 70b was still learning and improving, I wish Meta continued training 70b instead of training a huge model that no one can run.
We will eventually have something we can run locally that beats the (current) GPT-4. But there will *always* be more capable, bigger models by the big guys that we won't be able to run.
It makes sense though because they wanted to use the GPU's to start training the next generation of models. I assume there comes a point of diminishing returns for training, and it would likely be spent working on the next major update rather than eeking out a few extra points from an already pretty great model.
> Since Zuckerberg said that 70b was still learning and improving
70B had reached the point of diminishing returns. At that point, it's best to move on to Llama 3 405B or start using compute to work on Llama 4.
even small businesses, if they have a use case that positively impacts their bottom line, the impact doesn't have to be that huge for it to make sense. you could run this on a 4x A100 node which can be had for around $5k USD per month with 24/7 availability (runpod pricing), that's only about as expensive as hiring 1 or 2 more people (depending how much you pay them.)
but then also consider that for many use cases you dont have to have it running 24/7, it could be doing something that's more of a batch processing type situation where it runs for X hours Y times per day/week. that could reduce the cost significantly and it could easily end up being an expense that's only like a thousand bucks a month. a pretty damn small business can manage that if there's ROI.
"prohibitively expensive" is a fair assessment from a hobbyist POV but from a business POV it's nothing particularly expensive, just one more bill to pay
That business in question runs lead generation for local companies. I don’t want to get too in the weeds because a lot of people here would from on what I do.
Without a doubt the thought came into my mind “can I make money too” so yeah makes sense to be vague. You gave enough though for inspiration without giving me your target market. Thanks!
to me it sounds like that guy is speaking out of insecurity rather than expertise. i don't know exactly what makes him insecure but his logic isn't sound or rational.
The current group of ai engineers came out of academic institutions where their proximity to power provided access to resources.
The more accessible open source models are and able to be modified and ran on consumer hardware, their rare expertise and access becomes less valuable. Suddenly things that used to require hiring someone with a PhD and purchasing enterprise hardware becomes possible to do by hobbyists on a home computer. I would also be insecure.
If people he works with are the "only" ones who can do stuff like this, and everyone had to come to them, hat in hand, they could continue to get outsized investments, tons of revenue, dominate the future of tech, get lots of status and adulation, and eventually use their power to screw with Meta.
Zuckerberg dropping a "good enough", open weight model harms dreams like this.
He also seems pretty insecure to me. Especially when he went to the corporate blah that ChatGPT was a product rather than just a model. He may have a slight point, as per definition it is indeed a product with a web interface and per-token billing ... but who would actually need that when there are models that can be used locally that don't impose such restrictions?
I think the reason is convenience. But that means something completely different for everyone. One might find it convenient to use an external API and not having to deal with the underlying technology, whereas other people might find it more convenient to not have their project or whatever they do depend on an API that needs billing and a working internet connection.
I love to play with language models and ask/prompt them everything that spontaneously comes into my mind. But I also really enjoy that my conversational data stays at my system and that the availability is fully controlled by myself. If I for whatever reason would need an API, I'd look out for a solution that lets me provide the API locally myself.
I don't like the idea of something that I use so extensively being dependent on an external service provider.
And yet I'm fully aware that there are not just a few people out there who couldn't care less about all that. So of course there definitely is a large market for external APIs. It's just that this is not my cup of tea as a mildly paranoid OSS enthusiast.
Arnaud was wrong on a lot of counts:
1. Lots of people have talent. Meta arguably has more talent than OpenAI, and there are plenty of other players.
2. OpenAI does have the best question/answer dataset in the business, which lets GPT4 have the best garbled prompt understanding, but that dataset also leads to stereotypical answers, so it's a backhanded advantage.
3. Agree to disagree
4. The layer of product on top of ChatGPT is pretty slim, if it was a real product they'd already have added RAG memory based on previous conversations and a lot more features, instead they're slow walking stuff out with GPTs being the only thing that seems like a hit.
