Called IQcast, the feature tells users whether they have a low, medium or high chance of dropping below the target blood glucose range within the next one to four hours. These individual-specific predictions are generated by analyzing data collected through Sugar.IQ app and the Guardian Connect device. The Sugar.IQ app is currently available in the App Store for free download. The FDA-cleared app uses IBM Watson Health's AI and analytics tools to help users see how their glucose levels change during the day, and includes a smart food logging system, motivational insights, a glycemic assistant, a data tracker and a glycemic insights feature. Hypoglycemia -- defined by the American Diabetes Association as a blood glucose level lower than 70 mg/dL -- can lead to symptoms ranging from lightheadedness and lethargy to vision impairment and seizures.

The firm's chief strategy officer on how artificial intelligence is changing the game.

Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize that this scheme of writing is suboptimal in memory utilization and introduces redundant computation. To validate our hypothesis, we derive a theoretical bound on the amount of information stored in a RAM-like system and formulate an optimization problem that maximizes the bound. The proposed solution dubbed Uniform Writing is proved to be optimal under the assumption of equal timestep contributions. To relax this assumption, we introduce modifications to the original solution, resulting in a solution termed Cached Uniform Writing. This method aims to balance between maximizing memorization and forgetting via overwriting mechanisms. Through an extensive set of experiments, we empirically demonstrate the advantages of our solutions over other recurrent architectures, claiming the state-of-the-arts in various sequential modeling tasks.

Companies from a wide range of industries use machine learning data to do everyday business. From consumer marketing and workforce management to healthcare treatment decision solutions and public safety and policing solutions, whether you realize it or not your life is increasingly more affected by the outcomes of machine learning algorithms. Machine learning algorithms make decisions like who gets a bonus, a job interview, whether or not your credit card limit (or interest) is raised, and who gets into a clinical trial. Machine learning algorithms even help make decisions about who gets parole and who languishes in prison. The result is that people's lives and livelihood are affected by the decisions made by machines.

Do you want to learn how you can accelerate your AI strategy or get ahead of the latest AI trends? Or are you more curious to learn what results businesses are achieving by adopting AI? Either way, make sure you attend Think 2019 and experience Watson AI technology first-hand. Here's a sneak peek at five sessions you can't miss: Being able to explain the decisions your AI makes and have trust in them is crucial to accelerating adoption of AI in your business. In these sessions, you'll learn how AI OpenScale provides businesses with confidence in AI decisions and infuses AI throughout its full lifecycle with trust and transparency, explains outcomes, and automatically mitigates bias. However, there are still a variety of hurdles businesses need to overcome to scale and automate their AI.

Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider disease manifestations and provides physicians with treatment solutions from similar previous cases for reference. The proposed DSS supports natural language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease classification with the help of the ontology.

IBM has launched a unit designed for human resources to better find talent and recruit using artificial intelligence. The company's HR effort, dubbed IBM Talent & Transformation, includes select Watson AI-based services that can help HR become a growth engine to enable digital transformation. AI can be used to revamp workflow, employee engagement, recruitment and retention while providing a more diverse workforce, the company says. The Watson Talent Suite rolls up behavioral science, AI, and psychology and applies it to HR. Components include Watson Career Coach, a virtual coach that provides advice for career paths, and Watson Candidate Assistant, which looks through the history of job seekers and matches them with openings. These services were developed for IBM's internal HR team and the company claims it drove $107 billion in benefits in 2017 with better employee satisfaction.

Content provided by IBM with Insider Studios. In the summer of 1956, 10 scientists and mathematicians gathered at New Hampshire's Dartmouth College to brainstorm a new concept Assistant Professor John McCarthy called "artificial intelligence." According to the original proposal for the research project, McCarthy--along with fellow organizers from Harvard, Bell Labs and IBM--wanted to explore the idea of programming machines to use language and solve problems for humans while improving over time. It would be years before these lofty objectives were met, but the summer workshop is credited with launching the field of artificial intelligence (AI). Sixty years later, cognitive scientists, data analysts, UX designers and countless others are doing everything those pioneering scientists hoped for--and more.

Computers aren't going to replace creative pros -- but machine learning and artificial intelligence can be powerful tools in the storytelling process. The 60-second spot was directed by Oscar-winner Kevin Macdonald, working from a script that was developed by IBM's Watson AI system. To produce the spot for the Lexus ES executive sedan launching in Europe, the automaker enlisted its creative agency, The&Partnership London, along with technical partner Visual Voice. The agencies collaborated with the IBM Watson team to use AI to analyze 15 years' worth of footage, text and audio for car and luxury brand campaigns that have won Cannes Lions awards for creativity, as well as a range of other external data. Watson identified elements common to award-worthy commercials that were "both emotionally intelligent and entertaining," according to IBM.

The elite team of engineers and medical specialists assembled by IBM's Watson Health division had the innocuous code name "Project Josephine," but its mission could not have been more urgent: to fix the artificial intelligence software at the core of the company's campaign to tackle the $7 trillion global health care market. The predicament faced by IBM officials, STAT has found, was that it could not get its software to reliably understand and analyze language in patient medical records. That was critical for the company to deliver on multimillion-dollar contracts with hospitals and drug companies. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is a premium subscription that delivers daily market-moving biopharma coverage and in-depth science reporting from a team with decades of industry experience.