TechRepublic's Teena Maddox talked to Dave Neway, head of marketing at IBM Watson Advertising, at CES 2020 about how Watson can take advertising to the next level with artificial intelligence (AI). The following is an edited transcript of their conversation. Dave Neway: We had a major product announcement this week. On Tuesday, I'm very proud to announce that we launched a new offering called Advertising Accelerator with Watson, which as you can probably tell by the name, leverages Watson AI really to do three things. Anticipation: It helps advertisers better predict as opposed to react to the optimal combination of visual elements to drive the highest engagement for an audience.

My holiday reading this year was Gregory Zuckerman's The Man Who Solved the Market, which I finished in one long sitting on Christmas Eve. It tells a fascinating story of the legendary Jim Simons and his secretive hedge-fund firm, Renaissance Technologies. Without a doubt, Simons has an extremely successful career. Simons started a side project on the mathematical analysis of stock trading strategies when he worked at the Institute for Defense Analyses (IDA) as a Cold War codebreaker. After the IDA fired him for publicly speaking out against the Vietnam War, Simons joined the faculty at Stony Brook University, where he recruited top talents from across the country and built a world-class math department.

Canon has made a lot of noise about its new Deep Learning AF system, which sits at the heart of the manufacturer's latest flagship professional camera. It sounds incredibly clever, but there are plenty of questions – what is Deep Learning? Does the system learn as you shoot? Is it really artificial intelligence in a camera? Does it actually make the autofocus any better?

Recently a team of researchers from Imperial College London and Google Health created a computer vision model intended to diagnose cases of breast cancer from X-rays. As CNN reports, the model was reportedly trained on X-rays of over 29,000 women, and when pitted against six radiologists the model managed to outperform the assessments of the doctors. Currently, the NHS uses the combined decisions of two doctors in order to diagnose breast cancer from X-rays. If the two doctors end up disagreeing, a third will be brought in to consult on the images. While the doctors had access to the medical records of the patients, the AI device only had the mammograms to base its decisions on.

It's difficult to make predictions, especially about the future. But one fairly safe prediction is that data will continue eating the world in 2020 and the coming decade. The most important tech trend since the 1990s will no doubt accentuate its presence in our lives, for better or for worse. At the beginning of the last decade, IDC estimated that 1.2 zettabytes (1.2 trillion gigabytes) of new data were created in 2010, up from 0.8 zettabytes the year before. The amount of the newly created data in 2020 was predicted to grow 44X to reach 35 zettabytes (35 trillion gigabytes).

A recent report from the MIT Work of the Future Task Force finds that companies are still in the "early stages of adoption" when it comes to incorporating new technology into their workflows, while a 2018 Pew Research Center study showed that 65-90% of surveyed people think human-held jobs will be replaced by robots and computers. When and how future workplaces will ultimately change remain unanswered, but Daniel Huttenlocher, inaugural dean of the MIT Stephen A. Schwarzman College of Computing, has some ideas. He spoke Dec. 2 at the MIT Technology Review Future Compute event in Cambridge, Massachusetts, and discussed the future of machines and the digital workforce. "I think it's very hard to predict the future and particularly hard to predict the positive outcomes of the future," said Huttenlocher, PhD '88. "It's a lot easier to see a technology and say'Gee, that looks like it's going to pose a risk for a particular form of employment' … than to envision some whole new type of work that is very hard to see because of the way that the technology is going to change."

Is thermal imagery detailed enough to enable an AI model to recognize people's facial features? That's the question Intel and Gánsk University of Technology researchers sought to answer in a study recently presented at the Institute of Electrical and Electronics Engineers' 12th International Conference on Human System Interaction. These researchers investigated the performance of a model trained on visible light data that was subsequently retrained on thermal images. As the researchers point out in a paper describing their work, thermal imagery is often used in lieu of RGB camera data within environments where privacy is preferred or otherwise mandated, like medical facilities. That's because it's able to obscure personally identifying details like eye color and jaw line.

H. Kargupta: Connected Cars: How Distributed Data Mining Is Changing the Next Generation of Vehicle Telematics Products.

We have data-driven decision support systems implemented in Management Information Systems(MIS). Algorithms created by humans coded into MIS chew raw data and spit out decisions. The MIS systems were developed by human with substantial effort in software development. Once created, they allowed very little flexibility in deriving insights from new data sets. Now we have Machine Learning (ML) systems capable of making data-driven decisions or predictions without the need for explicit programming.

Many people may be familiar with the frustration of calling up their smartphone's speech-to-text function to dictate an email, only to find that it won't work because the phone isn't connected to the internet. Now, a new generation of edge artificial intelligence (AI) chips is set to reduce those frustrations by bringing the AI to the device.1 We predict that in 2020, more than 750 million edge AI chips--chips or parts of chips that perform or accelerate machine learning tasks on-device, rather than in a remote data center--will be sold. This number, representing a cool US$2.6 billion in revenue, is more than twice the 300 million edge AI chips Deloitte predicted would sell in 20172--a three-year compound annual growth rate (CAGR) of 36 percent. Further, we predict that the edge AI chip market will continue to grow much more quickly than the overall chip market. By 2024, we expect sales of edge AI chips to exceed 1.5 billion, possibly by a great deal.3 This represents annual unit sales growth of at least 20 percent, more than double the longer-term forecast of 9 percent CAGR for the overall semiconductor industry.4 These edge AI chips will likely find their way into an increasing number of consumer devices, such as high-end smartphones, tablets, smart speakers, and wearables.