Discover the latest developments in artificial intelligence, machine learning, and emerging technologies. Stay ahead of the AI revolution.
Amazon has introduced Alexa for Shopping, combining its Rufus shopping chatbot with Alexa+ across its app, website, and Echo Show devices. The assistant can answer product questions, compare items, track prices, and support shopping reminders.
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via cosine similarity — is effective for unstructured semantic search.
For AI systems to keep improving in knowledge work, they need either a reliable mechanism for autonomous self-improvement or human evaluators capable of catching errors and generating high-quality feedback. The industry has invested enormously in the first.
The company formerly known as Intercom just did something that no major customer service platform has attempted at scale: it built an AI agent whose sole job is to manage another AI agent. Fin Operator, announced Thursday at a live event in San Francisco, is a new AI-powered system designed specifically for the back-office teams that configure, monitor, and improve Fin, the company's customer-facing AI agent.
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI agents run. For the last two years, the enterprise AI race has mostly been framed as a model war: OpenAI’s GPT series versus Anthropic’s Claude versus Google’s Gemini, with smaller and open-source alternatives also coming in from the U.
A code migration agent finishes its run, and the pipeline looks green. But several pieces were never compiled — and it took days to catch.
When generative AI first moved from research labs into real-world business applications, enterprises made a tacit bargain: “Capability now, control later.” Feed your proprietary data into third-party AI models, and you will get powerful results.
For the first time since the AI race began, more American businesses are paying for Anthropic's Claude than for OpenAI's ChatGPT. Adoption of Anthropic rose 3.
For decades, the IQ test has been one of the most familiar — and most contested — yardsticks for human intelligence. Now, a startup project called AI IQ is applying the same metaphor to artificial intelligence, assigning estimated intelligence quotients to more than 50 of the world's most powerful language models and plotting them on a standard bell curve.
Varda Space Industries, a startup that’s been pitching its ability to perform drug experiments in space, says it has signed up the pharmaceutical company United Therapeutics in what may be remembered as a notable step toward in-orbit manufacturing. The idea of building things in outer space for use on Earth has so far been explored….
AI that can see and understand what's happening in a video — especially a live feed — is understandably an attractive product to lots of enterprises and organizations. Beyond acting as a security "watchdog" over sites and facilities, such an AI model could also be used to clip out the most exciting parts of marketing videos and repurpose them for social, identify inconsistencies and gaffs in videos and flag them for removal, and identify body language and actions of participants in controlled st.
A malicious Hugging Face repository that posed as an OpenAI release delivered infostealer malware to Windows machines and recorded about 244,000 downloads before removal, according to research from AI security firm HiddenLayer. The number of downloads may have been artificially inflated by the attackers to make the model seem more popular, so the extent of […] The post Hugging Face hosted malicious software masquerading as OpenAI release appeared first on AI News.
Is AI leaving the era of "turn-based" chat? Right now, all of us who use AI models regularly for work or in our personal lives know that the basic interaction mode across text, imagery, audio, and video remains the same: the human user provides an input, waits anywhere between milliseconds to minutes (or in some cases, for particularly tough queries, hours and days), and the AI model provides an output. But if AI is to really take on the load of jobs requiring natural interaction, it will need t.
Artificial intelligence is transforming how companies handle compliance. Background checks run in real-time.
AI agents choose tools from shared registries by matching natural-language descriptions. But no human is verifying whether those descriptions are true.
Here is a scenario that should concern every enterprise architect shipping autonomous AI systems right now: An observability agent is running in production. Its job is to detect infrastructure anomalies and trigger the appropriate response.
Just a few weeks after announcing Claude Managed Agents, Anthropic has updated the platform with three new capabilities that collapse infrastructure layers like memory, evaluation, and multi-agent orchestration, into a single runtime. This move could threaten the standalone tools that many enterprises cobble together.
Dario Amodei is not the kind of CEO who talks loosely about numbers. The Anthropic co-founder and chief executive, a former VP of research at OpenAI with a PhD in computational neuroscience from Princeton, has built a reputation for measured public statements — particularly around the financial performance of a company that, until recently, disclosed almost nothing about its business.
Anthropic on Tuesday unveiled a suite of updates to its Claude Managed Agents platform at its second annual Code with Claude developer conference in San Francisco, introducing a new capability called "dreaming" that lets AI agents learn from their own past sessions and improve over time — a step toward the kind of self-correcting, self-improving AI systems that enterprises have demanded before trusting agents with production workloads. The company also moved two previously experimental features .
Every LangChain pipeline your team hardcodes starts breaking the moment the query distribution shifts — and it always shifts. That bottleneck is what Sakana AI set out to eliminate.