But if this is to happen, the Cambridge University Professor believes a political rethink is required: "As to whether that will happen or not will depend on whether this county's politicians are prepared to learn from Scandinavian countries instead of the US. "Scandinavia has a good welfare state and high taxation, the opposite to the US, with its very inadequate welfare state and low taxes. "The present government seems to admire the US system more." The celebrated cosmologist and astrophysicist does however foresee a difficulty with collecting taxes from these multinational tech giants, based on current controversies over their tax arrangements. Facebook's UK tax bill, for example, is 0.62 percent of their £1.3billion

This week's milestones in the history of technology include the coining of the term "artificial intelligence," the digitization of the Library of Congress, and the first penny paper. The first issue of Scientific American is published by Rufus Porter as a weekly broadsheet subtitled "The Advocate of Industry and Enterprise, and Journal of Mechanical and Other Improvements." In an era of rapid innovation, Scientific American founded the first branch of the U.S. Patent Agency, in 1850, to provide technical help and legal advice to inventors. A Washington, D.C., branch was added in 1859. By 1900 more than 100,000 inventions had been patented thanks to Scientific American.

Artificial intelligence (AI) is advancing by leaps and bounds, with exciting new applications launching every day. But a blockchain-based startup believes the transition from an "information economy" to an "intelligence economy" will prove a bumpy ride for many people. Large corporations have built most of the common AI systems that many people use today. CEN says this centralization has the danger of monopolizing AI – leaving most of us out of the loop. According to CEN, this is not the only problem.

The rising use of deep learning and other big-data algorithms has led to an increasing demand for hardware platforms that are computationally powerful, yet energy-efficient. Due to the amount of data parallelism in these algorithms, high-performance 3D manycore platforms that incorporate both CPUs and GPUs present a promising direction. However, as systems use heterogeneity (e.g., a combination of CPUs, GPUs, and accelerators) to improve performance and efficiency, it becomes more pertinent to address the distinct and likely conflicting communication requirements (e.g., CPU memory access latency or GPU network throughput) that arise from such heterogeneity. Unfortunately, it is difficult to quickly explore the hardware design space and choose appropriate tradeoffs between these heterogeneous requirements. To address these challenges, we propose the design of a 3D Network-on-Chip (NoC) for heterogeneous manycore platforms that considers the appropriate design objectives for a 3D heterogeneous system and explores various tradeoffs using an efficient ML-based multi-objective optimization technique. The proposed design space exploration considers the various requirements of its heterogeneous components and generates a set of 3D NoC architectures that efficiently trades off these design objectives. Our findings show that by jointly considering these requirements (latency, throughput, temperature, and energy), we can achieve 9.6% better Energy-Delay Product on average at nearly iso-temperature conditions when compared to a thermally-optimized design for 3D heterogeneous NoCs. More importantly, our results suggest that our 3D NoCs optimized for a few applications can be generalized for unknown applications as well. Our results show that these generalized 3D NoCs only incur a 1.8% (36-tile system) and 1.1% (64-tile system) average performance loss compared to application-specific NoCs.

The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.

"I'm used to bad news," Merkel said, according to a participant's recollection. The German chancellor had just returned from China, where she spent a day in the Shenzhen tech hub visiting companies like ICarbonX, an artificial intelligence (AI) startup focused on disease detection. A trained physicist, Merkel had been impressed by what she saw. The money and manpower China poured into AI left the 64-year-old with little doubt that the country viewed the technology as its key to becoming a global superpower. "We really do have to walk the extra mile to make sure we're not left behind" -- Jörg Bienert, president of a new association representing more than 50 AI startups Germany, by contrast, had no plan for AI. So on her return to Berlin, Merkel met the country's top 32 AI experts at the chancellery to hear how the country was doing. Their assessment was sobering: Germany, they said, has a good track record in AI research, but it suffers from problems ranging from brain drain to a weak record in transforming basic research into real-world applications that are hampering its ability to compete in a new technology race. After three hours, Merkel left concerned -- and made her worries public a month later. "For centuries, or let's say since the age of Enlightenment, we in Europe were used to being the first ones to come up with technological innovations," she told a tech conference.

It was 2014, around the time when Travis Kalanick referred to Uber as his chick-magnet "Boober" in a GQ article, that I'd realized congestion in San Francisco had gone insane. Before there was Uber, getting across town took about ten minutes by car and there was nowhere to park, ever. With Boober in play, there was parking in places there never were spaces, but the streets were so jammed with empty, one-person "gig economy" cars circling, sitting in bus zones, mowing down bicyclists whilst fussing with their phones, still endlessly going nowhere, alone, that walking across the city was faster. To be fair, you wouldn't know there were 5,700 more vehicles a day on our roads if you'd just moved here. Nor if you were pouring Uber-delivered champagne over yourself in a tub of stock options while complaining about San Francisco's homeless from the comfort of your company-rental Airbnb where artists or Mexican families once lived.

Some of the best known examples of artificial intelligence are Siri and Alexa, which listen to human speech, recognize words, perform searches and translate the text results back into speech. But these and other AI technologies raise important issues like personal privacy rights and whether machines can ever make fair decisions. As Congress considers whether to make laws governing how AI systems function in society, a congressional committee has highlighted concerns around the types of AI algorithms that perform specific – if complex – tasks. Often called "narrow AI," these devices' capabilities are distinct from the still-hypothetical general AI machines, whose behavior would be virtually indistinguishable from human activity – more like the "Star Wars" robots R2-D2, BB-8 and C-3PO. Other examples of narrow AI include AlphaGo, a computer program that recently beat a human at the game of Go, and a medical device called OsteoDetect, which uses AI to help doctors identify wrist fractures. As a teacher and adviser of students researching the regulation of emerging technologies, I view the congressional report as a positive sign of how U.S. policymakers are approaching the unique challenges posed by AI technologies.

Payroll, which includes employees and their salaries or wages, can be defined as the amount of money a company pays its workers. However, the process is not that simple. Human resource details like employee benefits, salaries and records all fall under payroll, and organizing each employee's file can be difficult. But as technology advances, payroll is becoming a more seamless process. This is great news for small businesses that struggle to manage human resources.

Dr. Carol Reiley's achievements are too long to list. She co-founded Drive.ai, a self-driving car startup that raised $50 million in its second round of funding last year. Forbes magazine named her one of "20 Incredible Women in AI," and she built intelligent robot systems as a PhD candidate at Johns Hopkins University. But when she built a voice-activated human-robot interface, her own creation couldn't recognize her voice. Dr. Reiley used Microsoft's speech recognition API to build her interface.