At the Common Good in the Digital Age tech conference recently held in Vatican City, Pope Francis urged Facebook executives, venture capitalists, and government regulators to be wary of the impact of AI and other technologies. "If mankind's so-called technological progress were to become an enemy of the common good, this would lead to an unfortunate regression to a form of barbarism dictated by the law of the strongest," he said. In a related but contextually different conversation, this summer Joy Buolamwini testified before Congress with Rep. Alexandria Ocasio-Cortez (D-NY) that multiple audits found facial recognition technology generally works best on white men and worst on women of color. What these two events have in common is their relationship to power dynamics in the AI ethics debate. Arguments about AI ethics can wage without mention of the word "power," but it's often there just under the surface. In fact, it's rarely the direct focus, but it needs to be. Power in AI is like gravity, an invisible force that influences every consideration of ethics in artificial intelligence. Power provides the means to influence which use cases are relevant; which problems are priorities; and who the tools, products, and services are made to serve. It underlies debates about how corporations and countries create policy governing use of the technology.
The sustained success random forests has led naturally to the desire to better understand the statistical and mathematical properties of the procedure. Lin and Jeon (2006) introduced the potential nearest neighbor framework and Biau and Devroye (2010) later established related consistency properties. In the last several years, a number of important statistical properties of random forests have also been established whenever base learners are constructed with subsamples rather than bootstrap samples. Scornet et al. (2015) provided the first consistency result for Breiman's original random forest algorithm whenever the true underlying regression function is assumed to be additive. Despite the impressive volume of research from the past two decades and the exciting recent progress in establishing their statistical properties, a satisfying explanation for the sustained empirical success of random forests has yet to be provided.
Washington: Researchers have developed trained AI agents capable of adopting human design strategies. Big design problems require creative and exploratory decision making, a skill in which humans excel. When engineers use artificial intelligence (AI), they have traditionally applied it to a problem within a defined set of rules rather than having it generally follow human strategies to create something new. The findings were published in the -- ASME Journal of Mechanical Design. This research considers an AI framework that learns human design strategies through observation of human data to generate new designs without explicit goal information, bias, or guidance.
More and more cities are looking to go green. And renewable energy is, if current trends hold, the future of the energy industry. But as renewable energy technologies like wind farms are implemented at larger scales than ever, local officials are running into their limitations. The energy production of wind farms is hard to predict, and this makes energy grid design difficult. Experts hope that machine learning can be applied to renewable energy to solve this problem.
The American Civil Liberties Union (ACLU) is pressing forward with a lawsuit involving the facial recognition software offered by Amazon and Microsoft to government clients. In a complaint filed in a Massachusetts federal court, the ACLU asked for a variety of different records from the government, including inquiries to companies, meetings about the piloting or testing of facial recognition, voice recognition, and gait recognition technology, requests for proposals, and licensing agreements. At the heart of the lawsuit are Amazon's Rekognition and Microsoft's Face API, both facial recognition products that are available for customers of the companies' cloud platforms. The ACLU has also asked for more details on the US government's use of voice recognition and gait recognition, which is the automated process of comparing images of the way a person walks in order to identify them. Police in Shanghai and Beijing are already using gait-analysis tools to identify people.
The symptoms of angina--the pain that occurs in coronary artery disease--do not differ substantially between men and women, according to the results of an unusual new clinical trial led by MIT researchers. The findings could help overturn the prevailing notion that men and women experience angina differently, with men experiencing "typical angina"--pain-type sensations in the chest, for instance--and women experiencing "atypical angina" symptoms such as shortness of breath and pain-type sensations in the non-chest areas such as the arms, back, and shoulders. Instead, it appears that men and women's symptoms are largely the same, say Karthik Dinakar, a research scientist at the MIT Media Lab, and Catherine Kreatsoulas of the Harvard T.H. Chan School of Public Health. Dinakar and his colleagues presented the results of their HERMES angina trial at the European Society of Cardiology's annual congress in September. Their research is one of the first clinical trials accepted at the prestigious conference to use machine learning techniques, which were used to characterize the full range of symptoms experienced by individual patients and to capture nuances in how they described their symptoms in a natural language exchange.
China is imposing curfews and regulations on video game playing minors. China is imposing curfews and regulations on video game playing minors. Chinese officials are cracking down on youth online gaming, which they say negatively affects the health and learning of minors. Official guidelines released Tuesday outline a new curfew and time restrictions for gamers under 18. Six measures were outlined in the guidelines, aimed at preventing minors "from indulging in online games."
In the spring of last year, cybersecurity researcher Takeshi Sugawara walked into the lab of Kevin Fu, a professor he was visiting at the University of Michigan. He wanted to show off a strange trick he'd discovered. Sugawara pointed a high-powered laser at the microphone of his iPad--all inside of a black metal box, to avoid burning or blinding anyone--and had Fu put on a pair of earbuds to listen to the sound the iPad's mic picked up. As Sugawara varied the laser's intensity over time in the shape of a sine wave, fluctuating at about 1,000 times a second, Fu picked up a distinct high-pitched tone. The iPad's microphone had inexplicably converted the laser's light into an electrical signal, just as it would with sound.
AI is quickly revolutionizing the security camera industry. Several manufacturers sell cameras which use deep learning to detect cars, people, and other events. These smart cameras are generally expensive though, compared to their "dumb" counterparts. The data for the events would then be published to an MQTT topic, along with some metadata such as confidence level. OpenCV is generally how these pipelines start, but [Martin's] camera wouldn't send RTSP images over TCP the way OpenCV requires, only RTSP over UDP.
Machine learning and artificial intelligence are integral components of any modern organization's IT stack but these data-harvesting tools can have a dark side if appropriate risk management and planning protocols aren't in place. There's no denying the power and possibilities created by AI and machine learning. With this astounding power to build models designed to improve the efficiency and performance of everything from marketing and supply chain to sales and human resources comes considerable responsibility. A recent McKinsey report sheds some light on how companies in every industry should be wary of assuming that these relatively new and remarkably complex tools will always deliver the desired outcome as they're integrated with other applications and processes. These tools are just like every other tool that's ever existed: they're only as good as the people designing and using them.