Since most of us need more than a "machine learning only" server, I'll focus on the reality of how Intel Xeon SP Platinum processors remain the best choice for servers, including servers needing to do machine learning as part of their workload. Here is a partial run down of key software for accelerating deep learning on Intel Xeon Platinum processor versions enough that the best performance advantage of GPUs is closer to 2X than to 100X. There is also a good article in Parallel Universe Magazine, Issue 28, starting on page 26, titled Solving Real-World Machine Learning Problems with Intel Data Analytics Acceleration Library. High-core count CPUs (the Intel Xeon Phi processors – in particular the upcoming "Knights Mill" version), and FPGAs (Intel Xeon processors coupled with Intel/Altera FPGAs), offer highly flexible options excellent price/performance and power efficiencies.

It's a brand new year, and a good time to look into the future to see what the next 12 months will bring. I listened to my friend Bridget Karlin make her predictions on the radio program Coffee Break with Game-Changers, which compiled what 80 thought leaders in technology, business and academics foresee for companies and industry in the coming year. Karlin, who is Intel's managing director of Internet of Things (IoT) Strategy and Technology, made the prediction that in 2017, artificial intelligence in all its various forms will go mainstream. I've decided to add my voice and make my own predictions for the coming year. Along the way, it will help power innovations such as autonomous vehicles, precision farming, therapeutic drug discovery, and advanced fraud detection for financial institutions.

Machine Learning (ML) is an exciting new subfield of computer science. With origins in pattern recognition, today's hardware and software advances have made ML a new tool for many types of organizations I order to remain competitive. With today's hardware, massive amounts of data can be fed into a system, which can then use algorithms to determine possible outcomes of a task, and store that information for further use. As the amount of data that is ingested increases, the accuracy of the outcomes can improve. Similar to simulations that can give more accurate results with faster processing, more memory and improved algorithms, so can ML applications.

Its expected over 50 billion devices will be connected by 2020, these includes wearables, vehicles, city infrastructure and probably sensors in every home appliance. Intel is working hard towards this vision and identified these areas the chipmaker will be focusing advancely for 2017. Artificial intelligence, 5G networks, automated driving and virtual reality/merged reality. The global robotics and AI market is estimated to grow to $153 billion by 2020, which includes $83 billion for robotics and $70 billion for AI-based analytics. The technology supporting some nascent AI applications, such as natural language processing and bots will greatly improve, paving the way for more widespread adoption of AI.

SAN FRANCISCO, Nov. 17, 2016 – Intel Corporation today announced a range of new products, technologies and investments from the edge to the data center to help expand and accelerate the growth of artificial intelligence (AI). Intel sees AI transforming the way businesses operate and how people engage with the world. Intel is assembling the broadest set of technology options to drive AI capabilities in everything from smart factories and drones to sports, fraud detection and autonomous cars. At an industry gathering led by Intel CEO Brian Krzanich, Intel shared how both the promise and complexities of AI require an extensive set of leading technologies to choose from and an ecosystem that can scale beyond early adopters. As algorithms become complex and required data sets grow, Krzanich said Intel has the assets and know-how required to drive this computing transformation.

Intel announced a slew of products, technologies and investment in an effort to fix its position in the field of artificial intelligence. In the new move, Intel has assembled a set of technology options to drive AI capabilities in everything from smart factories and drones to sports, fraud detection and autonomous cars. Intel is increasing its focus on AI as it believes it can power the AI products released recently by companies like Facebook and Google. In a blog Intel CEO Brian Krzanich had said, "Intel is uniquely capable of enabling and accelerating the promise of AI. Intel is committed to AI and is making major investments in technology and developer resources to advance AI for business and society."

Colfax now offers a 2-hour Hands-On Workshop (HOW) video on the best practices for performance optimization for Intel Xeon Phi processor (formerly Knights Landing). Use links below the video to navigate the 10 episodes.

The vision that computers could emulate human reasoning and decision making arose in the 1940s--soon after the development of modern computers themselves. There have been long periods when progress was scant, but Artificial Intelligence is now poised to take off. To understand why now is the time, let's look for a minute at then. The challenge with AI has always been to understand how humans represent knowledge and how they apply it to make decisions. The idea was to capture the knowledge of experts along with a set of rules that governed how to apply it.

Yes, the title is a precious mess. But "Big Data" was always defined as a problem statement. For some, it represented the challenge of acquiring data from new sources. For others, it meant the herculean task of building a scalable infrastructure that could manage all the data. For a brave few, it meant the arcane art (or presumptive science) of extracting value from data using advanced data analysis techniques and tools.

In this special guest feature, Rob Farber from TechEnablement writes that modernized code can deliver significant speedups on machine learning applications. Benchmarks, customer experiences, and the technical literature have shown that code modernization can greatly increase application performance on both Intel Xeon and Intel Xeon Phi processors. Colfax Research recently published a study showing that image tagging performance using the open source NeuralTalk2 software can be improved 28x on Intel Xeon processors and by over 55x on the latest Intel Xeon Phi processors (specifically an Intel Xeon Phi processor 7210). For the study, Colfax Research focused on modernizing the C-language Torch middleware while only one line was changed in the high-level Lua scripts. NeuralTalk2 uses machine learning algorithms to analyze real-life photographs of complex scenes and produce a correct textual description of the objects in the scene and relationships between them (e.g., "a cat is sitting on a couch", "woman is holding a cell phone in her hand", "a horse-drawn carriage is moving through a field", etc.) Captioned examples are show in the figure below.