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.

JERUSALEM – The Israeli military has confirmed that a drone crashed in Syria earlier this week in unclear circumstances. In a statement, the military said the "Skylark" went down on Sunday and that the incident was being investigated. Tuesday's statement said there is "no risk of a breach of information." Hezbollah's media arm published photographs of what it said was a drone it had shot down after infiltrating Syrian airspace in the Golan Heights. Although Israel is not actively fighting in the Syrian civil war, it keeps close tabs on its enemies Iran and Lebanon's Iranian-backed Hezbollah militant group, which are both backing Syrian government forces.

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.

A few months ago I wrote a tutorial on how to classify images using Convolutional Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library. The pre-trained networks inside of Keras are capable of recognizing 1,000 different object categories, similar to objects we encounter in our day-to-day lives with high accuracy. Back then, the pre-trained ImageNet models were separate from the core Keras library, requiring us to clone a free-standing GitHub repo and then manually copy the code into our projects. This solution worked well enough; however, since my original blog post was published, the pre-trained networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) have been fully integrated into the Keras core (no need to clone down a separate repo anymore) -- these implementations can be found inside the applications sub-module. Because of this, I've decided to create a new, updated tutorial that demonstrates how to utilize these state-of-the-art networks in your own classification projects.

Machine learning couldn't be hotter. A type of artificial intelligence that enables computers to learn to perform tasks and make predictions without explicit programming, machine learning has caught fire among the hip tech set, but remains a somewhat futuristic concept for most enterprises. But thanks to technological advances and emerging frameworks, machine learning may soon hit the mainstream. Consulting firm Deloitte expects to see a big increase in the use and adoption of machine learning in the coming year. This is in large part because the technology is becoming much more pervasive.

Approach-avoidance may be the best way to characterize Americans' attitudes towards a driverless future. Three-quarters (78%) of U.S. drivers reported feeling afraid to ride in a fully self-driving vehicle, yet most of them --59% -- said they want autonomous vehicle technology in their next vehicle. So while American drivers seem ready embrace autonomous technology, they are not yet ready to give up full control. Those are the main findings a new survey released earlier this month by AAA. "A great race towards autonomy is underway and companies are vying to introduce the first driverless cars to our roadways," Greg Brannon, AAA's director of automotive engineering and industry relations, said in a statement.

What kind of existential problems does AI bring about? The medium-term challenge of AI is not killer robots, it's job replacement. This dynamic is already underway and the literature suggests it's a more powerful driver of job loss than trade, though trade receives much more attention. True AI has not arrived, and automation is not AI, but robots and human-written code are a reasonable preview of what employment challenges genuine AI will bring. Computers already manage warehouses, can drive reasonably well, and are making meaningful progress into areas like basic lawyering and radiology that we long considered to be immune to change.

HBO's Westworld features a common plot device--synthetic hosts rising up against their callous human creators. But is it more than just a plot twist? After all, smart people like Bill Gates and Steven Hawking have warned that artificial intelligence may be on a dangerous path and could threaten the survival of the human race. They're not the only ones worried. The Committee on Legal Affairs of the European Parliament recently issued a report calling on the EU to require intelligent robots to be registered, in part so their ethical character can be assessed.