H2O is Easy to Use and Deploy • h2o.ai/download and run anywhere, immediately • Client APIs: R, Python, Java, Scala, REST, Flow GUI • Spark (cf. H2O.ai - At the Core of AI 6. Powerful, Scalable Techniques for Deep Learning and AI Win your copy at our booth! H2O Book - Written by the Community 7. H2O.ai Machine Intelligence User Based Insurance WATCH NOW WATCH NOW "H2O is an enabler in how people are thinking about data." "We have many plans to use H2O across the different business units." There is no limit to scaling in H2O." "Working with the H2O team has been amazing." "The business value that we have gained from advanced analytics is enormous." WATCH NOW WATCH NOW 8 9. H2O.ai Machine Intelligence WATCH NOW WATCH NOW Matching TV Watching Behavior with Buying Behavior "Unlike other systems where I had to buy the whole package and just use 10-20%, I can customize H2O to suit my needs." "I am a big fan of open source.

Films have long made us aware of the potential of AI but many of these far-fetched ideas are now becoming a reality. In fact, 2016 may be the year we look back on in decades to come and declare was the time when AI went mainstream. And this suggestion has been supported by the increasing investment pouring into the space this year, with global heavyweights like Twitter buying British startups like Magic Pony Technology. AI is not so much sweeping across our world as seeping into it. A combination of enormous computing power and the latest deep-learning techniques are promising to give us better medical diagnoses, better products, better diets and better lives.

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network.

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Episode Summary: In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative to begin with, so that it's hard not to find traction. Financial firms have also invested heavily in AI in the past, and more are starting to tap into the financial applications of machine learning (ML) and deep learning. This week, we're joined by CEO and Co-founder of Kavout Alex Lu, whose company offers AI trading applications for enterprises and individuals. Lu speaks today about the kinds of patterns that traders now have access to in finance, and he gives examples of ways Kavout and other institutions are using artificial intelligence in stock trading to build better and more personalized products and services.

Scikits are Python-based scientific toolboxes built around SciPy, the Python library for scientific computing. Scikit-learn is an open source project focused on machine learning: classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. On the other hand, it has quite a nice selection of solid algorithms, and it uses Cython (the Python-to-C compiler) for functions that need to be fast, such as inner loops. Among the areas Scikit-learn does not cover are deep learning, reinforcement learning, graphical models, and sequence prediction. It is defined as being in and for Python, so it doesn't have APIs for other languages.

Microsoft is buying a deep learning startup based in Montreal, a global hub for deep learning research. But two years ago, this startup wasn't based in Montreal, and it had nothing to do with deep learning. Which just goes to show: striking it big in the world of tech is all about being in the right place at the right time with the right idea. Sam Pasupalak and Kaheer Suleman founded Maluuba in 2011 as students at the University of Waterloo, about 400 miles from Montreal. The company's name is an insider's nod to one of their undergraduate computer science classes.

When the world's smartest companies such as Microsoft, Google, Alphabet Inc., and Baidu are investing heavily in Artificial Intelligence (AI), the world is going to sit up and take notice. Chinese Internet giant Baidu spent USD1.5 billion on research and development. And as proof of China's strong focus on AI and Machine Learning, Sinovation Ventures, a venture capital firm, invested USD0.1 billion in "25 AI-related startups" in the last three years in China and the U.S. Research shows that although genuine intelligence may still be a bit far off, AI and Machine Learning technologies are still expected to reign in 2017. Try reading up on Microsoft Project Oxford, IBM Watson, Google Deep Mind, and Baidu Minwa, and you'll understand what I am trying to get at. In 2015, Gartner's Hype Cycle for Emerging Technologies introduced Machine Learning (ML), and the graph showed (Figure 1) that it would reach a plateau in 2 to 5 years.

Richard Socher carries a resume that would seem to make him rather attractive to the giants of the internet. He just finished a PhD at Stanford University, where he explored a form of artificial intelligence called "deep learning," teaching machines to recognize images and understand natural language using software that operates a bit like the networks of neurons in the human brain. In recent years, the giants of net--including Google, Facebook, Microsoft, and Baidu--have seized on deep learning as a path to the future of automated computer systems, and they've been hiring researcher after researcher from the relatively small community of academics that specialize in this rather complicated technology. Socher says the big names have knocked at his door--"I had some very, very attractive offers"--but he turned them down. He wanted to start his own company, a company that would build deep learning technologies anyone can use, not just the internet giants. That company is called MetaMind, and it's backed by $8 million in funding from Saleforce.com

Arguably, the open source movement -- the idea that a group of technologists freely contributing their own work and commenting on the work of others, can create a final product that is comparable with anything that a commercial enterprise might create -- has been one of the great innovation catalysts of the technology industry. It's no wonder, then, that a group of Silicon Valley luminaries -- including Elon Musk, Peter Thiel and Reid Hoffman -- have lined up to contribute $1 billion to a new open-source AI project known as OpenAI that is led by Ilya Sutskever, one of the world's top experts in machine learning. For now, we don't really know. The OpenAI website is basically just a single blog post outlining the organization's manifesto and an "About" page detailing all the technologists and engineers working on the project. Thus far, we only have a long announcement from the founding members that they are going to do something amazing.