Apple continues to bulk up on artificial intelligence and data crunching smarts. Ruslan Salakhutdinov, an associate professor at Carnegie Mellon University and its computer science school's machine learning department, said Monday via Twitter that he is joining Apple aapl as its director of A.I. research. He will continue to work at Carnegie Mellon while at Apple. Excited about joining Apple as a director of AI research in addition to my work at CMU. Apply to work with my teamhttps://t.co/U2hQl2GdhA Among Salakhutdinov's areas of research is the hot field of deep learning, an A.I. technique in which software systems called neural networks are given vast quantities of data to find patterns within.

Apple is making a visible push in the direction of AI today by hiring Carnegie Mellon University professor Ruslan Salakhutdinov for what appears to be a newly minted position: director of AI research. Salakhutdinov, who announced his new position on Twitter, works in the fields of deep learning and neural networks; he's published dozens of papers in the last couple of years alone. The topics he's worked on run the gamut, but the main thread is one of human-like understanding of various media: recognizing objects in images, actions in videos, and so on. We are looking for exceptional hands-on research scientists with a proven track record in a variety of machine learning methods; from the realms of deep learning, reinforcement learning, unsupervised learning, and computer perception. You will be joining a world-class, multidisciplinary team and will be participating in cutting-edge research in deep learning, machine intelligence, and artificial intelligence.

Apple is getting serious about artificial intelligence. It's just hired its first director of AI. And not just anyone -- it's hired Ruslan Salakhutdinov, an associate professor in machine learning at one of the top institutions for AI, Carnegie Mellon University. Salakhutdinov has been working on some pretty intense AI research. He primarily researches deep learning and neural networks, where computers learn from a large pool of examples.

Idling in rush-hour traffic can be mind numbing. It also carries other costs. Traffic congestion costs the U.S. economy 121 billion a year, mostly due to lost productivity, and produces about 25 billion kilograms of carbon dioxide emissions, Carnegie Mellon University professor of robotics Stephen Smith told the audience at a White House Frontiers Conference last week. In urban areas, drivers spend 40 percent of their time idling in traffic, he added. The big reason is that today's traffic signals are dumb.

In this blog post, we will learn techniques to classify urban sounds into categories using machine learning. Earlier blog posts covered classification problems where data can be easily expressed in vector form. For example, in the textual dataset, each word in the corpus becomes feature and tf-idf score becomes its value. Likewise, in anomaly detection dataset we saw two features "throughput" and "latency" that fed into a classifier. But when it comes to sound, feature extraction is not quite straightforward.

Outside a large warehouse in Pittsburgh, in an area along the Allegheny River that was once home to dozens of factories and foundries but now has shops and restaurants, I'm waiting for a different kind of technological revolution to arrive. I check my phone, look up, and notice it's already here. A white Ford Fusion, its roof bedazzled with futuristic--looking sensors, is idling nearby. Two people sit up front--one monitoring a computer, the other behind the wheel--but the car is in control. I hop in, press a button on a touch screen, and sit back as the self-driving Uber takes me for a ride. As we zip out onto the road toward downtown, the car stays neatly in its lane, threading deftly between an oncoming car and parked trucks that stick out into the street.

It'll likely take a long time before DARPA's autopilot system flies military planes on its own, but this latest demonstration proves that it works. Aurora Flight Sciences, the aviation company that's developing the technology for the agency, has successfully tested it on a Cessna Caravan turboprop aircraft. Aircrew Labor In-Cockpit Automation System or ALIAS is comprised of a robotic arm and a tablet-based user interface with speech recognition, among other components. When installed on a plane, it acts as the co-pilot in charge of flying the aircraft -- its human companions can chill and spend their time keeping an eye on the weather or looking out for any potential threats. This is the technology's third demonstration in merely a year, following two test runs on a simulator and a Diamond DA-42 plane.

Every year we like to get a Googler who is close with the ranking and search quality team to give us future thinking points to relay to the search marketing community. In part two of our interview with Gary Illyes of Google, we asked him that question. Well I guess you can guess that we are going to focus more and more on machine learning. But it will not take over the core algorithm. The other thing is that there is a very strong push for AMP everywhere.

Let's go through an example of Cancer Tissue Observations: Logistic regression is a popular method to predict a binary response. It is a special case of Generalized Linear models that predicts the probability of the outcome. Logistic regression measures the relationship between the Y "Label" and the X "Features" by estimating probabilities using a logistic function. The model predicts a probability which is used to predict the label class. Our data is from the Wisconsin Diagnostic Breast Cancer (WDBC) Data Set which categorizes breast tumor cases as either benign or malignant based on 9 features to predict the diagnosis.

Earlier this year, chatbots were barely a blip on the tech world's radar. Then, at Facebook's F8 developer conference in April, the company announced that its Messenger app would soon feature chatbots from brands including CNN and 1-800-Flowers. Seemingly overnight, chatbots became the "next big thing" in tech and the object of endless media coverage. Other messaging platforms, such as Kik, Line and Telegram, are also experimenting with chatbots developed by third-party companies. And Talkabot, a bot-related conference, took place in Austin in late September.