There is no dearth of articles around the web comparing and contrasting data science terminology. There are all sorts of articles written by all types of people relaying their opinions to anyone who will listen. So let me set the record straight, for those wondering if this is one of those types of posts. I think that, while there may be an awful lot of opinion pieces defining and comparing these related terms, the fact is that much of this terminology is fluid, is not entirely agreed-upon, and, frankly, being exposed to other peoples' views is one of the best ways to test and refine your own. So, while one may not agree entirely (or even minimally) with my opinion on much of this terminology, there may still be something one can get out of this.

Iris Capital is a pan-European venture capital fund manager specializing in digital economy. In such a world, many information platforms are available, but there is no software tool that applies the latest in machine and deep learning. Come to us to present us the next generation software tool or platform that automatically detects the right innovative teams/companies depending on who's looking for it and how innovation is defined. Our pitching competition is aimed at international early-stage start-ups between 1 and 5 years of existence. The 5 to 10 best start-ups will be evaluated on stage by a jury made of Iris Capital investors, large corporate innovation VP, leading start-up CEOs and media agencies.

With the massive growth of big data and the value of those data, machine learning is quickly becoming a technology that every organization should tap into. It has the ability to positively affect everything from analytics to customer relationship management (CRM). Many organizations are interested in these CRM improvements--as they should be. Machine learning in CRM can allow organizations a much more intimate look at their customer than has ever been possible in the past. Here are three ways in which machine learning can improve CRM.

Developing robust quantitative trading strategies is an intensive, rigorous, time-consuming process with no guarantee for success. In this webinar, you will learn how to apply techniques from the Artificial Intelligence and machine learning fields to improve the quantitative strategy development process and maximize your chances of success with every strategy. Attendees will learn practical applications that they can apply to their own trading and will come away with a strategy they can actually trade live. Attendees should have a basic understanding of quantitative and algorithmic trading. No programming experience is required.

What an utterly interesting time to be alive. We all remember how Deep Blue defeated Kasparov. Chess became a'solved problem' pretty soon after that historic event. And in the past week, we have witnessed an even more amazing feat: AlphaGo beating world-class Go player Lee Sedol. The game that knows more positions than there are atoms in the universe was no match for Google's DeepMind team.

You can find many additional references here (Python, Excel, Spark, R, Deep Learning, AI, SQL, NoSQL, Graph Databses, Visualization, etc.) as well as here, here, and here.

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Inbox by Gmail has a few useful features but probably the most interesting is Smart Reply, which was expanded to the desktop this week. As impressive as the A.I.-powered suggestions for replies to your emails are, they're just not very useful yet. Sure, Smart Replies tend to offer relevant options, but they're not very useful in most real-world cases. Open an invitation to party and you might get'Sounds good!,' 'Can't wait,' and Sorry, I can't be there!' as the suggestions. By the time I've added'Hi Bob,' and a few other bits of personalization, I might as well have written the whole thing myself.

Artificial intelligence isn't an industry so much as a technology poised to transform business across a wide variety of sectors--and probably more than you think. During a panel discussion Tuesday at the Las Vegas tech trade show CES, a group of A.I. experts talked about which industries are the most ripe for adoption and application of A.I., and why entrepreneurs and consumers alike stand to benefit significantly from the technology. The number one industry set be transformed by A.I. appears to be healthcare, with 400 million invested by health care companies in the technology as of last year, a figure that's projected to grow to 3 billion or more by 2020, according to data from the Beacon Center for the Study of Evolution in Action. The retail industry is close behind, with 100 million invested as of 2015, expected to reach 1.9 billion by 2020. Panelists pointed to manufacturing, financial services and government as the three followers to healthcare and retail.

The Go contest between Lee Sedol and AlphaGo, Google DeepMind's Go-playing computer program, was broadcast across South Korea. Scrambling to respond to the success of Google DeepMind's world-beating Go program AlphaGo, South Korea announced on 17 March that it would invest 863 million (1 trillion won) in artificial-intelligence (AI) research over the next five years. It is not immediately clear whether the cash represents new funding, or had been previously allocated to AI efforts. But it does include the founding of a high-profile, public–private research centre with participation from several Korean conglomerates, including Samsung, LG Electronics and Hyundai Motor, as well as the technology firm Naver, based near Seoul. "Thanks to the'AlphaGo shock', we have learned the importance of AI before it is too late" The timing of the announcement indicates the impact in South Korea of AlphaGo, which two days earlier wrapped up a 4–1 victory over grandmaster Lee Sedol in an exhibition match in Seoul.