When John Guttag keynotes the HIMSS and Healthcare IT News Big Data and Healthcare Analytics Forum in Boston on October 24, the MIT professor will describe the unique challenges of applying machine learning to healthcare – as well as the huge potential for efficiencies and quality improvements as these data techniques become more widespread across the industry. Guttag, who heads the Data Driven Inference Group at the MIT's Computer Science and Artificial Intelligence Laboratory, and his MIT students are currently working closely with Mass General on integrating machine learning into clinical workflows, specifically with the aim of reducing healthcare-associated infections. "I want to actually see things change in the system, not just write papers saying things could change," Guttag said. "The goal here is to have something good happen. I hope a year from now I'm able to say, 'Guess what, we've lowered the rate of nosocomial infections at MGH – and more importantly put together a description of how we've done it that is exportable to other organizations.'"

Amazon Web Services has unveiled a new generation of GPU-powered cloud computing instances aimed squarely at customers running machine learning applications. The P2's a major step up from the previous generation of GPU-powered AWS instances, and it has plenty of memory to burn. But it's built with an earlier generation of GPU, so it's less suited for the bleeding-edge machine learning work that needs the most recent advances in GPU technology. Amazon is currently billing the G2 as suitable for "graphics-intensive applications," rather than machine learning specifically. The P2, on the other hand, is definitely for machine learning.

With online viewership and sales growing rapidly, enterprises are interested in understanding how they analyze performance to positively impact business metrics. Deeper insight into the user experience is needed to understand why conversions are dropping and/or bounce rates are increasing or, preferably, to understand what has been helping these metrics improve. The digital performance management industry has evolved as application performance management companies have broadened their scope beyond synthetic testing that simulates users loading specific pages at regular intervals to include web and mobile testing, and real user monitoring (RUM). As synthetic monitoring gained popularity, performance engineers realized the variations that exist from real end users were not being captured. This led to the introduction of RUM - the process of capturing, analyzing and reporting data from a real end user's interaction with a website.

With up to 16 NVIDIA Tesla K80 GPUs, P2 instances are the most powerful GPU instances available in the cloud. P2 instances allow customers to build and deploy compute-intensive applications using the CUDA parallel computing platform or the OpenCL framework without up-front capital investments. To offer the best performance for these high performance computing applications, the largest P2 instance offers 16 GPUs with a combined 192 Gigabytes (GB) of video memory, 40,000 parallel processing cores, 70 teraflops of single precision floating point performance, over 23 teraflops of double precision floating point performance, and GPUDirect technology for higher bandwidth and lower latency peer-to-peer communication between GPUs. P2 instances also feature up to 732 GB of host memory, up to 64 vCPUs using custom Intel Xeon E5-2686 v4 (Broadwell) processors, dedicated network capacity for I/O operation, and enhanced networking through the Amazon EC2 Elastic Network Adaptor. Two years ago, we launched G2 instances to support customers running graphics and compute-intensive applications," said Matt Garman, Vice President, Amazon EC2. "Today, as customers embrace heavier GPU compute workloads such as artificial intelligence, high-performance computing, and big data processing, they need even higher GPU performance than what was previously available.

Artificial Intelligence Poised to Double Annual Economic Growth Rate in 12 Developed Economies and Boost Labor Productivity by up to 40 Percent by 2035, According to New Research by Accenture NEW YORK; Sept. 28, 2016 – Research released today from Accenture (NYSE: ACN) reveals that artificial intelligence (AI) could double annual economic growth rates by 2035 by changing the nature of work and spawning a new relationship between man and machine. The impact of AI technologies on business is projected to boost labor productivity by up to 40 percent by fundamentally changing the way work is done and reinforcing the role of people to drive growth in business. "AI is poised to transform business in ways we've not seen since the impact of computer technology in the late 20th century," said Paul Daugherty, chief technology officer, Accenture. "The combinatorial effect of AI, cloud, sophisticated analytics and other technologies is already starting to change how work is done by humans and computers, and how organizations interact with consumers in startling ways. Our research demonstrates that as AI matures, it can propel economic growth and potentially serve as a powerful remedy for stagnant productivity and labor shortages of recent decades."

ACCORDING to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about "the singularity" -- when machines become smarter than humans -- have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional.

Some scientists suggest that, instead of working on artificial intelligence that functions better than the human brain, we should be making computers like the brain. We may well be on this path after engineers from University of Massachusetts Amherst demonstrated devices that emulate the behavior of the brain's synapses. Their device uses "memristors,' components whose resistance relies on how much charge has passed through them in the past. That means they have the ability to store and process information, and have some characteristics that make them better than traditional integrated circuits. These memristors have been used before, but what makes this study unique is that two different kinds of memristors are being combined to better emulate the brain. On their own, memristors have been made to mimic synapses, using electrical fields. But these are based on physical processes, not biological ones. When used with diffusion-type memristors, however, the whole set up becomes more like how a regular synapse fires up. "In the past, people have used devices like transistors and capacitors to simulate synaptic dynamics, which can work, but those devices have very little resemblance to real biological systems.

The Future Today Institute has created a terrific, free report summarizing key technology trends and what they mean for tomorrow. I've embedded the report below so you can quickly flip through it. I read the whole report and highlighted the most noteworthy elements for book publishers below. That leads me (once again) to the topic of curation, a very important (current and) future publishing trend. Curation is becoming as important as creation, especially as we're bombarded with more information than we can possibly consume.

When the U.S. globalization push of the 1990s outsourced manufacturing, proponents could not have known the full impact this shift would have at home. Not only were many towns wiped out, their commerce to be replaced by service industries, but the manufacturing sector itself would struggle with the loss of infrastructure -- buildings and machinery -- and with manufacturing intelligence as well. Manufacturing intelligence, the kind of know-how and workers that we need to take back industries, was lost. The country's shrinking manufacturing base benefited many other global economies, however. China, of course, became a major trade partner.

Royal Bank of Scotland (RBS) is launching a new online "chatbot" that will answer customers' questions online and help direct them to the right places. The new online tool, dubbed "Luvo," will begin helping 10% of the bank's customers online from December, according to an emailed statement sent to Business Insider. It will be a web chat tool: as you're browsing, a little chat window will likely pop up and ask if you need any help. The rollout follows a 2-month trial of the tool with the 1,200 RBS staff handling small business inquiries. These staff could direct the SMEs to Luvo to help them with things like lost pins or corporate cards.