Welcome to Mossberg, a weekly commentary and reviews column on The Verge and Recode by veteran tech journalist Walt Mossberg, executive editor at The Verge and editor at large of Recode. I've been familiar with Siri longer than most people. Way back in 2009 -- two years before Apple incorporated the intelligent digital assistant into the iPhone -- I stood onstage with the inventors of the service while they debuted it at a tech conference I co-produced. At the time, it was just a third-party app on the iPhone App Store. Not long thereafter, Apple bought the company, and the assistant reemerged in 2011 with a splashy introduction as a core feature of the iPhone 4s.

Have you ever wondered what you would look like with Botox or dermal fillers? Practitioners are hoping they will soon be able to give people a more accurate picture of how they might look after going under the needle. Michael Molton at Epiclinic, a cosmetic clinic in South Australia, began developing his 3D imaging technique after becoming frustrated with 2D before-and-after photos. These are used to show prospective clients how a procedure may change their face, but the "after" shots are often enhanced with better lighting and make-up. "I wanted something that you couldn't fudge, like CT or MRI scans that are used in other areas of medicine," says Molton.

A new kind of computer, devised by researchers at Google DeepMind in the U.K., could broaden the abilities of today's best AI systems by giving them an important new feature--a kind of working memory. The researchers show that the computer, which consists of a large neural network connected to a unique form of memory, can perform relatively complex tasks by figuring out for itself what information to hold in its memory. The tasks include figuring out the best way to get from one station to another on London's spaghetti-like Underground transit network, after exploring diagrams of other types of networks and learning about the most salient features. The Google DeepMind researchers call their system a differentiable neural computer. It is differentiable in the sense that its behavior--including what to store in memory--can be learned using the mathematical process, called backpropagation, that underlies the working of neural networks.

An artificial intelligence algorithm has been developed by Google's DeepMind that is capable of working out the most efficient way of getting from one point to another on London's Tube network. The system, known as a differentiable neural computer (DNC), is able to combine basic reasoning with memory in a unique way to solve such problems. "Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from the data," states a paper that details the DNC in the journal Nature. "We show that it can learn tasks, such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs, such as transport networks and family trees." Google's DeepMind gained international media attention earlier this year after it developed the first machine capable of beating a human world champion at the board game Go.

October 9, 2016, 4:54 PM On 60 Minutes Overtime, Charlie Rose explores the labs at Carnegie Mellon on the cutting edge of A.I. See robots learning to go where humans can't

PlayStations are no longer things that sit underneath your TV. Now they get strapped to your face. Sony has released the PlayStation VR, a special headset that represents its first foray into virtual reality gaming. And it might represent many other people's first try with it, too, since it is much cheaper than competing headsets and doesn't require a huge computer system. The PS VR plugs into existing PlayStation 4 consoles with a series of wires.

Alexander the friendly robot visits the Indoor Park to interact with children by telling classic fairy tales, singing and dancing at Westfield London on August 10, 2016 in London, England. For most, Artificial intelligence (AI) – loosely defined as the ability of machines to mimic human intelligence – feels like a known entity borne out of sci-fi and pop culture. However the reality of AI the burgeoning startup sector growing around it is perhaps far more exciting than anything ever dreamed up in Hollywood. AI is a diverse field, mostly dominated by efforts in machine learning that use algorithm frameworks to gather unprecedented amounts of actionable information. Within that field is the subfield of deep learning, which might borrow from fields like computer vision and image recognition to analyze data (text, images, video, speech, music, etc.) in order to properly identify something. Think about a computer seeing millions of pictures of a dog.

Imagine solving mathematical problems where you could use the full physical range of computational possibilities within the laws of the universe, and be inspired by the sublime algorithmic intelligence of the human brain. This is precisely why the emerging field of quantum machine learning (QML) has received so much recent attention. In this blog post, we'd like to discuss the fundamental ideas and applied value of machine learning to computation in general, and then contextualize these ideas in a new way within the paradigm of quantum computation. Machine learning – a subfield of computer science related to computational statistics and pattern recognition – emerged in its modern incarnation in the mid-late 20th century as researchers attempted to build thinking machines. While first-generation artificial intelligence took inspiration from the computers of the 1980s to reason about intelligence and view humans like deterministic, syntactical machines, contemporary artificial intelligence instead chooses to build machines that have the adaptability and variability of human in "coping" with the ill-defined problem of being an individual with incomplete information in a complex world.

Hiring industry has changed very much with the advancement in technology. Today, most Recruiters go through the candidates social profiles(LinkedIn, Facebook, Twitter, Github) along with the Resumes to figure out if the candidate is a good fit for the company. All this sounds like one huge task which is generally spread over days depending on the number of candidates who have applied to the company. One third of new hires quit their job after about six (6) months. It is forecasted that the number of unemployed people will reach more than 212m by 2019. Thus these recruiters just shortlist the candidates based on some hard filters like GPA, Years of experience etc. to shorten the list.

During the past few years, the terms artificial intelligence and machine learning have begun showing up frequently in technology news and websites. Often the two are used as synonyms, but many experts argue that they have subtle but real differences. And of course, the experts sometimes disagree among themselves about what those differences are. In general, however, two things seem clear: first, the term artificial intelligence (AI) is older than the term machine learning (ML), and second, most people consider machine learning to be a subset of artificial intelligence. One of the best graphic representations of this relationship comes from Nvidia's blog.