In addition to fintech solutions, the Dutch Land Registry also turned to the incorporation of AI with the goal of setting up "cognitive systems to make predictable models" to see how blockchains and AI can operate in the national property scope. Holland's Ministry of Economic Affairs and Climate Policy initiated a national blockchain research project with a special unit in charge of recognizing possible application of ledger technology in the Netherlands.

It's the year 2075 and the newest generation doesn't remember life before AI. Even more frightening, they don't know the meaning of personal privacy – at least not in the way their grandparents remember it. Someone is always watching you, whether it be the government, your employer, insurance companies, the bad date you had last week, or some random hacker. Personalized surveillance is just a fact of life now. Nothing lives or dies without being monitored.

Artificial intelligence technology is now making its way into manufacturing, and the machine-learning technology and pattern-recognition software at its core could hold the key to transforming factories of the near future. While AI is poised to radically change many industries, the technology is well suited to manufacturing, says Andrew Ng, the creator of the deep-learning Google Brain project and an adjunct professor of computer science at Stanford University. "AI will perform manufacturing, quality control, shorten design time, and reduce materials waste, improve production reuse, perform predictive maintenance, and more," Ng says. The term artificial intelligence is used today as something of a catch-all for software that can train itself to perform certain tasks and to get better at those tasks over time, he says. For example, AI is behind the software that identifies your friends' faces in photographs.

The most exciting thing about smart speakers is that they are all so utterly imperfect. Each of the mainstream examples – Amazon's Echos, the Google Home, Sonos's wide range and the new Apple HomePod – packs within it stunning technology that would have been unimaginable just a couple of years ago. But they're also full of downsides, making choosing one a matter of deciding what you want, not simply settling on the best. Deciding is a matter of picking which things you want – and which things you definitely don't. Some sound good, but are terrible at talking back to you.

And for soccer (er, football) fans in the US, Fox Sports will be the TV network responsible for bringing them all 64 games from Russia, at least if they want to watch them in English. But, beyond its broadcast offerings, Fox Sports wants to keep people engaged in the competition in different ways. Aside from its partnership with Twitter, which comes in the form of a show that'll stream live from Russia, Fox Sports has teamed up with IBM to build the ultimate World Cup highlight machine. Powered by Watson artificial intelligence, this video hub lets you create on-demand clips from every FIFA World Cup tournament dating back to 1958. Fox Sports says there are 300 archived matches that Watson is capable of analyzing, which you can filter out by World Cup year, team, player, game, play type or any combination of these.

Soon you could be choosing a computer over a doctor when it comes to a cancer diagnosis. According to a new study, an artificial intelligent (AI) system outperformed dermatologists when it came to diagnosing skin cancer. The computer, a deep learning convolutional neural network (CNN), was trained by a team from Germany, France, and the US, by looking at over 100,000 images of cancerous moles and benign spots. After its training, the scientists put the computer to work by pitting it against 58 dermatologists from 17 countries around the world. After being shown images of different types of moles, the CNN was able to accurately detect skin cancer in 95 per cent of the images.

Grab your beverage of choice and enjoy the read! Do hit reply if you're up for a brainstorming session on use cases, new research or ways to future proof your SaaS or enterprise product by implementing ML where it makes sense. On the current "AI revolution": In a lovely piece, Prof. Michael Jordan of Berkeley explores many of the central tenets driving the excitement around AI today. He makes the case for a new engineering discipline, defines the differences between human-imitative AI (i.e. "The current focus on doing AI research via the gathering of data, the deployment of "deep learning" infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills -- with little in the way of emerging explanatory principles -- tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally-tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. These are classical goals in human-imitative AI, but in the current hubbub over the "AI revolution," it is easy to forget that they are not yet solved."

In 1997, he was the world's number one-ranked chess grandmaster, and considered to be among the best players of all time. But in May that year, he had a match against a powerful artificial intelligence (AI) system designed by IBM. Most experts assumed that Kasparov would win --chess had long been presumed to be one of those complex intellectual pursuits where humans would always be able to beat machines. But over the course of six hard-fought games, and in front of millions watching on television, IBM's AI software came out on top. I find this moment fascinating -- and I'm slightly ashamed to admit that I've spent hours watching each of the games on YouTube. The reason I'm a bit obsessed is that IBM's victory was something of a watershed moment -- a prelude to the AI era we're entering today, in which software is increasingly capable of carrying out tasks that previously required human-level intelligence to do.

These robots are the result of years and years of work in swarm robotics. It's not enough to build a little drone that can do bees' characteristic waggle dance; the researchers needed to create a team of tiny bots that have a hive-mind of their own. They also needed to be able to move, act, and learn as a unit rather than a jumbled mess of machinery. For example, engineers from the University of Graz used artificial intelligence to evolve the robots' behavior to be more like that of real-life bees as the drones themselves became more sophisticated. They also got two robotic swarms to interact with each other, flying around as cohesive units without individuals flying off on their own, or crashing into one another.

Abstract: In this thesis we study a perception problem in the context of autonomous driving. Specifically, we study the computer vision problem of 3D object detection, in which objects should be detected from various sensor data and their position in the 3D world should be estimated. We also study the application of Generative Adversarial Networks in domain adaptation techniques, aiming to improve the 3D object detection model's ability to transfer between different domains. The state-of-the-art Frustum-PointNet architecture for LiDAR-based 3D object detection was implemented and found to closely match its reported performance when trained and evaluated on the KITTI dataset. The architecture was also found to transfer reasonably well from the synthetic SYN dataset to KITTI, and is thus believed to be usable in a semi-automatic 3D bounding box annotation process.