In the era of self-driving cars, a scary but otherwise uneventful car crash can be huge news. This was the case in Tempe, Arizona, on Friday, when an Uber self-driving car was hit so hard that it rolled onto its side. There were no serious injuries reported. Uber has grounded its fleet of self-driving cars in Arizona as a result, a spokeswoman for the company told me. "We are continuing to look into this incident, and can confirm we had no backseat passengers in the vehicle," an Uber spokesperson said in a statement provided to The Atlantic.

Fei Fei Li, chief scientist of AI/ML for cloud services at Google Inc., speaks at Cloud Next '17 in front of an image of one of sister company Waymo's driverless cars. Deep learning has become the technology du jour of late and few companies have advanced the field as much across as many areas or integrated the technology as completely into their operations as Google and its Alphabet affiliates. In keeping with Google's push to externalize its innovations, the company's Next '17 cloud conference featured a number of AI-related announcements and a general theme of democratizing access to the world's most powerful deep learning systems. In recent years Google and its sister companies have become synonymous with advancing the AI revolution at a frenzied pace and infusing deep learning across the company's services. Perhaps most famously, last year Deep Mind's AlphaGo became the first machine to beat a top Go player, while Waymo's driverless cars have become symbols of the autonomous driving revolution.

DataRobot has raised $54 million in the first close of a Series C round led by New Enterprise Associates. The latest round brings the total amount raised by the Boston, Massachusetts-based company to $111 million, with "significant" additional funding expected in the second close of the round. Data scientists Jeremy Achin and Thomas DeGodoy founded DataRobot in 2012 on the belief that automated machine learning will not only increase productivity for data scientists, but will also open up the world of data science to non-data scientists. The DataRobot platform features hundreds of open-source machine learning algorithms, allowing users to quickly build predictive models. Chris Devaney, COO at DataRobot, told ZDNet that a data scientist would typically look at a set of data, prepare that data, and then train a predictive model -- a process that can take weeks or even months.

Amazon's Echo and Dot connected speakers are sold out, and 35 new products will have Alexa built-in this year. Did Alexa win over Siri, Cortana and Hey Google? LOS ANGELES -- Alexa is much better at Siri at so many things. But now that Amazon's Alexa personal digital assistant has joined Siri on the iPhone, we come to a sudden and unexpected realization. Siri, which has been maligned and criticized over the last five years, is actually more useful than Alexa on the phone.

This is the third installment of a new series called Deep Learning Research Review. Every couple weeks or so, I'll be summarizing and explaining research papers in specific subfields of Deep Learning. This week focuses on applying Deep Learning to Natural Language Processing. The last post was about reinforcement learning and the post before was on generative adversarial networks. Natural Language Processing (NLP) is all about creating systems that process or "understand" language in order to perform certain tasks. The traditional approach to NLP involved a lot of domain knowledge of linguistics itself. Understanding terms such as phonemes and morphemes was pretty standard, as there are whole linguistic classes dedicated to their study.

It's March of the Machines this week, so we had a look at some surprising companies hiring in emerging technologies such as IoT, AI and machine learning. It has never been a more exciting time to work in artificial intelligence (AI) or the internet of things (IoT). There are so many career options available to those who want to pursue these emerging technologies, and successful candidates can make a real difference in how society uses technology. But, while you might know that you want to work in AI or machine learning, you might be left wondering where exactly to start your job search. What companies are hiring in that sector?

By this point, a modified Lincoln MKZ driving itself around San Jose isn't anything special. With more than two dozen companies are testing autonomous tech in California, what's one more joining the pack? Not much, until you find out what's missing on the sedan cruising down the highway and winding through city streets at night. No radar tucked behind the body panels. In fact, according to Jianxiong Xiao, the car navigates using nothing but a handful of cameras he bought at Best Buy for $50 a pop.

In next movie Prof. Dr. Max Welling gives the latest developments in Machine Learning also related to recruitment. Deep learning is a machine learning method, as machine learning is a part of artificial intelligence. Unsupervised learning A child is learning by classifying objects. For example the child makes clusters like chairs and even if see's a chair what is not exactly the same as the chairs the child saw before, he can classify to the same group. Supervised learning The same example but now the father tells (labels) the cluster of chairs as "chairs" so the child can recognize chairs without seeing the same chair before.

In recent years, we have become increasingly good at training deep neural networks to learn a very accurate mapping from inputs to outputs, whether they are images, sentences, label predictions, etc. from large amounts of labeled data. What our models still frightfully lack is the ability to generalize to conditions that are different from the ones encountered during training. Every time you apply your model not to a carefully constructed dataset but to the real world. The real world is messy and contains an infinite number of novel scenarios, many of which your model has not encountered during training and for which it is in turn ill-prepared to make predictions. The ability to transfer knowledge to new conditions is generally known as transfer learning and is what we will discuss in the rest of this post. Over the course of this blog post, I will first contrast transfer learning with machine learning's most pervasive and successful paradigm, supervised learning. I will then outline reasons why transfer learning warrants our attention. Subsequently, I will give a more technical definition and detail different transfer learning scenarios.

The following article and talk are by Jessica Taylor and they were originally posted on MIRI. At the EA Global 2016 conference, I gave a talk on "Using Machine Learning to Address AI Risk": It is plausible that future artificial general intelligence systems will share many qualities in common with present-day machine learning systems. If so, how could we ensure that these systems robustly act as intended? We discuss the technical agenda for a new project at MIRI focused on this question. The talk serves as a quick survey (for a general audience) of the kinds of technical problems we're working on under the "Alignment for Advanced ML Systems" research agenda. Included below is a version of the talk in blog post form.1 This talk is about a new research agenda aimed at using machine learning to make AI systems safe even at very high capability levels.