#zero-shot-performance

#zero-shot-performance

"Claude has regressed to the point it cannot be trusted to perform complex engineering," an AMD senior director wrote in a widely shared post on GitHub.

AI Mode can use your previous conversations, along with places you've searched for or tapped on in Search and Maps to deliver more relevant options, personalized to you. So if AI Mode infers that you have a preference for Italian food, plant-based meals, and places that have outdoor seating, you may get results suggesting options like these.

Before you can even get the opportunity to impress a human interviewer, you will first need to impress the algorithm! More recently, AI has also been used to assist current employees in doing their jobs and then to help their employers evaluate how well employees are performing in those jobs.

What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.

A major difference between LLMs and LTMs is the type of data they're able to synthesize and use. LLMs use unstructured data-think text, social media posts, emails, etc. LTMs, on the other hand, can extract information or insights from structured data, which could be contained in tables, for instance. Since many enterprises rely on structured data, often contained in spreadsheets, to run their operations, LTMs could have an immediate use case for many organizations.

Since AlexNet5, deep learning has replaced heuristic hand-crafted features by unifying feature learning with deep neural networks. Later, Transformers6 and GPT-3 (ref. 1) further advanced sequence learning at scale, unifying structured tasks such as natural language processing. However, multimodal learning, spanning modalities such as images, video and text, has remained fragmented, relying on separate diffusion-based generation or compositional vision-language pipelines with many hand-crafted designs.

AI agents need skills - specific procedural knowledge - to perform tasks well, but they can't teach themselves, a new research suggests. The authors of the research have developed a new benchmark, SkillsBench, which evaluates agentic AI performance on 84 tasks across 11 domains including healthcare, manufacturing, cybersecurity and software engineering. The researchers looked at each task under three conditions:

OpenAI's GPT-5.2 Pro does better at solving sophisticated math problems than older versions of the company's top large language model, according to a new study by Epoch AI, a non-profit research institute.

The robotics industry, for now, faces the biggest challenge in teaching robots to operate in the messy real world. The unstructured environment means robots need massive amounts of data to learn. Gathering and structuring that data is the costliest thing in robotics and perhaps the biggest impediment, slowing the entire development process.

AI systems continued to advance rapidly over the past year, but the methods used to test and manage their risks did not keep pace, according to the International AI Safety Report 2026. The report, produced with inputs from more than 100 experts across over 30 countries, said that pre-deployment testing was increasingly failing to reflect how AI systems behaved once deployed in real-world environments, creating challenges for organisations that had expanded their use of AI across software development, cybersecurity, research, and business operations.