The government said it was concerned that smart vehicles, which allow drivers to do things such as access maps and travel information, could be targeted by hackers to access personal data, steal cars that use keyless entry systems, or take control of technology for malicious reasons.

Artificial intelligence is all over the news, with tech titans arguing over whether it will be a force for good or bad. An equally important question is whether AI will stratify society even more, and create a world of haves and have nots. AI is already impacting multiple industries and will take over many blue collar and white collar jobs in the years to come. The speed and severity with which this happens are what creates the biggest challenges for the US and countries around the world. Add to this the geopolitical implications, recently outlined in an important op ed by Kai Fu Lee, and even weak AI can be seen as a scary thing.

Retinitis pigmentosa (RP) is a rare genetic disorder that sabotages the retina's ability to respond to light by disabling rod photoreceptors. The National Eye Institute estimates that one in 4,000 people around the world suffer from the disorder. In the UK however, people who have lost their sight to RP may soon have hope as the National Health Service (NHS) funds the implantation of a device among ten patients to help treat this form of blindness. The Argus II Bionic Eye implant works in tandem with a small camera mounted on a pair of glasses that is worn by the patient. Images from the camera are converted into wireless signals via electrodes attached to the retina.

The subjugation of humanity by a race of supersmart, artificially intelligent beings is something that has been theorised by everyone from generations of moviemakers to New Zealand's fourth most popular folk parody duo. But the latest prophet of our cyber-fueled downfall must realise why people would be inclined to take his warnings with a grain of silicon. He is, after all, the same guy who's asking us to turn over control of our cars - and our lives - to a bunch of algorithms. Elon Musk, who hopes that one day everyone will ride in a self-driving, electric-powered Tesla, told a group of governors on Saturday that they needed to get on the ball and start regulating artificial intelligence, which he called a "fundamental risk to the existence of human civilisation." When pressed for better guidance, Musk said the government must get a better understanding of the latest achievements in artificial intelligence before it's too late.

Artificial intelligence and increasing automation is going to decimate middle class jobs, worsening inequality and risking significant political upheaval, Stephen Hawking has warned. In a column in The Guardian, the world-famous physicist wrote that "the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining." He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years and decades. The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines. Technology has already gutted many traditional manufacturing and working class jobs -- but now it may be poised to wreak similar havoc with the middle classes.

The following essay is reprinted with permission from The Conversation, an online publication covering the latest research. As an Artificial Intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb?

This article was posted by Tamis van der Laan. Tamis is a data science and machine learning specialist. In the past few decades, computer systems have achieved a whole lot. They have managed to organize and catalog the information produced by our civilization as a whole. They have relieved us from stringent cognitive tasks and increased our productivity significantly.

In recent years, randomized methods for numerical linear algebra have received growing interest as a general approach to large-scale problems. Typically, the essential ingredient of these methods is some form of randomized dimension reduction, which accelerates computations, but also creates random approximation error. In this way, the dimension reduction step encodes a tradeoff between cost and accuracy. However, the exact numerical relationship between cost and accuracy is typically unknown, and consequently, it may be difficult for the user to precisely know (1) how accurate a given solution is, or (2) how much computation is needed to achieve a given level of accuracy. In the current paper, we study randomized matrix multiplication (sketching) as a prototype setting for addressing these general problems. As a solution, we develop a bootstrap method for {directly estimating} the accuracy as a function of the reduced dimension (as opposed to deriving worst-case bounds on the accuracy in terms of the reduced dimension). From a computational standpoint, the proposed method does not substantially increase the cost of standard sketching methods, and this is made possible by an "extrapolation" technique. In addition, we provide both theoretical and empirical results to demonstrate the effectiveness of the proposed method.

While it's easier than ever to define and train deep neural networks (DNNs), understanding the learning process remains somewhat opaque. Monitoring the loss or classification error during training won't always prevent your model from learning the wrong thing or learning a proxy for your intended classification task. Once upon a time, the US Army wanted to use neural networks to automatically detect camouflaged enemy tanks. Wisely, the researchers had originally taken 200 photos, 100 photos of tanks and 100 photos of trees. They had used only 50 of each for the training set.

An official from a suburban Minneapolis mosque where an early morning explosion occurred says the blast happened in the imam's office during the first prayer of the day. The Star Tribune reports that Mohamed Omar, executive director of the Dar Al-Farooq Islamic Center in Bloomington, says the center occasionally receives threatening calls and emails. Bloomington police Chief Jeff Potts said Saturday that investigators are trying to determine the cause of the blast. Authorities say the explosion damaged one room but it didn't hurt anyone. Asad Zaman, director of the Muslim-American Association of Minnesota, says the organization is offering a $10,000 reward for information leading to an arrest and conviction.