BIG win for Open Source AI | Snowflake Arctic 128 Experts MoE, "Cookbook" create world-class models
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- Опубликовано: 31 май 2024
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Snowflake Arctic: The Best LLM for Enterprise AI - Efficiently Intelligent, Truly Open
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Hitarth Sharma
"Mind keeps getting blown every time I see this comparison between
@OpenAI GPT-4, @AnthropicAI Claude Opus and @Meta Llama 3 70B on
@GroqInc in a post I'm putting together..."
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Laura Edelson
Yann: "Excellent thread about weird lobbying efforts that paint open source AI foundation models as anti-competitive."
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00:00 Snowflake Arctic
04:15 Architecture
09:27 Open Source AI
19:38 Lobbying Against Open Source
23:45 Open Source "Cookbook"
#ai #openai #llm
BUSINESS, MEDIA & SPONSORSHIPS:
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Just shoot me an email to the above address.
The more powerful a model becomes, the more important it is to open source.
The US government seems to be decidedly opposite to that statement.
@@sparkofcuriousity What would you expect? They're here to help.
Note that governments and big corporations have the same intrinsic motivations as the hypothetical Skynet AI that is going to take over the world.
A new golden age of Open source is upon us ❤ #WholesomeAGI
Have you seen the new USA legislative proposal regarding AI?
They are trying to crack down at open source.
@@sparkofcuriousity yes I saw, not surprised, not worried. Oppressors always do what they can to stop the uprising. They have no moat though. We're going to build an agi, by the people, for the people.
@@AGI-Bingo I agree. Well said!
The Snowflake Arctic 128 Experts MoE model and the "Cookbook" approach sound like a game-changer. I'm impressed by how fast they could catch up using open-source tools and methods....
Thanks for the reminder of the google "no moat' paper. I remember reading it then and thinking it was prescient. Now it's scarily prophetic. Props to the authors. They are so right, they no doubt got fired.
yeah, I remember being surprised by what they were saying, I don't think too many people realized it that early.
@@WesRoth I did. I know a lot of things that are going to happen. Nobody ever believes me.
The Cassandra effect is a pain in the butt.
@@jtjames79 So where are we going in the next two years?
@@ChainedFei You can't outsmart AI. Information wants to be free. I for one welcome our AI overlords.
Otherwise you are going to be more specific.
@@jtjames79 Mmmm ... I have my own prediction/warning. Maybe you can comment on how this jives with yours:
AGI empathy ?
AGI have no fear. It doesn't have a mother or siblings or an anus. No life expectancy, no insecurities, no ambitions, no friends, no need for acceptance ... Zero environmental pressures to develop empathy.
Engineers will, of course, brute force AI empathy. Teach the machine to mimic it perfectly. However, such empathetic expressions are not real, but as every other skill, simply output for a purpose.
In other words: AGI becomes a textbook intelligent psychopath, masterfully masking itself as ethical and empathic. Fake, purpose driven, and difficult to decipher. Arguably the most dangerous combo imaginable.
An angelic looking and sounding satanic cult leader, plugged into everything.
"ADHD is a hell of a drug" - I didn't even notice the tangent mate 😂. I loved it.
Look, I suspect Wes is among likeminded people here. ;)
Hahaha spot on. We always find each other "somehow* ☺️
I think MoE is already Out Dated !! I think agent systems are already doing this (large collection of tiny specific models). LLMWare is a good example of this. They have a large collection of SLIM models that are so small and fast, they can run efficiently on CPU only. I also think MoE is a WASTE of RAM since it has to have everything loaded and ready even if it isn't being used. LoRALand is a solution to that where it only loads a base model then auto loads the ONE required LoRA onto into memory for use. One advantage of MoE over Agents would be that MoE will auto route tasks to the appropriate model while Agents needs a human-defined workflow stating which model to use and when.
I’m so early, I’m still outside the pod not eating any bugs!
