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With Cambridge-based Watson Health, IBM bets big on health care - The Boston Globe
When Deborah DiSanzo, a veteran Massachusetts health care technology executive, went looking for her next career move, she made what might seem like an unusual choice: IBM Corp. But IBM's shift from computer hardware to software and services has taken the New York company deep into the world of doctors, hospitals, and drug companies. DiSanzo signed on as leader of its new health care division, called Watson Health, and got lucky when IBM decided to plant the unit's headquarters in Kendall Square: no relocation necessary. IBM is making a big bet on health care, and it's doing it here in the technology and life sciences hub of Massachusetts. Since IBM Watson Health was launched in 2015, the company has made four acquisitions worth about 4 billion and forged numerous partnerships with major hospitals, drug makers, and other companies.
Artificial Intelligence, Rise of Agents and The Death of Choice
A.I. is easier than ever to leverage, but not what client-side digital marketing or agency folks are used to. First, it's not much to look at (in fact, it's nothing to look at - it's syntax, vocabulary and diction-based). The technology is different, it's not about pages, apps or ads (NLP Libraries!? Parsers?! And to do it right, it's a very tight combination of technical and creative, medium and message. Moreover, there are practical barriers to just getting started.
Drive.ai brings "emotional intelligence" to self-driving cars
Emerging from stealth mode, self-driving car start-up Drive.ai Drive.ai announced today its first commercial foray, a retrofit kit to make fleet vehicles, from delivery trucks to car services, self-driving. The kit includes a sensor array, computer and an LED sign to communicate with pedestrians and other drivers. Self-driving car technology is being developed by most major automakers, automotive equipment suppliers and start-up companies such as Drive.ai. The intent is to eliminate the 95 percent of fatal car accidents every year that are attributable to human error.
Could hackers tip a U.S. election? You bet.
Reports this week of Russian intrusions into U.S. election systems have startled many voters, but computer experts are not surprised. They have long warned that Americans vote in a way that's so insecure that hackers could change the outcome of races at the local, state and even national level. Multibillion-dollar investments in better election technology after the troubled 2000 presidential election count prompted widespread abandonment of flawed paper-based systems, such as punch ballots. But the rush to embrace electronic voting technology -- and leave old-fashioned paper tallies behind -- created new sets of vulnerabilities that have taken years to fix. "There are computers used in all points of the election process, and they can all be hacked," said Princeton computer scientist Andrew Appel, an expert in voting technologies.
HPE updates Vertica, boosts machine learning features in Haven OnDemand
Hewlett Packard Enterprise announced Tuesday the latest release of its Vertica analytics software, as well as new machine learning capabilities for Haven OnDemand. Vertica focuses on the analytics of structured information, such as data stored in rows and fields. As the latest iteration, Vertica 8, codenamed "Frontloader," introduces a unified architecture and new in-database analytics capabilities. As far and fast as cloud computing is embedding itself into the enterprise, there remain many cloud-resistant applications and services. HPE says Vertica 8 is designed to help businesses extract intelligence from data residing in multiple silos across the datacenter, including on-premise, private, and public clouds, and in Hadoop data lakes.
The Machine Learning Revolution Has Begun, Bringing Predictive Forecasting to Businesses of All Sizes - ERP Software Blog
A revolution that has been quietly brewing in the sphere of machine learning and predictive analytics is taking flight. For customers, the analytics revolution is putting powerful new analytical tools in the hands of financial planners and business managers in every area of the business, enabling more comprehensive, effective, and reliable planning. While the value of machine learning has been known for some time, the advances needed to make the practice accessible to a mainstream audience have been made only recently. As KDnuggets reports,[1] the convergence of three key trends is breaking down the barriers that have impeded the growth and employment of machine learning:[2] The abundance of data enables more features and better machine learning models to be created. Data scientists no longer are needed to manage the machine learning infrastructure or implement custom code.
Machine Learning at Work in the Wind Energy Domain (Channel 9)
With the growing focus on renewable energy, there is a need to accurately forecast energy production. In this video, we explore a typical work flow when forecasting wind energy and wrap up the conversation with possible predictive maintenance use cases for the wind turbines. Although the discussion focuses on wind energy domain, this work can be easily reused with minor tweaks for other renewable energy sources.
The 5 things we've learned about building bots โ Chatbots Magazine
We built BOTbot as an experiment to learn about self-service bot creation platforms, user/bot interaction, bot promotion and management. Below we run down the process we took to creating a code-free MVP, the results from our initial user interactions and how scripted journeys, rails, NLP and AI all have their place in business bots -- dependent on use case. After looking at various'quick&easy' bot platforms we decided to use Chatfuel because: We continue to be impressed with Chatfuel's simplicity and use it for demos and tests. But we don't use Chatfuel for live production bots because: Having chosen the platform to build on we launched BotBot without buttons, optimistically hoping to use NLP to create an open conversational dialogue about chatbots. Limited domains, where a data set is filtered across a handful of axis (e.g.
How to Ace the FAA's New Test and Become a Pro Drone Pilot
KC Sealock had not taken a standardized test since college. But here he was at 39 years old, long black beard flecked with grey, sitting in front of a computer at Jacksonville, Florida's Herlong Air Field, with a proctor peering on from behind a glass door. He spent two hours clicking at multiple choice questions about latitudes and longitudes, Class C airspace regulations, wing load factors, and more--60 in all. Finally, Sealock hovered his mouse hovered over the submit button. "I didn't know if I wanted to click," he says.