151–160 of 582 posts
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Funny you should say that. Google just announced today that they are giving all college students one year of free Gemini advanced. I wonder how much that will actually move the needle among the youth.
And every professor just groaned at the thought of having to read yet another AI-generated term paper.
Absolutely decimated on metrics by o4-mini, straight out of the gate, and not even that much cheaper on output tokens (o4-mini's thinking can't be turned off IIRC).
Not sure "decimated" is a fitting word for "slightly higher performance on some benchmarks".
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Nobody has really talked about what I think is an advantage just as powerful as the custom chips: Google Books. They already won a landmark fair use lawsuit against book publishers, digitized more books than anyone on earth, and used their Captcha service to crowdsource its OCR. They've got the best* legal cover and all of the best sources of human knowledge already there. Then Youtube for video. The chips of course…
The amount of text in books is surprisingly finite. My best estimate was that there are ~10¹³ tokens available in all books ( https://dynomight.net/scaling/#scaling-data ), which is less than frontier models are already being trained on. On the other hand, book tokens are probably much "better" than random internet tokens. Wikipedia for example seems to get much higher weight than other sources, and it's only ~3×10¹⁰…
One hidden note from Gemini 2.5 Flash when diving deep into the documentation: for image inputs, not only can the model be instructed to generated 2D bounding boxes of relevant subjects, but it can also create segmentation masks! https://ai.google.dev/gemini-api/docs/image-understanding#se... At this price point with the Flash model, creating segmentation masks is pretty nifty. The segmentation masks are a bit of a g…
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If the battle was between Altman and Pichai I'd have my doubts. But the battle is between Altman and Hassabis. I recall some advice on investment from Buffett regarding how he invests in the management team.
Could you please expand, on both your points?
I think Pichai has been an exceptional revenue maximizer but he lacks vision. I think he is probably capable of squeezing tremendous revenue out of AI once it has been achieved.
I like Hassabis in a "good vibe" way when I hear him speak. He reminds me of engineers that I have worked with personally and have gained my respect. He feels less like a product focused leader and more of a research focused leader (AlphaZero/AlphaFold) which I think will be critical to continue the advances necessary to push the envelope. I like his focus on games and his background in RL.
Google's war chest of Ad money gives Hassabis the flexibility to invest in non-revenue generating directions in a way that Altman is unlikely to be able to do. Altman made a decision to pivot the company towards product which led to the exodus of early research talent.
https://github.com/googleapis/python-genai/blob/473bf4b6b5a6...
class ThinkingConfig(_common.BaseModel):
"""The thinking features configuration."""
include_thoughts: Optional[bool] = Field(
default=None,
description="""Indicates whether to include thoughts in the response. If true, thoughts are returned only if the model supports thought and thoughts are available.
""",
)
thinking_budget: Optional[int] = Field(
default=None,
description="""Indicates the thinking budget in tokens.
""",
)
Anyone managed to get Gemini to spit out thought summaries in its API using this option?
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And every professor just groaned at the thought of having to read yet another AI-generated term paper.
They should just get AI to mark them. I genuinely think this is one thing AI would do better than humans.
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If the battle was between Altman and Pichai I'd have my doubts. But the battle is between Altman and Hassabis. I recall some advice on investment from Buffett regarding how he invests in the management team.
Could you please expand, on both your points?
Google making Gemini 2.5 Pro (Experimental) free was a big deal. I haven't tried the more expensive OpenAI models so I can't even compare, only to the free models I have used of theirs in the past. Gemini 2.5 Pro is so much of a step up (IME) that I've become sold on Google's models in general. It not only is smarter than me on most of the subjects I engage with it, it also isn't completely obsequious. The model push…
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It must have something to do with the way your wife is prompting. I've noticed this with my friends too. I usually get working code from Gemini 2.5 Pro on the first try, and with a couple of follow-up prompts, it often improves significantly, while my friends seem to struggle communicating their ideas to the AI and get worse results. Good news: Prompting is a skill you can develop.
Or we can just learn to write it ourselves in the same amount of time /shrug