Researchers, industry executives, and government officials have long puzzled over how self-driving cars might change the planet. If you could do something else while stuck in traffic, would it change the way you use your car? Would you be willing to live farther from work? Alternatively, would the advent of shared self-driving cars prod you to ditch your personal vehicle for shared Ubers, making trips more efficient? Self-driving cars aren't here yet, and it will likely be years, or decades, before most Americans have access to the technology, which is still in development.

The following paper, "Simba: Scaling Deep-Learning Inference with Chiplet-Based Architecture," by Shao et al. presents a scalable deep learning accelerator architecture that tackles issues ranging from chip integration technology to workload partitioning and non-uniform latency effects on deep neural network performance. Through a hardware prototype, they present a timely study of cross-layer issues that will inform next-generation deep learning hardware, software, and neural network architectures. Chip vendors face significant challenges with the continued slowing of Moore's Law causing the time between new technology nodes to increase, sky-rocketing manufacturing costs for silicon, and the end of Dennard scaling. In the absence of device scaling, domain specialization provides an opportunity for architects to deliver more performance and greater energy efficiency. However, domain specialization is an expensive proposition for chip manufacturers.

Classical computing using digital symbols--equivalent to a Turing Machine--is reaching its limits. It is undeniable that computing's historic exponential performance increases have improved the human condition. Yet such increases are a thing of the past due in large part to the constraints of physics and how today's systems are constructed. Hardware device designers struggle to eliminate the effects of nanometer-scale thermodynamic fluctuations, and the soaring cost of fabrication plants has eliminated all but a few companies as a source of future chips. Software developers' ability to imagine and program effective computational abstractions and implementations are clearly challenged in complex domains like economic systems, ecological systems, medicine, social systems, warfare, and autonomous vehicles.

Moore's Law and Dennard scaling are waning. Yet the demand for computer systems with ever-increasing computational capabilities and power/energy-efficiency continues unabated, fueled by advances in big data and machine learning. The future of fields as disparate as data analytics, robotics, vision, natural language processing, and more, rests on the continued scaling of system performance per watt, even as traditional CMOS scaling ends. The following paper proposes a surprising, novel, and creative approach to post-Moore's Law computing by rethinking the digital/analog boundary. The central idea is to revisit the idea of data representation and show how it is a critical design choice that cuts across hardware and software layers.

In May 1948, women were strikingly prominent in ACM. Founded just months earlier as the "Eastern Association for Computing Machinery," the new professional society boldly aimed to "advance the science, development, construction, and application of the new machinery for computing, reasoning, and other handling of information."36 No fewer than 27 women were ACM members, and many were leaders in the emerging field.a Among them were the pioneer programmers Jean Bartik, Ruth Lichterman, and Frances Snyder of ENIAC fame; the incomparable Grace Murray Hopper who soon energized programming languages; Florence Koons from the National Bureau of Standards and U.S. Census Bureau; and noted mathematician-programmer Ida Rhodes.26 During the war, Gertrude Blanch had organized a massive human computing effort (a mode of computation made visible in the 2016 film Hidden Figures47) and, for her later service to the US Air Force, became "one of the most well-known computer scientists and certainly the most visible woman in the field."24,25 Mina Rees, a mathematics Ph.D. like Hopper and Blanch, notably funded mathematics and computing through the Office of Naval Research (1946–1953), later serving as the first female president of the American Association for the Advancement of Science. In 1949, Rees was among the 33 women (including at least seven ACM women) who participated in an international conference at Harvard University, chairing a heavyweight session on "Recent Developments in Computing Machinery."29

Chinese autonomous vehicle startup Pony.ai has received a permit from California's Department of Motor Vehicles to test its driverless cars without human safety drivers behind the wheel on specified streets in three cities. Pony has been authorized to test autonomous vehicles with safety drivers in California since 2017, but the new permit will let it test six autonomous vehicles without safety drivers on specific streets in Fremont, Alameda County; Milpitas, Santa Clara County; and Irvine, Orange County. According to the DMV, the vehicles are designed to be driven on roads with speed limits of 45 miles per hour or less, in clear weather and light precipitation. The first testing will be in Fremont and Milpitas on weekdays between 10AM and 3PM. A total of 55 companies have active permits to test driverless vehicles in California according to the DMV, but Pony is only the eighth company to receive a driverless testing permit, joining fellow Chinese companies AutoX, Baidu, and WeRide, along with US companies Cruise, Nuro, Waymo, and Zoox.

On May 10, 40 advocacy groups sent an open letter demanding a permanent ban on the use of Amazon's facial recognition software, Rekognition, by US police. The letter was addressed to Jeff Bezos and Andy Jassy, the company's current and incoming CEOs, and came just weeks before Amazon's year-long moratorium on sales to law enforcement was set to expire. The letter contrasted Bezos's and Jassy's vocal support of Black Lives Matter campaigners during last summer's racial justice protests after the murder of George Floyd with reporting that other Amazon products have been used by law enforcement to identify protesters. On May 17, Amazon announced it would extend its moratorium indefinitely, joining competitors IBM and Microsoft in self-regulated purgatory. The move is a nod at the political power of the groups fighting to curb the technology--and recognition that new legislative battle grounds are starting to emerge.

IS THERE no problem artificial intelligence can't tackle? Methods such as deep learning are finding uses in everything from algorithms that recommend what you should purchase next to ones that predict someone's voting habits. The result is that AI has developed a somewhat mystical reputation as a tool that can digest many different types of data and accurately predict many different outcomes, an ability that could be of particular use for solving previously impenetrable problems within healthcare. However, AI is no panacea. Too often, it is turned to too quickly and in an impulsive way, resulting in claims that it works when it doesn't.

On May 18, Google CEO Sundar Pichai announced an impressive new tool: an AI system called LaMDA that can chat to users about any subject. To start, Google plans to integrate LaMDA into its main search portal, its voice assistant, and Workplace, its collection of cloud-based work software that includes Gmail, Docs, and Drive. But the eventual goal, said Pichai, is to create a conversational interface that allows people to retrieve any kind of information--text, visual, audio--across all Google's products just by asking. LaMDA's rollout signals yet another way in which language technologies are becoming enmeshed in our day-to-day lives. But Google's flashy presentation belied the ethical debate that now surrounds such cutting-edge systems.

President Barack Obama bumped fists with Nathan Copeland during a tour of innovation projects at the White House Frontiers Conference at the University of Pittsburgh in 2016. President Barack Obama bumped fists with Nathan Copeland during a tour of innovation projects at the White House Frontiers Conference at the University of Pittsburgh in 2016. A robotic arm with a sense of touch has allowed a man who is paralyzed to quickly perform tasks like pouring water from one cup into another. The robotic arm provides tactile feedback directly to the man's brain as he uses his thoughts to control the device, a team reports Thursday in the journal Science. Previous versions of the arm required the participant, Nathan Copeland, to guide the arm using vision alone.