San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies, reflecting a growing backlash against a technology that's creeping into airports, motor vehicle departments, stores, stadiums and home security cameras. Government agencies around the U.S. have used the technology for more than a decade to scan databases for suspects and prevent identity fraud. But recent advances in artificial intelligence have created more sophisticated computer vision tools, making it easier for police to pinpoint a missing child or protester in a moving crowd or for retailers to analyze a shopper's facial expressions as they peruse store shelves. Efforts to restrict its use are getting pushback from law enforcement groups and the tech industry, though it's far from a united front. Microsoft, while opposed to an outright ban, has urged lawmakers to set limits on the technology, warning that leaving it unchecked could enable an oppressive dystopia reminiscent of George Orwell's novel "1984."

Chip maker Xilinx on Tuesday held its annual "analyst day" event in New York, where it told Wall Street's bean counters what to expect from the stock. During the event the company fleshed out a little more how it will go after a vibrant market for data center chips, especially those for machine learning. That market is expected to rise to $6.1 billion by 2024 from $1.8 billion in 2020. The focus for Xilinx is a raft of new platform products that take its capabilities beyond the so-called field-programmable gate arrays, or FPGAs, that it has sold for decades. That requires selling developers of AI applications on the notion there's more than just the neural network itself that needs to be sped up in computers.

While blockchain and quantum computing continue to inch up the emerging technologies hype cycle, artificial intelligence (AI) is revolutionising the financial services industry by stealth. Fuelled by the three'V's of big data – velocity, volume and variety – AI tools are being deployed across the bank, from the customer front end to deep in the back office. AI is an umbrella term that covers robotic process automation, or the simple automation of processes such as data entry; natural language processing (NLP), most commonly used in chatbots; and machine learning, as is used in robo-advisory services and credit scoring. To date, much of the focus has been on customer benefits, such as improved experience and more personalised products; however, Cathy Bessant, Bank of America's chief operations and technology officer, believes that the more exciting application areas are in risk management and financial forecasting. "AI gives us the ability to take vast amounts of data and produce forecasts and risk assessments based on changing variables to understand their impact. The opportunity for fast, world-class risk management is huge," she says.

Artificial intelligence (AI) algorithms are generally hungry for data, a trend which is accelerating. A new breed of AI approaches, called lifelong learning machines, are being designed to pull data continually and indefinitely. But this is already happening with other AI approaches, albeit with human intervention. A steady stream of data is the fuel for coveted results. But, with the ever-increasing importance of data, the stakes of data bias are growing ever higher.

With leaders increasingly seeing artificial intelligence (AI) as helping to drive the next great economic expansion, a fear of missing out is spreading around the globe. Numerous nations have developed AI strategies to advance their capabilities, through investment, incentives, talent development, and risk management. As AI's importance to the next generation of technology grows, many leaders are worried that they will be left behind and not share in the gains. There is a growing realization of AI's importance, including its ability to provide competitive advantage and change work for the better. A majority of global early adopters say that AI technologies are especially important to their business success today--a belief that is increasing. A majority also say they are using AI technologies to move ahead of their competition, and that AI empowers their workforce. AI success depends on getting the execution right. Organizations often must excel at a wide range of practices to ensure AI success, including developing a strategy, pursuing the right use cases, building a data foundation, and cultivating a strong ability to experiment. These capabilities are critical now because, as AI becomes even easier to consume, the window for competitive differentiation will likely shrink. Early adopters from different countries display varying levels of AI maturity. Enthusiasm and experience vary among early adopters from different countries. Some are pursuing AI vigorously, while others are taking a more cautious approach.

Punchh, the leader in digital marketing solutions for physical retailers, today announced the launch of Punchh Deep Sentiment Analysis. The new product allows brands to extract valuable insights from customer reviews using Punchh's natural language comprehension engine built with industry-leading deep learning and artificial intelligence. Its natural language processing model achieves human-level performance, defined as more than 93 percent accurate, and features multi-language support. "In today's hyper-competitive climate, brands need to do everything they can to foster and nurture direct customer relationships, and paying attention to customer reviews is an essential part of that," said Shyam Rao, CEO of Punchh. "Manually reading every review is prohibitively time-consuming for most retailers, which leads to slower response times and poor customer experiences. Our solution uses AI and machine learning to help brands analyze reviews at scale and immediately identify critical information so they can focus on high-level insights and make quick decisions to strengthen customer relationships and increase loyalty."

Law firms and legal departments are not taking advantage of artificial intelligence and machine learning tools that could be helping them run more efficiently, according to a new Bloomberg Law survey. Not On Board: Some 54 percent of respondents to the survey said they don't use these tools, while only 23 percent did. Another 24 percent were unsure about whether they used AI and machine learning, which Molly Huie, Bloomberg Law's team leader for data analysis and surveys, said could...

A SIZABLE amount of real estate in the media is dedicated to artificial intelligence (AI) these days - and especially so in Singapore. From news of the government's commitment to AI research, policies delineating its ethical use, to announcements of the next big global firm setting up AI facilities in the Red Dot, the ubiquity of the technology foretells the central role that it is set to play in every aspect of society. At the enterprise level, adoption within day-to-day operations has been steadily ramping up - especially in sectors such as supply chain, where AI-led tech such as Internet of Things (IOT) analytics have shown a clear business case in its ability to substantially boost efficiency. Meanwhile, consumers are increasingly being exposed to AI as well - think, for example, of Google Pixel's AI-powered night sight, which enables photography in the dark. That said, and while it is already an aphorism that businesses need to adopt AI to maintain a competitive edge, it just as important that businesses refrain from regarding AI as one big, shiny, silver bullet.

Thom Golden, senior vice president of data science at Capture Higher Ed, took his family to Chaney's Dairy Barn in Bowling Green, Ky. The experience left him with more than just some premium homemade ice cream and an afternoon of fun in the country. He was able to see firsthand how Artificial Intelligence (AI) can open up new possibilities for a family business. "I've never been a dairy farmer, but I know enough to understand that it's exhausting," Thom says during a recent episode of The Weightlist, Capture's podcast that regularly discusses the areas between data, new technologies and enrollment management. He hosts the podcast with Brad Weiner, director of data science at Capture.

This paper introduces Seeker, a system that allows users to interactively refine search rankings in real time, through feedback in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding space, allowing the user to query using a representation of the item (like a tune for a song, or a picture for an object). However, this approach requires the user to possess an example representation of their desired item. Additionally, most current search systems do not allow the user to dynamically adapt the results with further feedback. On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: "Is this item similar to what you have in mind?" With this assumption, our algorithm allows for users to provide sequential feedback on search results to adapt the search feed. We show that our proposed approach works well both qualitatively and quantitatively. Unlike most previous representation-based search systems, we can quantify the quality of our algorithm by evaluating humans-in-the-loop experiments.