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.

Agilience mission is to find people and stories that matter to you, to your community, to your business. Their ambition is to nurture communities by pointing out key authorities who inspire and educate, and they do with Agilience Authority Index. Here we review their list of top 10 social media authorities in Artificial Intelligence and Machine Learning. The most trusted #MachineLearning brand in the world. Driven by the crazy idea that #DataScience must be simple & profitable.

The ability for machines to "visually understand the world around them" is the final piece of the puzzle needed for true artificial intelligence, according a researcher at one of the Australian Research Council's centres of excellence. "For an artificially intelligent agent or a robot to be able to operate in its environment, it needs to be able to do three things," said Stephen Gould, chief investigator at ARC's Centre of Excellence in Robotic Vision. "The first thing is to be able to see, the second thing is to be able to think, and the third thing is to be able to act." Gould said that we've long possessed the algorithms required for AI to think and reason, and make decisions. "And the machines that they're reasoning on are getting faster thanks to Moore's Law, and we now have distributed computing and specialised architecture that can run these algorithms," he added.

Artificial intelligence is a $15 billion dollar industry and growing. With more than 2,600 companies developing intelligent technology, the value of AI is expected to rise to more than $70 billion by 2020. And it's not just attracting the tech giants: USAA is using AI to protect its users from identity theft, and Under Armour has connected its health app, MyFitnessPal, to IBM Watson so users can get a more thorough read of their health. For designers, that represents a major business opportunity. But AI is also a challenge requiring every strength and skill they've learned and many they haven't.

Over the next decade, mobile, the Internet of Things, machine learning, robotics, and blockchain technologies will change a great deal about how the oil and gas industry works. Five technologies will change the oil and gas industry: mobile will speed oilfield transactions, increase efficiency, and improve safety by removing people from harm's way; the Internet of Things (IoT) will reduce the cost of repairs; machine learning will provide ever more optimal solutions to field challenges; robotics will upend the question of who does the work, and blockchain will make contracting faster and smoother than ever before. Adopting these technologies will be a challenge for many in our industry, requiring a change in mind-set. Engineers tend to focus less on investing for the future than on fixing what's broken now, as do companies trying to maximize their return on investment. But investments in these transformative technologies now will mean less to fix in the future, and more time to innovate, operate, and develop resources as fully as possible--which is what we're all trying to do, correct?

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.