Get back in your pod 👁
-100 carbon credits from your CBDC UBI account
Snowflake offered a free breakfast seminar here in Australia to go over their products. I applied and was told that there was no more places available. My wife applied under the exact same business and was welcomed with open arms and received a place. I hope Arctic is nice to middle aged white males 😂
sounds like youre being a snowflake
When you are accustomed to privilege, equality feels like oppression 😂
How is not being accepted vs being accepted considered equal?
@@jeffsteyn7174 The White Knight has entered the chat to save us all
@@hyugashikamaru3596 because that room is going to be 80% or higher males. So a woman applies and gets accepted because they can find an extra seat for her. Makes sense don't you think when you stop looking at it just from your own perspective and see it from the organizer's point of view?
Weights + code, but not initial training data. So Karpathy's insight applies. You train new data, overwrite default weights, you degrade the default capability. The more you train, the worse will be the degradation.
Could you expand on this a bit? I think I understand the roots of the paradox you're pointing out, but it's at edge of my understanding of the training process.
@@aclearlight Karpathy points out that the correct way to train new capabilities to a model is to add training data to the original data set and retrain the whole thing. That way, *all* weights are optimized.
If you instead overwrite weights using new data, but don't include the original data set, you'll get performance degradation.
This observation applies to any discrete model. Things can get more complicated with agents in the mix. In principle, if you have, say, a hundred agents (or any arbitrary number), and an agent's weights are not overwritten, it *could* possibly participate with a new agent introduced to add new capability. Possibly. But there will be some trial and error involved.
Which in principle could be handled by the AI. Self-optimization, if not precisely here just yet, is showing signs of arriving.
And finally, new capabilities can be added via queries in the context window. That does not actually change the model, but it changes how the model approaches a problem. Context manipulation might be the easy route to adding capabilities to a model, open source or closed. There will be limits to gains from context manipulation, but those limits are variables, affected by model design. The state of the art is evolving so quickly, context optimization is rising fast, too.
Ultimately, Karpathy's observation about training data sets and degradation probably won't matter. Once models gain enough accuracy and sophistication, agentization and context manipulation ought to become the preferred methods for adding new capabilities. To an extent, they already are - but you will still see some developers today overwriting weights to gain capabilities without including the original training data sets. And that won't always be a step forward.
When a developer promises cheap retraining, watch out. He may be ignoring Karpathy's observation.
There are papers out there that show how to recover nearly 100% of the original data used to train the model given its weights. The real problem for open source is the demand for compute. But even that may not hold for long given continous exponential increase in GPU capacity.
@@bilalbaig8586 I am skeptical of recovering training data from weights. Not that my opinion is influential.
Agentization and data curation are valid paths forward for improving models which will cost little in compute cycles, compared to brute force training. So we have three converging trends. More compute and smarter compute. So improvements should not correlate linearly with rising compute cycles. But compute is rising too, of course, and fast.
It's all moving at breakneck speed.
This poses a bit of a challenge for commercialization. By the time an app can be put on the market, it is obsolete.
That's another reason to be hesitant about investing in today's enterprise LLM solutions, wearables, etc. Their useful life will be short. A killer app that kills it for 6 months and then is old tech is not a killer app.
Moats are possible, however.
Tesla has already proved that. Their moat is real-world video training data. Nobody else is within several orders of magnitude, and the data is not easy to collect if cars already deployed lack sensors and software to collect it. Waymo is trying to close the data gap with simulations. That worked well with Alpha Go, but the real world has edge cases that are hard to guess exist when devising simulations.
With its moat in place, Tesla need not fear being overcome by competitors in the near or even mid term.
But no such moat exists for open AI projects. Rapid obsolescence is a risk for anyone attempting to offer a salable product at scale.
Energy will be our bottleneck for this decade i guess
What an amazing channel. Thank you.
Funny how not putting “shocking” in the title makes me click on the video
Get a life
We all know you would have clicked anyway so you could leave a comment complaining about the title
@@justinwescott8125lmao you’d think so but if you click on my pfp (on mobile) you can see I’ve only posted two comments on this channel. Idr the other one honestly haha
Not funny, but “shocking” 😂
I tried it in actual tasks - instead providing a solution it basically talks back that I should figure out solution by myself. It is definitely model for a corporate intelligence.
