The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.

LONDON -- The government is putting cutting edge technologies such as artificial intelligence (AI), 5G wireless internet, "smart" energy technology, and robotics at the heart of its new post-Brexit industrial strategy. Theresa May is set to launch the government's "Modern Industrial Strategy" on Monday at a regional meeting of the cabinet in the North West. The Prime Minister announced in a release on Sunday evening that the strategy would be focused around ten key strategic pillars, the first of which is: "Investing in science, research, and innovation." The government says that a £4.7 billion increase in research & development (R&D) funding announced in last year's Autumn Statement is central to the new industrial strategy. This investment will go to areas such as AI, "smart" energy technology, robotics, and 5G wireless, the only specific industries name-checked in the release.

Artificial intelligence is affecting everything from automobiles to health care to home automation and even sports. It's also going to have a measurable impact on software development, with developers becoming more like data scientists, an AI official with Nvidia believes. AI and deep learning will mean changes in how software is written, said Jim McHugh, vice president and general manager for Nvidia's DGX-1 supercomputer, which is used in deep learning and accelerated analytics. The long-standing paradigm of developers spending months simply writing features will change, he explained. With the advent of AI, data is incorporated to create the insight for software.

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Don't have the budget 23% Don't own or have access to the required data 8% Once enterprises overcome these obstacles, Forrester concludes, they stand to gain from AI driving accelerated transformation in customer-facing applications and developing an interconnected web of enterprise intelligence.

Every year at CES, there is a slew of announcements from automakers about their latest connected car or their next futuristic concept. However, one thing missing was the chipmakers that make these things possible. Last year gave us a glimpse of what these companies were doing, but this year, and all announced major automotive design wins, partnerships and products. Each company has a different approach to automotive wtih a different focus on the self-driving, connected car of the future and how they plan to enable it. At CES 2017, Intel's Automated Driving Group launched its first automotive-branded Intel GO solutions.

The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.

Tiny drones could scout high-rise buildings and underground tunnels for possible threats to US troops in cities of the future. But instead of spending years cooking up the necessary drone technologies in military research labs, the Pentagon might be better off shopping for the latest civilian drones coming soon to stores. US military leaders have discussed the need for a new generation of scout drones for some time. After all, kicking down doors is a dirty and dangerous business for US troops trying to clear enemy-held buildings. It would be far safer to deploy diminutive drone buddies to provide an initial peek inside, and identify any potential threats.

With another tsunami of hype about the wonders artificial intelligence can bring, IT professionals would be wise to seek higher ground until they can sort out exactly how machine learning business applications can help them. As DevOps is slowly taking over the IT landscape, its vital that IT pros understand it before jumping right into the movement. In this complimentary guide, discover an expert breakdown of how DevOps impacts day-to-day operations management in modern IT environments. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

Python is a popular language for machine learning. It is even the most popular one according to a study of mine recently published here and on KDnuggets. The above study generated quite a few reactions on social media. I just recently switched to Scala. Somewhat similar to python but with a number of advanced concepts.