If you've been checking out the XDA Depot then you know about all the sweet deals you can get on software, online courses, and more. Some of the best values are the course bundles that have huge discounts. Let's take a look at The Complete Machine Learning Bundle which is currently 95% off. Master artificial intelligence and be ahead of the curve with 10 courses & 63.5 hours of training in machine learning. This course will get you prepared to solve problems with NLP, recommendations, sentiment analysis, quant trading and computer vision.

His present superhuman AI agents can see, learn from experience, simulate our world and orchestrate meta-solutions (General-Purpose AI). The international metric consists of the state-of-the-art OpenAI Gym.

If you'll be attending the RSA Conference in February and are looking to learn some hacking tips and tricks, Cylance CEO and President Stuart McClure will be presenting on February 15th at 1:30 PM on Hacking Exposed NextGen. Stuart will demonstrate some live exploits and real-life hacking on everyday technology, and will also show you how to prevent them using simple countermeasures. We urge you to join Stuart for this amazing presentation at the Marriott Marquis in Yerba Buena 5. Reserve your seat today! In the meantime, get ready for RSA by watching Stuart's recent CBS News interview, during which he discusses why "who did the hacking" is a less important question than how to stop it: STUART: "Our approach is really quite different than anything else you'll find out there. We take a purely mathematical approach. We believe that you can actually identify attacks long before they ever start, and truly prevent them. This is done through what we call AI prediction. We've trained computers over millions and millions of files and attacks, to learn exactly what makes them up - the DNA of these attacks. By understanding the DNA mathematically, now we can prevent and protect against future attacks. So it looks like we're predicting attacks, when really, we've just learned through AI machine learning what the DNA of these attacks is. We see ourselves today as applying artificial intelligence to cybersecurity in a truly preventative and predictive way. We see the company expanding far beyond cybersecurity. The techniques that we are using are very applicable to other areas and fields of study. Anything that you want to try and classify effectively. For example, another application might be healthcare, or diagnostics - for example an MRI scan. Being able to detect early forms of cancer or disease. They take large amounts of data that you and I would fall asleep trying to process, and they never forget what they process. And they can learn from that to make decisions going forward."

When Binny Bansal, co-founder of India's largest retailer Flipkart was studying at IIT-Delhi, nobody at his institute was interested in the subject of artificial intelligence. "Because nothing was happening in India," Mr. Bansal told his co-panelists Satya Nadella, chief executive of Microsoft and Infosys co-founder Nandan Nilekani during a fireside chat at an event in Bengaluru. That was 15 years ago and a lot has changed since. On Monday, Mr. Nadella, who leads the world's largest software maker, announced a strategic partnership with Flipkart, where the e-commerce company would adopt the company's cloud computing platform, Microsoft Azure. Flipkart said that it planned to leverage artificial intelligence (AI), machine learning and analytics capabilities in Azure to optimise its data for innovative merchandising, advertising, marketing and customer service.

ORLANDO – From Watson to Siri, Alexa to Cortana, consumers and patients have become much more familiar with artificial intelligence and natural language processing in recent years. Pick your terminology: machine learning, cognitive computing, neural networks/deep learning. All are becoming more commonplace – in our smartphones, in our kitchens – and as they continue to evolve at a rapid pace, expectations are high for how they'll impact healthcare. As it sparks equal part doubt and hope (and not a little hype) from patients, physicians and technologists, a panel of IT experts at HIMSS17 discussed the future of AI in healthcare on Sunday afternoon. Kenneth Kleinberg, managing director at The Advisory Board Company, spoke with execs from two medical AI startups: Cory Kidd, CEO of Catalia Health, and Jay Parkinson, MD, founder and CMO of Sherpaa.

Facial recognition technology used to identify criminals can also identify lemurs with a near perfect level of accuracy. The system, called LemurFaceID, is a facial recognition system that works by identifying individual lemurs based on photographs of them. The system can facilitate the long-term research of known individual lemurs, which could help with understanding how lemurs evolve and how to best conserve them. The LemurFaceID system scored 98.7 per cent accuracy after 100 trials of using the system. Amateur photographers took photos of the lemurs in Ranomafana National Park (RNP), a rainforest in southeastern Madagascar.

Mark Cuban, chairman of AXS TV and owner of the Dallas Mavericks, listens to testimony during a Senate Judiciary Subcommittee hearing Dec. 7, 2016 in Washington, DC. (Photo: Mark Wilson, Getty Images) Add Mark Cuban to the list of tech visionaries exhorting the need to address the advance of robotics and artificial intelligence. "Automation is going to cause unemployment and we need to prepare for it," Cuban posted on Twitter with a link to an essay about the rise of robots in the workplace. Automation is going to cause unemployment and we need to prepare for it. Employment and jobs have been a hot topic recently with President Trump's emphasis on getting U.S. corporations to focus on jobs at home and his plans of tightening of trade and immigration policies to foster job growth. Automakers including Fiat Chrysler, GM and Ford, as well as Intel and Walmart have recently announced plans that the companies say will create new jobs.

TensorFlow is the machine-learning library open sourced by Google in November 2015. It gained over 11,000 stars on GitHub in its first week after launch, and has built up quite a community since then: at the time of this writing, TensorFlow has over 45,000 stars, 13,000 commits and 21,000 forks. This is the second installment in our interview series with Jeff Dean, Google Senior Fellow and lead of the Google Brain research team. In our first installment, we talked about the landscape of machine learning: its past, present and future. In this installment, we'll cover TensorFlow: why we built it originally, how to use it, and what its future may hold.

Prior to that, Siri made adding an event to your calendar or quickly looking up the weather as easy as a simple push of a button on your smart phone. At what point does "Siri, tell me the weather" or "Alexa, play music" become "I'll have a cheeseburger and fries" or "What aisle can I find the peanut butter on?" or "Does this shirt come in small"? As we move away from assistants who perform simple automated tasks, 2017 has the potential to turn voice assistant technology into a virtual customer service functionality. The convergence of chatbots and virtual voice assistants has the potential to completely change the retail experience. For years, we have heard rumors about quick service retail (or QSR) replacing humans with touch screen tablets.

Earlier this week we looked at how deep nets can learn intuitive physics given an input of objects and the relations between them. If only there was some way to look at a 2D scene (e.g., an image from a camera) and build a 3D model of the objects in it and their relationships… Today's paper choice is a big step in that direction, learning the 3D structure of objects from 2D observations. The 2D projection of a scene is a complex function of the attributes and positions of the camera, lights and objects that make up the scene. If endowed with 3D understanding agents can abstract away from this complexity to form stable disentangled representations, e.g., recognizing that a chair is a chair whether seen from above or from the side, under different lighting conditions, or under partial occlusion. Moreover, such representations would allow agents to determine downstream properties of these elements more easily and with less training, e.g., enabling intuitive physical reasoning… The approach described is this paper uses an unsupervised deep learning end-to-end model and "demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner."