We talk to a lot of analysts at Lab41. A recurring theme of these conversations is what they frequently refer to as "result provenance." Translation -- "Are these results any good? I don't have a whole lot of time to research them any further, so will these results hold up under scrutiny?" The old adage about lies, damn lies, and statistics has been around for just about forever.

Measurement of true customer satisfaction has always been the Holy Grail of customer experience. Sure, there are several methods currently in use, including net promoter scores (NPS), customer effort scores, and customer satisfaction scores (CSAT). Typically these approaches are fed by customer surveys--quite often the results are too little and too late. But what would happen if we disrupted customer satisfaction measurement using the Internet of Things (IoT) and artificial intelligence (AI)? The first step to this new disruptive approach is to have customers opt in and provide the data streams that could be fed into an IoT Complex Event Processor (CEP engine), which is a collection of technology components that can process millions of events from mobile devices, connected products, website clicks, social media posts, and pretty much any type of message that can be generated by computers.

As you may have noticed, we've been researching artificial intelligence (AI) and its economic and educational implications. From healthcare to transportation, we believe it is incredibly important for young people and adults to be learning about AI, and we are writing more about it to equip teachers and parents with information to help young people ask good questions about the implications of AI on their lives and livelihoods. To get the scoop, I sat Tom Vander Ark down for a podcast interview on AI. You'll also hear from Gerald Huff, a senior Silicon Valley software engineer, who shares his thoughts on AI and what it means for students and the transportation industry. Listen to the podcast, read excerpts from the interview below and be sure follow the campaign at #AskAboutAI.

Recently Tom (@neuralmarket) and I had the chance to work together with Amanda Shiga (@AmandaShiga) from Nonlinear Digital to build web analytics process using RapidMiner. Amanda has an on-going pilot project to apply data mining techniques to clickstream and user behavior data collected from her client's website. The website has a number of value-weighted micro-conversions, such as newsletter signup, or downloading a whitepaper, or event registration. For online retailers, seeing the visitors convert to paying customers is the ultimate goal. The focus of web analytics nowadays has shifted from getting visitors to a website to turning the web visitors into high value customers.

In this post-Tinder world, your profile picture is everything. The world swipes right (acceptance!) or swipes left (rejection!) based solely on what your photo looks like. Not what you look like. What your photo looks like. So, when hunting for dates and other forms of conjugation, you better get that photo right.

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