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Zillow Upgrades AI Search 🟢

The enhanced search feature allows users to find homes based on driving distance from specific locations and affordability criteria, streamlining the house-hunting process.

What’s happening in AI right now

New Players, Bold Strategies, and Billion-Dollar Bets

The headlines this week have been dominated by massive funding rounds for AI startups. Ilya Sutskever, former OpenAI chief scientist, has secured an eye-watering $1 billion round for Safe Superintelligence (SSI) from prominent investors including Andreessen Horowitz and Sequoia Capital. SSI's mission is to develop advanced AI systems with safety as a priority reflecting the growing concerns about the potential risks of unchecked AI development.

Similarly, Japanese startup Sakana AI has raised $100 million in Series A funding, positioning itself to challenge established AI labs with a unique collective intelligence approach. These investments underscore a critical trend: the AI race is no longer just about capability, but also about responsibility and innovation in development methodologies.

Democratizing AI: from sports to government

While big tech battles for supremacy, smaller players are finding innovative niches that could democratize AI's benefits. SportAI, an Oslo-based startup, has secured $1.8 million for its platform providing real-time sports technique coaching and analysis. This development signals AI's potential to make elite-level coaching accessible to a broader audience, potentially revolutionizing how we approach sports training and performance analysis.

In the public sector, CivStart's launch of its sixth Govtech Accelerator Program focuses on AI solutions for local governments. This initiative highlights the growing recognition of AI's potential in enhancing public sector operations, while also emphasizing the need for ethical considerations and governance standards in AI implementation.

Education and infrastructure: building the ecosystem

Recognizing that the AI revolution requires a skilled workforce, the National Science Foundation (NSF) is investing nearly $8 million in five projects under the EducateAI initiative. This investment aims to expand AI education across the United States, with a focus on increasing diversity and accessibility. It's a crucial step towards democratizing AI knowledge and ensuring a diverse range of perspectives in the field's development.

News roundup

The top stories in AI today.

FUTURE OF WORK

Jobs are changing — are you?

NEW LAUNCHES

The latest features & products in AI innovation.

GADGETS

Computers, phones, wearables & other AI gizmos.

FUNDRAISING

Who’s getting that bread?

GOVERNMENT

Press releases, regulation, defense & politics.

AI research changing the world

The latest breakthroughs and most pivotal papers — broken down in language anyone can understand.

Unveiling the Next Generation of AI for Code

Imagine a world where artificial intelligence can not only understand code but also write, edit, and improve it just like a seasoned developer. The latest research from the BigCode project, an open scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), is turning this vision into reality. Their new release, StarCoder2, along with The Stack v2, represents a significant leap forward in AI's ability to generate code, promising to revolutionize how we develop software. StarCoder2 is a family of cutting-edge LLMs designed specifically for code generation. Trained on The Stack v2, the largest pre-training corpus for Code LLMs to date, these models boast an impressive 3B, 7B, and 15B parameters. The Stack v2, built upon the Software Heritage archive, is an expansive dataset 10 times larger than its predecessor, encompassing a raw dataset of 67.5 TB. After meticulous cleaning, filtering, and incorporation of various high-quality code-related datasets, the training set expanded to approximately 3TB (900B+ tokens). The rigorous evaluations of StarCoder2 models reveal state-of-the-art performance across multiple benchmarks.

Notably, StarCoder2-3B and StarCoder2-15B stand out as leaders within their respective size classes. This breakthrough showcases their capabilities in code completion, editing, and reasoning, setting new standards for AI in software engineering. Moreover, the BigCode project's commitment to transparency and open science is evident in their full disclosure of training data, model weights, and development details. This approach empowers the broader engineering and scientific community to build upon these advancements, fostering an environment of collaboration and innovation.

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