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Finding leaked passwords with AI: How we built Copilot secret scanning

In October 2024, we announced the general availability of Copilot secret scanning, leveraging AI to detect generic passwords in users’ codebases. This post describes how Copilot secret scanning works under the hood, the challenges we ran into when developing it, and the framework we use for testing and iteration. What is Copilot secret scanning? Copilot secret scanning is a feature of GitHub Secret Protection, which protects millions of repositories on GitHub by detecting hundreds of pattern types through our partner program. The precision of these detections is paramount for security teams and developers when dealing with security alerts. Historically, our

Key ex-OpenAI researcher subpoenaed in AI copyright case

Alec Radford, a researcher who helped develop many of OpenAI’s key AI technologies, has been subpoenaed in a copyright case against the AI startup, according to a court filing Tuesday. The filing, submitted by an attorney for the plaintiffs to the U.S. District Court in the Northern District of California, indicated that Radford was served a subpoena on February 25. Radford, who left OpenAI late last year to pursue independent research, was the lead author of OpenAI’s seminal research paper on generative pre-trained transformers (GPTs). GPTs underpin OpenAI’s most popular products, including the company’s AI-powered chatbot platform, ChatGPT. Radford joined

2.0 Flash, Flash-Lite, Pro Experimental

In December, we kicked off the agentic era by releasing an experimental version of Gemini 2.0 Flash — our highly efficient workhorse model for developers with low latency and enhanced performance. Earlier this year, we updated 2.0 Flash Thinking Experimental in Google AI Studio, which improved its performance by combining Flash’s speed with the ability to reason through more complex problems. And last week, we made an updated 2.0 Flash available to all users of the Gemini app on desktop and mobile, helping everyone discover new ways to create, interact and collaborate with Gemini. Today, we’re making the updated Gemini

Alternative Data Use Grows Strongly Among Investors, Thanks to AI

(Zakharchuk/Shutterstock) Investment advisors are expanding their use of alternative data thanks to generative AI and the competitive advantages they plan to obtain through it, according to the latest report on alternative data from Lowenstein Sandler. Alternative data is the investment arena refers to anything that doesn’t appear in company filings, press releases, analyst reports, and other traditional sources. Investors are looking to alternative data like company credit card transactions, geolocation, mobile device data, and social media in order to gain a potentially lucrative signal that can be exploited for competitive advantage. Lowenstein Sandler is a law firm that has been

A Practical Guide to Implementing DeepSearch / DeepResearch

A Practical Guide to Implementing DeepSearch / DeepResearch. I really like the definitions Han Xiao from Jina AI proposes for the terms DeepSearch and DeepResearch in this piece: DeepSearch runs through an iterative loop of searching, reading, and reasoning until it finds the optimal answer. […] DeepResearch builds upon DeepSearch by adding a structured framework for generating long research reports. I’ve recently found myself cooling a little on the classic RAG pattern of finding relevant documents and dumping them into the context for a single call to an LLM. I think this definition of DeepSearch helps explain why. RAG is

Klarna CEO doubts that other companies will replace Salesforce with AI

The founder and CEO of IPO-bound fintech Klarna took to X to once again explain why his company ditched Salesforce’s flagship CRM product about a year ago in favor of its own homegrown AI system.  But this time, Sebastian Siemiatkowski emphasized that he doesn’t think others will — or should — follow his lead. “I don’t think it is the end of Salesforce; might be the opposite,” he wrote. The news that Klarna had developed its own in-house AI system based on OpenAI’s ChatGPT that allowed it to drop its contract for Salesforce CRM went viral in September. This came

Reddit – Dive into anything

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Reddit – Dive into anything

We value your privacy Reddit and its partners use cookies and similar technologies to provide you with a better experience. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. For more information, please see our Cookie Notice and our Privacy Policy. Source link

Does Microsoft’s Majorana chip meet enterprise needs?

Pragmatism over hype To be clear, I’m not dismissing quantum computing entirely. The Majorana 1 chip undoubtedly represents a substantial leap forward in hardware design and computational potential. Innovation in this field has a rightful place in academia, research, and industries that rely on extreme precision and computation: climate modeling, molecular biology, etc. Microsoft, Google, IBM, and others will continue to push boundaries, and that’s good for science and humanity. But for the average enterprise that spends its days managing cloud costs and dealing with ever-increasing volumes of user data, the promises of quantum computing glitter from a distance but

[2410.13770] Probing the Latent Hierarchical Structure of Data via Diffusion Models

[Submitted on 17 Oct 2024 (v1), last revised 28 Feb 2025 (this version, v2)] View a PDF of the paper titled Probing the Latent Hierarchical Structure of Data via Diffusion Models, by Antonio Sclocchi and 3 other authors View PDF HTML (experimental) Abstract:High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessing the latent variables underlying such a data structure remains a challenge. In this work, we show that forward-backward experiments in diffusion-based models, where data is