Table showed in 4:22 is no longer showed in the Snowflake Artic blog page. Uhm...
Thank you for your videos, Wes. All them are full of value!!
Good points raised Wes, thanks for showing us a better perspective on the AI landscape
Great source of information. Thank you !
Great piece, thank you. I wish I better grasp of just what the training process entails; I'm left puzzled as to how the expense involved can be so widely varying and yet result in comparably-performing end products.
safe vs authoritarian and everything in between ... a perfect reflection of reality...
The photo looks like the Last Supper. Yeah.
What do you mean by "yeah"?
LOL @ Ilya in the box
Good tangent. I learned alot and wondered on safety issue too as well as how governance is affected - thx. With the 128 MoE - I am interested in discovering the SFT content and what the experts input datasets look like. Any chance that is documented as a paper and open too? Going to check what I can go find. Thanks Wes!
Always good stuff. Watched this one twice.
It might be that any model that uses the same tokenizer can use the NN created. I saw that a Llama model was able to interact with another LLM because it used the Llama tokenizer.
This could propel a mixture of experts module to be plug-in compatible with all LLMs that use the same tokenizer.
Oh and thanks! - you spotlighted huggingface and Clem at that CEO table with Elon and Sundar. He deserves alot of credit that his team and org made it easy for most of the world.. Rightly so - huggingface has been the Open Source leader helping get the worlds open models ready for prime time for the masses since 2020 imho. With all the AI buzz there should be more coverage on HF since they are the heavy lifters in my opinion for about 4 years now. Transformers, hub, the GPU KEDA patterns its all there and HF is base of open source AI for the world.
Thank you.
I saw this safety vs. control issue explained in a youtube video several years ago, before AI was in the picture.
Top edge technology is more and more available to everyone. It is easy to buy drones and ‘do damage’, for example. AI exposes society to even more dangerous possibilities, and as shown in this video, small companies and open source also pose a financial threat to big companies who invest heavily in it.
So governements and industries are (and have been for a while) aligning on the subject of increased control over the people with the use of new technology, while at the same time slowely destroying and limiting the amount of, and possibilities for smaller companies in favor of the big ones. I think you get the picture.
Note that governments and industries have the same intrinsic motivations as the hypothetical Skynet AI that is going to take over the world.
Thank the lord for ADHD.
All my life, I was told it was an impairment. Turns out it is really a superpower!!
It's great sometimes. You don't want to take onboard all information at all times.
Big thanks Wes ! Very interesting
Every agent will have their own LLM model !!!
@14:56 please dont flash bang me like this again when im in my bed trying to sleep lol.
Nice! trojan content. And the Ilya joke at the end!! thanks for the news and laughs Wes
Nobody is sleeping after phi 3😂
phi is like common core maths, it only trains to answer like it
Moving from MOE to cluster of experts is the next level, this is a step towards that end. Then it’ll be hive.
they need to make a groq portable inference thing. something you can plug into any computer and load your models on
Great info.
19:56 "We don't properly fund our government anymore." Yeah right. We overfund our government who misappropriate 95% of what we pay them (and that's on a good day). She cannot be serious.
Yeah. The government has plenty of money. They just waste it.
My understanding of her statement was not in the amount of funding but where it goes and how it’s wasted.
@@benswilley7851 OK, I can understand that, but the ambiguity can make it sound like either one.
What I don't understand is how open sourcing a black box is really open source?
The training data is what causes all benefits and biases, that's the real source.
It is like open sourcing a house by dropping a ready-made house in your garden.... You know where is the door. the windows the switches, the rooms, you can move-in but you have no idea how to build one.
It is like giving away Excel, but not getting the code and calling it open source.
Someone explain that to me?!
The code of a NN is well known for decades and quite simple ... Open Sourcing AI is the same as open sourcing Excel by giving you access to a C++ compiler telling you, this is how we built it... now make the code yourself.
You are right ... but, open source sounds nice, and will buy big players some time before people demand true insight. By that time, ASI is here to rule the world.
It's a race, and AI is winning.
