This blog has been talking a lot about Machine Learning (ML) with regard to tabular data. That makes sense because predictive algorithms based on tabular data are often easy to implement and have a lot of potential to improve outcomes. Also, we have access to a lot of tabular data from the EHR. However, ML is capable of doing a lot more than predicting probabilities on tabular data, and there are incredible opportunities in other areas of healthcare. One in particular is in Radiology and Pathology departments.

I published a post about the current status of "Data Scientist" in Japan, as a periodic follow-up analysis since two years ago. Its trend still remains, but it's beyond my anticipation at that time. Indeed growing trend of "Artificial Intelligence" in Japan is steeper than that in English, and "Data Scientist" is now getting to be forgotten by people, although in the global market data scientist is still a major role spreading data science including both statistics and machine learning across industries. Although I did not explicitly mention in the post, I guess that Japanese people may think that data scientist is a professional for statistical analysis although artificial intelligence engineer is one for machine learning or artificial intelligence as a misleading technology. Two years ago, already I've had seen some disappointment at "Data Science" powered by statistics and supported by data scientists.

We know that we can use artificial intelligence (AI) for trading stocks, and some hedge funds are making a killing in this space. In order to feed these algorithms, there are generally two types of financial data you can use; fundamental and market data. If you think about all the data related to a stock that describes the way it trades, you're thinking about market data. Using this data to make trades would be referred to as "technical trading" because it ignores the fundamentals behind stocks. People that do technical trading are often thought of as speculators by investors who invest based on fundamental data, things like profitability, revenues, valuations, etc.

As more of our lives move into the digital sphere, data has become incredibly valuable. There's so much digital information floating around that commentators have hailed the beginning of an era of'big data'. This basically refers to huge datasets that are much larger than traditional collections of information. This info has been generated by growing digitisation, especially from online financial transactions and social media. It's a never-ending paradox – the more digital society becomes, the more data there is. . .

With innovations augmenting in every sector, there comes an imperative need for banking and commerce to reinvent the customer experiences in the financial services. Artificial intelligence (Al) in this regard, plays a crucial role, where they could extend the creative problem solving capabilities and productivity of human workforce and deliver superior business results. In the same context, BW Businessworld in collaboration with Wipro Limited and Intel, brought together industry experts, for an exclusive discussion and set the stage for a dialogue on'Artificial Intelligence and Machine Learning in Banking' on 15th Dec at JW Marriott Sahar, Mumbai. Leading a way forward, the forum discussed the possibilities that Al has opened across businesses. The evening was commenced by Mr Surajit Roy, BFSI Vertical Head - IME, Wipro Ltd, who welcomed the participants and the delegates, setting the tone for discussion with the recent influx of digitalisation and Al in the sector.

Two summers ago, Magna International Inc. and Royal Bank of Canada brought together some of Canada's brightest entrepreneurs and innovators to brainstorm what Canada needs to foster the development of emerging technologies and tech companies. Fast forward to today and we are on the cusp of the next great technological revolution, driven by artificial intelligence (AI). The term may conjure up visions of science-fiction movies with apocalyptic outcomes, but the reality is we are already living in a world infused with AI, from music and movie recommendation services, to driver assistance systems in cars, to virtual personal assistants in our phone or in our home. Over the past few years, the pace of technological innovation and exploration of AI has increased exponentially, with futuristic ideas such as self-driving cars becoming tangible products of today. At the recently concluded World Economic Forum in Davos, Switzerland, one of the major themes centred on AI as the Fourth Industrial Revolution.

Artificial intelligence (AI) is all the buzz this year. According to CB Insights, as of June 15, this year, more than 200 AI venture financing deals have been completed already totaling $1.5B in dollar volume. If the latter half of the year continues at this pace, 2016 will be a record year. Based on analysis by CB Insights, most of the deals being done are at a series B or C stage, indicating that startups in this space are beginning to see success. AI, as most people now know, has several applications in health technologies, marketing & sales, business analysis and financial services.

This article has been updated as of September 30, 2016 to reflect new research and industry progress in the artificial intelligence market. Wall Street, venture capitalists, technology executives – all have important reasons to understand the growth and opportunity in artificial intelligence, but the inherent vagueness of the term makes any single valuation extremely difficult. Indeed, the term "artificial intelligence" is notorious for having a relatively amorphous definition, itself. In order to put together an executive brief for market size and projected growth of AI, I've molded this article around (a) AI-related industry market research forecasts, and (b) a limited number of reputable research sources for further insight into AI valuation and forecasting, in addition to select and relevant quotes. Bear in mind that different market research firms define "artificial intelligence" according to varying criteria.

Artificial intelligence startups raised $5.02 billion worldwide in 2016 to hit a five-year high, as application of the technology expands from online services and finance to such far-ranging fields as agriculture. This trend is likely to continue through 2017, as investors scout out promising technologies and talent. AI-related startup funding rounds totaled 658 last year, up from 493 rounds in 2015, when funding reached $3.12 billion, according to U.S.-based research firm CB Insights. So-called mega-rounds are also increasing. For example, Texas-based StackPath, a startup offering AI-based cybersecurity services, took in $180 million.

Japanese video game designer Hideo Kojima worked for Konami for three decades, so it was a bit of a shock when he officially exited the company in 2015. In the wake of his exit, there were reports about a corporate restructuring that did not include him as one of the directors. This week, the renowned game designer and director has finally opened up about the severance of his ties with Konami. In an interview with BBC Radio 1's Newsbeat, Kojima may have hinted at the reason behind his exit from Konami. "I have more freedom now because the final decision comes down to me," he said.