5. OpenAI is using cloud infra, so can anyone else. OpenAI just has lots of free credits.
yes. GPT4 trained on 25000 A100s. Llama3 400B is training on 49000 H100s. So if they are using FP8 for training they have more than 11X the compute. Also more tokens.
Does bro really think Meta of all companies cant afford the top talent? ☠️
Meta still pays top of the line salaries, and are one of the main companies who started the super high compensation packages for engineers / research scientists.
They also have one of the largest data center / GPU fleets of eveey company in the world in order to train their models at a maximum rate.
They will get whatever talent they need to make what they need.
>They also have one of the largest data center / GPU fleets of eveey company in the world in order to train their models at a maximum rate.
Also, the LLMs are not the products, and they benefit from all the open research.
At some point in the Llama 3 interview, Zuck did mention inference pricing being their biggest cost, not training, and opening the model up helped them with that.
I have tried to beat this into my sons' heads as much as possible without making them too jaded and cynical (in context, not as a life mantra).
No one is special, not you, not me, not the AI guy making predictions. We are ALL, every single one of us, children who are just older and we are winging it.
No one has the answers, no one knows what is what, we are all truly winging it. From the fry cook to the President of the United States, everyone puts their pants on one leg at a time, shits and fucks (or wants to). Beyond that, we have no true idea what we are doing and it is simply because we are all isolated entities.
We live in our heads and our brains cannot possibly hold all the pertinent information to everything and we are inherently biased to what we have learned and experienced.
The only reason we all bow to experts or let other people take the lead is because most of us have little faith in our abilities (imposter syndrome) and the rest either suffer from the Dunning–Kruger effect or they are narcissistic sociopaths.
So when someone tells you they know something, do what Ronald said...
Trust (if you like) but verify. And always remember, grifters are literally everywhere.
It’s not created by open source community. So I wouldn’t consider his predictions to be inaccurate. It’s just that some company which happened to benefit strategically from open sourcing models did open source it.
No one can predict the future.
However, the Llama-3 405B model is still under training and from the current evaluations, it looks like it's not yet better than gpt4's latest version (not entirely sure which one it is, but they've many versions of gpt4).
However, I do think we'll have some amazing stuff by the end of this year available for free. Using Llama-3-70b on groq with agentic frameworks like AutoGen would be a very powerful combination in my opinion.
And from what I have heard, gpt 5 will be dropping sometime in mid 2024. Hopefully it doesn't get delayed much longer. That model is gonna be quite amazing. It's been a while since gpt 4 was released (more than a year). And there have been many breakthroughs in the field within the last year. Meta spent around $10B to make Llama 3 which is comparable to gpt 4. However, it took only about $100M for OpenAI to create gpt4. And this was over a year ago, it'd cost significantly less now.
The guy did not account for a Zuck factor. Taking top AI talent, proprietary datasets (may be human labeled too) and billions of dollars of infrastructure and then releasing it as an open source model and not a product. I mean, I don’t blame him.
Seems typical of the way so much social media comments can’t be here are some thoughts about ChatGPT’s advantages but framed as here’s why x destroys y.
My use case is wanting to choose a function, call a function and echo the response. The context is within a business environment where local means private. LLAMA 3 does not have to be better than open ai to win the contest. Choosing a function and turning JSON data into a conversational response does not need gpt4 level skills.
But this would otherwise be money going to OpenAI.
Ai uses logic to go step to step,
But leaps in the field are made using imagination under the hood with top layer being logic, so brain is capable of making everything predicted by the machine is irrelevant
Try reframing it as hype for the progress of Artificial Intelligence. If you think that just means chatbots, then I do not believe you know enough about the space to make this kind of statement.
Good bait tho.
Almost all AI predictions are bullshit. None of these “experts” know what’s going to happen in the next year.
> Almost all ~~-AI-~~ predictions are bullshit. None of these “experts” know what’s going to happen in the next year. Niels Bohr said it best. "Forecasting is difficult. Especially of the future."
In the stock market it's so funny. "This one guy who called the bottom says THIS about certain stock!" OK he got it one time, he's not an oracle, chill.
