CoreWeave, the AI Hyperscaler, today announced it has reached an agreement to acquire Weights & Biases, a leading AI developer platform. The acquisition extends CoreWeave's purpose-built cloud platform by enabling an end-to-end experience for customers, enhancing functionality for the world's leading AI labs and enterprises to build, tune and deploy AI applications.
The transaction combines CoreWeave's infrastructure and managed cloud services with the Weights & Biases AI developer platform for AI model training and AI application evaluation and monitoring. Collectively, CoreWeave and Weights & Biases intend to provide customers with a powerful application development workflow to accelerate AI roadmaps and bring innovations to market faster.
The combination will result in one team dedicated to customers with intense focus on the end-user experience and productivity. CoreWeave plans to bring new innovations to the Weights & Biases platform while maintaining the functionality and dependability that users know and love. Weights & Biases' customers will also be able to continue deploying workloads wherever they like, be it on-premise or with the infrastructure provider of their choice.
Weights & Biases was founded in 2017 by Lukas Biewald, Chris Van Pelt, and Shawn Lewis, on a mission to build the best tools for AI developers. Since then, the company has become a leading player in the MLOps and LLMOps space with a platform used by over 1 million AI engineers. Its trusted software products are used industry-wide by marquee enterprises, including OpenAI, Meta, NVIDIA, Snowflake, AstraZeneca, Toyota, Canva, Square, and Wayve to build and train AI models and develop and deploy AI applications.
Weights & Biases has built a phenomenal platform to help organizations of any size and across a range of industries to build, deploy and monitor AI training and inference applications, said Michael Intrator, co-founder and CEO of CoreWeave.
More on CoreWeave’s acquisition of Weights & Bias on PR Newswire
Automating Contracts With Generative AI, ECM, And Orchestration Hubs | IBM
Say goodbye to manual contract processing! IBM’s Brandon Swink explores how Generative AI, ECM, and Orchestration Hubs streamline document management and improve efficiency. Discover how these technologies transform your approach to complex documents.
UPCOMING INTERVIEW ON THE (A)BSOLUTELY (I)NCREDIBLE PODCAST
I’m expanding the (A)bsolutely (I)ncredible Podcast to include interviews with thought leaders who are passionately working on revolutionary transformation, powered by AI.
On the inaugural episode, I welcome Theodora Lau, the Founder of Unconventional Ventures, and author of a the new book being published by Palgrave Macmillan.
Banking on (Artificial) Intelligence: Navigating the Realities of AI in Financial Services. Theodora has been recognized as one American Banker’s Top 20 most influential women in Fintech, and the host of the amazing One Vision Podcast.
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As an author of multiple books on the future of finance this should be an interesting and insightful interview thats available to all our Neural News Network subscribers.
If you would like your executive, brand, or product featured on the all new (A)bsolutely (I)ncredible Podcast please message Bruce Burke directly for additional information.
Two Pioneers Of Reinforcement Learning Win Turing Award “Extremely Rewarding”
Having machines learn from experience was once considered a dead end. It's now critical to artificial intelligence, and work in the field has won two men the highest honor in computer science.
This year's Turing Award — often called the Nobel Prize of computer science — is going to Andrew Barto and Richard Sutton, the pioneers of a key approach that underlies much of today's artificial intelligence. Reinforcement learning, as the technique is known, posits that computers can learn from their own experiences, using a system of rewards similar to how researchers have trained animals.
In a joint interview, Barto and Sutton said the award is extremely rewarding, especially given that for much of their career, the technology they pursued was out of vogue. When we started, it was extremely unfashionable to do what we were doing, Barto told Axios. It had been dismissed, actually, by many people. There were periods of time when I could not get funding because I was not doing the current fashionable topic, and I wasn't going to change to what was fashionable, he said.
Sutton added that it was particularly gratifying to be given this award since it was Alan Turing who proposed the notion of computers learning from their own experiences in a 1950s paper, though it would take decades for there to be enough computing power to test out the notion.
Sutton, now a computer science professor at Canada's University of Alberta, was Barto's student at the University of Massachusetts in the late 1970s. Throughout the 1980s, the pair wrote a series of influential papers, culminating in their seminal 1998 textbook: Reinforcement Learning: An Introduction, which has been cited in more than 70,000 academic papers.
The approach finally gained prominence in the last decade as DeepMind's AlphaGo began to defeat human players. Reinforcement learning from human feedback is a key method for the training of large language models, while the approach has also proven useful in everything from programming robots to automating chip design.
More on Barto and Sutton’s Turing Award honors on AXIOS
AI Watching AI: The Rise Of Guardian Agents | Daryl Plummer | Top Of Mind
In this episode of Top of Mind, Gartner Distinguished VP Analyst Daryl Plummer explores the emerging world of guardian agents — AI designed to monitor other AI. Learn how guardian agents will become critical for organizations deploying AI agents for quality control, system observation and security from rogue AI behavior.