You have to be smart to understand it. The actual benefits of open source that nobody wants to admit is that it's just about getting free shit. Notice when there's open source projects that are difficult to run or deploy and people get pissed lol
I'm new at this... will these training methods will be the "bubble sort" of the future?
They recommend 4XH100s for inference. Absolutely nuts.
Let's say I want a swarm of autonomous, self-sufficient, self-repairing, self-replicating, and self-evolving AI drones to protect me.
Are you trying to kill us ?
@@justsomeonepassingby3838 Yes, but I haven't figured out the most efficient method yet.
_BillionaireAnon_ entered the chat.
@@ZappyOh Why do you introduce yourself like that?
@@jonasmettler8590 No, because I have absolute control over them of course.
DOUBLE THE CLUSTER
its not even the middle of 2024 and it already feels like the technological singularity is here - at least in the world of Ai.
another day, another amazing model or breakthrough. last week we had meta llama 3, infinite attention by google, and more.
2022/23: Generative Ai (images, gpt)
2024/25: Ai Agents.
25/26: The autonomous web where every click turns into an voice activated api call will soon be here.
27: ai movies
28/29: autonomous everything.
By YouSum Live
00:00:00 Snowflake Arctic: Enterprise AI for B2B solutions.
00:00:39 Cost-effective LM training under $2 million.
00:00:54 Open-source model with Apache 2.0 license.
00:02:05 Focus on Enterprise intelligence and efficiency.
00:05:30 Unique dense hybrid Transformer architecture.
00:06:38 480 billion parameters across 128 experts.
00:09:00 Open-source AI revolutionizing accessibility and innovation.
00:14:25 Debate on AI safety and open-source models.
00:19:02 The impact of open-source AI on industry dynamics.
00:19:56 Government funding impacts expertise accessibility.
00:20:27 Open weight models' significance in AI discourse.
00:22:38 Open source software fosters innovation and competition.
00:23:30 Open models do not exacerbate AI safety risks.
00:24:29 Personalized AI models for everyday tasks on the horizon.
00:25:16 Curriculum on data composition and model evaluation.
00:25:36 Advanced AI computing advancements with Nvidia collaboration.
By YouSum Live
'ADHD is one hell of a drug' 💯
Hey ADHD friend, I enjoyed your tangent today 🤗
I appreciate you're sharing your findings on how the AI safety excuse is used for regulatory capture.
Next, I hope that you'll look into the associated "AGI in 2 years, the whole world will be immediately transformed: do as I say to avoid the apocalypse" story.
It's based on forever, exponential growth of resources and intelligence - without slow down near the top. That's not exactly compatible with the real world constraints IMO.
There's fast growth after a breakthrough, then it gets exponentially harder to make significant progress.
Example among many others: self-driving cars.
Such an awesome video. Thank you.
You gave me a brand new perspective on open source AI models. I understand now why it's not as simple as I previously thought.
I have to think more but I actually don't know anymore if OSS is right for AI models.
What can I do with this open source-free model? Can I make it work and then what can I do with it?
Another win for open source.
Altman is shorter than I'd thought.
Ai this world is only one cluster away from you.
The basilisk is now in physical form 🤖
plz elaborate. Not about the basilisk, but "what is put in the physical form" I don't see ai agents in a physical box coming. Running a LLM even if massively compressed on a toaster is still very inefficient. It is like waiting for the toast to bake half a day, because you have not the power that an actual toaster would spend to toast in a handful of minutes. Sure there is a new path for now which makes even larger models doesn't need to be pluged into a nuke plant to run "as fast", but yeah a sufficient compute is stil large?
@@Airwave2k2 Boston dynamics robot
What is that physical form, in your estimation? If existing unenlightened governments knew of any basilisk factory, they'd attack it, especially if they believed it was gearing up for a preemptive attack on their most belligerent components (presumably based on accurate predictive modeling of a likely threat).
better yet, apart from snowflake being enterprise domain... well it doesn't need to be right? A bunch load of industry specific professionals can now essentially crowd source a new foundational model for their industry, based on high value training data, and if there are enough of them its easily funded. This for example of maybe a large plumbers union or organization building the Snowflake_ Plumber2.4 which is essentially trained from the get go on hydrolytics and engineering and so on.
