What better way to create a movie trailer about an artificially enhanced human than to use the reality behind the premise; artificial intelligence (AI). That's just what a partnership between IBM Research and 20th Century Fox recently set out to do, when they used machine learning techniques to produce what they described as the "first ever cognitive movie trailer." You'll have to judge the merits of the result yourself, but what is beyond doubt is this is just one example of the many ways AI and machine learning techniques are already changing the face of the entertainment industry. It's only makes sense that creative industries are leading the pack when it comes to the adoption of and experimentation with AI. Media, entertainment, and advertising are all the on the cutting edge when it comes to the adoption of AI and machine learning.

For those following along here or on my Twitter account it's no secret that I am currently enrolled at Metis in their 12-week data science bootcamp, which marries the structure of daily morning problem solving with highly self-guided and project-based afternoons/evenings/weekends. The expectations are high, and the deadlines are "intentionally unfair", giving the three months a hackathon-lite vibe. Some projects featured on this blog, this post included, accompany projects completed and presented for Metis. For this project I scraped Box Office Mojo in order to build a predictive linear regression model. At first blush, predicting domestic box office gross is hardly worthy of machine learning: instinctively we know it must be a function of increasing marketing and production budgets.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

It seems to me that the hype about AI makes really difficult for experienced investors to understand where the real value and innovation are. I would like then to humbly try to bring some clarity to what is happening on the investment side of the artificial intelligence industry. We have seen as in the past the development of AI has been stopped by the absence of funding, and thus studying the current investment market is crucial to identify where AI is going. First of all, it should be clear that investing in AI is extremely cumbersome: the level of technical complexity goes out of the pure commercial scope, and not all the venture capitalists are able to fully comprehend the functional details of machine learning. This is why the figures of the "Advisors" and "Scientist-in-Residence" are becoming extremely important nowadays.

Alan Turing Institute lends support to'Commission on Artificial Intelligence' published by the Science and Technology Committee. The establishment of an AI ethics board in the UK has taken a big step forward, with the Alan Turing Institute agreeing to work with the UK government to explore the ethics questions surrounding the development of artificial development. In a letter to the Science and Technology Committee, the Alan Turing Institute welcomed the Committee's recent Report on Robotics and Artificial Intelligence and put itself forward as an institution prepared to take a leading role in taking AI forward. The letter to Committee chair Stephen Metcalfe was in response to a report published by the Committee on 12 October 2016, in which it recommended that a'standing Commission on Artificial Intelligence' be established at the Alan Turing Institute to examine the social, ethical and legal implications of recent and potential developments in AI. "Your Report recommends that a standing Commission on Artificial Intelligence be Should this recommendation be taken forward, we would very much welcome the opportunity to lead the creation of the Commission." MP Stephen Metcalfe returned support in kind, saying in response to the Institute's letter: "We welcome the Alan Turing Institute's support for our report on Robotics and Artificial Intelligence and are pleased that, as the UK's new data science research institute, it is ready to lead the standing Commission on Artificial Intelligence that we recommended establishing" Debate surrounding the ethics concerned with AI has been gathering speed in recent times, with Melanie Mitchell recently telling CBR that the AI community is'not very well prepared' when it comes to the ethical issues that come with using AI in life-critical areas.

The bio industry is evolving and tech will play a large part in bio's next chapter. In fact, biology is looking more and more like programming lately, from "digital therapeutics" to "computational biomedicine" and "cloud bio". But how can this help with, for example, cancer? And how does it affect investing in bio startups (hint: software lets you de-risk at every stage). This interview of a16z bio fund general partner Vijay Pande (conducted by David Clark of VenCap International) discusses the challenges -- and possibilities -- of software brought to healthcare startups, including the difference between computer science x bio (as opposed to the'biotech' of yore) and, therefore, why now.

Increasingly becoming a necessity in Ecology and Earth Science research, handling complex data can be a tough nut when traditional statistical methods are applied. As its first publication, the new technologically-advanced Open Access journal One Ecosystem features a review paper describing the benefits of using machine learning technologies when working with highly-dimensional and non-linear data. Natural sciences, such as Ecology and Earth science, focus on the complex interactions between biotic and abiotic systems in order to infer understand these systems and make predictions. Traditional statistical methods can impose unrealistic assumptions that result in unsound conclusions as the era of'big data' meets ecology and earth science. Machine-learning-based methods, capable of inferring missing data and handling complex interactions, are more apt for handling complex scientific data.

Splunk announced new versions of Splunk Enterprise, Splunk IT Service Intelligence (ITSI), Splunk Enterprise Security (ES) and Splunk User Behavior Analytics (UBA). These products leverage machine learning to speed-up and facilitate extracting insights from machine-generated data. Splunk was founded in 2003 and brought "big data" to Wall Street's attention with its 2012 IPO. It has always focused on machine-generated data and its platform captures, indexes and analyzes real-time data in a searchable repository, on premises or in the cloud. Machine learning extends the Splunk platform by adding outlier and anomaly detection, adaptive thresholding and predictive analytics capabilities, applying over 25 commonly-used machine learning algorithms or custom algorithms to build data models that forecast future events.

Imagine one day walking into a gorgeous structure--like LA's famous Walt Disney Concert Hall--only to discover it was designed by a computer system and constructed by automated robotic arms. Ai Build, a London-based startup, aims to pave the way to 3D printing on large scales. The company is equipping industrial-grade Kuka robotic arms with artificial intelligence and "3D printing guns" to 3D print large structures that focus on maximizing efficiency with labor and materials. Founder and CEO Daghan Cam dreamed up the technology while considering traditional commercial construction and wondering what a more efficient and automated process might look like. In October, the company partnered with engineering consulting firm Arup Engineers to debut the 3D printed "Daedalus Pavilion" at the GPU Technology Conference in Amsterdam.

The world of computing has shifted to new platforms and services--like mobile and the cloud--and is promising to head in even bolder new directions, such as virtual reality and augmented reality. So, how does one of the biggest names in computing respond to a changing landscape? To find out, The Wall Street Journal's editor in chief, Gerard Baker, spoke with Microsoft Chief Executive Officer Satya Nadella. Here are edited excerpts of their conversation. BAKER: Your customers are happy.