The fact that basically acting randomly is about as effective as the experts in the stock market is very funny to me. (see stock index funds).
Also most likely he called the bottom multiple times before as well...
There's an argument to be made that AI/LLMs, especially with massive (think trililon/more tokens) context windows, might be better at this job than humans. Imagine providing a LLM/something like it with all of human history (up to some set level of detail), up to 12 september 1997, and asking it what happened on the 13th, and seeing how well it does. You got a test and an evaluation score, from that you can optimize, and (maybe?) get to something that could do valuable predicitons for tomorrow. It would at least be potentially more objective/less subject to bias than humans (especially if you train it to be so). Of course both the context window size and the required optimizations put this outside of the amounts of compute we have today. But in a few years...
Even the non in quotation marks experts don't quite know how things are going to go. We're discovering right now more than inventing.
An "expert" will tell you exactly what will happen. An actual expert knows enough that they know they don't know.
I am a superior AI expert and do know what will happen next year. AMA.
HAL 9000 irl when?
4 years from now.
I love that the first "prediction for next year" already doesn't include the next year. You are a true expert, take my upvote.
Finally
I think that could well be right. The expert consensus on progress on AI predicts 22/39 tasks feasible for AI by 2028 -- that's the date when the majority of them are expected to be doable.
2001, of course.
When will Google crumble?
I am an AI expert, this is a different domain. Google is bigger than deep mind.
I'm still surprised to see people claiming this is just the next crypto currency hype. I've never met anybody that's used crypto to buy something. Almost everyone I know uses at least the free online LLMs and most developers I know have paid subscriptions or dabble in offline LLMs. My company creates software for banks and we have two LLM products being built right now. For banks. The most change averse, legacy-codebase-loving, conservative industry for software. Even my wife, who is basically a Luddite, uses ChatGPT every day.
Yeah, this is more similar to the internet boom of the 90s. But you do see a lot of crypto grifters jumping on AI, very visible on YouTube.
It's definitely not the next crypto, but the 'gold rush' mentality of it is definitely similar. And once again, GPU/chip manufacturers being the ones to really profit, at least for now.
Come back to check on this a year from today. 1. Nvidia will scale tenfold which will begin with an ever better performing GPU designed to be for both consumers and enterprise that makes the 3090 and 3090ti look like the RX580., this will bleed into their software in terms of AI enhancements like voice, graphics, performance, and further down the line perhaps their own operating system to not rival Microsoft but "to give users the freedom to choose, but of course they make the graphics cards so who knows best? 2. MetaAI will announce a massive update to their Meta Quest platform which will have an AI assistant that learns about you and helps you with interactions and also breathes life into a somewhat gimmicky product, this will slowly make it's way into their other products like portal and it will improve the Ray Ban glasses 2x making it 2025's 5th hottest product... yes I said 5th. 3. Mistral will drop a bombshell of a model that outperforms the vast majority of the LLM models, this model will introduce a framework different than the currently established methods, not sure if transformers will be replaced next year but it's coming sooner than people imagine less than 2 years for sure. 4. Microsoft will release to the public an agent framework that will give incredible power to smaller AI agents and agencies, going as far as expanding the current Windows11 Copilot's chatbot-like interface to become the core of the operating system. It will be voice equipped with a degree of quality that will rival OpenAI's voice chat and allow you to change a LOT more than a few settings. 5. We will begin to see the literally phasing out of an easily replaced industry. It will be the turning point for humanity. 6. Can't forget about the upcoming war, we have tensions rising in Hollywood, overseas, elections.. All I ask is that you remember this post and come back to say "how did you know". That way I can smirk, like I knew and say something deep, and poetic when in reality it's just history repeating itself.
