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Is this the gemini-exp model on LMArena?
Yes, LMArena shows Gemini-2.0-Flash-Exp ranking 3rd right now, after Gemini-Exp-1206 and ChatGPT-4o-latest_(2024-11-20), and ahead of o1-preview and o1-mini: https://lmarena.ai/?leaderboard
EDIT: probably not deep research.. is it Google testing their equivalent of o1? who knows..
This naming is confusing... Anyway, I'm glad that this Google release is actually available right away! I pay for Gemini Advanced and I see "Gemini Flash 2.0" as an option in the model selector. I've been going through Advent of Code this year, and testing each problem with each model (GPT-4o, o1, o1 Pro, Claude Sonnet, Opus, Gemini Pro 1.5). Gemini has done decent, but is probably the weakest of the bunch. It failed…
I use a Claude and Gemini a lot for coding and I realized there is no good or best model. Every model has it's upside and downside. I was trying to get authentication working according to the newer guidelines of Manifest V3 for browser extensions and every model is terrible. It is one use case where there is not much information or right documentation so every model makesup stuff. But this is my experience and I don'…
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These models take an instruction, along with any contextual information, and are trained to produce valid output. That production of output is a form of reasoning via _some_ type of logical processing. No? Maybe better to say computational reasoning. That’s a mouthful.
Static computation is not reasoning (these models are not building up an argument from premises, they are merely finding statistically likely completions). Computational thinking/reasoning would be breaking down a problem into an algorithmic steps. The model is doing neither. I wouldn't confuse the fact that it can break it into steps if you ask it, because again that is just regurgitation. It's not going through tha…
When asked, “If Alice has 3 apples and gives 2 to Bob, how many does she have left?”, the model doesn’t just retrieve a memorized answer—it infers the logical steps (subtracting 2 from 3) to generate the correct result, showcasing reasoning built on the interplay of its scale and architecture rather than explicit data recall.
OT: I’m not entirely sure why, but "agentic" sets my teeth on edge. I don't mind the concept, but the word itself has that hollow, buzzwordy flavor I associate with overblown LinkedIn jargon, particularly as it is not actually in the dictionary...unlike perfectly serviceable entries such as "versatile", "multifaceted" or "autonomous"
I'm personally very glad that the word has adhered itself to a bunch of AI stuff, because people had started talking about "living more agentically" which I found much more aggravating. Now if anyone states that out loud you immediately picture them walking into doors and misunderstanding simple questions, so it will hopefully die out.
Think of Google as of a tanker ship. It takes a while to change course, but it has great momentum. Sundar just needs to make sure the course is right.
Publishers are being squeezed and going under, or replacing humans with hallucinated genai slop.
It’s like we’re taking the private equity model of extracting value and killing something off to the entire web.
I’m not sure where this is headed, but I don’t think Sundar has any strategy here other than playing catch up.
Demis’ goal is pretty transparently positioning himself to take over.
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You can run model >100x faster in cloud compared to on device with DDR RAM. This would make up for the reception.
And you can’t run the cloud model at all if you can’t talk to the cloud.
Is this the gemini-exp model on LMArena?
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If the model weights is not open, you can't run it on device anyways.
The Pixel 9 runs many small proprietary Gemini models on the internal TPU.
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i don’t think they need to win the on device market. we need to separate inference and training - the real winners are those who have the training compute. you can always have other companies help with inference
I don’t think the AI market will ever really be a healthy one until inference vastly outnumbers training. What does it say about AI if training is done more than inference? I agree that the in-device inference market is not important yet.
inference hardware is a commodity in a way that training is not
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You're still focused about inference :) inference basically does not matter, it is a commodity
You’re still focused about training :) training doesn’t matter if inference costs are high and people don’t pay for them