We're all getting used to the thought that in a not-so-distant future, competition for jobs won't just be other humans, it will also be an intelligent robot, self-driving car, or other artificial agent. But in our gut, we know this can't be the full truth, that there's a more nuanced story. We at least believe that elite human skills will remain valuable even as automation eats the world. The hard part is figuring out which ones will be the most valuable and where they will be the most prized. As a parent, this can be a particularly vexing problem when thinking about how to advise your kids.

Robots are about convenience, so why do we fear them? They bring us a better life and freedom from repetitive tasks. When I have children one day, I want them to grow up with helpful robots. What can Alexa or Pepper or Kuri be in ten years time? Alexa is my conversational partner of choice.

Whether it's opening Google Maps for directions, shopping on Amazon, or watching movies on Netflix, 2016 was the year that bots truly hit mainstream. This seismic development -- which has critical implications for businesses, consumers, and, indeed, humankind -- is the result of three developments that came to a head in 2016: First, an unprecedented capacity to analyze data; second, a backlash (limited though it was) to protect what data can be analyzed; and third, a welcoming of automation into our lives that we couldn't have fathomed even a few years ago. At this moment, we have more data than ever before. We're creating information at a bewildering pace -- approximately 2.5 quintillion bytes of data daily, which is enough to fill 57.5 billion iPads (at 32 gigabytes apiece). Big data is now a given, affecting every industry and function.

I often get asked on how to get started with Machine Learning. Most of the time, people have troubles understanding the maths behind all things. And I have to admit, I don't like the maths either. Math is an abstract way of describing things. And I think the way machine learning is described is too abstract to understand it easily. I probably try to describe things with foo code or a bit of JS to explain what I'm talking about.

Ricoh has announced the Pentax KP, the follow-up to the K-3 II, which features a new'high sensitivity' 24MP sensor and improved in-body image stabilization system. The new CMOS sensor brings with it a top ISO of 819,200 and an electronic shutter that tops out at 1/24000 sec (the mechanical shutter goes to 1/6000 sec). The KP uses the new 5-axis'Shake Reduction II' IBIS system, first seen on the K-1 full-framer, which offers up to 5 stops of stabilization according to Ricoh. As with other Pentax models, the KP supports Pixel Shift Resolution as well as AA Filter Simulation. The KP uses the same SAFOX 11 autofocus system as the K-3 II, meaning that it has 27 points, 25 of which are cross-type.

They'll be some of the weirder selfies you've ever taken, but a study using artificial intelligence to analyze images of skin lesions suggests that smartphones may soon help humans detect skin cancer. Published today in Nature, the study began with an unremarkable image-recognition network provided by Google, pre-trained to identify objects in images. Led by Stanford professor and former Google exec Sebastian Thrun, researchers showed the AI thousands and thousands of medical images--129,450 from Stanford University Medical Center and 18 open-source repositories, to be exact--which are labeled to tell the machine what it's looking at. After looking at hundreds of images of a specific lesion, the AI begins to understand similarities between the images. The algorithm learns to differentiate lesions from healthy skin, potentially based on traits like coloration and contrast.

The more time I spend engaging with entrepreneurs, investors, and analysts about the startup ecosystem, the more I hear the same old, cliché set of ideas and predictions repeated ad nauseam. One more interesting prediction I've heard, though, relates to artificial intelligence. Specifically, a lot of folks are under the impression that AI is just a buzzword, a sticker placed by founders on their companies to give them some measure of differentiation and hype. Here are the general arguments I've heard from people skeptical about the latest wave of AI startup activity: I get the impression that many of these theorists are working backwards, presupposing that AI is doomed to failure and then looking for reasons why. Argument: Current applications of AI don't live up to expectations.

Today begins week three of the poker tournament between Libratus, an AI system built by researchers from Carnegie Mellon University, and four of the world's top pros. While the humans plan to soldier on, a gallows humor has taken hold. With a little over 80,000 hands played, out of 120,000 total, the humans are down by roughly $750,000, a massive amount that will be all but impossible to come back from. "We're all down about the price of a small house," said Jason Les, chatting with onlookers about the score while he played. The players don't actually have to pay the AI anything, and in fact all get paid depending on how well they perform relative to one another. "It's not about the money, it's about preserving human dignity," quipped Les.

Ever since algorithms began recognizing patterns faster and better than humans, computers have been making doctors' lives easier and diagnoses more accurate. But widely used tools like automated cell counters, which can quickly point to diseases like malaria and leukemia by getting a head count on different kind of blood cells, are beginning to look quaint next to the deep learning and neural networks coming online. Today, hospitals can outfit their existing computer systems with a $1,000 graphics processor and speed-boost their capacity up to 260 million images per day. That's basically equivalent to all the MRIs, CT scans, and other images that all the radiologists in America look at each day. Unleashing that kind of AI on the medical world's mountains of patient data could speed up diagnoses and get patients on the path to recovery much sooner.

I often get asked on how to get started with Machine Learning. Most of the time, people have troubles understanding the maths behind all things. And I have to admit, I don't like the maths either. Math is an abstract way of describing things. And I think the way machine learning is described is too abstract to understand it easily. I probably try to describe things with foo code or a bit of JS to explain what I'm talking about.