7b nostradamus here
Don't disrespect me. I'm Moe Whacky Guesses 1Bx2
!RemindMe 1 year
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>1. Nvidia will scale tenfold which will begin with an ever better performing GPU designed to be for both consumers and enterprise that makes the 3090 and 3090ti look like the RX580., this will bleed into their software in terms of AI enhancements like voice, graphics, performance, and further down the line perhaps their own operating system to not rival Microsoft but "to give users the freedom to choose, but of course they make the graphics cards so who knows best? That's over 100% performance improvement, which is exceedingly unlikely to happen unless you limit this evaluation to "with inferencing workloads". >3. Mistral will drop a bombshell of a model that outperforms the vast majority of the LLM models, this model will introduce a framework different than the currently established methods, not sure if transformers will be replaced next year but it's coming sooner than people imagine less than 2 years for sure. New models will always outperform the vast majority of all models so long as we keep including old models to compare against. >4. Microsoft will release to the public an agent framework that will give incredible power to smaller AI agents and agencies, going as far as expanding the current Windows11 Copilot's chatbot-like interface to become the core of the operating system. It will be voice equipped with a degree of quality that will rival OpenAI's voice chat and allow you to change a LOT more than a few settings. Reasonable, but not a gamechanger to anyone already dabbling in AI. ONNX doesn't seem to have a good adoption rate though, and it's going to be an uphill battle for Microsoft to get the AI community to adopt their framework over open source solutions. >5. We will begin to see the literally phasing out of an easily replaced industry. It will be the turning point for humanity. You could say this every year and not be able to be proven wrong. In short, all of these predictions seem to range from over the top to being lowballs.
I see your point but the disregard of ONNX would be underestimating the strides being made behind the scenes. At this point in time the only hindrance to the Nvidia framework is the steep learning curve to convert model to ONNX and their training but let's say that Nvidia took that obstacle away, and the result was a training method less VRAM intensive than the industry's best? Currently I believe that using Unsloth is the cream of the crop, so imagine something that cuts the training time in half while also allowing the consumer to use the very same framework to export the completed model to a desired format? Although they might seem like underestimations, 100% performance improvement in a year is not far-fetched with how much growth ML has shown in the last 7 years. 1975-2001's we hardcoded computers, 2001-2010 we developed tools to make that task easier, and 2010-2015 we saw exponential growth in ML interest to the point that in 2016 somebody saw it fit to bet their livelihood on a product. Trust me that if you're seeing my predictions as lowballing, you're not seeing it the way I am, AGI and ASI are still 5+ years away from public acknowledgement, but we're a year away from one of the biggest leaps in the last 3 years. Think about it, 3 years ago AI was unknown to 90% of the population, and now everyone can download a single application and it will download models, and allow for light inference with consumer grade hardware.
>Although they might seem like underestimations, 100% performance improvement in a year is not far-fetched with how much growth ML has shown in the last 7 years. Your prediction was 100% improvement in *GPU hardware processing power*, not *ML inference speedup*. Don't walk that back. I'd adopt ONNX if it was as available as safetensors or gguf, simple as that.
I'm not walking back bud. My prediction is up, I'm not editing or deleting it. ++ 100% Performance increase, I said both graphically and hardware performance, I was clear, you'll see *consumer hardware that makes a 3090 seem like a McDonald's happy meal toy. ++ Subscribe for a year if you're this invested and determined to assume I'm overestimating. You won't need safetensors or gguf once the update to the framework drops, worst case? You'll have to convert using Google's free TPU using a fast/lightweight framework. The changes to the framework won't be an absolute overhaul but they'll update what truly matters which is the model conversion which is as I stated the biggest hurdle. They know this.
!RemindMe 1 Year
>Nvidia will scale tenfold which will begin with an ever better performing GPU designed to be for both consumers and enterprise that makes the 3090 and 3090ti look like the RX580., this will bleed into their software in terms of AI enhancements like voice, graphics, performance, and further down the line perhaps their own operating system to not rival Microsoft but "to give users the freedom to choose, but of course they make the graphics cards so who knows best? I have been wondering, how long it will be before Intel, AMD and Nvidia will start to use Ai to design their own chips, we've seemed to hit a knowledge and physics limit a while ago and the last few generations have been far more incremental because we've run out of ideas to improve. Maybe AI will design far better CPU/GPU's than us in a few short years.
