Google DeepMind researchers have developed a new AI system that excels in fact-checking, outperforming human annotators and saving costs, but critics question what ‘superhuman’ really means in this context.Read More
What makes this achievement particularly notable is the efficiency of the model—it achieves these speeds without compromising on precision.Read More
Listen to this article 3PL expert Sean Pineau will share his insights into automation and AI in the warehouse. Source: Locus Robotics Artificial intelligence promises to revolutionize robotics and industries including supply chain and logistics. For all of the hype around generative AI, robotics developers, integrators, and warehouse operators need to separate the facts from fiction. At the 2024 Robotics Summit & Expo, Locus Robotics will offer some help in demystifying AI in the warehouse.
SydeLabs launched its red teaming solution, SydeBox, on March 1, 2024, and has since seen adoption from 15+ enterprises that have detected over 10,000 vulnerabilities across 50+ applications/models.Read More
Too Long; Didn’t Read Open Source Software (OSS) faces gender biases despite its collaborative nature, affecting women’s participation and contributions. Studies show women are underrepresented, face communication barriers, and encounter biases in evaluation processes. This limits diversity, innovation, and profitability in OSS. To address this, OSS communities must implement inclusive guidelines, offer education and networking opportunities, use bias mitigation tools, and seek continuous improvement and feedback to create a more welcoming and equitable environment for
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Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data’s patterns (prior probability), collecting evidence (e.g., new time series data), and continuously updating that assumption to form a posterior probability distribution. Traditional Bayesian approaches like Gaussian processes (GPs) and Structural Time Series are extensively used for modeling time series data, e.g., the commonly