The insurance sector is one of the most old-fashioned and resistant to change -- so artificial intelligence will have an even greater effect. The insurance sector is one of the most old-fashioned and resistant to change, and this is why AI will have a greater impact on that with respect to more receptive industries. The collection of data of new types (i.e., unstructured data such as reports, images, contracts, etc.) and the use of new algorithms are disrupting the sector in several ways. This is a really simplistic representation of the insurance business in the last fifty years, and I am aware that insurance experts might disagree with me in many different ways. There are a couple of further features to be pointed out: first of all, insurance has historically been sold not bought, which means that brokers and agents were essential to tracking new customers and to even retain old ones. In addition, it is an industry which is by definition rich of data because they collected anything they could, but is also one of the less advanced because either many of those data are unstructured or semi-structured, or the model used are quite old and simple.

Artificial intelligence (AI) on its own isn't enough to compete – companies need industry-specific solutions to business problems. So said Martin Schroeter, IBM's senior vice-president and chief financial officer, on the company's quarterly earnings call. Cognitive computing technology (IBM's term for AI) is just "table stakes," said Schroeter, claiming that his company is going the extra mile. IBM is building datasets for Watson to serve specific industries, including healthcare and finance. "You need more than public data or algorithms to solve real-world problems," Schroeter said.

Clear link found between AI adoption and business growth. Infosys has released its multinational research findings on the business impact of Artificial Intelligence, market maturity and expectations. A clear link has been shown to exist between a company's revenue growth and its AI maturity, with those reporting faster revenue growths more likely to be ahead in AI adoption. The report also found that AI is perceived as a long-term strategic priority in innovation. The majority of respondents, totalling to 85 percent, aim to train employees on the benefits and use of AI.

Scientists have created what they say is a vaccine against the kind of fake and hyper-partisan news that is spreading quickly across Facebook. The researchers claim that by showing people lies, they can teach them to better see the truth. The solution works in a similar way to a real vaccine – exposing people to a small amount of the problem to help them better respond to larger amounts of it. The solution could help social networks and news organisations battle against fake news, which has been credited with helping the vote for both Brexit and for Donald Trump. The study claims that if people are shown well-established facts about climate change and then lies about it, the latter will cancel out the former.

The field of machine learning arose somewhat independently of the field of statistics. As a result, machine learning experts tend not to emphasize probabilistic thinking. Probabilistic thinking and understanding uncertainty and variation are hallmarks of statistics. By the way, one of the best books about probabilistic thinking is Nate Silver's The Signal and The Noise: Why So Many Predictions Fail But Some Don't. In the medical field, a classic paper is David Spiegelhalter's Probabilistic Prediction in Patient Management and Clinical Trials.

Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. Specialists describe deep learning as akin to a rocketship that needs a really big engine (a model) and a lot of fuel (the data) in order to go anywhere interesting. To get a better understanding of the issues involved in building compute systems for deep learning, I spoke with one of the foremost experts on this subject: Greg Diamos, senior researcher at Baidu. Diamos has long worked to combine advances in software and hardware to make computers run faster.

It's no secret that marketers and search optimization pros can be a bit obsessed with Google's zoo of algorithm updates. We've weathered the Penguins and Pandas that thankfully stopped old hacks from working these past couple of years; but what about what the next algo Zebra or Walrus, or maybe that bigger Elephant in the room affecting search traffic – personalization? This one's even bigger and less cuddly than the two that came before. The thing we should really be aware of, however, is the fact that Google's latest animal doesn't have a name because personalization is invisible. Still, I'm going to call it Elephant because that just feels right to me.

What makes a cartoon caption funny? As one algorithm found: a simple readable sentence, a negation, and a pronoun--but not "he" or "she." The algorithm went on to pick the funniest captions for thousands of the New Yorker's cartoons, and in most cases, it matched the intuition of its editors. Algorithms are getting much better at understanding language, and we are becoming more aware of this through stories like that of IBM Watson winning the Jeopardy quiz. Google released the word2vec tool, and Facebook followed by publishing their speed optimized deep learning modules.

The best view is always from the top. This holds true for the constellation of satellites hurling millions of pixels in images down to earth every day. In this age of breathtaking photography advancements, from drones and action cameras to submersible mobile phones, what has changed for an almost 60-year-old technology? It turns out that deep learning, i.e. an advanced machine learning technique that develops multilayered mathematical structures to classify images, is changing the way computers interpret large sets of satellite data. DigitalGlobe, a Colorado-based satellite imagery provider that supplies to such clients as Google Maps, is using a cloud-based, geospatial big data (GBD) platform to intelligently extract relevant images and points of interest from a vast compendium of above-earth photos.

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 release says that the £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 and robotics, Number 10 says.