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Reasoning AI is leveling up, and without breaking the bank!

A team at UC Berkeley’s Sky Computing Lab just released a new model for what’s practically pocket change in the AI world, where training models typically cost millions.

Here’s what we have for you today:

AI that can reason just got a whole lot cheaper and a whole lot more exciting.

Researchers at UC Berkeley’s Sky Computing Lab have unveiled Sky-T1-32B-Preview, a 32-billion-parameter reasoning model that’s open source, impressively smart, and costs less to train than a fancy dinner in San Francisco—just $450!

(Okay, maybe not that fancy of a San Francisco dinner, but you get the idea.)

So, what’s Sky-T1? It’s no ordinary AI model. This brainy creation was designed to tackle complex problems, from crunching challenging math equations to solving tricky coding puzzles.

And here’s the kicker: it’s holding its own against OpenAI’s earlier o1 model in several benchmarks.

What’s even more remarkable is how NovaSky pulled this off.

They relied on synthetic training data (basically AI-generated data) and a streamlined process that included help from Alibaba’s QwQ model and OpenAI’s GPT-4o-mini.

The entire training session wrapped up in just 19 hours on a cluster of 8 Nvidia H100 GPUs. And the results?

Sky-T1 crushed math and coding benchmarks, even outperforming the early version of o1 in some areas.

However, it did stumble on advanced science questions—so maybe don’t count on it for your quantum physics homework just yet.

Also, while OpenAI’s full o1 model remains more powerful overall—and with the even smarter o3 model on the horizon, what truly sets Sky-T1 apart is its open-source nature.

This means anyone with the right resources can replicate, customize, or build on it, unlocking new opportunities for researchers, developers, and innovators everywhere.

This isn’t just exciting, it’s a game changer.

NovaSky is proving that cutting-edge reasoning AI, which once cost millions to train, can now be developed affordably.

Want to know more about how they pulled it off? Click here to dive into the details!

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OpenAI is diving back into robotics, and this time, they’re going all in.

After disbanding its robotics division, OpenAI has relaunched the team with a renewed focus on creating “adaptive” and “versatile” robots.

With custom sensors, human-like adaptability, and ambitions for mass production, OpenAI’s vision could redefine what robots can do—and where they belong.

Caitlin Kalinowski, OpenAI’s hardware lead, recently shared updates on X, revealing plans for robots powered by advanced AI models and featuring cutting-edge sensors designed in-house.

The newly posted job listings on social media by OpenAI provide even more details.

OpenAI aims to develop “general-purpose” robots capable of working in dynamic, real-world environments.

These machines won’t just be prototypes—they’re gearing up for mass production, with one role specifically targeting designs for over a million units.

Hints of humanoid designs have also surfaced, with job descriptions mentioning limbs and systems optimized for human-like intelligence.

Plus, OpenAI plans to test prototypes using contract workers as it refines the models.

The robotics sector is buzzing with innovation, pulling in $6.4 billion in VC funding last year.

Startups like Figure and X1, backed by OpenAI, are racing to create humanoid robots, while companies like Bright Machines, Collaborative Robotics, and Bear Robotics focus on specialized tasks.

OpenAI’s move into robotics adds serious competition to an already dynamic field, but it’s not all smooth sailing.

Robotics has a history of bold claims and dashed hopes. The challenge of marrying physical hardware with high-level AI remains daunting.

Still, OpenAI’s expertise in AI gives it a unique advantage as it strives to integrate intelligence into physical platforms seamlessly.

Robotics isn’t the only hardware frontier OpenAI is exploring.

The company is also collaborating with Jony Ive on a secret device and developing custom AI chips, showcasing its ambitions beyond software.

Click here to explore how they’re pushing boundaries in AI and hardware!

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That's all we've got for you today.

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