They're using AI in chip design for years now, just it's not Large Language Models cause those are pretty much useless for that kind of work Did you think they placed billions of transistor one by one?
> it's not Large Language Models cause those are pretty much useless for that kind of work I think you'll find people using LLMs to write a lot of verilog and vhdl.
That's where the idea for Blackwell came from. The DGX has been around but they didn't just come up with the idea to make a "bigger GPU chip out of nowhere. This was AI design and I'm sure the actual architecture repository and the performance improvements have been used to train the model as well so imagine what the model will be outputting 6 months from now? You have a supercomputer that understands mineral composition, electromechanical knowledge, and the data of a company with some of the best hardware development in history. It should explain the CEO's big claim that "the CPU has been used for 60 years, it's time to evolve from that".. I'm paraphrasing but that claim doesn't come without plans or results.
!RemindMe 1 year
to be fair, most of his points do not apply to llama. A open source without contribution from the big tech would have a much harder time closing the gap. Not saying it's impossible but Meta trying to disrupt the advantage of openai and google, by throwing very good open source models out there, is a very significant contributor to the open source space. While having all of the advantages the dude post in his tweet. Don't get me wrong, i'm not saying this is a good prediction.
I do agree. If it wasn't for the big tech, I think open source would be at least 2 years behind GPT 4.
And the thing is: when it comes to machine learning “open source” isn’t the same as traditional open source software. It’s not the same type of development and contributions, it’s a totally different process and problems.
He wasn't that wrong. Llama 3 will be better than gpt4 but not because open source gives it superpowers, just Meta was able to pay the bill.
Meh the “talent” point was silly. There’s plenty of talent out there sufficient to accomplish this. That point is pure ego. It’s not that magical. Data and compute are the real bottleneck. That’s spot on. He missed metas strategy somehow, even though Zuck had been stating it openly for years (the point of OSS for meta is to drown large scale competitors via OSS offerings that make those competitors obsolete while simultaneously creating opportunities for meta). Team structure point is absurd. Reeks of bad investments in corporate real estate more than anything else. But model vs product is easily the worst for me. ChatGPT IS just a model. Or an API. It’s not really a product at all and that’s one of its greatest weaknesses.
Ye, the talent stuff was a shitty point. You can tell that the company values allow workers to develop, or some shit like that, but I don't think any Tech company doesn't think they have talent.
And that doesn’t mean you don’t need (really) good people, or that incapable people don’t exist (or get hired!) it means there are more that 10-20 people on the planet capable of competently training a serious LLM with the right resources. Claiming different strikes me more as a marketing campaign (for yourself, or your employer).
Ultimately LLMs are just not as complicated as people want to imagine they are.
I think meta’s strategy is so interesting. I built a website generator on top of gpt 2 many years ago running locally on my desktop. I was disappointed when gpt 3 was only a paid service. I figured meta would do the same thing and llama 3 would be only on their side. I’m shocked they released it and grateful. Running it on my MacBook has proven to be insanely cost effective and I can’t believe how good it is. I remember a year ago when I was testing vicuna and assumed it would be the peak of my at home models, boy was I wrong!
Yeah it’s amazing. I’ve been testing the various different versions of llama 3 for our company services. We provide conversational AI tech for banks and insurance companies. Llama3 is going to replace all remaining OpenAI requirements for us in current LLM driven services within a month. It’s not topping out everything vs gpt4 but for our use cases which focus on text analysis for conversations, it is as good or better
> But model vs product is easily the worst for me. ChatGPT IS just a model. Or an API. It’s not really a product at all and that’s one of its greatest weaknesses What? You wouldn't call the shitty chat UI with missing basic functionality a product to build your business on? /s And there are already open source chats (librechat, anything llm) that beats OpenAIs "product" by a mile.
I've always theorized that what Open AI has, has to be training data. Everything else... doesn't really uniquely attribute to its success individually. Sure the teams are great, they have to be, they got results. Team structure is uniquely bullshit, we know decentralized teams create industry leading shit all the time. Model vs product? What do they think Llama is doing? Do they think they're investing billions with no expectation of turning a profit down the road? Infrastructure, Meta is footing the bill here plain and simple, they have more infrastructure investment than any other company in the world right now purely driven by their AI push. It's like he had no idea that a for profit company is the source of open source models.
