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Gemini 3 Deep Think

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Re: Gemini 3 Deep Think

#211

Post by zeroonetwothree »

hmmmmmmmmmmmmmm wrote: post #143
casey2 wrote: post #131

Earlier quoted context omitted.

I think you are making that confusion. Any robotic system in the place of his parents would fail with a few hours. There are more novel tasks in a day than ARC provides.

Children have great levels of fluid intelligence, that's how they are able to learn to quickly navigate in a world that they are still very new to. Seniors with decreasing capacity increasingly rely on crystallised intelligence, that's why they can still perform tasks like driving a car but can fail at completely novel tasks, sometimes even using a smartphone if they have not used one before.

It really depends on motivation. My 90 year old grandmother can use a smartphone just fine since she needs it to see pictures of her (great) grandkids.

Re: Gemini 3 Deep Think

#212

Post by brokencode »

lm28469 wrote: post #174
brokencode wrote: post #165

Earlier quoted context omitted.

They are using the current models to help develop even smarter models. Each generation of model can help even more for the next generation. I don’t think it’s hyperbolic to say that we may be only a single digit number of years away from the singularity.

I must be holding these things wrong because I'm not seeing any of these God like superpowers everyone seem to enjoy.

Who said they’re godlike today?

And yes, you are probably using them wrong if you don’t find them useful or don’t see the rapid improvement.

Re: Gemini 3 Deep Think

#213

Post by futureshock »

simianwords wrote: post #6

OT but my intuition says that there’s a spectrum - non thinking models - thinking models - best of N models like deep think an gpt pro Each one is of a certain computational complexity. Simplifying a bit, I think they map to - linear, quadratic and n^3 respectively. I think there are certain class of problems that can’t be solved without thinking because it necessarily involves writing in a scratchpad. And same for b…

I think step 4 is the agent swarm. Manager model gets the prompt and spins up a swarm of looping subagents, maybe assigns them different approaches or subtasks, then reviews results, refines the context files and redeploys the swarm on a loop till the problem is solved or your credit card is declined.

Re: Gemini 3 Deep Think

#214

Post by chermanowicz »

wiseowise wrote: post #158
xnx wrote: post #15

Google is absolutely running away with it. The greatest trick they ever pulled was letting people think they were behind.

Their models might be impressive, but their products absolutely suck donkey balls. I’ve given Gemini web/cli two months and ran away back to ChatGPT. Seriously, it would just COMPLETELY forget context mid dialog. When asked about improving air quality it just gave me a list of (mediocre) air purifiers without asking for any context whatsoever, and I can list thousands of conversations like that. Shopping or comparing…

It's so capable at some things, and others are garbage. I uploaded a photo of some words for a spelling bee and asked it to quiz my kid on the words. The first word it asked, wasn't on the list. After multiple attempts to get it to start asking only the words in the uploaded pic, it did, and then would get the spellings wrong in the Q&A. I gave up.

Re: Gemini 3 Deep Think

#215

Post by lern_too_spel »

jetter wrote: post #133

it is interesting that the video demo is generating .stl model. I run a lot of tests of LLMs generating OpenSCAD code (as I have recently launched https://modelrift.com text-to-CAD AI editor) and Gemini 3 family LLMs are actually giving the best price-to-performance ratio now. But they are very, VERY far from being able to spit out a complex OpenSCAD model in one shot. So, I had to implement a full fledged "screensho…

If you want that to get better, you need to produce a 3d model benchmark and popularize it. You can start with a pelican riding a bicycle with working bicycle.

Re: Gemini 3 Deep Think

#216

Post by woah »

mapontosevenths wrote: post #204
modeless wrote: post #101

Earlier quoted context omitted.

