Facial recognition can log you into your iPhone, track criminals through crowds and identify loyal customers in stores. The technology -- which is imperfect but improving rapidly -- is based on algorithms that learn how to recognize human faces and the hundreds of ways in which each one is unique. To do this well, the algorithms must be fed hundreds of thousands of images of a diverse array of faces. Increasingly, those photos are coming from the internet, where they're swept up by the millions without the knowledge of the people who posted them, categorized by age, gender, skin tone and dozens of other metrics, and shared with researchers at universities and companies. As the algorithms get more advanced -- meaning they are better able to identify women and people of color, a task they have historically struggled with -- legal experts and civil rights advocates are sounding the alarm on researchers' use of photos of ordinary people.

When a syringe-wielding drill thief tried sticking up a Home Depot near Yankee Stadium, police figured out quickly that it wasn't a one-off. A man had also used a syringe a few weeks earlier while stealing a drill at another Home Depot 7 miles (11 kilometers) south in Manhattan. The match, though, wasn't made by an officer looking through files. It was done by pattern-recognition computer software developed by the New York Police Department. The software, dubbed Patternizr, allows crime analysts stationed in each of the department's 77 precincts to compare robberies, larcenies and thefts to hundreds of thousands of crimes logged in the NYPD's database, transforming their hunt for crime patterns with the click of a button.

A project team of Komeito, the junior partner in the Liberal Democratic Party-led ruling coalition, has presented to Foreign Minister Taro Kono its proposals for an international agreement to regulate robotic weapons development. Deployment of lethal autonomous weapons systems, or LAWS, cannot be overlooked in terms of international humanitarian law and ethics, according to the proposals released Monday. Komeito called for agreeing on a document, such as a political declaration or a code of conduct, within the framework of the Convention on Certain Conventional Weapons. Kono said he will refer to the proposals. Ethical issues and military advantages of such weapons have been under discussion within the framework of the convention since 2014.

Thousands of people in Moscow and other Russian cities took to the streets over the weekend to protest legislation they fear could lead to widespread internet censorship in the country. The protests, which were some of the biggest protests in the Russian capital in years, came in response to a bill in parliament that would route all internet traffic through servers in Russia, making virtual private networks (VPNs) ineffective. Critics say the bill would create an internet firewall similar to China's, calling it an online "iron curtain", but advocates claim it is necessary to protect the country from state-backed cyber attacks. We'll tell you what's true. You can form your own view.

Apple acquired Power by Proxi in October 2017. One of the first patent applications surfacing from Power by Proxi under Apple's name showed that their expertise was with an "object detection system." Such a system is crucial on a multi-device charging pad like AirPower. A user may pull their iPhone out of their pocket to charge it on AirPower and accidentally place along with it an aluminum gum wrapper, some loose change or a transit pass with a magnetic strip on the mat which could cause havoc if not a fire if the charging coils are accidentally overheated. Apple has both acquired and filed patents (01 & 02) working on this problem of foreign objects dumped on a charging pad.

Roundup Hello, here's a quick recap on all the latest AI-related news beyond what we've already reported this week. You may have seen news reports that autonomous cars are unlikely to detect pedestrians crossing the road if they have dark skin, and thus run them over. And yes, the internal alarm bells in your head should be going off, as a closer look at the research behind the stories shows all those headlines screaming about racist AI are a little off the mark. The academic paper at the heart of the matter described a series of experiments testing different computer vision models, such as the Faster R-CNN model and R-50-FPN, on images of pedestrians with different skin tones. The study's authors, based at the Georgia Institute of Technology in the US, described how they paid humans to look through the collection of roughly 3,500 photos, and individually tag people in the snaps as either "LS" for light skin or "DS" for dark skin, and then trained the neural networks using this dataset.

Machine learning models often excel in the accuracy of their predictions but are opaque due to their non-linear and non-parametric structure. This makes statistical inference challenging and disqualifies them from many applications where model interpretability is crucial. This paper proposes the Shapley regression framework as an approach for statistical inference on non-linear or non-parametric models. Inference is performed based on the Shapley value decomposition of a model, a pay-off concept from cooperative game theory. I show that universal approximators from machine learning are estimation consistent and introduce hypothesis tests for individual variable contributions, model bias and parametric functional forms. The inference properties of state-of-the-art machine learning models - like artificial neural networks, support vector machines and random forests - are investigated using numerical simulations and real-world data. The proposed framework is unique in the sense that it is identical to the conventional case of statistical inference on a linear model if the model is linear in parameters. This makes it a well-motivated extension to more general models and strengthens the case for the use of machine learning to inform decisions.

And we shouldn't expect the hype to die down any time soon. One day, of course, AI will become just another commonplace technology in our daily lives – much like the smartphone, the internet, the desktop and the pocket calculator all became at various points throughout recent history. But for now, AI is still an exciting new technology that's bursting with as yet unrealized potential, and 2019 will undoubtedly see some astonishing breakthroughs, as well as continued attention and enthusiasm from the media and tech spheres. But what sort of things can we expect? With 2018 now coming to a close, let's take a peep into our crystal ball and start asking what the future holds for AI and the impact it will have on businesses over the next twelve months.

NEW YORK - When a syringe-wielding drill thief tried sticking up a Home Depot near Yankee Stadium, police figured out quickly that it wasn't a one-off. A man had also used a syringe a few weeks earlier while stealing a drill at another Home Depot 7 miles (11 km) south in Manhattan. The match, though, wasn't made by an officer looking through files. It was done by pattern-recognition computer software developed by the New York Police Department. The software, dubbed Patternizr, allows crime analysts stationed in each of the department's 77 precincts to compare robberies, larcenies and thefts to hundreds of thousands of crimes logged in the NYPD's database, transforming their hunt for crime patterns with the click of a button.

The world's biggest consumer electronics show was held last month and wandering around the seemingly endless stalls of emerging new products, it was impossible to avoid the claims of artificial intelligence in some form or another. Some gadgets were, of course, smarter than others. From facial recognition food bowls for your pets to handheld speech recognition and language translation devices, smart tech and self-learning algorithms abound. The actual intelligence of some smart products is debatable but the trend is undeniable.Source:Supplied Encompassing terms including deep learning, machine learning, neural networks and general artificial intelligence which seeks to build computers with a capacity to think and learn like humans, it can be hard to pin down what AI truly means. But it's clearly here to stay.