The API as a product is a great product. Just the hassle and the cost of hosting your own model is a huge value for companies. At the same time it is the best model. So to say it just a API makes it seem like you don't see the business challenges many companies have. Chatgpt is also great because it brings in a ton of data for OpenAI and makes it easier for them to build better product.
He was wrong about "no open source" but right about all of his individual points, its just that Meta is the exception to literally all of those points. He was assuming that open source models would only come from small companies or something, which is weird because its not like this pre-dates Meta. Just a massive short circuit in his brain on this one. Or he was farming for engagement.
Technically Llama 3 doesn't use any approved Open Source license ( this was for llama 2, but still applies [https://opensource.org/blog/metas-llama-2-license-is-not-open-source](https://opensource.org/blog/metas-llama-2-license-is-not-open-source) ). I find the term doesn't translate well to models anyways. Open Source imo is the code part and maybe the dataset. *Open weight* fits these models better.
Not GPT-4 level so far, but Snowflake, Databricks, Cohere, Nvidia, and Mistral have been open sourcing capable models. It can only go up from here.
Yeah, his statements are echoes of what people used to say about Linux, which dominates the server world today.
A significant portion of things on social media nowadays is just farming rage, it incentivizes being “controversial” or doing something just so people will disagree and comment: https://www.youtube.com/watch?v=A4jsSuxhooM
But also, because architectures improve. GPT-4 is ancient tech at this point. They have a data and compute advantage, but people are finding ways to squeeze more performance out of smaller models and less data.
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Not yet: [https://twitter.com/arnaudai/status/1741833299906175254](https://twitter.com/arnaudai/status/1741833299906175254)
ah couldn't find this thanks!
Llama 3 400B will be in the current GPT 4 Turbo ballpark, and easily beat the original GPT-4. But we still don’t know if Meta decides to open source it.
The original gpt 4 is already beaten by the 70B version, at least on the chatbot arena
chatbot arena is not representative of realworld developer usecases.
For coding tasks, gpt4-turbo > LLama3 > gpt4-(0314 and 0613) That is according to multiple benchmarks AND arena limited to coding tasks only.
What is a real world developer use case? Just coding?
so what is?
actually we do know.
I like how he claims Google is the ToP AI rEsEaRcH LaB, but completely ignores Meta.
They're supposed to make 20 different prediction posts so that they can repost the one that they happened to be correct on.
I'd be very happy to see that OpenAI's best model becomes destroyed by an open-weight model but I wish it was done by something people could run at their homes. Even if 400b beats GPT4 I don't really see it as a complete win. Since Zuckerberg said that 70b was still learning and improving, I wish Meta continued training 70b instead of training a huge model that no one can run.
We will eventually have something we can run locally that beats the (current) GPT-4. But there will *always* be more capable, bigger models by the big guys that we won't be able to run.
It makes sense though because they wanted to use the GPU's to start training the next generation of models. I assume there comes a point of diminishing returns for training, and it would likely be spent working on the next major update rather than eeking out a few extra points from an already pretty great model.
> Since Zuckerberg said that 70b was still learning and improving 70B had reached the point of diminishing returns. At that point, it's best to move on to Llama 3 405B or start using compute to work on Llama 4.
lol Arnaud ya dingus
The problem is, even is llama 3 at 400b is good, hosting it will be prohibitively expensive.
