Verily and its sister company Google Research are developing an AI-powered test for heart disease that analyses retinal imagery. Verily is Google's standalone healthcare division, and has embarked on many collaborations since being spun out in 2015, and AI is emerging as one of the most exciting areas in digital health. The paper, which is yet to be peer reviewed, describes a technology that analyses imagery of the inner surface of the back of the eye to identify signs of heart disease. The system was originally trained using images from 284,335 heart disease patients, during which time it identified features specific to the disease through machine learning. The test was then validated using two independent datasets from 12,026 and 999 patients respectively. According to the authors of the paper, the system could accurately identify a number of heart disease-related risk factors, such as smoking status, HbA1c levels, blood pressure, and past major cardiac events as well as age and gender.

Jimoh Ovbiagele is the Chief Technology Officer & co-founder of ROSS Intelligence. He is a self-taught programmer, starting at the age of 10, who founded several startups in college and worked on self-driving cars. When he was 21, Jimoh came up with the idea for and co-founded ROSS Intelligence. Two years later, he was named by the American Bar Association as a Legal Rebel and by Forbes as one of their 30 Under 30. He speaks around the world -- from Canada to China -- about artificial intelligence and the future of law.

Breathlessly promoting its signature brand -- Watson -- IBM sought to capture the world's imagination, and it quickly zeroed in on a high-profile target: cancer. But three years after IBM began selling Watson to recommend the best cancer treatments to doctors around the world, a STAT investigation has found that the supercomputer isn't living up to the lofty expectations IBM created for it. It is still struggling with the basic step of learning about different forms of cancer. Only a few dozen hospitals have adopted the system, which is a long way from IBM's goal of establishing dominance in a multibillion-dollar market. And at foreign hospitals, physicians complained its advice is biased toward American patients and methods of care. STAT examined Watson for Oncology's use, marketing, and performance in hospitals across the world, from South Korea to Slovakia to South Florida. Reporters interviewed dozens of doctors, IBM executives, artificial intelligence experts, and others familiar with the system's underlying technology and rollout. The interviews suggest that IBM, in its rush to bolster flagging revenue, unleashed a product without fully assessing the challenges of deploying it in hospitals globally. While it has emphatically marketed Watson for cancer care, IBM hasn't published any scientific papers demonstrating how the technology affects physicians and patients. As a result, its flaws are getting exposed on the front lines of care by doctors and researchers who say that the system, while promising in some respects, remains undeveloped. "Watson for Oncology is in their toddler stage, and we have to wait and actively engage, hopefully to help them grow healthy," said Dr. Taewoo Kang, a South Korean cancer specialist who has used the product. At its heart, Watson for Oncology uses the cloud-based supercomputer to digest massive amounts of data -- from doctor's notes to medical studies to clinical guidelines. But its treatment recommendations are not based on its own insights from these data.

Features Tutorials Opinions News Meetings Jobs Academic Tweets Image of the week Features 277 Data Science Key Terms, Explained Top 10 Machine Learning Use Cases: Part 1 Search Millions of Documents for Thousands of Keywords in a Flash Cartoon: Future Machine Learning Class Data Science: (not) the preferred nomenclature Tutorials, Overviews Visualizing Cross-validation Code A Vision for Making Deep Learning Simple Detecting Facial Features Using Deep Learning What we learned labeling 1 million images Next Generation Data Manipulation with R and dplyr Learning Machine Learning… with Flashcards Using GRAKN.AI to Detect Patterns in Credit Fraud Data Opinions Closing the Insights-to-Action Gap Connecting the dots for a Deep Learning App Are physicians worried about computers machine learning their jobs? News New books on Data Science and Machine Learning from Chapman & Hall/CRC Press - Save 20% Top Stories, Aug 28-Sep 3: Python Overtakes R in Data Science, Machine Learning; 277 Data Science Key Terms WCAI Analytics Accelerator Challenge KDD Cup 2018 Call for Proposals Meetings What data has to teach us about deep learning? Crunch Data Engineering Conf., Budapest, Oct 18-20 Global AI Conference, New York City, October 23-24 Learn from experts at Netflix, Facebook, Tesla, DeepMind ... at Deep Learning/AI Assistant Summits, San Francisco, Jan 25-26 Upcoming Meetings in AI, Analytics, Big Data, Data Science, Machine Learning: September 2017 and Beyond Jobs Adobe: Sr. Data Science Engineer Academic U. of Tulsa: Assistant/Associate Professor of Business Analytics Top Tweets Top KDnuggets tweets, Aug 23-29: Python overtakes R, becomes the leader in #DataScience, #MachineLearning; I built a #chatbot in 2 hours Image of the week KDnuggets Cartoon: Future Machine Learning Class Visualizing Cross-validation Code A Vision for Making Deep Learning Simple Detecting Facial Features Using Deep Learning What we learned labeling 1 million images Next Generation Data Manipulation with R and dplyr Learning Machine Learning… with Flashcards Using GRAKN.AI to Detect Patterns in Credit Fraud Data Closing the Insights-to-Action Gap Connecting the dots for a Deep Learning App Are physicians worried about computers machine learning their jobs? Are physicians worried about computers machine learning their jobs? What data has to teach us about deep learning?