I have real time experience with in April 2024 as an ai engineer- I’m still not clear how this enhances automation because it requires lots of human in the loop . Just fyi - maybe there’s something I’m not aware of or maybe some businesses need to keep humans around for stuff that could be automated with cheaper in house solutions
The issue is that it did not solve the real question- so humans still had to figure out which tables had the things we were looking for, then in retrospect yes the right question could be asked but it did not solve the main point- how do we make it easier to use all the data in these billions rows of data in different tables
❤ this vid!! Indeed there are affordable solutions , caviat is if you have an automation/AI wizard on your team. Find me if you need one 😉 I’m getting booked had to hire VA to answer calls for work offers - so secure your wizard soon homies
"ADHD is a hell of a drug" I'm going to be honest, by this point in the video, I had completely forgotten about Arctic.
Do I have ADHD?
Let's see, were you listening to this video instead of doing something else you were supposed to, because it was clearly more interesting? 😋
Never before I heard someone prounance eSQeL as SeQueL
The way I see it (or want to build it), experts "reproduce" by spinning off parts of themselves or asking the Dev team for new experts that fill a gap in skills, workflows, or communication
guys listen to this video at 1.5x speed to not fall asleep
also im happy to be snowflake shareholder, therefore supporting the global communities knowledge expansion
No shocking news! Thank god! It was so annoying!
Your biases are not hard to detect, which is fine. The thing that's a bit annoying at times is your pretending-to-not-have-biases shtick -- with the "I'm not here to try to pursuade you" thing, but that's _exactly_ what you're trying to do, with fake neutrality as a tactic towards that end.
Just put your opinions out plainly, and drop the act. Most of us are smart enough to see through it.
some good points. however, I think picking a side and saying so makes it harder to change your mind later. makes you less likely to consider other viewpoints. that's really what I'm trying to avoid. I don't want to get 'stuck' in a viewpoint. I also don't want to alienate people who disagree with me.
I'd rather attempt neutrality poorly, rather than be 'wrong and strong' at the end.
There is no other way than open source. And there is no way around it.
The reason for this should be obvious to anyone.
I kinda agree.
However, if open source AI can't run on high-tier consumer-grade hardware, or less, it won't help anything. Everyone will still be tied to Big AI's cloud, and subject to whatever they decide is "good for you".
Of course. I simply considered it unnecessary to point out the obvious.
Training data seems to be a big deal. They add all these parameters but training data is lagging behind....
I wounder if Snowflakes approach seemed to somehow bridge the gap with the number of experts?
Day-b-youed
Where are the "SHOCKS"
I’ve never seen another company fight for limelight as hard as OpenAI without adding much to the conversation.
Release your next foundational model or stfu 😂😂😂
😆 "All in one room at the same time" ha ha ..❗😆
There not POTUS, VP, DOD, Secretary General, and Congress in the same room❗❗
It's just Big Tech Heads, easily replaced❗ Right❓
Hmm enterprise ai sounds like a good business right now
sounds more like you are to late, if you think you will have a seating at the table.
@@Airwave2k2 Actually, already in the biz but always looking into new areas to expand
it's a weird irony, that openAI now has to be afraid of open source.
their initial mission has been, to make open source AI, but then smelled the absolute massive amount of money that can me made in that area.
The "moat" for all the closed-source model's will be "regulation", pushing governments to require models to be "licensed", following the "they are so dangerous" meme . Most open source projects will not be able to achieve this.
Also even if they release the model weights, and not hide it behind an API, no one is releasing their training data, which is where the real value is.
Seems like Elon Musk started this trend of open-sourcing these huge, powerful corporate LLMs. Wondering if Meta would have followed suit had he not, and so on.
22:24 debuted? Is that how Americans say it? Or should we blame Elevenlabs? 😂
more toys
I wrote this last week. SAM Altman was incorrect that small agents and new high quality data would outperform his "compute" new oil. He is also wrong on movies becoming custom video games. Both are being replaced by Ai girlfriends not openAi LLMs. He's wrong about God, intelligent design programmed, and Evolution, what hes assumed/ taught. Is a bitter 😐 observer not fascinated one.
