Lenovo this week unveiled a new addition to the Moto Z family at IFA 2016. It's the Moto Z Play, and as you might expect from similarly-named phones from recent history, this model is made to be more affordable than the flagships it shares its name with. Get a closer look in our hands-on video.

The Weka machine learning workbench is an easy to use and powerful platform for applied machine learning. Even though it is easy to use, you may still require some help or advice when using it on your own problems. In this post you will discover resources that you can use to get more help with Weka. How to Get More Help For the Weka Machine Learning Workbench Photo by Susanne Nilsson, some rights reserved. Your installation of Weka provides documentation that you can consult for simpler questions around how to use the platform.

A couple weeks ago,we discussed the concepts of both linear classification and parameterized learning. This type of learning allows us to take a set of input data and class labels, and actually learn a function that maps the input to the output predictions, simply by defining a set of parameters and optimizing over them. Our linear classification tutorial focused mainly on the concept of a scoring function and how it can be used to map the input data to class labels. But in order to actually "learn" the mapping from the input data to class labels, we need to discuss two important concepts: In today and next week's blog posts, we'll be discussing two loss functions that you'll commonly see when applying Machine Learning, Neural Networks, and Deep Learning algorithms: To learn more about your first loss function, Multi-class SVM loss, just keep reading. At the most basic level, a loss function is simply used to quantify how "good" or "bad" a given predictor is at classifying the input data points in a dataset.

The huge amount of data we generate is a mixed blessing. It gives us better insight into human behavior, but it's also a lot more stuff to sort through. For helping us wade through our data, and make better use of it, we'll turn to AI (artificial intelligence) for things as complicated as navigating the sales journey to just figuring out what the doggone temperature is outside. He talks about how our relationship with our data will change as we develop better tools to interact with it, and better insights to learn from it. Coenraets: "I've been building apps for many, many years using all the different technologies that came and went and looking at the different ways people access information," Coenraets says.

Package Name: International Stocks Forecast Length: 7 Days (08/28/2016 – 09/04/2016) I Know First Average: 6.85% The highest returning stock for the 7 days period was. The package's overall average return was 6.85%, providing investors with a 6.35% premium, over the SP500's return of 0.50%, during the same period. Mechel Sp ADR (MTL) engages in mining and steel businesses in Asia,the Russian Federation, the Commonwealth of Independent States, Europe, the Middle East, the United States, and internationally. Mechel PAO works in three different sectors,Mining, Steel, and Power.

In the age of Facebook Messenger, WhatsApp, Slack and AI assistant (Cortana, Siri, Ok Google) we can finally communicate intelligently with our software. With growing Artificial Intelligence, Chatbots are helping us to find products, places, food and even solving customer service issues. Chatbots can convince you of remarkable things like the necessity of clicking a link or they are humans, not robots. Automation using bots allows companies to minimize their costs while still maintaining a high price point, thanks to the ease of use and quality of service. Brands capitalizing on digital experience are using chatbots to gain competitive advantage.

Robots may not yet have the authority to influence their human masters, according to new research. The study stationed a cyborg guard beside a table of food marked with a'reserved' sign in a student common room. Researchers from New York-based Cornell University used a mObi robot manufactured by Bossa Nova in a simple test. While the robot is not designed to look particularly menacing or authoritative, it has cameras that enable it to'see' what people around it are doing. The behaviour of hundreds of students was captured by a hidden GoPro action camera, reports New Scientist. The results showed that a disappointing seven per cent snaffled reserved food from the table, despite the robot guard's presence.

A 12-year-old who read The Lord Of The Rings aged five has become the youngest Cornell University freshman in the Ivy School's history. Jeremy Shuler was home-schooled by his parents - both aerospace engineers from Grand Prairie, Texas - and started reading books in English and Korean aged two. To help get him into Cornell, Jeremy's parents moved to Ithaca, where his father, Andy Shuler, took up a post at Lockheed Martin Upstate New York. A 12-year-old who started studying calculus aged 6 has become the youngest Cornell University freshman in the Ivy School's history With his bowl-cut hair, cherubic face and frequent happy laughter, Jeremy is clearly still a child despite his advanced intelligence. He swung in his chair while his parents, who he calls Mommy and Daddy, recounted his early years during an interview at the engineering school where his grandfather is a professor, his father got his doctorate and Jeremy is now an undergrad.

Many of the latest machine learning and data science tools purport to be easy to work with compared to previous generations of such frameworks and libraries. Chinese search engine giant Baidu now has an open source project in the same vein: a machine learning system it claims is easier to train and use because it exposes its functions through Python libraries. PaddlePaddle -- "Paddle" stands for "PArallel Distributed Deep LEarning" -- was developed by Baidu to augment many of its own products with deep learning. Baidu touted PaddlePaddle's speech transcription in Chinese, either for transcribing broadcasts or as a speech-to-text system to replace keyboards in smartphones. The company claims it needed 20,000 hours of audio as training material to achieve these results with its framework.

NVIDIA Corporation (NASDAQ:NVDA) was a PC graphics chip company who has matured into a specialized platform company who targets primarily four markets (Gaming, Professional Visualization, Data Center and Automotive), continuing to provide cutting edge visual computing solutions. The company consists of two reporting operating segments, GPU and Tegra Processor. While GPU remains the core strength of NVDA, the company's significant and consistent investment in research and development has been the key driver of sales that have grown from just over 4B in FY14 to an estimated 7B in FY17. NVDA remains the market leader in gaming with key growth contributions coming from professional visualization, deep learning and automotive. In gaming, only one third of the active GeForce users have upgraded to Maxwell with Pascal starting to ship in FQ2, meaning there is substantial scope for future uptake during an upgrade cycle.