The Pentagon's oft-repeated line on artificial intelligence is this: we need much more of it, and quickly, in order to help humans and machines work better alongside one another. But a survey of existing weapons finds that the U.S. military more commonly uses AI not to help but to replace human operators, and, increasingly, human decision making. The report from the Elon Musk-funded Future of Life Institute does not forecast Terminators capable of high-level reasoning. At their smartest, our most advanced artificially intelligent weapons are still operating at the level of insects … armed with very real and dangerous stingers. So where does AI exist most commonly on military weapons?

Albert Einstein spent the fall of 1915 overeating and sleeping in. He'd discovered a flaw in his theory of gravity, and competitors circled overhead. So Einstein relaxed a bit, went back to the blackboard and, months later, wrote the equation that rules the universe. More than a century later, today's Einsteins - the developers who've changed our everyday life with apps - also find themselves at an impasse. With the consumer experience evolving through artificial intelligence (AI) in ways big and small - from tastemaking playlists to facial recognition - the enterprise now faces a competitive imperative to integrate AI into the apps their products and services run on.

Artificial intelligence is beginning to make real music. In June, Google shared a machine-generated track created as part of its Project Magenta. Last week, Brian Eno launched a new video that uses A.I. to accompany his 2016 album The Ship. Now, researchers at Sony have unveiled a pair of songs composed through A.I. using the company's Flow Machines software. One, titled "Daddy's Car," is in the style of the Beatles.

Platforms such as these reveal insights from large amounts of unstructured data. That's how Facebook is able to recognize faces in photographs with nearly 98% accuracy. It's this technology that powers much of the managed services technology behind programmatic native advertising. As a matter of fact, Paul Roetzer of PR 20/20 gave a whole presentation on the topic at Content Marketing World. IBM's Watson project has earned just as much media attention as the iPhone 7 announcement – with good reason, too.

On 19 September the chief executive of the Law Society, Catherine Dixon, delivered a keynote address at the International Bar Association conference in Washington DC. The Law Society is the professional body that represents more than 170,000 solicitors in England and Wales. Our vision is to be valued and trusted as a vital partner to represent, promote and support solicitors while upholding the rule of law, legal independence, ethical values and the principle of justice for all. We also fulfil an important public interest function which aims to ensure access to justice, protect human rights and freedoms and uphold the rule of law. As part of our work to develop our strategy, we published a piece of research to identify what the future will bring for the legal profession.

Microsoft CEO Satya Nadella took the stage at his company's massive Ignite conference to lay out his vision for how deep learning and artificial intelligence will transform the company. "AI is at the intersection of our ambitions," Nadella said, noting how it will allow us "to reason over large amounts of data and convert that into intelligence." He likened AI to the arrival of books and the web and joked that we will soon create so much data that "we are getting to a point where we don't even know what to name things." "In this information explosion, what remains scarce is human intention and time -- our ability to make sense out of all of this information," he said. At Microsoft, this transformation is currently happening in a few different areas, with agents like Cortana, applications like SwiftKey and Office 365, and developer tools and platforms like the Cortana Intelligence Suite and the Azure cloud computing platform.

All those cars on California's famously gridlocked highways could be doing more than using energy. They could be producing it. The California Energy Commission is investing 2 million to study whether piezoelectric crystals can be used to produce electricity from the mechanical energy created by vehicles driving on roads. The commission is choosing a company or university to take on small-scale field tests. It will study how the small crystals, which generate energy when compressed, could produce electricity for the grid if installed under asphalt.

In the Summer of 2015 a team of hackers attempted to take control of an unmanned military helicopter known as Little Bird. The helicopter, which is similar to the piloted version long-favored for US special operations missions, was stationed at a Boeing facility in Arizona. The hackers had a head start: At the time they began the operation, they already had access to one part of the drone's computer system. From there, all they needed to do was hack into Little Bird's onboard flight-control computer, and the drone was theirs. When the project started, a "Red Team" of hackers could have taken over the helicopter almost as easily as it could break into your home Wi-Fi. But in the intervening months, engineers from the Defense Advanced Research Projects Agency had implemented a new kind of security mechanism--a software system that couldn't be commandeered.

Investigators eager to uncover the genetic basis of autism could now have hundreds of promising new leads thanks to a study by Princeton University and Simons Foundation researchers. In the first effort of its kind, the research team developed a machine-learning program that scoured the whole human genome to predict which genes may contribute to autism spectrum disorder (ASD). The results of the program's analyses -- a rogue's gallery of 2,500 candidate genes -- vastly expand on the 65 autism-risk genes currently known. Researchers have recently estimated that 400 to 1,000 genes underpin the complex neurodevelopmental disorder. This newest research provides a manageable, "highly enriched" pool from which to pin down the full suite of ASD-related genes, the researchers said.

Machine learning applications for NBA coaches and players might seem like an odd choice for me to write about. Let us get something out of the way: I don't know much about basketball. However, one of the perks of working in data science and machine learning is that I can still say somewhat insightful things about sports, as long as I have data. So with that expertise I'll weigh in to offer some machine learning applications for basketball. During a conversation with my good colleague Ray Wright, who does know quite a bit about basketball and had been looking at historical data from NBA games, we suddenly realized something about player shooting.