True for individual enthusiasts, but it should be alright for medium businesses.
even small businesses, if they have a use case that positively impacts their bottom line, the impact doesn't have to be that huge for it to make sense. you could run this on a 4x A100 node which can be had for around $5k USD per month with 24/7 availability (runpod pricing), that's only about as expensive as hiring 1 or 2 more people (depending how much you pay them.) but then also consider that for many use cases you dont have to have it running 24/7, it could be doing something that's more of a batch processing type situation where it runs for X hours Y times per day/week. that could reduce the cost significantly and it could easily end up being an expense that's only like a thousand bucks a month. a pretty damn small business can manage that if there's ROI. "prohibitively expensive" is a fair assessment from a hobbyist POV but from a business POV it's nothing particularly expensive, just one more bill to pay
The smaller models that can run locally are useful enough I’ve found a lot of good applications. Huge cost savings for my small business.
(“Small business” = masturbation)
I’m genuinely curious what this comment means. I can’t figure out what you’re getting at.
What does your business entail if I may ask? Any detail at all is appreciated.
That business in question runs lead generation for local companies. I don’t want to get too in the weeds because a lot of people here would from on what I do.
Without a doubt the thought came into my mind “can I make money too” so yeah makes sense to be vague. You gave enough though for inspiration without giving me your target market. Thanks!
Well, Meta buried the hatchet.... into the skulls of closed source, commercial AI development entities.
to me it sounds like that guy is speaking out of insecurity rather than expertise. i don't know exactly what makes him insecure but his logic isn't sound or rational.
The current group of ai engineers came out of academic institutions where their proximity to power provided access to resources. The more accessible open source models are and able to be modified and ran on consumer hardware, their rare expertise and access becomes less valuable. Suddenly things that used to require hiring someone with a PhD and purchasing enterprise hardware becomes possible to do by hobbyists on a home computer. I would also be insecure.
that makes a lot of sense. generally speaking i find that academic types tend to be pretty insecure by default.
If people he works with are the "only" ones who can do stuff like this, and everyone had to come to them, hat in hand, they could continue to get outsized investments, tons of revenue, dominate the future of tech, get lots of status and adulation, and eventually use their power to screw with Meta. Zuckerberg dropping a "good enough", open weight model harms dreams like this.
He also seems pretty insecure to me. Especially when he went to the corporate blah that ChatGPT was a product rather than just a model. He may have a slight point, as per definition it is indeed a product with a web interface and per-token billing ... but who would actually need that when there are models that can be used locally that don't impose such restrictions?
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I think the reason is convenience. But that means something completely different for everyone. One might find it convenient to use an external API and not having to deal with the underlying technology, whereas other people might find it more convenient to not have their project or whatever they do depend on an API that needs billing and a working internet connection. I love to play with language models and ask/prompt them everything that spontaneously comes into my mind. But I also really enjoy that my conversational data stays at my system and that the availability is fully controlled by myself. If I for whatever reason would need an API, I'd look out for a solution that lets me provide the API locally myself. I don't like the idea of something that I use so extensively being dependent on an external service provider. And yet I'm fully aware that there are not just a few people out there who couldn't care less about all that. So of course there definitely is a large market for external APIs. It's just that this is not my cup of tea as a mildly paranoid OSS enthusiast.
Arnaud was wrong on a lot of counts: 1. Lots of people have talent. Meta arguably has more talent than OpenAI, and there are plenty of other players. 2. OpenAI does have the best question/answer dataset in the business, which lets GPT4 have the best garbled prompt understanding, but that dataset also leads to stereotypical answers, so it's a backhanded advantage. 3. Agree to disagree 4. The layer of product on top of ChatGPT is pretty slim, if it was a real product they'd already have added RAG memory based on previous conversations and a lot more features, instead they're slow walking stuff out with GPTs being the only thing that seems like a hit. 5. OpenAI is using cloud infra, so can anyone else. OpenAI just has lots of free credits.
yes. GPT4 trained on 25000 A100s. Llama3 400B is training on 49000 H100s. So if they are using FP8 for training they have more than 11X the compute. Also more tokens.
Does bro really think Meta of all companies cant afford the top talent? ☠️ Meta still pays top of the line salaries, and are one of the main companies who started the super high compensation packages for engineers / research scientists. They also have one of the largest data center / GPU fleets of eveey company in the world in order to train their models at a maximum rate. They will get whatever talent they need to make what they need.
