At least three artificial intelligence researchers have resigned from Meta's new superintelligence lab, just two months after CEO Mark Zuckerberg first announced the initiative. Two of the staffers have returned to OpenAI, where they both previously worked, after less than one-month stints at Meta, WIRED has confirmed. Ethan Knight worked at the ChatGPT maker earlier in his career but joined Meta from Elon Musk's xAI. A third researcher, Rishabh Agarwal, announced publicly on Monday he was leaving Meta's lab as well. He joined the tech giant in April to work on generative AI projects before switching to a role at Meta Superintelligence Labs (MSL), according to his LinkedIn profile.

As AI systems become increasingly capable, understanding their offensive cyber potential is critical for informed governance and responsible deployment. However, it's hard to accurately bound their capabilities, and some prior evaluations dramatically underestimated them. The art of extracting maximum task-specific performance from AIs is called "AI elicitation", and today's safety organizations typically conduct it in-house. In this paper, we explore crowdsourcing elicitation efforts as an alternative to in-house elicitation work. We host open-access AI tracks at two Capture The Flag (CTF) competitions: AI vs. Humans (400 teams) and Cyber Apocalypse (8000 teams). The AI teams achieve outstanding performance at both events, ranking top-5% and top-10% respectively for a total of \$7500 in bounties. This impressive performance suggests that open-market elicitation may offer an effective complement to in-house elicitation. We propose elicitation bounties as a practical mechanism for maintaining timely, cost-effective situational awareness of emerging AI capabilities. Another advantage of open elicitations is the option to collect human performance data at scale. Applying METR's methodology, we found that AI agents can reliably solve cyber challenges requiring one hour or less of effort from a median human CTF participant.

Recent advancements in generative artificial intelligence (AI) have transformed collaborative work processes, yet the impact on team performance remains underexplored. Here we examine the role of generative AI in enhancing or replacing traditional team dynamics using a randomized controlled experiment with 435 participants across 122 teams. We show that teams augmented with generative AI significantly outperformed those relying solely on human collaboration across various performance measures. Interestingly, teams with multiple AIs did not exhibit further gains, indicating diminishing returns with increased AI integration. Our analysis suggests that centralized AI usage by a few team members is more effective than distributed engagement. Additionally, individual-AI pairs matched the performance of conventional teams, suggesting a reduced need for traditional team structures in some contexts. However, despite this capability, individual-AI pairs still fell short of the performance levels achieved by AI-assisted teams. These findings underscore that while generative AI can replace some traditional team functions, more comprehensively integrating AI within team structures provides superior benefits, enhancing overall effectiveness beyond individual efforts.

In an interview, Meta's president of global affairs, Nick Clegg, said the tools "are really quite important" in that they provide, in a lot of ways, "the most comprehensive access to publicly available content across Facebook and Instagram of anything that we've built to date." The Content Library will also help the company meet new regulatory requirements and obligations on data sharing and transparency, as the company notes in a blog post Tuesday. The library and associated API were first released as a beta version several months ago and allow researchers to access near-real-time data about pages, posts, groups, and events on Facebook and creator and business accounts on Instagram, as well as the associated numbers of reactions, shares, comments, and post view counts. While all this data is publicly available--as in, anyone can see public posts, reactions, and comments on Facebook--the new library makes it easier for researchers to search and analyze this content at scale. Meta says that to protect user privacy, this data will be accessible only through a virtual "clean room" and not downloadable.

Meta is not pivoting away from its signature product, the metaverse. Or at least that's what the Meta chief executive, Mark Zuckerberg, is arguing. Despite reports that sales teams at Meta have spent less time pitching the metaverse to advertisers, Zuckerberg claimed on the tech firm's latest quarterly earnings call that it's business as usual over at the company formerly known as Facebook. "A narrative has developed that we're somehow moving away from focusing on the metaverse vision, so I just want to say upfront that that's not accurate," the CEO said. But neither is the virtual reality world the only product Meta has bet its future on, Zuckerberg argued: "We've been focusing on both AI and the metaverse for years now, and we will continue to focus on both."

Artificial intelligence (AI) and machine learning (ML) have become increasingly important in the modern business landscape, revolutionizing industries by automating processes, providing valuable insights, and enhancing decision-making. At the heart of these AI-driven solutions are machine learning engineers who play a crucial role in developing, implementing, and maintaining ML algorithms and models for various applications. As AI continues to permeate every aspect of our lives, assembling a capable and skilled AI team is more important than ever for businesses looking to stay competitive and innovative. In this blog post, we will guide you through the process of hiring a machine learning engineer for your AI team, from defining project requirements to fostering a collaborative and supportive work environment. Before starting the hiring process, it's essential to have a clear understanding of your project requirements, as this will help you identify the specific skills and expertise needed for your machine learning engineer.

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In Early February 2022, Sony's "first AI breakthrough", GT Sophy, made its appearance on the cover page of Nature magazine [2]. GT Sophy is a racing AI built to match with world-class level players in Gran Turismo Sport, the latest installation of the legendary game series on PlayStation 4. GT7 is famous for its extremely realistic simulation of real-life racing experience, which largely complicates the production of GT Sophy at the early stage. Every tiny decision that GT Sophy makes may change the result of the race entirely. Thus, there is little simplification can be done to the training process. Sony's AI team needs to take all possible factors, like drifting effects caused by the passage of nearby cars, to perform any estimation.

It's true there has been progress around data protection in the U.S. thanks to the passing of several laws, such as the California Consumer Privacy Act (CCPA), and nonbinding documents, such as the Blueprint for an AI Bill of Rights. Yet, there currently aren't any standard regulations that dictate how technology companies should mitigate AI bias and discrimination. As a result, many companies are falling behind in building ethical, privacy-first tools. Nearly 80% of data scientists in the U.S. are male and 66% are white, which shows an inherent lack of diversity and demographic representation in the development of automated decision-making tools, often leading to skewed data results. Significant improvements in design review processes are needed to ensure technology companies take all people into account when creating and modifying their products.

Most businesses are new to artificial intelligence and face daunting challenges when trying to scale their efforts that seek to derive business value from data. Get artificial intelligence right, and generate $460 billion in additional revenues. That's the estimated gains today's companies may see if they do three things: improve data practices, trust in advanced AI, and integrate AI with business operations. However, most companies have not gotten the memo. That's the word from Infosys Knowledge Institute, which finds in its latest study that while the potential for AI-driven gains are significant, most companies are still struggling to "convert data science to business value."