Teenagers can often be portrayed as irresponsible individuals, seeking short-term satisfaction without regard for the consequences. But a new study shows adolescents' love for instant gratification can actually help improve their memory. The researchers found teenagers were better at a picture-based learning game than adults when positive reinforcement was involved, and they argue this sensitivity to reward could be part of an evolutionary adaptation to learn from their environment. Teenagers can often be portrayed as irresponsible individuals, seeking short-term satisfaction without regard for the consequences. But a new study shows adolescents' love for instant gratification, which also makes them thrill-seekers, can actually help improve their memory Teens' brains are wired differently from adults, the study found, explaining both their wild behaviour and their ability to memorise.

Back in 2010, an Intel researcher said 1,000-core processors would be feasible. We're in that era, and the race to make chips faster and more power efficient is gaining steam. The latest mega-chip is a 1,024-core processor called Epiphany V, which was announced by Adapteva on Wednesday. Adapteva claims it will have enough juice to outperform some of the latest gaming and server processors. It has a mere 24 more cores than the 1,000-core KiloCore, a test chip made by researchers at University of California, Davis.

One of the major benefits of working with R and Python for analytics is that there're always new and freely-available treats from their vibrant open source ecosystems. And now more and more, data scientists are able to reap the benefits of working with data in R, Python and other platforms simultaneously, as vendors introduce performant products with APIs to both R and Python -- in addition to perhaps Java, Scala and Spark. An example with which I'm currently quite smitten is H2O. H2O brands as "AI for Business" that "makes it possible for anyone to easily apply math and predictive analytics to solve today's most challenging business problems." What sets H2O apart is its comprehensive, open source, cross-platform, machine learning infrastructure architected from the ground up for scalability and speed.

Time Series prediction is a difficult problem both to frame and to address with machine learning. In this post you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library. The problem we are going to look at in this post is the international airline passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. Below is a sample of the first few lines of the file.

Arguably it is machine intelligence, along with a vast sea of data to apply it to. While you may never have as much data to process as Google does, you can use the very same machine learning and neural network library as Google. That library, TensorFlow, was developed by the Google Brain team over the past several years and released to open source in November 2015. TensorFlow does computation using data flow graphs. Google uses TensorFlow internally for many of its products, both in its datacenters and on mobile devices.

Visualize ML Python package to visualize some processes involved in Machine learning. If you have subscribed this blog, please make sure to change the feed address.

Are you implementing a machine learning algorithm at the moment? Implementing algorithms from scratch is one of the biggest mistakes I see beginners make. Don't Implement Machine Learning Algorithms Photo by kirandulo, some rights reserved. Why do I have to implement algorithms from scratch? It seems that a lot of developers get caught in this challenge.

Providers now need to start preparing for "machine learning" medicine, two professors write in a New England Journal of Medicine perspective. Ziad Obermeyer, an assistant professor at Harvard Medical School, and Ezekiel Emanuel, chair of the Department of Medical Ethics and Health Policy at University of Pennsylvania, write that computers eventually will be more effective than human providers at making a range of health care decisions--from determining the most cost-effective surgical supplies to deciding which tests a patient can skip. For example, they predict computer programs will be able to analyze radiographs for anomalies at a pixel level, a much more precise level of detail than the human eye is capable of detecting. "We can point this very powerful tool at a medical problem and say, 'I'm going to show you a bunch of people who had heart attacks, and a bunch who didn't. Go learn how to tell them apart,'" Obermeyer says in an interview with STAT News.

Not everyone needs to learn how to program the robots, but we'll all need to get comfortable working with algorithms and bots as well as people. Will they be friends or foes? And what can individuals do to position themselves for success in this brave new world? I just read an incredible statistic in a Harvard Business Review article: "By 2020, the US economy is expected to create 55 million job openings; and 24 million of these will be entirely new positions. One could argue the items on that list have always been valuable career currency, but it's only now -- in the face of competition from AI technologies -- that they're getting their full due.

Google on Tuesday officially announced a major change in its strategy to go after the smartphone market, as the search giant unveiled a'family of products' -- Pixel, Daydream, Home, and WiFi -- to venture into a new category of products which have both'hardware and software made by Google'. Taking the stage at the event, Sundar Pichai, CEO of Google, noted that when Google was founded in 1998, there were about 300 million people using the internet, the vast majority of whom were sitting at desktop computers and looking for answers that came in the form of blue links. But today, the internet community is closer to three billion people, and users are searching for all kinds of help across different contexts and devices, from cars and your classrooms to homes and the phones in people's pockets. When I look at where computing is heading, I see how machine learning and artificial intelligence are unlocking capabilities that were unthinkable only a few years ago. This means that the power of the software -- the'smarts' -- really matter for hardware more than ever before.