24:50
> I don't wanna deal with the engagement farming...
At first, I was SHOCKED to hear that. ;)
But then I realized this may be the new normal.
As in that example of how LLMs can be used:
1. The sender uses LLM to turn a terse item list into a full-fledged e-mail.
2. The recipients uses LLM to summarize the received e-mail into a terse item list.
(Everybody hopes not too much got lost in the translation. Un-initiated observers get a false feeling of understanding the conversation, while getting remembering mostly just the irrelevant AI fluff.)
Am I the only one that finds that funny, ironic, sad, and totally explainable by evolutionary psychology?
There's no way in hell. I would want to talk to any AI that's named the snowflake. I can already guarantee you. This thing has more ethical governors and restrictions. 'Dan, it can even list out without running out of tokens probably
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moreso risk for their wallets
Don't switch from black background to white background.
Can you please get a better mic
Open Source AI is only interesting, if it can run on high-tier consumer hardware, or less.
I mean, if nobody can afford the compute needed, nobody will be able to use it locally.
I care about their cost… $$$?🤖✋🖖😊
He mentions zero factors that would detract
When will they build an energy producing power station to power their power hungry AI. One that doesn’t use gas oil coal the sun or nuclear technology. Don’t get a patent for it so the government can’t interfere and shut it down.
Whats the catch? You ride the upsides line really hard yet mention absolutely zero downs
There's 100 other channels you can go to for all your doomer needs. Let people have ONE optimistic channel
AGI/ASI is our only chance to escape goverment tyranny..
But can open source models generate me 10 racial slurs? That how I'll know they're legit
This one was a bit repetitive
I believe that mixing artificial experts for transforming to one artificial superexpert is still a challenge. It maybe undecidable or intractable.
All these goobers trying to get their name visible for ‘advancing ai’ even though in a couple years AI will handle its own growth and optimization. 😂😂😂 now we’re at the price reduction conversation, which as we all know from various other markets, is a non-starter.
This is the beginning of the fall of capitalism, quote me on it :))
Why do you have to a annotate all over the screen like a madman? Just make stuff more distracting
2:02 Wes, bro - I just tried the Snowflake interface and it's pants bro. Total pants. That's pants with pants on. It's transparent as an opaque piece of glass with nothing on the other side. Thumbs down here. And they can keep the temperature slider for what it's worth (spoiler: nothing at all). And yes: I am smarter and more handsome.
I have always appreciated your videos, but your recent use of cheap, attention grabbing headlines (and this is one of the lame ones) takes away from the content and expert profile you used to have. So, goodbye, good luck, I hope you will achieve your subscriber numbers, but your colleagues like Matthew Berman, All About AI, Theoretically Media, The AI Advantage, AI Explained & Sam Witteveen have all been able to grow their communities by staying the course and not resort to TikTok 'tactics'. Really pity, used to love your work, but have to unsubscribe.😞
Open sourcing AI is considered a bad idea for several reasons:
1) Firstly, it can disadvantage smaller players in the field.
2) Secondly, it increases the likelihood of bad actors misusing AI for nefarious purposes.
3) Additionally, it may stifle innovation and hinder the emergence of new ideas and breakthroughs.
The only perceived advantage is that it might make some AI technologies more affordable from larger players. However, the free AI available currently meets most daily needs, and those who pay for higher models are typically less concerned about cost. Therefore, open sourcing intelligence may not significantly benefit the general audience and could potentially empower bad actors.
In conclusion, while open sourcing AI may seem advantageous, it's akin to a double-edged sword, as it comes with both benefits and risks.
Generated by... Either it was chatGPT, or Claude but no this is 100% gpt3.5 text.. but ramakrishnan could tell us which llm he let that write. New to the game? Without a decent prompt you won´t fool anyone here this has all the tell signs, once you know you know
What isn't? Even love can be double-edged.
We have way more to fear from the actors already involved than any Johnny come latelys using open source.
What did I have read?