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How 'Chain Of Draft' Could Cut AI Costs By 90% While Improving Performance
A team of researchers at Zoom Communications has developed a breakthrough technique that could dramatically reduce the cost and computational resources needed for AI systems to tackle complex reasoning problems, potentially transforming how enterprises deploy AI at scale.
The method, called chain of draft (CoD), enables large language models (LLMs) to solve problems with minimal words — using as little as 7.6% of the text required by current methods while maintaining or even improving accuracy. The findings were published in a paper last week on the research repository arXiv.
“By reducing verbosity and focusing on critical insights, CoD matches or surpasses CoT (chain-of-thought) in accuracy while using as little as only 7.6% of the tokens, significantly reducing cost and latency across various reasoning tasks,” write the authors, led by Silei Xu, a researcher at Zoom. COD draws inspiration from how humans solve complex problems. Rather than articulating every detail when working through a math problem or logical puzzle, people typically jot down only essential information in abbreviated form.
“When solving complex tasks — whether mathematical problems, drafting essays or coding — we often jot down only the critical pieces of information that help us progress,” the researchers explain. “By emulating this behavior, LLMs can focus on advancing toward solutions without the overhead of verbose reasoning.”
More on Zoom’s Chain of Draft methodology on VentureBeat
One-Person Enterprise | World Economic Forum Annual Meeting | Davos 2025
How far are we from a true one-person unicorn and what does this mean for the future of employment and capital? Panelists Benjamine Liu, Kanjun Qiu, Dan Murphy, Mitchell Green, Sarah Franklin, and Richard Socher discuss this engaging topic during the recent World Economic Forum in Davos, Switzerland.
As AI evolves from a support tool to a co-pilot, the dynamics of building and running a company are changing. Companies are increasingly relying on technology enabling one individual to achieve what previously required entire teams.
Wayne Rasanen’s Award Winning DecaTxt 3 | A One-Handed Keyboard
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The DecaTxt 3 uses a unique "chord" system, similar to a piano. By pressing different combinations of the two keys at each fingertip, you can generate any letter or symbol.
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The DecaTxt 3 is a perfect solution for people with hand tremors, poor motor skills, conditions like MS, limb loss, or even vision impairment. It connects via Bluetooth and can be strapped to either hand, making it comfortable and versatile for everyone.
The new 55th Annual R&D Award Winner, DecaTxt 3 has been featured in a new issue of the Florida Alliance for Assistive Services & Technology (FAAST) Newsletter.
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Meta Wants To Give Small Businesses An AI Boost With Customer Support Agents For Instagram And Facebook Platforms
The tech giant is offering its AI agent to businesses on its platforms free of charge during the pilot program.
Soon, all businesses will be able to use Meta’s AI to power live, 24/7 customer service that can interact with customers on behalf of businesses on Instagram, Facebook, and WhatsApp. Meta announced advancements in business AI—including the customer service AI agent that will make purchases and can respond to voice prompts from a user—at a conference Tuesday morning.
“We are at an amazing and historic moment,” said Clara Shih, the VP of Business AI at Meta. “We are on the cusp of AI being in the hands of every consumer and every business.” Over 600 million conversations between a person and a business occur in a day on all of Meta’s social platforms. With its new pilot program, Meta hopes to utilize its Llama AI model to help entrepreneurs and small business owners scale their businesses.
As soon as today, Meta users may begin to see business AI featured on ads from brands. Users can ask questions live on the ad page, and the AI agent will answer using data from the business’s Meta footprint (analyzing previous posts and customer service messages). Business owners can also input their own data sources. Businesses can design these agents, which can operate both straight off of ads and also through DMs or Messenger, at no cost right now as the pilot program kicks off. Meta is currently working on specifics for pricing in the future.
Business owners can also delegate which tasks they want the AI agent to handle, and which tasks they want the AI to hand off to a live customer service representative. Tasks that the business AI can tackle include managing returns and exchanges, providing product recommendations, handling purchases, and similar services. Shih said that providing these tools to all businesses, regardless of size, is “democratizing access” to powerful technology, which mainly large businesses have previously been able to implement.
More on Meta’s AI-powered customer service on Fast Company
Meta Head of Business AI, Clara Shih On Leading In A Male-Dominated Industry
CNBC's Julia Boorstin sits down with Clara Shih, Meta's Head of Business AI, discussing why she is laser-focused on embedding trust and safety into Meta’s AI products. Clara is in the inner circle of women leading AI development at the company, standing out in a male-dominated industry, and tackling burnout.
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