We present the first sub-microsecond transformer implementation on an FPGA achieving competitive performance for state-of-the-art high-energy physics benchmarks. Transformers have shown exceptional performance on multiple tasks in modern machine learning applications, including jet tagging at the CERN Large Hadron Collider (LHC). However, their computational complexity prohibits use in real-time applications, such as the hardware trigger system of the collider experiments up until now. In this work, we demonstrate the first application of transformers for jet tagging on FPGAs, achieving $\mathcal{O}(100)$ nanosecond latency with superior performance compared to alternative baseline models. We leverage high-granularity quantization and distributed arithmetic optimization to fit the entire transformer model on a single FPGA, achieving the required throughput and latency. Furthermore, we add multi-head attention and linear attention support to hls4ml, making our work accessible to the broader fast machine learning community. This work advances the next-generation trigger systems for the High Luminosity LHC, enabling the use of transformers for real-time applications in high-energy physics and beyond.

When the federal government closed Castle Air Force Base in Merced County in the 1990s, the dilapidated buildings and vast expanse of aging tarmac left behind seemed more like a liability than an opportunity. But by 2018, the old runways that once carried B-52 bombers had found a new and unexpected customer: Google, which was testing its experimental self-driving vehicles there, far from the prying eyes of Silicon Valley. At the urging of then-state Assemblyman Adam Gray, California gave Merced County 6.5 million that year to expand the self-driving testing program at the old base. A few years later, Gray invested there, too. In 2022, a company in which Gray is a minority owner bought four apartment buildings on the former base from Merced County, according to a Times review of business filings, property records and Gray's financial disclosures.

Machine learning analysis of data gathered by acoustic recording devices is a promising new tool for monitoring the marbled murrelet and other secretive, hard-to-study species, research by Oregon State University and the U.S. Forest Service has shown. The threatened marbled murrelet is an iconic Pacific Northwest seabird that's closely related to puffins and murres, but unlike those birds, murrelets raise their young as far as 60 miles inland in mature and old-growth forests. "There are very few species like it," said co-author Matt Betts of the OSU College of Forestry. "And there's no other bird that feeds in the ocean and travels such long distances to inland nest sites. This behavior is super unusual and it makes studying this bird really challenging."

Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications. However, a crucial challenge facing these applications is their need for ultra low-latency and on-detector ML capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled with the rapid advances in scientific instrumentation that is resulting in growing data rates, there is a need for ultra-fast ML at the extreme edge. Fast ML at the edge is essential for reducing and filtering scientific data in real-time to accelerate science experimentation and enable more profound insights. To accelerate real-time scientific edge ML hardware and software solutions, we need well-constrained benchmark tasks with enough specifications to be generically applicable and accessible. These benchmarks can guide the design of future edge ML hardware for scientific applications capable of meeting the nanosecond and microsecond level latency requirements. To this end, we present an initial set of scientific ML benchmarks, covering a variety of ML and embedded system techniques.

Employers have a responsibility to inspect artificial intelligence tools for disability bias and should have plans to provide reasonable accommodations, the Equal Employment Opportunity Commission and Justice Department said in guidance documents. The guidance released Thursday is the first from the federal government on the use of AI hiring tools that focuses on their impact on people with disabilities. The guidance also seeks to inform workers of their right to inquire about a company's use of AI and to request accommodations, the agencies said. "Today we are sounding an alarm regarding the dangers of blind reliance on AI and other technologies that are increasingly used by employers," Assistant Attorney General Kristen Clarke told reporters. The DOJ enforces disability discrimination laws with respect to state and local government employers, while the EEOC enforces such laws in the private sector and federal employers.

Today's managers and executives need to oversee humans and machines in this age of AI and RPA, but should machines be managed as humans in a way that some suggest? As artificial intelligence and robotics process automation (RPA) usage continue to expand in enterprises, managers and executives need to learn how to supervise more than just human employees. They need to manage the human-machine workforce. Some suggest that intelligent machines should be managed like people. More specifically, they suggest that, like people, virtual employees should have a job title and key performance indicators (KPIs).

AI can be utilized to make CRM progressively powerful and exact. Presently, numerous organizations are utilizing different AI instruments to assess potential leads, information from web-based life, fundamental suggestions, and for programmed information passage, information investigation to fortify advertising and deals exercises. The eCommerce business is blasting more than ever with the presentation of artificial intelligence (AI). It is seeing a re-imagined structure that takes clients to another degree of experience and satisfaction. Customarily, retail organizations were fulfilled by simply making an eCommerce site for their physical stores, yet it isn--t sufficient at this point.

AI can be utilized to make CRM progressively powerful and exact. Presently, numerous organizations are utilizing different AI instruments to assess potential leads, information from web-based life, fundamental suggestions, and for programmed information passage, information investigation to fortify advertising and deals exercises. The eCommerce business is blasting more than ever with the presentation of artificial intelligence (AI). It is seeing a re-imagined structure that takes clients to another degree of experience and satisfaction. Customarily, retail organizations were fulfilled by simply making an eCommerce site for their physical stores, yet it isn--t sufficient at this point.

AI can be utilized to make CRM progressively powerful and exact. Presently, numerous organizations are utilizing different AI instruments to assess potential leads, information from web-based life, fundamental suggestions, and for programmed information passage, information investigation to fortify advertising and deals exercises. The eCommerce business is blasting more than ever with the presentation of artificial intelligence (AI). It is seeing a re-imagined structure that takes clients to another degree of experience and satisfaction. Customarily, retail organizations were fulfilled by simply making an eCommerce site for their physical stores, yet it isn--t sufficient at this point.

WASHINGTON, November 12, 2019 - The advent of artificial intelligence raises the concern of whether online algorithms harm or help user bias, experts said at a Tuesday Brookings panel. The remarkable lack of transparency is evident in how companies analyze algorithms, said Solon Barocas, information science professor at Cornell University. Before technology became ubiquitous, it was easier for people to recognized blatant discrimination from companies. Now, he said, it's more difficult to detect these signs from an online platform. The reasons creditors provide to customers for adverse decisions, Barocas said, are not entirely useful.