On Tuesday, in an 8-1 tally, the San Francisco Board of Supervisors voted to ban the use of facial recognition software by city departments, including police. Supporters of the ban cited racial inequality in audits of facial recognition software from companies like Amazon and Microsoft, as well as dystopian surveillance happening now in China. At the core of arguments around the regulation of facial recognition software use is the question of whether a temporary moratorium should be put in place until police and governments adopt policies and standards or it should be permanently banned. Some believe facial recognition software can be used to exonerate the innocent and that more time is needed to gather information. Others, like San Francisco Supervisor Aaron Peskin, believe that even if AI systems achieve racial parity, facial recognition is a "uniquely dangerous and oppressive technology."
Large scale search advertising systems have many challenges in Natural Language Understanding and Computer Vision areas such as query and ads understanding, semantic representation, fast ads retrieval and relevance modeling, product image understanding and product detection. In his insightful talk, Bruce Zhang from Microsoft AI & Research will walk us through these various challenges and share how the Microsoft team has developed and deployed cutting-edge technologies, based on deep learning and ads domain data, in their Ads stack to improve ad quality and increase Revenue Per 1000 search (RPM). In addition, he will also share deep learning techniques used in Bing Ads such as query/ads semantic embedding models and KNN search service, query tagging model, generative models for query rewriting, DNN based query-keyword relevance model, visual product recognition models, product detection and description generation models for Product Ads. Who is this talk for? If your work touches machine learning, this talk is for you.
Deep learning computer vision startup allegro.ai is set to showcase its latest product offering, hosted at the Intel partner booth (booth #307), during the Embedded Vision Summit which will take place in Santa Clara, California on May 20-May 23, 2019. The company's platform and product suite simplify the process of developing and managing deep learning-powered perception solutions - such as for autonomous vehicles, medical imaging, drones, security, logistics and other use cases. The platform enables engineering and product managers to get the visibility and control they need, while research scientists focus their time on research and creative output. The result is meaningfully higher quality products, faster time-to-market, increased returns to scale, and materially lower costs. The company's investors include Robert Bosch Venture Capital GmbH, Samsung Catalyst Fund, Hyundai Motor Company, and other venture funds.
Recently San Francisco passed – in an 8-to-1 vote -- a ban on local agencies to use facial recognition technologies. The move is likely not to be a one-off either. Other local governments are exploring similar prohibitions, so as to deal with the potential Orwellian risks that the technology may harm people's privacy. "In the mad dash towards AI and analytics, we often turn a blind eye to their long-range societal implications which can lead to startling conclusions," said Kon Leong, who is the CEO of ZL Technologies. Yet some tech companies are getting proactive.
Over the next year, the recipients will work on things like a nerve-sensing wearable wristband. Another project seeks to develop a wearable cap that reads a person's EEG data and communicates it to the cloud to provide seizure warnings and alerts. Other tools will rely on speech recognition, AI-powered chatbots and apps for people with vision impairment. This year's grantees include the University of California, Berkeley; Massachusetts Eye and Ear, a teaching hospital of Harvard Medical School; Voiceitt in Israel; Birmingham City University in the United Kingdom; University of Sydney in Australia; Pison Technology of Boston; and Our Ability, of Glenmont, New York. "What stands out the most about this round of grantees is how so many of them are taking standard AI capabilities, like a chatbot or data collection, and truly revolutionizing the value of technology," Microsoft's Senior Accessibility Architect Mary Bellard said in a blog post.
The U.S. Department of Defense (DoD) visited Silicon Valley Thursday to ask for ethical guidance on how the military should develop or acquire autonomous systems. The public comment meeting was held as part of a Defense Innovation Board effort to create AI ethics guidelines and recommendations for the DoD. A draft copy of the report is due out this summer. Microsoft director of ethics and society Mira Lane posed a series of questions at the event, which was held at Stanford University. She argued that AI doesn't need to be implemented the way Hollywood has envisioned it and said it is imperative to consider the impact of AI on soldiers' lives, responsible use of the technology, and the consequences of an international AI arms race.
An artificial intelligence (AI) trained on the photos of a dog, crab, and duck (top) would be vulnerable to deception because these photos contain subtle features that could be manipulated. The images on the bottom row don't contain these subtle features, and are thus better for training secure AI. NEW ORLEANS, LOUISIANA--A hacked message in a streamed song makes Alexa send money to a foreign entity. A self-driving car crashes after a prankster strategically places stickers on a stop sign so the car misinterprets it as a speed limit sign. Fortunately these haven't happened yet, but hacks like this, sometimes called adversarial attacks, could become commonplace--unless artificial intelligence (AI) finds a way to outsmart them.
Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier to entry is the cross-disciplinary approach required to create such tools, requiring knowledge from many people across many fields. We aim to drive the field forward by unpacking that barrier, providing a brief introduction to core concepts and terms that define digital medicine. Specifically, we contrast "clinical research" versus routine "clinical care," outlining the security, ethical, regulatory, and legal issues developers must consider as digital medicine products go to market. We classify types of digital measurements and how to use and validate these measures in different settings. To make this resource engaging and accessible, we have included illustrations and figures ...
Background: We think that AI is all-knowing, but actually it's only partly so. When it comes to the AI behind driverless technology, for instance, sensors can take fantastically granular pictures of streets and hazards of all types, and AI can be fed with the experience of every type of driving situation. But autonomous technology companies still need humans to inform the AI what it's looking at -- to circle things like trees, stop signs and crosswalks. The big picture: The winners are AI companies, which are mostly in the U.S., Europe, and China. The losers are workers in both rich and relatively poor countries, who are paid little.