All you need to follow along are eyes, Python, a computer, and a bit of machine learning magic. He gives a good introduction to the idea. You take a histogram of the colors in a properly cropped and filtered photo of the object you want to identify. You then feed that into a neural network and train it to identify the different superheroes by color. When you feed it a new image later, it will compare the new image's histogram to its model and output confidences as to which set it belongs.

Evernote is trying to improve its digital-note-taking service, but it needs to access and read customer notes to do so. In a corporate blog post published this week, the company said it's updating the terms of its privacy policy to include a change that lets its employees access user notes to improve its machine-learning technologies. Machine learning technologies can allow powerful software to sift through data to find patterns and better automate tasks. Evernote said the decision to have some of its employees read user notes, effective Jan. 27, 2017, was done to "make sure that our machine learning technologies are working correctly, in order to surface the most relevant content and features to you." The company elaborated that although its current "computer systems do a pretty good job, sometimes a limited amount of human review is simply unavoidable in order to make sure everything is working exactly as it should."

The rapid and inexorable progress in the development of artificial intelligence has raised understandable concerns over the risks the technology poses to job security. There is a palpable fear that humans will quickly become obsolete, with some suggesting that up to half of existing jobs could be automated. This viewpoint is oversimplified, write Ravin Jesuthasan, Tracy Malcolm, and George Zarkadakis for the Harvard Business Review, as it fails to consider that automation will affect only certain tasks within specific jobs of a given occupation (when evaluated this way, only 9% of jobs are at a high risk of automation). In this more nuanced understanding, AI will simply force us to rethink our job descriptions, rather than eliminating work altogether. Already AI is having a significant impact on how we do our jobs, from streamlining administrative tasks to expediting medical research, and this rapid evolution requires a new way of thinking about the value of an employee and her performance.

The next stage of evolution for AI is democratization. That means making it available to businesses of all sizes, and not just to companies like Microsoft, Google or Apple. The opportunity in front of us is using AI to transform how we operate our businesses, no matter the size or industry. Contemporary AI techniques have given us magic in areas like speech recognition and image labeling, but there is much more work to be done. Think of your business and where you or your team make decisions about resource allocation.

Autonomous machines shining lasers at ants -- and spraying water at bewildered cats -- for the amusement of cackling grandchildren. Hobbyists are just getting started with deep-learning technologies that give them cheap, off-the-shelf capabilities that put Ronald Reagan's Star Wars program to shame. In the latest edition of the AI Podcast, NVIDIA engineer Bob Bond and Make: Magazine Executive Editor Mike Senese explain to host Michael Copeland how they've taken the once esoteric technology of deep learning and put it to work on offbeat projects that can be tackled on budgets of a few hundred bucks. "One of the big things that's happening -- and it's happening in real time right now -- is the technology is finally hitting a point where we, as consumers, have access to this type of capability," Senese says. Bond, a veteran engineer, is no technical novice.

Everything you need to know.

Uber's self-driving cars are making the move to San Francisco, in a new expansion of its pilot project with autonomous vehicles that will see Volvo SUVs outfitted with sensors and supercomputers begin picking up passengers in the city. The autonomous cars won't operate completely driverless, for the time being – as in Pittsburgh, where Uber launched self-driving Ford Focus vehicles this fall, each SUV will have a safety driver and Uber test engineer onboard to handle manual driving when needed and monitor progress with the tests. But the cars will still be picking up ordinary passengers – any customers who request uberX using the standard consumer-facing mobile app are eligible for a ride in one of the new XC90s operated by Uber's Advanced Technologies Group (ATG). There's a difference here beyond the geography; this is the third generation of Uber's autonomous vehicle, which is distinct from the second-generation Fords that were used in the Pittsburgh pilot. Uber has a more direct relationship with Volvo in turning its new XC90s into cars with autonomous capabilities; the Fords were essentially purchased stock off the line, while Uber's partnership with Volvo means it can do more in terms of integrating its own sensor array into the ones available on board the vehicle already.

With the amount of published research, patents, white papers, and other written knowledge out there, it's hard to be even reasonably sure you're aware of the goings-on around a certain topic or field. Omnity is a search engine made to make it easier by extracting the gist of documents you give it and finding related ones from a library of millions -- and now supports over a hundred languages. The process is simple and free, at least for the public-facing databases Omnity has assembled, comprising U.S. patents, SEC filings, PubMed papers, clinical trials, Library of Congress collections, and more. You upload a document or text snippet, and the system scans it, looking for the least common words and phrases -- which generally indicate things like topic, experiment type, equipment used, that sort of thing. It then looks through its own libraries to find documents with similar or related phrases that appear in a manner that suggests relevance. For example, say you put in the results of your clinical trial testing a food additive on a certain strain of mice, and found it resulted in a certain condition.

Over 3/4 of Marketing Leaders believe the future belongs to Artificial Intelligence. In October 2015, in my post entitled "The Future Of Sales Is AI: Are Your Sales Teams Prepared?" I shared a bold prediction from LeadGenius cofounder Anand Kulkarni: "In just 10 years most salespeoples' jobs will be replaced by artificial intelligence." As I wrote then and as I believe today, sales is not going away. Salespeople are not going away.

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