Drive down the 280 freeway in San Francisco and you might believe AI is everywhere, and everything. Nearly every billboard advertises an AI related product: "We've Automated 2,412 BDRs." "All that AI and still no ROI?" "Cheap on-demand GPU clusters." It's hard to know if you're interpreting the industry jargon correctly while zooming past in your vehicle. The signs are just one example of the tech industry's en-masse pivot to AI, a technology that the executives who have the most to gain from it say will be universe-shifting, inevitable and unavoidable. In California's tech heartland, every company is now an AI company, just like every company became a tech company sometime in the 2010s.

Scientists have discovered a new brain glitch that is the exact opposite of deja vu. While deja vu is the unsettling sense that you've lived a moment before, jamais vu is when something familiar suddenly feels alien -- like encountering it for the very first time. You've likely felt it: walking through your hometown and suddenly feeling lost, or repeating a common word until it sounds strange and meaningless. Repetition is often the trigger. The brain, overloaded by familiarity, short-circuits, making the ordinary feel bizarre.

Just a week in, the AI sector has already seen its first battle of wits under the new Trump administration. The clash stems from two key pieces of news: the announcement of the Stargate project, which would spend 500 billion--more than the Apollo space program--on new AI data centers, and the release of a powerful new open-source model from China. Together, they raise important questions the industry needs to answer about the extent to which the race for more data centers--with their heavy environmental toll--is really necessary. If, in dissecting DeepSeek R1, AI companies discover some lessons about how to make models use existing resources more effectively, perhaps constructing more and more data centers won't be the only winning formula for better AI. This story originally appeared in The Algorithm, our weekly newsletter on AI.

However, not everyone sees Stargate as having the same public benefit. Environmental groups say it could strain local grids and further drive up the cost of energy for the rest of us, who aren't guzzling it to train and deploy AI models. Previous research has also shown that data centers tend to be built in areas that use much more carbon-intensive sources of energy, like coal, than the national average. It's not clear how much, if at all, Stargate will rely on renewable energy. Even louder critics of Stargate, though, include Elon Musk.

We study the recent progress on dynamic view synthesis (DVS) from monocular video. Though existing approaches have demonstrated impressive results, we show a discrepancy between the practical capture process and the existing experimental protocols, which effectively leaks in multi-view signals during training. We define effective multi-view factors (EMFs) to quantify the amount of multi-view signal present in the input capture sequence based on the relative camera-scene motion. We introduce two new metrics: co-visibility masked image metrics and correspondence accuracy, which overcome the issue in existing protocols. We also propose a new iPhone dataset that includes more diverse real-life deformation sequences.

Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks by leveraging vast amounts of online texts. Unlike conventional models, LLMs can adapt to new domains through prompt engineering without the need for retraining, making them suitable for various business functions, such as strategic planning, project implementation, and data-driven decision-making. However, their limitations in terms of bias, contextual understanding, and sensitivity to prompts raise concerns about their readiness for real-world applications. This paper thoroughly examines the usefulness and readiness of LLMs for business processes. The limitations and capacities of LLMs are evaluated through experiments conducted on four accessible LLMs using real-world data. The findings have significant implications for organizations seeking to leverage generative AI and provide valuable insights into future research directions. To the best of our knowledge, this represents the first quantified study of LLMs applied to core business operations and challenges.

You probably already own an AI PC. In the past few months, Intel and PC makers have beat the drum of the AI PC loudly and in concert with AMD, Intel, and Qualcomm. It's no secret that "AI" is the new "metaverse" -- you know, that thing that everyone was talking up a few years ago -- and executives and investors alike want to use AI to boost sales and stock prices. And it's true that AI does depend on the NPUs found in chips like Intel's Core Ultra, the brand that Intel is positioning as synonymous with on-chip AI. The same goes for AMD's Ryzen 8000 series -- which beat Intel to the desktop with an NPU -- as well as Qualcomm's Snapdragon X Elite.

Venture investors are realizing that generative artificial intelligence might not be enough to stem yearslong startup downturn

The state of the art in brain-computer interface is rapidly evolving. There are two primary approaches described in the literature. Is there a good reason to risk an invasive brain-computer interface when the non-invasive headset or similar approaches are not full explored avenues? Right now, every piece of consumer technology is backdoored. Government actors are given the keys and non-government actors either stealthily transmit the backdoor key or at the least, they're hackable through the backdoor.

Artificial Intelligence (AI) is the new black, the shiny new object, the answer to every marketer's prayers, and the end of creativity. The recent emergence of AI from the arcane halls of academia and the backrooms of data science has been prompted by stories of drones, robots and driverless cars undertaken by tech giants like Amazon. But the hype exceeds the day-to-day reality. AI has a fifty-year history of mathematical and computer science development, experimentation and thought. What makes it exciting is the confluence of large data sets, improved platforms and software, faster and more robust processing capabilities and a growing cadre of data scientists eager to exploit a wider range of applications.