U.S. transportation authorities are investigating the deadly collision of a Tesla Model S car. And many reports say the fatal crash has heightened concern about self-driving cars. As NPR's Sonari Glinton points out, what Tesla's Model S has are self-driving features, autonomous elements meant to assist drivers rather than replace them. Virtually all major car and tech companies are pursuing self-driving technology as the future of transportation. But Tesla and Google are the earliest innovators, taking very different approaches.

The idea that university CS programs are taking bright young minds and fashioning them into algorithm and data structure whiz-kids defies the observations of almost any incoming CS student or their instructor. Many CS freshmen enter college already having a passion for computers and likely a privileged amount of access to technology and mentorship. Like myself, they were given computers as children by parents who were themselves close to technology. They have computer usage skills (how to configure your machine, how to fix basic computer problems) and have parents (or tutors) who introduced them to programming. For those without that background, freshman CS can prove very challenging.

December's ICCV 2015 conference in Santiago, Chile has come and gone, but that's no reason not to know about its top papers. Get an update on which computer vision papers and researchers won awards.

Python Developer - Artificial Intelligence/Machine Learning/API Python Developer urgently needed by the fastest growing healthcare startup in Europe for a brand new team building an incredible new product which will be used to predict illness and risk factors for their customers. The Python Developer who join this team will be building their new predictive engine to help them analyse future risk and illness. Data will be taken form a variety of sources like wearables and test results. This will be achieved through a combination of machine learning and deep learning algorithms. There may also be some work on their Back End microservices environment so any API exposure would be a bonus.

To learn more about creating a modern IT environment, click: http://aka.ms/GuideModernIT. For any novice in machine learning – the biggest challenge is to determine the algorithm to use to train the model. This tutorial attempts to identify the various use-cases for training models and which algorithm one can use in a particular use-case.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

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Softmax Regression (synonyms: Multinomial Logistic, Maximum Entropy Classifier, or just Multi-class Logistic Regression) is a generalization of logistic regression that we can use for multi-class classification (under the assumption that the classes are mutually exclusive). In contrast, we use the (standard) Logistic Regression model in binary classification tasks. Now, let me briefly explain how that works and how softmax regression differs from logistic regression. As the name suggests, in softmax regression (SMR), we replace the sigmoid logistic function by the so-called softmax function?: Now, this softmax function computes the probability that this training sample x(i) belongs to class j given the weight and net input z(i). So, we compute the probability p(y j x(i); wj) for each class label in j 1, ..., k.

It's widely known that tech has a diversity issue, Artificial Intelligence is no exception. However, when you're training algorithms to act like you and think like you, diversity all of a sudden becomes a pretty serious issue. "Earlier this month, Bill Gates took the stage at the Recode conference to talk about philanthropy with his wife, Melinda. They discussed mobile payments, contraception, and billionaires giving away their fortunes. Then the conversation turned to artificial intelligence, and Gates grinned and swiveled in his giant red leather chair. "Certainly, it's the most exciting thing going on," he said. It's the big dream that anybody who's ever been in computer science has been thinking about."

With search engine optimization (SEO) maintaining its presence as one of the most effective marketing techniques on the Internet, it's no wonder that over time, new technical changes in algorithms have made the task of ranking both sites and keywords more difficult to understand and execute. While this used to be an easy task that involved including keywords into the content of your website, Google has continually progressed its ranking technology to provide more quality results to its users. With the launch of RankBrain, Google's new algorithm solution, earlier this year, has it stepped into the field of artificial intelligence? One might think so, as RankBrain queries for so much more than the keywords of the past, taking SEO to the next level. Understanding RankBrain, boils down into recognizing that Google wants to provide users ranking content that is relevant. The way users are using Google to search has changed, as now it is seeing complete questions and more complicated keyword searches being inputted.