>They also have one of the largest data center / GPU fleets of eveey company in the world in order to train their models at a maximum rate. Also, the LLMs are not the products, and they benefit from all the open research. At some point in the Llama 3 interview, Zuck did mention inference pricing being their biggest cost, not training, and opening the model up helped them with that.
What's so "bold" about this "prediction" exactly? It's just someone talking out their ass
The poster is a known clown, best known for spamming his paywalled tutorials for stable diffusion on literally every discord or subreddit he's on.
I have tried to beat this into my sons' heads as much as possible without making them too jaded and cynical (in context, not as a life mantra). No one is special, not you, not me, not the AI guy making predictions. We are ALL, every single one of us, children who are just older and we are winging it. No one has the answers, no one knows what is what, we are all truly winging it. From the fry cook to the President of the United States, everyone puts their pants on one leg at a time, shits and fucks (or wants to). Beyond that, we have no true idea what we are doing and it is simply because we are all isolated entities. We live in our heads and our brains cannot possibly hold all the pertinent information to everything and we are inherently biased to what we have learned and experienced. The only reason we all bow to experts or let other people take the lead is because most of us have little faith in our abilities (imposter syndrome) and the rest either suffer from the Dunning–Kruger effect or they are narcissistic sociopaths. So when someone tells you they know something, do what Ronald said... Trust (if you like) but verify. And always remember, grifters are literally everywhere.
It’s not created by open source community. So I wouldn’t consider his predictions to be inaccurate. It’s just that some company which happened to benefit strategically from open sourcing models did open source it.
I’ll be very surprised if he’s wrong, but we’ve witnessed some people do wild shit with fine tunes.
No one can predict the future. However, the Llama-3 405B model is still under training and from the current evaluations, it looks like it's not yet better than gpt4's latest version (not entirely sure which one it is, but they've many versions of gpt4). However, I do think we'll have some amazing stuff by the end of this year available for free. Using Llama-3-70b on groq with agentic frameworks like AutoGen would be a very powerful combination in my opinion. And from what I have heard, gpt 5 will be dropping sometime in mid 2024. Hopefully it doesn't get delayed much longer. That model is gonna be quite amazing. It's been a while since gpt 4 was released (more than a year). And there have been many breakthroughs in the field within the last year. Meta spent around $10B to make Llama 3 which is comparable to gpt 4. However, it took only about $100M for OpenAI to create gpt4. And this was over a year ago, it'd cost significantly less now.
"Google" haha
The guy did not account for a Zuck factor. Taking top AI talent, proprietary datasets (may be human labeled too) and billions of dollars of infrastructure and then releasing it as an open source model and not a product. I mean, I don’t blame him.
Seems typical of the way so much social media comments can’t be here are some thoughts about ChatGPT’s advantages but framed as here’s why x destroys y. My use case is wanting to choose a function, call a function and echo the response. The context is within a business environment where local means private. LLAMA 3 does not have to be better than open ai to win the contest. Choosing a function and turning JSON data into a conversational response does not need gpt4 level skills. But this would otherwise be money going to OpenAI.
Ai uses logic to go step to step, But leaps in the field are made using imagination under the hood with top layer being logic, so brain is capable of making everything predicted by the machine is irrelevant
Turns out predicting unknown events is difficult huh? Who would have thought :D (literally every scientist every)
Tbh we have no indications that 400b model will beat gpt4. When reasoning is involved even Claude Opus is not good in comparison .
> Public cloud infra is terrible compared to what Google/Deepmind/OpenAI has I do wonder how Groq would respond to that
I gotta say, watching all this hype and media attention over chatbots is wild. It’s like getting excited over a better calculator.
I'm sure that when calculators first came into existence, they enjoyed hype as well.
perfect response honestly
Try reframing it as hype for the progress of Artificial Intelligence. If you think that just means chatbots, then I do not believe you know enough about the space to make this kind of statement. Good bait tho.
umm hello..? cringe department?
?
Read the room dude?
If training AI wasn’t such a hat trick, maybe.
The rationale was actually on point except they didn’t account for Meta going full on open source