Manoj Saxena is the executive chairman of CognitiveScale and a founding managing director of The Entrepreneurs' Fund IV (TEF), a $100m seed fund focused exclusively on the cognitive computing space. Saxena is also the chairman of Federal Reserve Bank of Dallas, San Antonio branch and Chairman, SparkCognition an Austin based cognitive security and safety analytics company. Prior to joining TEF, Saxena was general manager, IBM Watson, where his team built the world's first cognitive systems in healthcare, financial services, and retail. Earlier he founded, built and sold two Austin based software startups. Saxena will be speaking at Gigaom AI Now in San Francisco, February 15-16th.

Politicians are the lowest form of life on earth. Liberal Democrats are the lowest form of politicians.

When Donald Trump met with technology leaders in December to tell them he wanted them to create jobs in the U.S., their heads probably tilted to the side, as if you tried to explain physics to your dog and she just watched your lips moving and wondered when, among all those unfamiliar sounds, she was going to hear the word treat. They're in business to help us do more with less. They like innovation and disruption and software eating the world. In his own Chance-the-gardener way, the president-elect might be onto something. His victory was a middle-finger salute from those who feel left out by technology and globalization.

It's hard to bet against the continued rise of automation, robots, and artificial intelligence (AI), all of which are already having major impacts on how we work, learn, shop, and play. But being able to predict that robotics and AI will change our lives is a lot easier than predicting they will change our lives. In a recent forecast for 2017 and beyond, for instance, analyst firm IDC said we can expect to continue seeing robotic and AI technologies keep growing more affordable, more capable, and easier to use. The Obama White House said it expects the same, but also warns in a new report that "growth will not be costless" and could harm workers lacking the skills to compete in an AI-driven economy. How the incoming administration plans to address such issues is also uncertain. While President-elect Donald Trump's campaign promised to revive U.S. manufacturing and spend $1 trillion on the nation's infrastructure, he has also tapped Hardee's/Carl's Jr. chief Andrew Puzder -- who supports the use of automation to save on employment costs -- as secretary of the U.S. Department of Labor.

Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model (HASMM): a versatile probabilistic model that is capable of capturing the modern electronic health record (EHR) data. Unlike existing models, the HASMM accommodates irregularly sampled, temporally correlated, and informatively censored physiological data, and can describe non-stationary clinical state transitions. Learning the HASMM parameters from the EHR data is achieved via a novel forward-filtering backward-sampling Monte-Carlo EM algorithm that exploits the knowledge of the endpoint clinical outcomes (informative censoring) in the EHR data, and implements the E-step by sequentially sampling the patients' clinical states in the reversetime direction while conditioning on the future states. Real-time inferences are drawn via a forward-filtering algorithm that operates on a virtually constructed discrete-time embedded Markov chain that mirrors the patient's continuous-time state trajectory. We demonstrate the prognostic utility of the HASMM in a critical care prognosis setting using a real-world dataset for patients admitted to the Ronald Reagan UCLA Medical Center. In particular, we show that using HASMMs, a patient's clinical deterioration can be predicted 8-9 hours prior to intensive care unit admission, with a 22% AUC gain compared to the Rothman index, which is the state-of-the-art critical care risk scoring technology.

While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by only a small set of event information, comprising entity names and dates, the model is leveraged by unsupervised learning of semantic vector representations on extensive text data. We demonstrate applicability to the study of financial risk based on news (6.6M articles), particularly bank distress and government interventions (243 events), where indices can signal the level of bank-stress-related reporting at the entity level, or aggregated at national or European level, while being coupled with explanations. Thus, we exemplify how text, as timely, widely available and descriptive data, can serve as a useful complementary source of information for financial and systemic risk analytics.

AI has been hot in 2016, and it's not cooling off anytime soon. The investments, acquisitions, trials, reorganizations and breakthroughs of the past year have set the AI industry up to have tremendous impact over the next twelve months. We'll stop talking about far-fetched, man-versus-machine Skynet predictions and instead figure out how to harness AI to turn the slag pile of big data into the orderly summit of our dreams. The hype curve will calm down as people realize what AI can do and is doing, and thus form more realistic pictures of what it will do. We'll embrace AI as critical for our economic productivity.

With the advent of drones, robots and self-driving vehicles a recent report released by the White House contended that artificial intelligence could transform the economy. The report lists strategies that will increase the benefits and diminish the costs of AI, helping to create opportunities in cyberdefense and fraud detection. Although the report clearly states that the precise economic impact is difficult to estimate, it highlights five possible effects of an economy driven by AI, including an increased demand for higher technical skills and uneven distribution of the impact across sectors, wage levels, etc. A depiction of AI has often been seen on television shows. In 1962, I sing the Body Electric, an episode of The Twilight Zone, significantly captured the unique concept of AI by presenting a robotic grandmother adjusting smoothly in a human family.

It can read lips and create new food recipes. It can win at chess, Jeopardy and the game Go. Every major technology company appears to be integrating it into how they organize and operate their business. And it seems like just about every new app in existence claims its software uses some sort of machine learning to make life even better. Artificial intelligence is splashed across headlines like never before.

Early on an unusually blustery day in June, Kevin Esvelt climbed aboard a ferry at Woods Hole, bound for Nantucket Island. Esvelt, an assistant professor of biological engineering at the Massachusetts Institute of Technology, was on his way to present to local health officials a plan for ridding the island of one of its most persistent problems: Lyme disease. He had been up for much of the night working on his slides, and the fatigue showed. He had misaligned the buttons on his gray pin-striped shirt, and the rings around his deep-blue eyes made him look like a sandy-haired raccoon. Esvelt, who is thirty-four, directs the "sculpting evolution" group at M.I.T., where he and his colleagues are attempting to design molecular tools capable of fundamentally altering the natural world. If the residents of Nantucket agree, Esvelt intends to use those tools to rewrite the DNA of white-footed mice to make them immune to the bacteria that cause Lyme and other tick-borne diseases. He and his team would breed the mice in the laboratory and then, as an initial experiment, release them on an uninhabited island. If the number of infected ticks begins to plummet, he would seek permission to repeat the process on Nantucket and on nearby Martha's Vineyard. More than a quarter of Nantucket's residents have been infected with Lyme, which has become one of the most rapidly spreading diseases in the United States. The illness is often accompanied by a red bull's-eye rash, along with fever and chills. When the disease is caught early enough, it can be cured in most cases with a single course of antibiotics. For many people, though, pain and neurological symptoms can persist for years. In communities throughout the Northeast, the fear of ticks has changed the nature of summer itself--few parents these days would permit a child to run barefoot through the grass or wander blithely into the woods. "What if we could wave our hands and make this problem go away?" Esvelt asked the two dozen officials and members of the public who had assembled at the island's police station for his presentation. He explained that white-footed mice are the principal reservoir of Lyme disease, which they pass, through ticks, to humans.