Articles for category: AI News

DeepSeek didn’t deep-six AI startup funding after all

DeepSeek? Who’s that? The Chinese cheap artificial intelligence model maker was supposed to cool investor interest in money-burning AI startups, but that was hardly the case this week. If anything, the AI wars are heating up even more, with a hot new model from Elon Musk’s xAI, the launch of former OpenAI Chief Technology Officer Mira Murati’s new startup Thinking Machines Lab, Ilia Sutskever’s startup Safer Superintelligence raising $1 billion, and a whole lot of industry-focused AI startups raising rounds of up to $100 million each. Oh, and OpenAI reached 400 million active weekly users. Vultures are circling Intel, which looks

Making money from AI: Searching for a ‘killer app’

While AI startups promise major disruption, what they’re delivering hasn’t lived up to the hype. This undercuts the massive investments that Big Tech firms are seeking in the sector, AI Now co-ED Sarah Myers West told FT’s Madhumita Murgia. Listen here The post Making money from AI: Searching for a ‘killer app’ appeared first on AI Now Institute. Source link

The ethics of AI and how they affect you

Having worked with AI since 2018, I’m watching its slow but steady pick-up alongside the unstructured bandwagon-jumping with considerable interest. Now that the initial fear has subsided somewhat about a robotic takeover, discussion about the ethics that will surround the integration of AI into everyday business structures has taken its place.   A whole new range of roles will be required to handle ethics, governance and compliance, all of which are going to gain enormous value and importance to organisations. Probably the most essential of these will be an AI Ethics Specialist, who will be required to ensure Agentic AI systems

Creating artificial doubt significantly improves AI math accuracy

What makes an AI system good at math? Not raw computational power, but something that seems almost contradictory: being neurotically careful about being right. When AI researchers talk about mathematical reasoning, they typically focus on scaling up – bigger models, more parameters, larger datasets. But in practice, mathematical ability isn’t about how much compute you have for your model. It’s actually about whether machines can learn to verify their own work, because at least 90% of reasoning errors come from models confidently stating wrong intermediate steps. I guess this sounds obvious once you understand it. Any mathematician would tell you

Amazon announces next-gen AI-powered Alexa+

Just a heads up, if you buy something through our links, we may get a small share of the sale. It’s one of the ways we keep the lights on here. Click here for more. Amazon has announced Alexa+, a new amped-up version of Alexa based on cutting-edge large language models with vast capabilities that redefine the way we interact with digital assistants. Alexa+ will roll out in the US in the next few weeks. The monthly fee is $19.99, but Amazon Prime members can access it at no cost.  Amazon says Alexa+ is way smarter. The conversations feel natural

The next step in autonomous AI

China’s recently unveiled AI agent, Manus, represents a significant leap forward. Introduced by the Chinese startup Monica, Manus is described as a fully autonomous AI agent capable of handling a wide range of tasks with minimal human intervention.  Since its launch on March 6, 2025, Manus has attracted considerable global attention, sparking discussions about its technological implications, ethical considerations, and potential impact on the AI landscape. This article explores what makes Manus unique, examines the perspectives of its supporters and critics, and considers the broader implications of its development. The emergence of Manus Manus differs from conventional AI systems’ ability

Noteworthy AI Research Papers of 2024 (Part One)

To kick off the year, I’ve finally been able to complete the draft of this AI Research Highlights of 2024 article. It covers a variety of topics, from mixture-of-experts models to new LLM scaling laws for precision. Reflecting on all the major research highlights of 2024 would probably require writing an entire book. It’s been an extraordinarily productive year, even for such a fast-moving field. To keep things reasonably concise, I decided to focus exclusively on LLM research this year. But even then, how does one choose a subset of papers from such an eventful year? The simplest approach I