Research & Benchmarks - Page 28 of 30
Academic AI research, performance benchmarks, scientific breakthroughs, and peer-reviewed studies advancing artificial intelligence frontiers.
Academic AI research, performance benchmarks, scientific breakthroughs, and peer-reviewed studies advancing artificial intelligence frontiers.
About one in ten articles in a US newspaper now has some machine-generated text in it. Readers are rarely told. This is not a future scenario.
Video editing is a stubborn, tedious craft. Adobe’s new AI research project, Frame Forward, seems designed to gut the most painful part of it: removing something from a moving picture.
In New Delhi this week, a nondescript training lab opened its doors. Its purpose, however, is anything but small.
There’s a basic problem with the machines we’re told will reason. We have no real idea if they are. We see the answer, but the work is a mess of unreadable math. A team at Meta’s labs may have found a way to read that mess.
Building with AI often means blindly trusting a black box. You feed it a prompt, and you get a strange, inexplicable reply. Figuring out why is a professional nightmare. Google’s latest move in AI Studio aims to fix that.
Software bugs have long been the Achilles' heel of complex coding projects. Now, OpenAI might have found a game-changing solution with Aardvark, an AI system that approaches software vulnerability hunting like an expert human researcher.
Pinterest thinks you need a robot to tell you what to wear. On Thursday, the company will launch an AI shopping assistant that suggests outfits based on your own saved pins. It’s a chatbot for clothes.
Google's AI research continues to push boundaries with a significant upgrade to NotebookLM. The platform can now process massive amounts of information like never before, handling up to 1 million tokens in a single interaction.
Audio is the bane of video editors. Adobe's latest AI project is an attempt to fix that, promising to swap the music and tweak the emotion in a voiceover with something like a button press. It's a repair tool for bad audio. The pitch is simple.
Computers hate hard math. We knew that. But a Google research project just found a new way for them to hate it. The AI for Math Initiative, a collaboration with mathematicians, has identified novel structures within complex computational problems.
Most project management tools are digital naggers. You click through menus forever. Linear, beloved by actual developers, now targets that friction with a new MCP server. Boss an AI around instead. Tell it to move a ticket. Assign a bug.
India just wrote a $1.2 billion check for its AI independence. The national IndiaAI Mission commits Rs 10,300 crore to domestic infrastructure.
Software can now fake a famous author's voice. That’s the claim. But researchers at Stony Brook University and Columbia Law School put it to a hard test.
Every business is confused. They have boardrooms full of people asking questions they can't define, and they expect data scientists to find the answers.
Consensus is trying to fix the most tedious part of a scientist's job: reading. Its new tool, Scholar Agent, uses GPT-5 to automate the slog of literature review. The platform promises to turn weeks of paper-chasing into minutes.
Everyone wants AI to be greener. Researcher Miranda Schwacke thinks the answer might be in our heads. Her work connects brain science to sustainable AI development. It's not about making chatbots faster.
AI was supposed to make the hard jobs easier. For data engineers, it seems to be doing the opposite. A new survey from MIT Technology Review Insights, done with Snowflake, polled 400 senior tech executives.
OpenAI is building a lab to do its homework. Announcing a new "OpenAI for Science" team led by former Instagram product VP Kevin Weil, the company has one directive: apply AI models to physics and math. This isn't about drafting emails.
OpenAI's new Sora video tool has developers talking, but not about the next viral dance. They're arguing it marks the end of social media as a human space. The platform feels different. Vine and TikTok were built on people.
Running machine learning experiments at scale is mostly an infrastructure problem. Models are easier to write than the plumbing to run them. Databricks sells a solution.
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