Though the two countries are now in a race to develop atomic technology, China's most advanced reactor was the result of collaboration with American scientists. This April, in a speech given at the Shanghai branch of the Chinese Academy of Sciences, the physicist Xu Hongjie announced a breakthrough. For over a decade, his team had been working on an experimental nuclear reactor that runs on a lava-hot solution of fissile material and molten salt, rather than on solid fuel. The reactor, which went online two years ago, was a feat in itself. It is still the only one of its kind in operation in the world, and has the potential to be both safer and more efficient than the water-cooled nuclear plants that dominate the industry. Now, Xu explained, his team had been able to refuel the reactor without shutting it down, demonstrating a level of mastery over their new system. As dazzling as that was, the timing of Xu's speech also freighted the topic with geopolitical import. Only a few months earlier, DeepSeek, the Chinese artificial-intelligence company, had set alarms ringing through the U.S. tech world when it became clear that the relatively small Chinese startup, operating under U.S. export controls, had created a large language model that rivalled anything devised by the behemoths of Silicon Valley.

In the World Wide Web, reliable time series forecasts provide the forward-looking signals that drive resource planning, cache placement, and anomaly response, enabling platforms to operate efficiently as user behavior and content distributions evolve. Compared with other domains, time series forecasting for Web applications requires much faster responsiveness to support real-time decision making. We present KAIROS, a non-autoregressive time series forecasting framework that directly models segment-level multi-peak distributions. Unlike autoregressive approaches, KAIROS avoids error accumulation and achieves just-in-time inference, while improving over existing non-autoregressive models that collapse to over-smoothed predictions. Trained on the large-scale corpus, KAIROS demonstrates strong zero-shot generalization on six widely used benchmarks, delivering forecasting performance comparable to state-of-the-art foundation models with similar scale, at a fraction of their inference cost. Beyond empirical results, KAIROS highlights the importance of non-autoregressive design as a scalable paradigm for foundation models in time series.

In 2021, Google set a lofty goal of achieving net-zero carbon emissions by 2030. Yet in the years since then, the company has moved in the opposite direction as it invests in energy-intensive artificial intelligence. In its latest sustainability report, Google said its carbon emissions had increased 51% between 2019 and 2024. New research aims to debunk even that enormous figure and provide context to Google's sustainability reports, painting a bleaker picture. A report authored by non-profit advocacy group Kairos Fellowship found that, between 2019 and 2024, Google's carbon emissions actually went up by 65%.

Google has signed a "world first" deal to buy energy from a fleet of mini nuclear reactors to generate the power needed for the rise in use of artificial intelligence. The US tech corporation has ordered six or seven small nuclear reactors (SMRs) from California's Kairos Power, with the first due to be completed by 2030 and the remainder by 2035. Google hopes the deal will provide a low carbon solution to power datacentres, which require huge volumes of electricity. The US company, owned by Alphabet, said nuclear provided "a clean, round-the-clock power source that can help us reliably meet electricity demands". The explosive growth of generative AI, as well as cloud storage, has increased tech companies' electricity demands.

Online inference is becoming a key service product for many businesses, deployed in cloud platforms to meet customer demands. Despite their revenue-generation capability, these services need to operate under tight Quality-of-Service (QoS) and cost budget constraints. This paper introduces KAIROS, a novel runtime framework that maximizes the query throughput while meeting QoS target and a cost budget. KAIROS designs and implements novel techniques to build a pool of heterogeneous compute hardware without online exploration overhead, and distribute inference queries optimally at runtime. Our evaluation using industry-grade deep learning (DL) models shows that KAIROS yields up to 2X the throughput of an optimal homogeneous solution, and outperforms state-of-the-art schemes by up to 70%, despite advantageous implementations of the competing schemes to ignore their exploration overhead.

Creating native ads sounds like a simple, straightforward task. That's not until you are required to produce them on a large-scale basis with speed, consistency, timeliness, and variations. Leveraging on Knorex proprietary AI engine, KAIROS; producing ads can now be faster and easier than ever. KAIROS analyzes the destination URL provided and generates five variations of ads with different creatives, headlines, and body texts within seconds. Marketers get to select the ones that best fit their ad campaign while retaining the flexibility to tweak them further.

Creating native ads sounds like a simple, straightforward task. That's not until you are required to produce them on a large-scale basis with speed, consistency, timeliness, and variations. Leveraging on Knorex proprietary AI engine, KAIROS; producing ads can now be faster and easier than ever. KAIROS analyzes the destination URL provided and generates five variations of ads with different creatives, headlines, and body texts within seconds. Marketers get to select the ones that best fit their ad campaign while retaining the flexibility to tweak them further.

You'll be pleased to hear you are not alone. This article gives you a seriously thorough guide to getting the most out of Kairos face recognition, and explains how-- with some simple tweaks of a concept called thresholds-- you'll be rubbing your eyes in amazement in no time. Face recognition technology is the biometric identification of a person by comparing a live capture or digital image with a stored, or'enrolled' image for that person. During the enrollment process, the subject establishes her identity in the system by presenting her biometric image for scanning. Next, enrolled biometrics is processed by biometric and encryption algorithms which allow matching algorithms to predict, by comparison, how closely the processed face matches the enrolled face. This enables the software to make a confident identification.

Kairos, facial recognition solution provider, has introduced an on-premise version of its Facial Recognition APITrack this API. An on-premise version of the API allows users to better secure their applications, and reduces latency that can arise when using Kairos' hosted version. "As we've matured as a company, so has the market, and our customers' needs have evolved too," Cole Calistra, Kairos CTO, commented in a press release. "In response we began work earlier this year on the biggest ever project at Kairos. Taking our popular cloud service and'containerizing' it, enabling businesses to install a private version of [Kairos]."

Kairos, the facial recognition startup that found itself in turmoil following the ouster of founder and then-CEO Brian Brackeen last October, has raised $4 million in funding from E. Jay Saunders, CEO of Domus Semo Sancus. This brings Kairos's total funding to $17 million. As of November, Kairos had just enough money to get through Q1 of this year. At the time, Brackeen was looking to raise $5 million for the company and had already secured $3.5 million from Beyond Capital Markets, contingent upon Brackeen rejoining the company. Fast-forward to today and Brackeen is still out of the company and the interim CEO, Melissa Doval, has been appointed permanent CEO.