Electrical engineer Gilbert Herrera was appointed research director of the US National Security Agency in late 2021, just as an AI revolution was brewing inside the US tech industry. The NSA, sometimes jokingly said to stand for No Such Agency, has long hired top math and computer science talent. Its technical leaders have been early and avid users of advanced computing and AI. And yet when Herrera spoke with me by phone about the implications of the latest AI boom from NSA headquarters in Fort Meade, Maryland, it seemed that, like many others, the agency has been stunned by the recent success of the large language models behind ChatGPT and other hit AI products. The conversation has been lightly edited for clarity and length.

For decades, the shift towards Ecommerce has continued steadily across the globe, with growth consistently outpacing (and eating into) bricks and mortar retailing. The COVID pandemic has simply accelerated that shift. Yet many consumer goods companies are still behind the curve in their use of technology. Ella, my 12-year-old daughter, has illustrated this point to me several times during the past year. It begins when she shows me her phone and says, "look daddy, check out this new arts-and-crafts or room decor video on TikTok."

Mumbai: Early this month at the Intel AI Devcon 2018 in Bengaluru, a holographic avatar called Ella listened intently to composer Kevin Doucette playing notes on his synthesizer. When he paused, she began composing her own notes, complementing his music in real-time. Ella was learning about features such as tempo, scale and pitch from the music data that was being sent in real-time to an Intel Movidius Neural Compute Stick. Intel used a class of artificial neural networks, the recurrent neural network or RNN that depends on previous calculations to work on current ones, to perform this artificial intelligence (AI) task. This Neural Compute Stick is simply a case in point that Intel--a company which most people identify with central processing units (CPUs) inside personal computers (PCs), mobiles and servers--is widening its portfolio to stay in the AI race that has strong contenders including Nvidia, Microsoft, Google, Facebook, IBM, Amazon, Apple, Alibaba and Baidu.

Medtronic's mission is to alleviate pain, restore health, and extend life through the application of biomedical engineering, explains Elaine Gee, PhD, Senior Principal Algorithm Engineer specializing in Artificial Intelligence at Medtronic. It's a mission Gee is well equipped for. With over 15 years' experience in modeling, bioinformatics, and engineering, she drives machine learning algorithm development and analytics to support next-generation medical devices for diabetes management. On behalf of AI Trends, Ben Lakin, from Cambridge Innovation Institute, sat down with Gee to discuss her most recent focus: algorithm development related to glucose sensing to improve the accuracy and performance of continuous glucose monitoring devices, also known as CGMs. Editor's Note: Gee will be giving a featured presentation on Advancing Continuous Glucose Monitoring Sensor Development with Machine Learning at Sensors Summit in San Diego, December 10-12.

In my experience as a business transformation solutions expert, I know that deals are only closed when both buyer and seller see the value. For the seller, this means optimizing revenues and margins. To do this consistently, enterprises must not only know what solutions to offer their customers but also be able to gauge their customers' willingness to pay. In a competitive environment where many players offer similar services and solutions, the ability to consistently offer a price that is well within the customer's zone of price comfort is vital to success. Enterprises in the business to business (B2B) space generally have well-defined policies that govern not only pricing and margin requirements but also discounts, preferential payment terms, and so on.

Mumbai: Early this month at the Intel AI Devcon 2018 in Bengaluru, a holographic avatar called Ella listened intently to composer Kevin Doucette playing notes on his synthesizer. When he paused, she began composing her own notes, complementing his music in real-time. Ella was learning about features such as tempo, scale and pitch from the music data that was being sent in real-time to an Intel Movidius Neural Compute Stick. Intel used a class of artificial neural networks, the recurrent neural network or RNN that depends on previous calculations to work on current ones, to perform this artificial intelligence (AI) task. This Neural Compute Stick is simply a case in point that Intel--a company which most people identify with central processing units (CPUs) inside personal computers (PCs), mobiles and servers--is widening its portfolio to stay in the AI race that has strong contenders including Nvidia, Microsoft, Google, Facebook, IBM, Amazon, Apple, Alibaba and Baidu.

Developers using the edge computing platform may be interested in a new piece of hardware created by UP based in the Netherlands called UP AI Edge. As the name suggests it adds artificial intelligence and hardware acceleration'ON' the Edge of the Internet of Things. Watch the demonstration video below to learn more about the UP hardware and its specifications. The UP AI Edge platform offers developers and users and ultra-compact form factor, combining three powerful Intel technologies together into one device providing an Intel Apollo Lake SoC, Intel Movidius Myriad 2 VPU and Intel Cyclone 10GX FPGA. "Not long ago the trend has changed from the Internet of Things to Artificial Intelligence. At UP, we didn't only stop at talking about this shift but we began to actually make it! Not forgetting the post-it sized computing platform, UP Core, we've been launching each exciting project successively. With refueled excitement, we bring you UP AI Edge. The first embedded, ultra compact, high performance, low power consumption, Artificial Intelligence platform for Edge computing."

Artificial intelligence is an emerging solution for some of the most important problems facing health care today, including medical imaging, clinical decision support, pharmaceuticals, and more. There is clearly significant value in applying AI to medicine, but there are many challenges as well. Who will reap the benefits? How can health care startups make sure their proprietary AI technology is protected? And how do they maintain their AI edge?

That's the question Mary Cummings of Duke University puts forward in a new paper for the think tank Chatham House. Citing R&D spending in recent years, Cummings argues that companies like Google and Facebook could outpace militaries when it comes to the science of artificial intelligence, which in turn could lead to potentially dangerous technologies going into use before they have been properly tested. The matter will probably come down to who will be the first to make automated systems truly autonomous--or at least more autonomous then they are now. As Cummings explains, there's a range of capabilities when it comes to processing information and making decisions. At one end there's the pilot reading instrument displays and making quick adjustments at the controls--behavior that eventually becomes automatic.