This month OpenAI published a paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning" by Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever which shows Evolution Strategies (ES) can be a strong alternative to Reinforcement Learning (RL) and have a number of advantages like ease of implementation, invariance to the length of the episode and settings with sparse rewards, better exploration behaviour than policy gradient methods, ease to scale in a distributed setting. Running on a computing cluster of 80 machines and 1,440 CPU cores, authors' implementation was able to train a 3D MuJoCo humanoid walker in only 10 minutes (A3C on 32 cores takes about 10 hours). Using 720 cores they can also obtain comparable performance to A3C on Atari while cutting down the training time from 1 day to 1 hour. The communication overhead of implementing ES in a distributed setting is lower than for reinforcement learning methods such as policy gradients and Q-learning. By not requiring backpropagation, black box optimizers (the ones make no assumptions about the structure of the function being optimized) reduce the amount of computation per episode by about two thirds, and memory by potentially much more.
First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems. Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with Artificial Intelligence (AI) to avoid these problems and challenges in the first place. So what is machine learning, and how can it help your business? Machine learning is a subset of AI that lets computers'learn' without explicitly being programmed.
Ocado, the world's largest online-only supermarket, has been evaluating the feasibility of robotic picking and packing of shopping orders in its highly-automated warehouses through the SoMa project, a Horizon 2020 framework programme for research and innovation funded by the European Union. One of the main challenges of robotic manipulation has been the handling of easily damageable and unpredictably shaped objects such as fruit and vegetable groceries. These products have unique shapes and should be handled in a way that does not cause damage or bruising. To avoid damaging sensitive items, the project uses a compliant gripper (i.e. one that possesses spring-like properties) in conjunction with an industrial robot arm. The variation in shape of the target objects imposes another set of constraints on the design of a suitable gripper.
Technology is the great equalizer. In every industry and in nearly every department, technology is and should be central to performance and achievement capacity. The assembly line modernized the means of production in the early 1900s, the telephone revolutionized communication, computers changed nearly everything in the 1980s, and today the frontier of technology is big data and artificial intelligence (A.I.). Much has been made of those two trends in the last year. Every company under the sun has made bold claims about how much data they can capture and utilize.
Real-time bidding is an aspect of digital marketing that can seem overly complex for the average bear, so it was only a matter of time before AI entered the picture. This week, Google brought machine learning into the process to help make it easier. Tapping some of the same artificial-intelligence technologies that have already appeared in Google Photos and AlphaGo, Smart Bidding is a new capability for conversion-based automated bidding across AdWords and DoubleClick Search to help companies determine their optimal bid for any given campaign or portfolio. It can factor in millions of signals, Google says, and continually refines models of users' conversion performance at different bid levels. "Smart Bidding's learning capabilities quickly maximize the accuracy of your bidding models to improve how you optimize the long-tail," Anthony Chavez, Google's product management director for search ads, wrote in a blog post explaining the new service.
Can technology build a better Buffett? Nevertheless, the world has yet to see anything like a Wall Street version of Deep Blue, the artificially intelligent machine that defeated chess grand master Gary Kasparov in 1997. Today those early adopters of AI, like Fidelity Investments and Batterymarch Financial, refuse to even talk about the technology. Still, artificial intelligence has steadily improved in sophistication and quietly made itself indispensable on Wall Street. According to Andrew Lo, director of the Laboratory for Financial Engineering at MIT, every investment firm embracing a math-driven strategy uses some form of AI in its research, and Lo expects the terminology to appear again soon in promotions for retail investments like mutual funds and privately managed accounts.
"Managing the Data" is a new column about customer and audience data strategy written by longtime AdExchanger contributor Chris O'Hara. In 1960, the US Navy coined a design principle: Keep it simple, stupid. When it comes to advertising and marketing technology, we haven't enjoyed a lot of "simple" over the last dozen years or so. In an increasingly data-driven world where delivering a relevant customer experience makes all the difference, we have embraced complexity over simplicity, dealing in acronyms, algorithms and now machine learning and artificial intelligence (AI). When the numbers are reconciled and the demand side pays the supply side, what we have been mostly doing is pushing a lot of data into digital advertising channels and munching around the edges of performance, trying to optimize sub-1% click-through rates.
Despite a rise in Intel's overall revenue of 9.1% YoY in 3Q16 to a record high of $15.8 billion, largely due to unexpectedly high sales in the PC market, just two segments - the Client Computing Group and the Data Center Group - contributed 85% of the company's earnings. With the PC market in structural decline, increasing big cloud provider data center market control, and greatly increased data center competition, Intel is faced with shrinking defensive flexibility, which does not bode well for its long-term future. In 1997, in a public relations landmark for AI, International Business Machines Corporation (NYSE:IBM) created Deep Blue, which beat the then reigning world champion Garry Kasparov at chess. Dominant players in the global AI market are IBM, QlikTech International AB, Alphabet Inc. (NASDAQ:GOOG) (NASDAQ:GOOGL), MicroStrategy Inc. (NASDAQ:MSTR), Microsoft Corporation (NASDAQ:MSFT), Brighterion Inc., IntelliResponse Systems Inc., eGain Corporation (NASDAQ:EGAN), Nvidia Corporation(NASDAQ:NVDA), Next IT Corporation, and Nuance Communications, Inc. (NASDAQ:NUAN).
Vincent Granville *** (DSC) - Dr. Vincent Granville is a visiory data scientist with 15 years of big data, predictive modeling, digital and business alytics experience. Vincent is widely recognized as the leading expert in scoring technology, fraud detection and web traffic optimization and growth. Over the last ten years, he has worked in real-time credit card fraud detection with Visa, advertising mix optimization with CNET, change point detection with Microsoft, online user experience with Wells Fargo, search intelligence with InfoSpace, automated bidding with eBay, click fraud detection with major search engines, ad networks and large advertising clients. Most recently, Vincent launched Data Science Central, the leading social network for big data, business alytics and data science practitioners. Vincent is a former post-doctorate of Cambridge University and the tiol Institute of Statistical Sciences.
Intel announces AI strategy to drive breakthrough performance, democratize access and maximize societal benefits. Intel introduces industry's most comprehensive data center compute portfolio for AI: the new Intel Nervana platform. Intel aims to deliver up to 100x reduction in the time to train a deep learning model over the next three years compared to GPU solutions. Intel reinforces commitment to an open AI ecosystem through an array of developer tools built for ease of use and cross-compatibility, laying the foundation for greater innovation. Intel announces AI strategy to drive breakthrough performance, democratize access and maximize societal benefits.