This new offering is available to service providers such as security integrators, telecoms and alarm and monitoring companies for reselling to their customers. IvedaAI includes a powerful self-contained server with artificial intelligence (AI) software, capable of searching a combination of objects from dozens to thousands of cameras in less than one second. Video analytics have been around for many years, but adoption has been slow because of inaccuracies and high cost. IvedaAI employs 30 patents in AI, big data analytics and cloud computing. It applies a deep learning algorithm (trained, not programmed), automates processes and uses natural language.
New article says that to take full advantage of deep-learning solutions in healthcare, the US and China should collaborate, not compete. In a new commentary article, titled'It Takes a Planet', Eric Topol, MD, of Scripps Research and Kai-Fu Li, PhD, CEO of the China-based tech investment firm Sinovation Ventures have argued for more collaboration between China and the US on artificial intelligence (AI) development. This comes in the wake of the US government ordering the AI company iCarbonX in China to divest its majority ownership stake in the Massachusetts-based company PatientsLikeMe. "Chinese academics and companies already have unfettered access to personal health data," they write. "To compete in AI health, US companies will need access to clinical data on a similar scale. How will that be possible if the current isolationist policy continues?"
I recently launched a new research project called RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning. I haven't seen a lot of content on /r/machinelearning that deals with biomedical data, so I thought that this project might be interesting to some of you! RIDDLE uses deep MLPs to estimate race and ethnicity information from electronic medical records. The underlying methodology is not anything new, but I think the application is meaningful. RIDDLE is primarily useful for epidemiology research where race & ethnicity can be powerful confounders.
This article was first posted on The Conversation. The world is full of connected devices -- and more are coming. In 2017, there were an estimated 8.4 billion internet-enabled thermostats, cameras, streetlights and other electronics. By 2020 that number could exceed 20 billion, and by 2030 there could be 500 billion or more. Because they'll all be online all the time, each of those devices -- whether a voice-recognition personal assistant or a pay-by-phone parking meter or a temperature sensor deep in an industrial robot -- will be vulnerable to a cyberattack and could even be part of one.
The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI provided new numbers regarding business leaders' assessment of China as a global AI leader, the current worldwide ranking of China's AI-related entrepreneurial and research activities, plans for AI adoption by U.S. enterprises and expectations regarding its impact on jobs, and the use of AI in face recognition, physical security monitoring, cashierless retail, categorizing open-ended survey responses, and detecting plant diseases and atrial fibrillation. A doctor examines a magnetic resonance image on a computer screen during the CHAIN Cup at the China National Convention Center in Beijing, June 30, 2018. A computer running artificial intelligence software defeated two teams of human doctors in accurately recognizing maladies in magnetic resonance images in a contest that was billed as the world's first competition in neuroimaging between AI and human experts. The U.S. Department of Homeland Security estimates face recognition will scrutinize 97% of outbound airline passengers by 2023 [The Economist] More than 4.5 million websites use reCAPTCHA and the system collects hundreds of millions of daily solves or more than 100 person-years of labor every day; Google/reCAPTCHA has extracted to date over $7 billion of free labor [hcaptcha] The Bureau of Labor Statistics' injury and illness database is built upon text-based descriptions of work-related injuries and illnesses it receives from workplaces across the country each year; categorizing the description into actionable data used to be done manually, but this year, the BLS has done 80% of that automatically using deep neural networks [governmentCIO] The AI market worldwide is estimated to grow by $75.54 billion from 2019 to 2023 [Technavio] The AI market worldwide is estimated to reach $202.57 Data is eating the world quote of the week: "The market for data labeling passed $500 million in 2018 and it will reach $1.2 billion by 2023, according to the research firm Cognilytica. This kind of work, the study showed, accounted for 80 percent of the time spent building A.I. technology"--The New York Times AI is "mimicking the brain" quote of the week: "Computer vision… is nothing like the human sort"--The Economist Robots are eating the world quote of the week: "A human can certainly move a part faster than a cobot [collaborative robot]. However, it does not take coffee breaks and continues to work for several hours after we have already gone home"--Pekka Myller, Ket-Met Robots are eating the world quote of the 19th century: "[A Linotype] could work like six men and do everything but drink, swear, and go out on strike"--Mark Twain