Germany is gearing up to lay down the ethical foundations for self-driving cars, banning A.I. from making decisions that could harm one group of people over another. The country's transport minister has outlined the basis for future legal guidelines for driverless vehicles, rules that echo Isaac Asimov's three laws of robotics, which manufacturers will be expected to work towards ahead of formal legalization. Dobrindt has created an ethics commission to work out the specifics in terms of regulation, but the above rules will serve as a starting point for future laws. The third rule may seem to suggest that the manufacturer cannot depend on the driver stepping in during an emergency, but Dobrindt indicated that drivers will be expected to have a basic awareness at all times. In practice, this will likely mean sleeping at the wheel is forbidden, but reading a book is allowed.

The Fifa 17 demo has been released, letting players get their first chance to play the game – but just for four days. The download will only be available until 13 September. After that, fans will have no way of getting a go at the game until 29 September, when the full game comes out. The demo allows people to play through a single match of the kind that they'll get when the full game comes out. Players can choose from matches between 12 different teams from around the world, including Chelsea and Manchester City from the UK.

Telefónica Open Future_, Telefónica's startup accelerator that helps the best entrepreneurs grow and build successful businesses, and PAPIs.io Artificial Intelligence (AI) has a track-record of improving the way we make decisions. So why not use it to decide which startups to invest in, and take advantage of all the startup data that is available? The AI Startup Battle, powered by PreSeries (a joint venture between BigML and Telefónica Open Future_), is a unique experience you don't want to miss, where you'll witness real-world and high-stakes AI. As an early stage startup, you will enjoy a great opportunity to secure seed investment, and get press coverage in one of the technology capitals of the world.

The system is called Recognition and will be running for three months in London up to 27 November 2016, both online and in a small exhibition at the Tate. A nice touch is that it will also take in feedback from matching selections made by viewers themselves at the art museum on Millbank. Created by Fabrica, it's the winner of the IK Prize 2016 for digital innovation, awarded by Tate Britain, in partnership with Microsoft. You can see it in action above, in the match of LS Lowry's Industrial Landscape (from 1955) with a recent construction image of Changi Airport in Singapore… Apparently, Recognition employs multiple artificial intelligence technologies. For example, there is natural language processing to interpret image captions and text, analysing context and subject matter.

Welcome to the first in a series of blog posts that is designed to get you quickly up to speed with deep learning; from first principles, all the way to discussions of some of the intricate details, with the purposes of achieving respectable performance on two established machine learning benchmarks: MNIST (classification of handwritten digits) and CIFAR-10 (classification of small images across 10 distinct classes--airplane, automobile, bird, cat, deer, dog, frog, horse, ship & truck). The accelerated growth of deep learning has lead to the development of several very convenient frameworks, which allow us to rapidly construct and prototype our models, as well as offering a no-hassle access to established benchmarks such as the aforementioned two. The particular environment we will be using is Keras, which I've found to be the most convenient and intuitive for essential use, but still expressive enough to allow detailed model tinkering when it is necessary. By the end of this part of the tutoral, you should be capable of understanding and producing a simple multilayer perceptron (MLP) deep learning model in Keras, achieving a respectable level of accuracy on MNIST. The next tutorial in the series will explore techniques for handling larger image classification tasks (such as CIFAR-10).

Just months after Microsoft's Tay artificial intelligence sent racist messages on Twitter, another AI seems to have followed suit. More than 6,000 selfies of individuals who live all over the world and range in ages of 18 to 69 were judged by a robot in a beauty contest last week. But when the results came in, there was something missing - it turned out the robots did not like people with dark skin. The Beauty.AI beauty contest put together of robot judges to determine the winners. Beauty.AI used five algorithms to act as judges in a beauty contest.

What will the future look like, and how will be it be shaped by new and emerging technologies? Well, according to new research from Nesta, 60% of people in the UK believe new technological innovations will improve their future wellbeing. In the run up to Nesta's FutureFest event, which will take place on 17th and 18th September in London's Tabacco Dock, the innovation foundation commissioned market research agency, ComRes, to explore how consumers in the UK feel about emerging technologies, such as augmented reality, driverless cars and artificial intelligence, and whether they believe the future innovations that will be developed within the next 20 years will be beneficial to their lives. Many of the responses were upbeat: almost half of respondents (49%) said DNA sequencing and editing represents a future opportunity for the healthcare sector, while 68% think future technologies will help to improve food production. Around a third (36%) of those surveyed in London said they would be willing to be microchipped in order to secure access to their work devices and homes, while 28% were open to eating meals in pill form and 27% were up for dating a partner they were paired up with through pheromone or DNA-matching.

Whilst I can't fully refute either scenario, I can speculate on the potential for near-future political applications of AI which have the potential to be both disruptive and unintuitively desirable. Automatic, near-instant, ubiquitous fact checking UK fact-checking organisation Full Fact recently released a report that covered the state of various fact checking technologies in use and in development. It covered areas like overall existing semantic data standards in use and their challenges, and trends seem to be that natural language and contextual language recognition is going from strength to strength. The techniques in play include quickly responding to previously debunked claims, data mining statistics on demand and drawing confidence-based correlations from less reliable but plentiful data like news and social media reports. The convergent goal of these various projects appears to be moving fact checking from a post-hoc arm-chair analyst world, to a force that for example, in the middle of a debate or news report, is avalible to instantly challenge, rate or question any given factual statement within seconds of it being said.

I've been putting out more JavaScript courses, playing with new technologies, and I got really deep into React and Redux. Been having a lot of fun with those. Redux introduces a more functional approach to JavaScript state management, which is pretty amazing. I've enjoyed building it into my projects, and it has really simplified things like unit testing for your app's state management. I really appreciate the simplicity of Reducers in Redux. If you're not familiar with Redux, the reducers are regular reducer functions for the purposes of app state management. Using reducers for state management has been really amazing and has totally transformed the way I think about building apps, so I've been really happy with it. I've also played with Angular 2 just a little and I've been playing with TypeScript. I have mixed feelings about both of those so far. Angular 2, compared to React and Redux, feels like it has a lot more overhead and not a lot of benefit. Like writing unit tests for the views in Angular 2 is much more complicated than writing unit tests for things like Pure Components in React. Maybe as I use it more I'll start to "feel" it a little more. In TypeScript, I really like the IDE(Integrated development environment) type hinting that it gives you, and I like the Type Inference capability so you don't have to manually annotate everything, which is fantastic ... an amazing feature. I like that more than the type annotations available with Tern.js, but the problem is that since it needs to see how the types flow through the program, sometimes it will infer types that are a little too strict and you have to go in and manually loosen up the type annotations. Sometimes, it's really hard to do that especially if you use any kind of complicated functional programming techniques, which I tend to do once in a while.

A recent study shows how artificial intelligence (machine learning) could help improve the accuracy of lung diseases diagnosis. The presence of artificial intelligence (AI) in the broad and complex field of medicine has continually improved over time. As a matter of fact, AI's potential to enhance the accuracy and efficiency when it comes to disease diagnostics is simply undeniable. Following the influences of artificial intelligence in the various subspecialties of medicine, a team of researchers developed an AI-based algorithm process that can learn and perform predictive data analysis. According to Tech 2, Belgium's University of Leuven professor and senior study author Wim Janssens explained that the AI algorithm process can provide experts more accurate lung diseases diagnosis by simulating physicians' complex reasoning in a "more standardized and objective way." Based on the study, researchers successively probed how the concept of machine learning could assist them in analyzing the complete lung function tests.