This week, Intel announced the acquisition of machine learning startups Nervana Systems for a rumored 400M. This move is intended to improve Intel's artificial intelligence(AI) and machine learning (ML) capabilities in its next generation architecture. The acquisition follows the news of Apple's acquisition of Seattle-based AI startup Turi for about 200M. These series of M&A events are signaling that we are entering a time of consolidation in the AI and ML markets.

In the musical "Hamilton," George Washington exclaims: "We are outgunned, outmanned, outnumbered, outplanned. We gotta make an all-out stand. Ayo, I'm gonna need a right-hand man." I recently saw this performance and the remark made me think to myself: Like George and his nascent collection of militia trying to defend New York City from a British armada and 32,000 troops, are clinicians about to face an overwhelming onslaught with respect to digital health and cognitive computing? Closer than ever I now wonder if these technologies have matured to a point where a convergence is happening.

Some people got a good laugh when a chatbot was given its own Twitter account – and was then transformed into a Holocaust-denying racist in 24 hours and swiftly taken offline. The Microsoft chatbot – called Tay – is a piece of software which can communicate with others with no human involvement. This bot was equipped with artificial intelligence but was easily manipulated by Twitter users. Teaching a robot works just like drilling an innocent, unknowing child, as artificial intelligence learns from the sum of its experiences, much like human beings. The more often a subject is talked about, opinions expressed, and certain wordings used, the more likely the software is to consider it normal and use it.

Two of today's major business and intellectual trends offer complementary insights about the challenge of making forecasts in a complex and rapidly changing world. Forty years of behavioral science research into the psychology of probabilistic reasoning have revealed the surprising extent to which people routinely base judgments and forecasts on systematically biased mental heuristics rather than careful assessments of evidence. These findings have fundamental implications for decision making, ranging from the quotidian (scouting baseball players and underwriting insurance contracts) to the strategic (estimating the time, expense, and likely success of a project or business initiative) to the existential (estimating security and terrorism risks). The bottom line: Unaided judgment is an unreliable guide to action. Consider psychologist Philip Tetlock's celebrated multiyear study concluding that even top journalists, historians, and political experts do little better than random chance at forecasting such political events as revolutions and regime changes.1 The second trend is the increasing ubiquity of data-driven decision making and artificial intelligence applications. Once again, an important lesson comes from behavioral science: A body of research dating back to the 1950s has established that even simple predictive models outperform human experts' ability to make predictions and forecasts. This implies that judiciously constructed predictive models can augment human intelligence by helping humans avoid common cognitive traps.

The next time you're hired, you might find yourself getting information about payroll, vacations, and expenses by talking to a chatbot instead of consulting a handbook for new employees or talking to someone in HR. A startup called Talla, based in Boston, is working on chatbots designed to help new workers get up to speed and be more productive. The company is using advanced machine learning and natural language processing techniques in an effort to create software that is smarter than the average bot. Talla recently launched a simple prototype bot for managing to-do lists on the workplace communications platform Slack. So far, about 600 companies have added the chatbot to their Slack channel and are using it, says May.

It ain't rocket packs, not quite yet, but it does feel like the future. The smartest brands in history: Google anticipating your search bar entries, Facebook putting together a personalized news feed, Amazon knowing every embarrassing detail of your interest in cookbooks and novelty toilet paper, and Netflix inventing a whole new genre based on your viewing preferences ("Nostalgic Hawaiian Sci-Fi Family Cop Documentaries"). All of these technologies are powered by artificial intelligence: systems based on invisible, complex systems that process more data than has ever been produced -- learning from it, responding, and predicting. As a New York Times story reports, interest in AI in the 1980s created a minor boom. Without any viable business uses, though, it led to an "AI winter."

Build predictive models and discover useful patterns from observed data. Learn how to get started using machine learning tools to detect patterns and build predictive models from your data sets. Build predictive models and discover useful patterns from observed data. Engineers and data scientists work with large amounts of data in a variety of formats such as sensor, image, video, telemetry, databases, and more. They use machine learning to find patterns in data and to build models that predict future outcomes based on historical data.

The Watson Conversation service is a critical component you can use to rapidly deploy a range of bots across a host of channels that will enable your software to build dialog with humans and stimulate conversation. You simply supply your expertise in the form of intents, entities, and well-crafted conversations and the end result is a trained, natural-conversation model that can be integrated into simple chatbots and sophisticated virtual agents designed for mobile devices, messaging platforms, and even robots. In this tour of a simulated, in-vehicle application, you can see how voice commands direct the automobile to perform certain functions such as turning on and off lights and screen wipers. "The application is configured to send signals to the Watson Conversation service. The service, in turn, has been trained to recognize intents and entities that may be uttered by a typical driver. The service is also trained in a dialog flow which allows the service to respond in a natural way to the user's input."

Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post you will discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model.