Backed by Silicon Valley titans, Leading the Future has raised huge sums to promote pro-AI candidates on both side of the aisle. The Spanish Springs shopping center in Sparks, Nevada, hosts many businesses, including a Spirit Halloween, a hearing aid supplier, and a Vietnamese restaurant. It's also home to a consulting agency that's received millions of dollars from one of the biggest pro-AI super PAC networks ahead of November's midterm elections . The problem is that the company--Lantern Production Consultants LLC--doesn't seem to exist anywhere beyond a post office box in the Spanish Springs shopping complex. Lantern Production Consultants has been paid around $20 million since last year by Think Big, part of a network of pro-AI super PACs funded by the Leading the Future PAC, for a wide array of services, including polling, digital advertising, research, and strategy, according to federal campaign finance records reviewed by WIRED.

Atlas of Surveillance makes it easy to see what surveillance technology your local police department uses. It's a free database that shows where automated license plate readers and other surveillance tech is being used. Flock cameras are a hot topic right now. They're automated license plate readers with concerning privacy implications, and you probably drive past more of them than you realize. If you're curious about what your local police department is using, the Atlas of Surveillance gives you a simple way to find out. Created by the Electronic Frontier Foundation and the University of Nevada, Reno's Reynolds School of Journalism, the site collects public information about surveillance tech used by law enforcement across the US.

However,batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately,efficient model-parallel algorithms tend tobe complicated todiscover, describe, and to implement, particularly on large clusters.

While significant progress has been made in research and development on open-source and cost-efficient large-language models (LLMs), serving scalability remains a critical challenge, particularly for small organizations and individuals seeking to deploy and test their LLM innovations. Inspired by peer-to-peer networks that leverage decentralized overlay nodes to increase throughput and availability, we propose GenTorrent, an LLM serving overlay that harnesses computing resources from decentralized contributors. We identify four key research problems inherent to enabling such a decentralized infrastructure: 1) overlay network organization; 2) LLM communication privacy; 3) overlay forwarding for resource efficiency; and 4) verification of serving quality. This work presents the first systematic study of these fundamental problems in the context of decentralized LLM serving. Evaluation results from a prototype implemented on a set of decentralized nodes demonstrate that GenTorrent achieves a latency reduction of over 50% compared to the baseline design without overlay forwarding. Furthermore, the security features introduce minimal overhead to serving latency and throughput. We believe this work pioneers a new direction for democratizing and scaling future AI serving capabilities.

Zanskar uses AI to identify hidden geothermal systems--and claims it has found one that could fuel a power plant, the first such discovery by industry in decades. A geothermal startup said Thursday that it has hit gold in Nevada--metaphorically speaking. Zanskar, which uses AI to find hidden geothermal resources deep underground, says that it has identified a new commercially viable site for a potential power plant. The discovery, the company claims, is the first of its kind made by the industry in decades. The find is the culmination of years of research on how to find these resources--and points to the growing promise of geothermal energy .

High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training.

All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters.