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Author: Sally Ward-Foxton

Sally Ward-Foxton covers AI technology and related issues for EETimes.com and all aspects of the European industry for EETimes Europe magazine. Sally has spent more than 15 years writing about the electronics industry from London, UK. She has written for Electronic Design, ECN, Electronic Specifier: Design, Components in Electronics, and many more. She holds a Masters' degree in Electrical and Electronic Engineering from the University of Cambridge.

Panel: Global Leadership in AI Depends on Gaining Public Trust

By Sally Ward-Foxton

The support of the public is paramount to any country or region’s successful global leadership in transforming businesses, markets and economies using the technology, a panel discussion at CES concluded.

To start, the panelists, which included representatives from industry, trade associations, politics and government, were in agreement that leadership in AI is not a simple win-lose conundrum.

“I would not agree with the idea that it’s a zero-sum game, that one nation is leading and that it’s a race and someday you declare a victory, and then everybody else is a loser… I think everyone can benefit from the advantages of artificial intelligence,” said Lynne Parker, deputy CTO for the United States, from the White House’s Office of Science and Technology Policy.

Parker’s definition for a nation that leads in the field of AI is one with a lot of companies leading in terms of innovation, one with many leading universities in that field with cutting-edge ideas, and one with a strong innovation ecosystem which works closely with academia and the industry to foster innovation. The US is leading, according to those metrics, she argued.

“There will be winners, but maybe it’ll be first place, second place, third place, fourth place, and not winner take all,” said Michael Beckerman, president and CEO of the Internet Association. “From the companies standpoint, certainly [leadership] will come from innovation and the ability to put in place transparency and safeguards to ensure there’s not bias or discrimination through artificial intelligence, and ensuring that ethics are set up in a way that meets our common goals and standards… and from the government standpoint, making sure that policies are in place that both encourage and allow for innovation.”

These policies must ensure there are safeguards for both government and private sector use of AI, Beckerman said, pointing out that some of the riskier potential applications in terms of public trust are for government applications of the technology.

The panelists also agreed that consumer trust was of paramount importance, in a way that hasn’t been seen with other technology trends in the past.

“For both private sector and public sector use, you can’t truly win or succeed with AI deployments, with scaling AI, unless you have consumer and citizen trust,” said Adelina Cooke, North American AI policy lead at Accenture. “Any [leadership] race is going to need to engender trust among the population. When we are thinking about scaling [up AI applications], it’s not just feasibility and innovation, it’s making sure that you have the proper governance and responsible oversight within an organization [that’s important].”

Government Role
Panelists took different views on the role governments should play in increasing AI leadership in their countries.

The White House’s Lynne Parker described the US government’s hands-off approach to regulation of the use of AI technology.

“Certainly, I think at the beginning, the role of the federal government is not to get in the way,” she said. “We want to foster innovation and make sure it’s being used in ways we can all benefit from, but… there are many areas in which we need to have more oversight.”

AI presents a unique challenge, she said, in that there are many existing laws that protect Americans from things like discrimination, and the country has a robust legal system to help enforce these laws. If these laws are enforced at the state and local government level, companies have to deal with a patchwork of laws and regulations, which hampers innovation in every locale.

“At some point, the federal government needs to step up and say, okay, we’re actually hampering innovation by not having regulatory oversight or a process for it, or having any consistency,” she said.

The White House released a draft memo earlier this week which will establish consistent guidelines for regulatory agencies, which should help protect the public and also help the innovation ecosystem by providing companies with some predictability in terms of regulatory approach, she said.

Italian member of parliament Mattia Fantinati detailed both the European Commission’s approach and the approach in Italy.

The European Commission’s strategy for AI leadership is based several key ideas. These include boosting technological and industrial capacity, uptake of AI across the economy with private-public partnerships, being prepared for the socioeconomic changes which will happen quickly, and ensuring a legal and ethical framework for innovation to flourish within.

Italian initiatives for adoption of AI are focused on small and medium enterprises (SMEs), reflecting the country’s economy, he said.

“Most developed countries have adopted an AI strategy that reflects their social and political system,” he said, noting that Italy is home to many SMEs in manufacturing and handicrafts. “My role is to… create a collaboration between the masters of handicraft and artificial intelligence. It’s not easy, but we have to do it, because the European strategy is focused on the SME.”

The European Commission’s strategy includes using public funding to stimulate private investment, particularly with early-stage startups.

