To complement existing offerings like Sparkling Water and Steam, H2O.ai is releasing Deep Water, a new tool to help businesses make deep learning a part of everyday operations. Deep Water will open up new possibilities for the TensorFlow, MXNet and Caffe communities to engage with H2O.ai. This also means that the GPU is set to become a greater part of business operations for the entire Fortune 500, not just tech companies. SriSatish Ambati, CEO of H2O.ai, says his company has found a sweet spot with predictive analytics. Ambati gave me the example of an insurance provider using H2O to analyze images of roofs and provide insights for preventative maintenance.

A year ago, in a report on Big Data and investment management, Citi Business Advisory Services predicted that "with the improved volume, velocity and variety of data inherent in the big data approach, the innovation seen in systematic trading models over the past decade could accelerate." One of the platforms highlighted in the Citi report was DataSift, a service that promises to "integrate social, blog and news data in a single place." Or as Citi put it, DataSift aggregates "marquee data source partners, including Edgar Online, Wikipedia, and WordPress." Edgar, of course, is consistent with old-fashioned ideas of what hedge fund managers through various third parties should keep track of. But Wikipedia presence on this short list might pull up short those who still think of it as a pastime for nerds who like to think of themselves as editors.

WorkFusion, the leading provider of intelligent automation, has closed a $35 million Series D financing round led by Georgian Partners with participation from existing investors Mohr Davidow Ventures, iNovia, Nokia Growth Partners (NGP), Greycroft and RTP Ventures, bringing total funds raised to $71 million. The funding will accelerate customer adoption of AI-powered automation as they realize the immense productivity gains from digitizing business processes. "We invested in WorkFusion not only because it aligns with our thesis that AI will transform business, but most importantly because the company has made machine learning practical and powerful for enterprise operations," said Justin LaFayette, Managing Partner at Georgian Partners. "WorkFusion has category leading products and a strong leadership aligned by a powerful vision of helping businesses drive rapid productivity improvements with AI." WorkFusion transforms operations through self-learning, process-level automation that eliminates up to 90% of manual back-office work and AI-powered chat bots that increase front office service center capacity by 5x. "IDC estimates the AI and cognitive systems market will grow from $8 billion in 2016 to over $47 billion in 2020 and businesses are looking to invest in the most innovative and comprehensive automation technologies on the market," said Max Yankelevich, WorkFusion CEO.

It's easy to think of AI as cold, unbiased, objective. Not quite, suggests Narrative Science Chief Scientist Kris Hammond explains, because we never know when AI will repeat our biases back to us. "Just as our biases creep into how we talk to, we train, we teach our children, they creep into the way we talk to, train, and teach our AI systems," says Hammond, also a professor of Computer Science at Northwestern University and founder of the University of Chicago's Artificial Intelligence Laboratory. Narrative Science uses machine learning to turn data into stories that help people better understand the world around them. Its natural language generation platform, Quill, has generated headlines by literally generating headlines: automating the production of earnings reports and sports stories, among other tasks.

LendingRobot, an automated online investment-management service that calls itself a "robo-advisor," is launching a hedge fund that the company says is administered without human intervention, making use of Amazon Web Services' machine learning, compute servers and blockchain technology. Until now, the business of Seattle-based LendingRobot has been focused on helping manage investments in so-called peer-to-peer loans. In such loans, would-be borrowers too risky for banks seek loans from individuals. Companies including Lending Club and Prosper grade loan applications by risk and make them available to individual investors, most of whom spread their risk by investing small amounts in many individual loans. The investment is attractive, as it can yield 8-10 percent interest paid monthly.

The dream of building computers or robots that communicate like humans has been with us for many decades now. And if market trends and investment levels are any guide, it's something we would really like to have. MarketsandMarkets says the natural language processing (NLP) industry will be worth $16.07 billion by 2021, growing at a rate of 16.1 percent, and deep learning is estimated to reach $1.7 billion by 2022, growing at a CAGR of 65.3 percent between 2016 and 2022. Of course, if you've played with any chatbots, you will know that it's a promise that is yet to be fulfilled. There's an "uncanny valley" where, at one end, we sense we're not talking to a real person and, at the other end, the machine just doesn't "get" what we mean.

Is human intelligence heading toward a cyborg-dominated future? In a recent tweet, the Tesla and SpaceX CEO teased that a brain-computer system that links human brains to a computer interface -- a "neural lace" -- may be announced early this year, reported TechCrunch. Musk first mentioned the neural lace concept (the addition of a digital layer of intelligence to the human brain) at Recode's Code Conference last year. The brain-computer system would create a"symbiosis with machines," Musk said, according to TechCrunch. "We're already a cyborg -- I mean, you have a digital or partial version of yourself in the form of your emails and your social media and all the things that you do, and you have basically superpowers with your computer and your phone and the applications that are there," Musk said at the conference.

Publishing data on Kaggle is a way organizations can reach a diverse audience of data scientists with an enthusiasm for learning, knowledge, and collaboration. For Dr. Erin Miller of START, the National Consortium for the Study of Terrorism and Responses to Terrorism, making her organization's Global Terrorism Database available for analysis by Kaggle users has brought new awareness to their cause. In this Open Data Spotlight, Erin discusses how setting aside agendas and focusing on understanding this unparalleled dataset of over 150,000 attack events allows users to undertake constructive analyses that may defy common conceptions about terrorism. Read on to learn more about the Global Terrorism Database project and the ways users of open data can make valuable contributions to the organizations that make them possible. My role started out (more than 12 years ago) as a graduate assistant cleaning raw data, and now I manage the project team, workflow, resources, and interaction with end users and related research projects.

The hysteria surrounding both machine learning and artificial intelligence is substantiating mathematician Alan Turing's assertion that eventually "no one will be able to speak of machines thinking without expecting to be contradicted". Indeed now, the concept of the'rise of robots' is no longer merely a plot in a Hollywood sci-fi blockbuster, but an area being explored by academics, researchers and technologists across the world. Silicon Valley start-up Lumiata, for example, uses AI to map out current and future health trajectories of individuals and provide detailed reasons behind every prediction. Ash Damle, founder and CEO of the company, told BusinessCloud that AI solves an issue of scale. "If we gave physicians enough time to go through all of the data and look for patterns and individuals, they could get some wonderful insight, but the reality is that people are constrained," he said.

New AI vs. humans poker challenge is set to kick off on Wednesday. The poker-playing algorithm humans will be facing this time around was named Libratus. After successfully solving games like chess and even Go, scientists seem bent to crack poker next. The research into an artificial intelligence that could stand up to top-tier poker players has been going on for some time now. The results so far haven't been very encouraging for these scientists, but they aren't giving up.