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AlphaCode was the first AI to write code at a human level in competitive programming. 🛠 Using a specialized version of Gemini, we created AlphaCode 2, which excels on this task. We estimate it performs better than 85% of participants on 12 recent Codeforces contests. ↓

Google DeepMind

1,465,405 subscribers

293,039 次观看 • 2 年前 •via X (Twitter)

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Google DeepMind 的头像

Google DeepMind2 年前

We’re excited to announce 𝗚𝗲𝗺𝗶𝗻𝗶: @Google’s largest and most capable AI model. Built to be natively multimodal, it can understand and operate across text, code, audio, image and video - and achieves state-of-the-art performance across many tasks. 🧵

Google DeepMind 的头像

Google DeepMind2 年前

We’ve optimized Gemini 1.0 for three different sizes, meaning it can run on everything from data centers to mobile phones. 🔨 1️⃣ Ultra: our largest one for highly complex tasks 2️⃣ Pro: our best one for scaling across many tasks 3️⃣ Nano: our most efficient one for devices

Google DeepMind 的头像

Google DeepMind2 年前

Gemini Ultra outperforms human experts on MMLU (massive multitask language understanding): one of the most popular methods of benchmarking AI models. It involves a combination of 57 test subjects from math to history to law and more. ↓

Google DeepMind 的头像

Google DeepMind2 年前

Gemini is also one of the world’s leading foundation models for coding. It can understand, explain, and generate high-quality code in programming languages like Python, Java, C++, and Go.

Google DeepMind 的头像

Google DeepMind2 年前

As Gemini is natively multimodal, it better understands nuanced information and can answer questions relating to complicated topics. This makes it especially good at explaining reasoning in subjects like math and physics - which could be helpful when it comes to homework. ↓

Alexandre Becker 的头像

Alexandre Becker2 年前

@RemiLeblond @codeforces Congrats @RemiLeblond 👏

aptr322 的头像

aptr3222 年前

@codeforces are there api or how it's reachable?

Shashwat Srivastava 的头像

Shashwat Srivastava2 年前

@codeforces Sound's alarming 😬

Thao Nguyen🇻🇳 的头像

Thao Nguyen🇻🇳2 年前

Using a specialized version of Gemini, THEY created Alpha code 2, which excels on this task

Anonidk2003 的头像

Anonidk20032 年前

@codeforces now thats some hyped up stuff, ask it to participate in a real contest

Vipul Chaudhary 的头像

Vipul Chaudhary2 年前

@codeforces These 15% will save earth from these AIs

Azz 的头像

Azz2 年前

@codeforces Gimana tanggapannya nih gan @DeeGeeDow

Heaton 的头像

Heaton2 年前

@codeforces

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The Great Programmer Layoffs Are Coming Google’s new AlphaCode 2 now beats 85% of human programmers. Last year’s AlphaCode was median human level. Yes, in just 1 year, AI went from outperforming 46% to 85% of coders. Sit with that. And it’s actually crazier than that, because for cost reasons they didn’t even use Gemini Ultra, they used the weaker Gemini Pro (~ChatGPT-3.5 ish). How powerful would Gemini Ultra be? Also interesting: Google DeepMind CEO Demis Hassabis said they are also working on research along similar lines to Q*. Sam Altman confirmed Q* was an "unfortunate leak" in a recent interview. Junior devs will likely be the first to go, seniors will have more time.

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AI that had human-level intelligence would actually be way above human level in capability. Obviously, if we trained a human-level AI, we could just run way more instances of it in parallel. This is a huge advantage. But it's not the only one. Right now, LLMs are much less sample-efficient than humans, but they can process each individual piece of data much more quickly. A model that was a human-level learner would be able to gain the equivalent of thousands of years of education. AIs can also be way more task-oriented than humans. Even a really well-motivated human worker can only operate with ‘human-level’ effort for relatively short spans of time. AGI could do it continuously.

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Gemini 3 is truly a great model. It created the Rubik's cube simulation on the first try, in seconds, with ~300 lines of code. Flawless.

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