Atlantic reporter Alex Reisner recently uncovered four datasets of music being used to train AI models and made them fully searchable for the public. Two of the sets are absolutely enormous at 12 million and 9 million tracks. The other two are much smaller, but still represent a significant amount of training data at over 100,000 songs each.
According to Reisner, the sets have been downloaded thousands of times and, while it's impossible to know exactly who has used them, Google and Stability have both confirmed they have in research papers. Some of the sources, like the Free Music Archive dataset, are free to stream for personal use but re …
Read the full story at The Verge.
The billionaire founder and CEO of the New York-based hedge fund Third Point LLC is deploying tens of millions of dollars into two hyperscaler names. The latest 13F filing from the U.S. Securities and Exchange Commission (SEC) shows that Dan Loeb opened a new stake in Meta (META) in Q1 of this year, accumulating 90,000 […]
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The ARD standard could redefine enterprise AI integration, potentially marginalizing non-compliant tools and boosting major backers' market dominance.
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Enterprises implementing agentic AI face a challenge: Which tools should they allow their agents to use, where can they be found, and how can they be used safely? A new protocol, Agentic Resource Discovery, or ARD, aims to let agents answer those questions for themselves. Behind it are Google, Microsoft, Cisco, Nvidia, Salesforce and others.
ARD aims to standardize the way that tools and services are shared across systems within a corporate domain. For example, when investigating a production problem, an agent may want to query engineering documentation and open support tickets, deployment history and observability systems, all of which could be managed by different registries and across different silos. There is no common layer that pulls them together. ARD has been designed to be that layer.
It operates across two levels. Catalogs and Registries. In the first, an organization publishes a catalog setting out its available capabilities. The Registries layer act as a form of search engi
Enterprises implementing agentic AI face a challenge: Which tools should they allow their agents to use, where can they be found, and how can they be used safely? A new protocol, Agentic Resource Discovery, or ARD, aims to let agents answer those questions for themselves. Behind it are Google, Microsoft, Cisco, Nvidia, Salesforce and others.
ARD aims to standardize the way that tools and services are shared across systems within a corporate domain. For example, when investigating a production problem, an agent may want to query engineering documentation and open support tickets, deployment history and observability systems, all of which could be managed by different registries and across different silos. There is no common layer that pulls them together. ARD has been designed to be that layer.
It operates across two levels. Catalogs and Registries. In the first, an organization publishes a catalog setting out its available capabilities. The Registries layer act as a form of search engi
An IT executive changing jobs usually attracts little attention outside a narrow group of people, but Noam Shazeer’s move from Google to OpenAI is as momentous as any high-value soccer transfer.
He announced the news in a post on X: “I’m excited to share that I’ll be joining OpenAI and look forward to working with the exceptional team there.”
Shazeer initially achieved fame as one of the eight co-authors of the influential AI paper Attention Is All You Need, published when he was working at Google Brain. He is also one of the creators of the transformer technology that lies at the heart of modern AI models.
He left Google when the company failed to back his chatbot Meena and was tempted back when Google subsequently bought the company he founded, Character.AI, for $2.7 billion. That company achieved notoriety when it was sued by a grieving mother, who alleged that a Character.AI chatbot had contributed to her son’s death by suicide. The company subsequently settling out of court.
Shaze
An IT executive changing jobs usually attracts little attention outside a narrow group of people, but Noam Shazeer’s move from Google to OpenAI is as momentous as any high-value soccer transfer.
He announced the news in a post on X: “I’m excited to share that I’ll be joining OpenAI and look forward to working with the exceptional team there.”
Shazeer initially achieved fame as one of the eight co-authors of the influential AI paper Attention Is All You Need, published when he was working at Google Brain. He is also one of the creators of the transformer technology that lies at the heart of modern AI models.
He left Google when the company failed to back his chatbot Meena and was tempted back when Google subsequently bought the company he founded, Character.AI, for $2.7 billion. That company achieved notoriety when it was sued by a grieving mother, who alleged that a Character.AI chatbot had contributed to her son’s death by suicide. The company subsequently settling out of court.
Shaze
Google, Microsoft, OpenAI, and others want to help enterprises demonstrate that their AI applications are behaving themselves through the creation of a new foundation.
The Appia Foundation will, it explained rather impenetrably, “establish modular specifications that provide a connecting layer to bridge foundational global standards with practical, trusted assessments across the global AI value chain.”
Those specifications will help AI users ascertain whether the systems they are using meet all the obligations that apply to them in the form of standards and regulations, it said. It’s a challenging task with so much regional variation in requirements, and where the EU, for example, is more tightly controlled than the US.
The Foundation has established a set of criteria to demonstrate conformity with what is expected. There are two layers: the Requirements and Guidance layers will help users determine what is actually required, while the Assessment Enablement layer will look at how those
Google, Microsoft, OpenAI, and others want to help enterprises demonstrate that their AI applications are behaving themselves through the creation of a new foundation.
The Appia Foundation will, it explained rather impenetrably, “establish modular specifications that provide a connecting layer to bridge foundational global standards with practical, trusted assessments across the global AI value chain.”
Those specifications will help AI users ascertain whether the systems they are using meet all the obligations that apply to them in the form of standards and regulations, it said. It’s a challenging task with so much regional variation in requirements, and where the EU, for example, is more tightly controlled than the US.
The Foundation has established a set of criteria to demonstrate conformity with what is expected. There are two layers: the Requirements and Guidance layers will help users determine what is actually required, while the Assessment Enablement layer will look at how those
