MIT researchers unveil VaxSeer, an AI tool that accurately predicts dominant flu strains and optimizes vaccine design months ahead, outperforming WHO selections in a 10-year study by simulating virus evolution and vaccine responses using deep learning models.
Meta's profits surge from proven machine learning models as generative AI hype overshadows the continued success of conventional AI techniques powering applications across sectors, potentially signaling overlooked investment opportunities in traditional AI firms.
Anthropic announces it will train AI models on user data like chat logs and coding sessions unless users opt out by September 28th, extending data retention to 5 years for those who don't opt out.
Google Translate unveils groundbreaking features allowing live multilingual conversations across over 70 languages and personalized AI-powered language learning with interactive speaking practice, leveraging cutting-edge models like Gemini for enhanced translation accuracy.
AWS CEO Matt Garman dismisses the notion of replacing junior developers with AI as the 'dumbest thing,' emphasizing their importance for talent development and learning, while suggesting AI will enhance enterprises by freeing workers from mundane tasks to focus on creativity.
In a groundbreaking initiative, Bill Gates offers a $1 million prize for global innovators to leverage artificial intelligence in developing solutions to accelerate Alzheimer's disease research, with the resulting tools to be made publicly available.
Open weight large language models reveal inconsistent performance across hosted providers, with those running the latest vLLM framework generally outperforming older versions, while conformance suites and compatibility tests aim to ensure consistent implementation.
GitHub Copilot introduces multiple AI models tailored for diverse coding needs, allowing users to choose the best model based on task requirements, balancing speed, accuracy, and cost efficiency for general coding, deep reasoning, fast responses, or working with visuals.
AI judges, powered by large language models, are emerging to automatically evaluate outputs from machine learning systems, offering various evaluation methods like comparing outputs, scoring, and pass/fail judgments, but require testing against human evaluators and cost considerations.
A new 'Efficient Agents' AI framework achieves 96.7% of top performance at only 72.6% of the cost, enabling highly scalable deployment by using moderately-priced language models, limiting planning steps, broad simple tool usage, and lean memory management.