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An analysis of DeepSeek's R1-Zero and R1

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Re: An analysis of DeepSeek's R1-Zero and R1

#41

Post by moralestapia »

pants2 wrote: post #34
vidarh wrote: post #26

Earlier quoted context omitted.

For inference Nvidia has more significant competition than for training. See Groq, Google's TPU's etc.

People talk about Groq and Cerberus as competitors but it seems to me their manufacturing process makes the availability of those chips extremely limited. You can call up Nvidia and order $10B worth of GPUs and have them delivered the next week. Can't say the same for these specialty competitors.

You mean Cerebras.

>call up Nvidia and order $10B worth of GPUs

Doubt it.

No idea about Groq, but Cerebras might give you a similar timeline than nVidia. Each of their wafers are 50x-100x H100s so they need to make less of them, in absolute units.

But cooling, power, etc... nVidia might have an advantage as their ecosystem is huge and more "liquid" in a sense.

Re: An analysis of DeepSeek's R1-Zero and R1

#42

Post by ClumsyPilot »

polishdude20 wrote: post #6

I predict that the future of LLM's when it comes to coding and software creation is in "custom individually tailored apps". Imagine telling an AI agent what app you want, the requirements and all that and it just builds everything needed from backend to frontend, asks for your input on how things should work, clarifying questions etc. It tests the software by compiling and running it reading errors and failed tests a…

> Imagine telling an AI agent … requirements… asks for your input on how things should work, clarifying questions etc.

That’s hard work. I watch people do that every day, and always get something wrong.

Also what about deploying the application, paying for database or cloud resource that will run it, etc?

Re: An analysis of DeepSeek's R1-Zero and R1

#43

Post by fragmede »

optimalsolver wrote: post #25
dagelf wrote: post #2

Well o3 scored 75% on AGI-1, R1 and o1 only 25%.... watch this space though....

But can o3 write a symphony? Seriously though, I'd like to hear suggestions on how to automatically evaluate an AI model's creativity, no humans in the loop.

we'd have to create a numerical scale for creativity, from boring to Dali, with milliEschers and MegaGeigers somewhere in there as well

Re: An analysis of DeepSeek's R1-Zero and R1

#44

Post by jcims »

spyckie2 wrote: post #22

> But now with reasoning systems and verifiers, we can create brand new legitimate data to train on. This can either be done offline where the developer pays to create the data or at inference time where the end user pays! > This is a fascinating shift in economics and suggests there could be a runaway power concentrating moment for AI system developers who have the largest number of paying customers. Those customers…

>You might be lowering the cost of your training corpus by a few million dollars, but I highly doubt you are getting novel, high quality data.

The large foundational models don't really need more empirical data about the world. ChatGPT already 'knows' way more than I do, probably by many orders of magnitude. Yet it's still spewing nonsense at me regularly because it doesn't know how to think like a human or interact with me in a human-like way. To that end, the ability for a company like OpenAI to collect novel data from interacting with real humans is a material advantage over their competition.

Re: An analysis of DeepSeek's R1-Zero and R1

#45

Post by aprilthird2021 »

polishdude20 wrote: post #6

I predict that the future of LLM's when it comes to coding and software creation is in "custom individually tailored apps". Imagine telling an AI agent what app you want, the requirements and all that and it just builds everything needed from backend to frontend, asks for your input on how things should work, clarifying questions etc. It tests the software by compiling and running it reading errors and failed tests a…

> auto run your code, compile it, feed errors back to the LLM,

Can't wait for companies to juice profits by having the LLM run excessive cycles or get stuck in a loop and run up my bill

Re: An analysis of DeepSeek's R1-Zero and R1

#46

Post by nine_k »

mohsen1 wrote: post #31
spyckie2 wrote: post #22

> But now with reasoning systems and verifiers, we can create brand new legitimate data to train on. This can either be done offline where the developer pays to create the data or at inference time where the end user pays! > This is a fascinating shift in economics and suggests there could be a runaway power concentrating moment for AI system developers who have the largest number of paying customers. Those customers…

every time you respond to an AI model "no, you got that wrong, do it this way" you provide a very valuable piece of data to train on. With reasoning tokens there is just a lot more of that data to train on now

This assumes that you give honest feedback.

Efforts to feed deployed AI models various epistemic poisons abound in the wild.

Re: An analysis of DeepSeek's R1-Zero and R1

#47

Post by ClumsyPilot »

talldayo wrote: post #14
mohsen1 wrote: post #8

The idea that a lot of compute is moving towards inference has a huge consequence for the current "AI investments". This is bad news for NVDA particularly. The inference focused solutions have better economics than paying NVDA those huge margins (e.g. Grog)

Nvidia can actually charge larger margins if inference compute goes down. It would enable them to manufacture more units of smaller GPUs using inferior and cheaper silicon, all of which would increase the profits per unit sold as well as the number of units they can manufacture. The industry has to find a way to separate itself from Nvidia's GPGPU technology if they want to stop being gouged. The issue is that nobody…

I think future of inference is on the client side

You can do inference on almost any hardware, I do not see any edge for NVIDIA here

I can download DeepSeek 30b model and run inference at good speed on AMD GPU ms and even on CPU. Apple silicon works fine too. I get >50 tokens/s on £300 AMD GPUs.

The main bottleneck appears to be memory, not processing power.

Re: An analysis of DeepSeek's R1-Zero and R1

#48

Post by jacobsenscott »

polishdude20 wrote: post #6

I predict that the future of LLM's when it comes to coding and software creation is in "custom individually tailored apps". Imagine telling an AI agent what app you want, the requirements and all that and it just builds everything needed from backend to frontend, asks for your input on how things should work, clarifying questions etc. It tests the software by compiling and running it reading errors and failed tests a…

What's it called when you describe an app with sufficient detail that a computer can carry out the processes you want? Where will the record of those clarifying questions and updates be kept? What if one developer asks the AI to surreptitiously round off pennies and put those pennies into their bank account? Where will that change be recorded, will humans be able to recognize it? What if two developers give it conflicting instructions? Who's reviewing this stream of instructions to the LLM?

"AI" driven programming has a long way to go before it is just a better code completion.

Re: An analysis of DeepSeek's R1-Zero and R1

#49

Post by jvanderbot »

jeffbee wrote: post #39
mohsen1 wrote: post #31

Earlier quoted context omitted.

every time you respond to an AI model "no, you got that wrong, do it this way" you provide a very valuable piece of data to train on. With reasoning tokens there is just a lot more of that data to train on now

If such labels are collected and used to retrain the model then yes. But these models are not learning online.

Really? Isn't that the point of RL used in the way R1 did?

Provide a cost function (vs labels) and have it argue itself to greatness as measured by that cost function?

I believe that's what GP meant by "respond", not telling GPT they were wrong.

Re: An analysis of DeepSeek's R1-Zero and R1

#50

Post by fragmede »

polishdude20 wrote: post #6

I predict that the future of LLM's when it comes to coding and software creation is in "custom individually tailored apps". Imagine telling an AI agent what app you want, the requirements and all that and it just builds everything needed from backend to frontend, asks for your input on how things should work, clarifying questions etc. It tests the software by compiling and running it reading errors and failed tests a…

I mean, we're halfway there, with aider and open-interpreter, just give it a couple of years
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