François Chollet, creator of ARC-AGI, has consistently said that solving the benchmark does not mean we have AGI. It has always been meant as a stepping stone to encourage progress in the correct direction rather than as an indicator of reaching the destination. That's why he is working on ARC-AGI-3 (to be released in a few weeks) and ARC-AGI-4. His definition of reaching AGI, as I understand it, is when it becomes i…

> His definition of reaching AGI, as I understand it, is when it becomes impossible to construct the next version of ARC-AGI because we can no longer find tasks that are feasible for normal humans but unsolved by AI. That is the best definition I've yet to read. If something claims to be conscious and we can't prove it's not, we have no choice but to believe it. Thats said, I'm reminded of the impossible voting tests…

> If something claims to be conscious and we can't prove it's not, we have no choice but to believe it.

Can you "prove" that GPT2 isn't concious?

Re: Gemini 3 Deep Think

#217

Post by Razengan »

bitpush wrote: post #180
xnx wrote: post #151

Earlier quoted context omitted.

Google privacy cred is ... excellent? The worst data breach I know of them having was a flaw that allowed access to names and emails of 500k users.

Link? Are you conflating with "500k Gmail accounts leaked [by a third party]" with Gmail having a breach? Afaik, Google has had no breaches ever.

Google is the breach.

Re: Gemini 3 Deep Think

#218

Post by bigbadfeline »

jstummbillig wrote: post #65
fishpham wrote: post #32

Earlier quoted context omitted.

Yes, but benchmarks like this are often flawed because leading model labs frequently participate in 'benchmarkmaxxing' - ie improvements on ARC-AGI2 don't necessarily indicate similar improvements in other areas (though it does seem like this is a step function increase in intelligence for the Gemini line of models)

Could it also be that the models are just a lot better than a year ago?

> Could it also be that the models are just a lot better than a year ago?

No, the proof is in the pudding.

After AI we're having higher prices, higher deficits and lower standard of living. Electricity, computers and everything else costs more. "Doing better" can only be justified by that real benchmark.

If Gemini 3 DT was better we would have falling prices of electricity and everything else at least until they get to pre-2019 levels.

Re: Gemini 3 Deep Think

#219

Post by estearum »

mapontosevenths wrote: post #204
modeless wrote: post #101

Earlier quoted context omitted.

François Chollet, creator of ARC-AGI, has consistently said that solving the benchmark does not mean we have AGI. It has always been meant as a stepping stone to encourage progress in the correct direction rather than as an indicator of reaching the destination. That's why he is working on ARC-AGI-3 (to be released in a few weeks) and ARC-AGI-4. His definition of reaching AGI, as I understand it, is when it becomes i…

> His definition of reaching AGI, as I understand it, is when it becomes impossible to construct the next version of ARC-AGI because we can no longer find tasks that are feasible for normal humans but unsolved by AI. That is the best definition I've yet to read. If something claims to be conscious and we can't prove it's not, we have no choice but to believe it. Thats said, I'm reminded of the impossible voting tests…

> If something claims to be conscious and we can't prove it's not, we have no choice but to believe it.

This is not a good test.

A dog won't claim to be conscious but clearly is, despite you not being able to prove one way or the other.

GPT-3 will claim to be conscious and (probably) isn't, despite you not being able to prove one way or the other.

Re: Gemini 3 Deep Think

#220

Post by simianwords »

futureshock wrote: post #213
simianwords wrote: post #6

OT but my intuition says that there’s a spectrum - non thinking models - thinking models - best of N models like deep think an gpt pro Each one is of a certain computational complexity. Simplifying a bit, I think they map to - linear, quadratic and n^3 respectively. I think there are certain class of problems that can’t be solved without thinking because it necessarily involves writing in a scratchpad. And same for b…

I think step 4 is the agent swarm. Manager model gets the prompt and spins up a swarm of looping subagents, maybe assigns them different approaches or subtasks, then reviews results, refines the context files and redeploys the swarm on a loop till the problem is solved or your credit card is declined.

i think this is the right answer

edit: i don't know how this is meaningfully different from 3

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