Expectations for artificial intelligence (AI) are sky-high, but what are businesses actually doing now? The goal of this report is to present a realistic baseline that allows companies to compare their AI ambitions and efforts. Building on data rather than conjecture, the research is based on a global survey of more than 3,000 executives, managers, and analysts across industries and in-depth interviews with more than 30 technology experts and executives. The gap between ambition and execution is large at most companies. Three-quarters of executives believe AI will enable their companies to move into new businesses.

The success of artificial intelligence (AI) algorithms hinges on the ability to gain easy access to the right kind of data in sufficient volume. Put more simply, AI depends on good data. Even Google--which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business--makes no bones about the critical role of data in AI. Peter Norvig, Google's director of research, has said: "We don't have better algorithms, we just have more data." Companies increasingly realize that data is critical to their success--and they are paying striking sums to acquire it. Microsoft's US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data. Think, for example, of Oracle pursuing an M&A-led strategy for its Oracle Data Cloud data aggregation service, or IBM buying, within the past two years, both The Weather Company and Truven Health Analytics. Early returns for companies making such investments are promising. Still, to unlock the full value of AI algorithms, companies must have access to large data sets, apply abundant data-processing power, and have the skills to interpret results strategically.

When Norman Borlaug, the father of the green revolution, won the Nobel Prize in 1970, the Nobel Committee remarked that "more than any other single person of this age, he has helped provide bread for a hungry world." Borlaug's introduction of disease resistant high-yielding crop varieties and advanced agricultural practices was a game changer, as agriculture yields increased tremendously and helped save millions from starvation. Half a century after Borlaug received the Nobel Prize, we live in a world where yield growth is plateauing and the total land under cultivation is decreasing. Changing weather patterns and water availability is altering productivity in certain agricultural regions. At the same time, world population continues to grow and is projected to reach at least 9 billion people by 2050, much of the growth is clustered in developing countries, where rapid economic expansion is allowing for increased calorie availability and consumption with an increased demand for protein.

The authors believe that, while the study offers novel approaches for forensics, the work has serious implications for data privacy, deidentification and adequately informed consent. The team concludes that much more public deliberation is needed as more and more genomes are generated and placed in public databases. For the IRB approved study, 1,061 ethnically diverse people ranging in age from 18 to 82 participated by having their genomes sequenced to an average depth of at least 30x. Researchers also collected phenotype data in the form of 3D facial images, voice samples, eye and skin color, age, height, and weight. The team predicted eye color, skin color and sex with high accuracy, but other more complex genetic traits proved more difficult.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Consumers also have the option of managing devices through smart home hubs such as those offered by Samsung SmartThings, Amazon Echo, and Google Home, which allow owners to monitor any type of connected device through a single interface. However, ReportLinker found that few respondents – just 9% – say they use such hubs. This could change, however, if it becomes more convenient to control home devices from your smartphone. Both Google and Apple have developed all-in-one apps that enable consumers to operate multiple smart devices right from a single app on a smartphone or tablet.