We can use unsupervised machine learning to identify which drugs are associated with which adverse events. Specifically, machine learning can help us to create clusters based on gender, age, outcome of adverse event, route drug was administered, purpose the drug was used for, body mass index, etc. This can help for quickly discovering hidden associations between drugs and adverse events. Clustering is a non-supervised learning technique which has wide applications. Some examples where clustering is commonly applied are market segmentation, social network analytics, and astronomical data analysis.

Click here to discover dozens of free data science and machine learning related books. Also, most of the upcoming Data Science 2.0 book is available for free here. An earlier version, Data Science 1.0 (also free, somewhat outdated) can be found here. How can data scientists stay abreast and viable with all the tools, languages, skills, machine learning etc..? Each of these can be specializations taking decades to master.

Online Web Based Business showed first retail, and then, many other sectors, that Internet'location' was rapidly becoming more important than the physical location in attracting business. As Internet based business expands and transforms with IoT then understanding the concept of'interactions', rather than the format of IT based'Transactions', becomes necessary. The Enterprise orchestrates IoT event'Interactions' from the market into an the fastest optimum response using its internal IoT assets is the new competitive winner. Each stage of development of Internet based capabilities has increased the focus on winning business through smarter competitive Internet connected interactions. The recognition of the differences between activities and technologies of the so-called'Front Office' versus the'Back Office' become even more apparent with the introduction of IoT.

In my column this week at Above the Law, I have an exclusive first look at a new product that uses machine learning to help prevent mistakes in drafting legal documents. Called jEugene Compass, the product will be formally announced tomorrow morning. What separates it from other proofreading programs for lawyers is that it incorporates machine learning. It can learn acceptable usages and language from prior documents you have created. You can also train it to apply specified rules and to alert you to specific issues.

LONDON--(BUSINESS WIRE)--According to the latest market study released by Technavio, the global artificial intelligence in education market is expected to grow at a CAGR of more than 39% during the forecast period. This research report titled'Global Artificial Intelligence in Education Market 2016-2020' provides an in-depth analysis of the market in terms of revenue and emerging market trends. This market research report also includes an up to date analysis and forecasts for various market segments and all geographical regions. "High student dropout rates coupled with macro-economic pressure to strengthen the general education levels is pushing educational institutions to adopt mechanisms to improve learning quality. Institutions are investing in artificial intelligence technologies, such as augmented reality and virtual reality, to positively impact the education industry," says Jhansi Mary, a lead analyst at Technavio for education technology research.

Artificial intelligence has been widely hyped for its potential to transform a broad swath of industries, from cybersecurity to medicine. Now, we might start to get a clearer picture for how it could be used to change the way we shop. Macy's announced Wednesday that it has teamed up with IBM Watson to use artificial intelligence as a customer service tool in 10 of its stores. The retailer dubbed the pilot program "Macy's On Call," and it will allow customers to type in questions on their phones and receive answers. Unlike some chatbots that can only regurgitate preprogrammed responses based on keywords, IBM Watson will learn over time to give better answers that are customized to individual stores.

Modern music gets a lot of flack for sounding mass-produced, particularly genres like pop and country. Flow Machines is a project funded by European Research Council, and has created Artificial Intelligence tools that can compose music. The project's FlowComposer tool draws on a database of over 13,000 leadsheets, basic scores of the melodies and harmonies of tracks, ranging from the Beatles to Broadway hits. The tool can compose songs from this data automatically, or be used as a tool by composers, allowing musicians to create tracks that are a blend between automatic compositions and their own original work. While the concept of music partially composed by AIs may be frightening, particularly for music purists like myself, Flow Machines says the project doesn't want to replace human creativity and musicality.

If you are a strong-armed NFL quarterback who reads defenses like genre fiction, a movie star whose name alone can open a film in China, or a stock picker who beats Buffet every time, congratulations: you are almost as valuable as a data scientist or machine learning engineer with a PhD from Stanford, MIT, or Carnegie Mellon. At least it seems that way. Every company in Silicon Valley -- increasingly, every company everywhere -- is frantically competing for those human prizes, in a human resources version of a truffle hunt. As businesses now realize that their competitiveness relies on machine learning and artificial intelligence in general, job openings for those trained in the field well exceed all the people in the world who aren't locked up by Facebook, Google, and other superpowers. But what if you could get the benefits of AI without having to hire those hard-to-find and expensive-to-woo talents?

Several machine learning based investment models are already deployed and functioning. Some of them focus on technical analysis and apply investment models based upon daily fluctuations in the market. Having the ability to observe patterns, provide quick feedback, and adjust course, these algorithms take positions, and exit from positions, while accumulating trading profits and simultaneously learning from each transaction. Then there are other models that are based upon the fundamentals of the stock. The one that I found of great interest is the one developed by SignalFire.

Robots' potential to take over the world is a commonly expressed fear in the world of AI, but at least one Turing Award winner doesn't see it happening that way. Rather than replacing mankind, technology will create a new kind of human that will coexist with its predecessors while taking advantage of new tech-enabled tools. So argued Raj Reddy, former founding director of Carnegie Mellon University's Robotics Institute and 1994 winner of the Turing Award, at the Heidelberg Laureate Forum in Germany last week. "I could not have predicted much of what has happened in AI," Reddy told an audience of journalists at a press conference. "Four or five things happened in AI in the last decade that I didn't think would happen in my lifetime," including achievements in language translation and AI's triumph at the game of Go.