USA vs. China
Asked by an audience member about Kai Fu Lee’s 2018 book “AI Superpowers: China, Silicon Valley and the New World Order” in which he details China’s strengths in this arena, Parker again referenced the importance of public trust.

She mentioned Lee’s postulation that China is very good at taking existing ideas and implementing them.

“At the same time, I think we, as the free world, also care about exactly how these technologies are used,” she said. “We want to make sure that we don’t use the technologies in ways that are inconsistent with the values of our nations.”

AI Can Map the World for Disaster Preparedness

By Sally Ward-Foxton

LAS VEGAS — Intel has developed AI models to identify geographical features from satellite imagery for the creation of accurate, up-to-date maps. The company has been working closely with the Red Cross on its Missing Maps project, which aims to create maps for areas of the developing world to improve disaster preparedness. Many areas of the developing world do not have up-to-date maps, which means that aid organizations can struggle to work efficiently in the event of natural disasters or epidemics.

“As someone who’s been on the ground with the Red Cross, having access to accurate maps is extremely important in disaster planning and emergency response,” said Dale Kunce, co-founder of Missing Maps and CEO of American Red Cross Cascades Region. “But there are entire parts of the world that are unmapped, which makes planning and responding to disasters much more difficult. This is why we’re collaborating with Intel to use AI to map vulnerable areas and identify roads, bridges, buildings, and cities.”

“If you don’t know where all the roads are before a hurricane hits, after it hits, you have no idea where flooding has occurred or which roads are washed out and which aren’t,” said Alexei Bastidas, deep-learning data scientist at Intel AI Lab, in an Intel podcast on the subject. “If you don’t have an accurate enough map of what was there beforehand, it really prevents you from responding to the disaster as it’s ongoing. The other thing to consider is that a lot of these disasters … are weather events — cyclones, typhoons, hurricanes, even volcanic eruptions. These weather events can occlude the satellite sensor; they create clouds … It makes it extremely challenging for somebody like the Red Cross to respond to an event.”

At present, Missing Maps uses a team of volunteers to go though satellite images and identify roads, towns, bridges, and other infrastructure. The volunteers manually update an open-source map called Open Street Map, which is laborious and time-consuming.

Intel’s AI Lab, in collaboration with Mila and CrowdAI, developed an image-segmentation model and used it to identify unmapped bridges in Uganda from satellite pictures. Object-detection approaches were discounted due to performance in favor of segmentation. Bridges were selected as a trial feature because they are critical infrastructure and are particularly vulnerable to natural disasters such as floods. Seventy previously unmapped bridges were discovered by the system; the Ugandan National Society can use this data to better plan evacuation and aid-delivery routes.

Uganda Map
The system identified 70 bridges across Uganda that were previously unmapped by either Open Street Map or the Ugandan Bureau of Statistics. (Image: Intel)

Satellite imagery can be particularly challenging to work with. The lack of an obvious frame of reference for up and down is challenging, said Bastidas. Also, images are not always taken from directly above, meaning the same feature may be seen from different angles. Differences in the local terrain as well as styles of infrastructure and architecture make it hard to train models on labelled data from other parts of the world. Even in images from the same country, terrain may look very different in summer and winter, and features such as bridges show huge variation in size and style.

Intel’s training dataset therefore had to come exclusively from Uganda. In fact, a section of Northern Uganda was used, which includes multiple views of the same bridges to enable models to learn about seasonal and nadir-angle changes.

The models started by looking for waterways and highway features, and any areas where a highway crossed a waterway was marked as a candidate point for a bridge. Known bridge locations within 30 m of any candidate points were discarded. Bounding boxes were added around these intersections, and then satellite images from areas in the bounding boxes were pulled. The models could then interpret the images to see whether they contained a bridge.

The models ran on second-generation Intel Xeon scalable processors (Cascade Lake) with DL Boost and nGraph. Bastidas said that these processors were chosen for their giant size; satellite images are often 1,024 square pixels, and it was desirable for the chip to process an entire image at once.

According to Bastidas, the next steps for the project may include the generation of models that can aid human mapping volunteers, perhaps predicting bridge locations but leaving the final decision to human eyes.

“We are also interested in trying to come up with ways to leverage existing open-source data to make models that are more robust, more generalizable, and can [work] with more tolerance for this geographically distinct area,” he said.

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