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#439357 How the Financial Industry Can Apply AI ...
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THE INSTITUTE Artificial intelligence is transforming the financial services industry. The technology is being used to determine creditworthiness, identify money laundering, and detect fraud.
AI also is helping to personalize services and recommend new offerings by developing a better understanding of customers. Chatbots and other AI assistants have made it easier for clients to get answers to their questions, 24/7.
Although confidence in financial institutions is high, according to the Banking Exchange, that’s not the case with AI. Many people have raised concerns about bias, discrimination, privacy, surveillance, and transparency.
Regulations are starting to take shape to address such concerns. In April the European Commission released the first legal framework to govern use of the technology, as reported in IEEE Spectrum. The proposed European regulations cover a variety of AI applications including credit checks, chatbots, and social credit scoring, which assesses an individual’s creditworthiness based on behavior. The U.S. Federal Trade Commission in April said it expects AI to be used truthfully, fairly, and equitably when it comes to decisions about credit, insurance, and other services.
To ensure the financial industry is addressing such issues, IEEE recently launched a free guide, “Trusted Data and Artificial Intelligence Systems (AIS) for Financial Services.” The authors of the nearly 100-page playbook want to ensure that those involved in developing the technologies are not neglecting human well-being and ethical considerations.
More than 50 representatives from major banks, credit unions, pension funds, and legal and compliance groups in Canada, the United Kingdom, and the United States provided input, as did AI experts from academia and technology companies.
“This IEEE finance playbook is a milestone achievement and provides a much-needed practical road map for organizations globally to develop their trusted data and ethical AI systems.”
“We are in the business of trust. A primary goal of financial services organizations is to use client and member data to generate new products and services that deliver value,” Sami Ahmed says. He is a member of IEEE industry executive steering committee that oversaw the playbook’s creation.
Ahmed is senior vice president of data and advanced analytics of OMERS, Ontario’s municipal government employees’ pension fund and one of the largest institutional investors in Canada.
“Best-in-class guidance assembled from industry experts in IEEE’s finance playbook,” he says, “addresses emerging risks such as bias, fairness, explainability, and privacy in our data and algorithms to inform smarter business decisions and uphold that trust.”
The playbook includes a road map to help organizations develop their systems. To provide a theoretical framework, the document incorporates IEEE’s “Ethically Aligned Design” report, the IEEE 7000 series of AI standards and projects, and the Ethics Certification Program for Autonomous and Intelligent Systems.
“Design looks completely different when a product has already been developed or is in prototype form,” says John C.Havens, executive director of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. “The primary message of ethically aligned design is to use the methodology outlined in the document to address these issues at the outset.”
Havens adds that although IEEE isn’t well known by financial services regulatory bodies, it does have a lot of credibility in harnessing the technical community and creating consensus-based material.
“That is why IEEE is the right place to publish this playbook, which sets the groundwork for standards development in the future,” he says.
IEEE Member Pavel Abdur-Rahman, chair of the IEEE industry executive steering committee, says the document was necessary to accomplish three things. One was to “verticalize the discussion within financial services for a very industry-specific capability building dialog. Another was to involve industry participants in the cocreation of this playbook, not only to curate best practices but also to develop and drive adoption of the IEEE standards into their organizations.” Lastly, he says, “it’s the first step toward creating recommended practices for MLOps [machine-learning operations], data cooperatives, and data products and marketplaces.
Abdur-Rahman is the head of trusted data and AI at IBM Canada.
ROAD MAP AND RESOURCES
The playbook has two sections, a road map for how to build trusted AI systems and resources from experts.
The road map helps organizations identify where they are in the process of adopting responsible ethically aligned design: early, developing, advanced, or mature stage. This section also outlines 20 ways that trusted data and AI can provide value to operating units within a financial organization. Called use cases, the examples include cybersecurity, loan and deposit pricing, improving operational efficiency, and talent acquisition. Graphs are used to break down potential ethical concerns for each use case.
The key resources section includes best practices, educational videos, guidelines, and reports on codes of conduct, ethical challenges, building bots responsibly, and other topics. Among the groups contributing resources are the European Commission, IBM, the IEEE Standards Association, Microsoft, and the World Economic Forum. Also included is a report on the impact the coronavirus pandemic has had on the financial services industry in Canada. Supplemental information includes a list of 84 documents on ethical guidelines.
“We are at a critical junction of industrial-scale AI adoption and acceleration,” says Amy Shi-Nash, a member of the steering committee and the global head of analytics and data science for HSBC. “This IEEE finance playbook is a milestone achievement and provides a much-needed practical road map for organizations globally to develop their trusted data and ethical AI systems.”
To get an evaluation of the readiness of your organization’s AI system, you can anonymously take a 20-minute survey.
IEEE membership offers a wide range of benefits and opportunities for those who share a common interest in technology. If you are not already a member, consider joining IEEE and becoming part of a worldwide network of more than 400,000 students and professionals. Continue reading
#439105 This Robot Taught Itself to Walk in a ...
Recently, in a Berkeley lab, a robot called Cassie taught itself to walk, a little like a toddler might. Through trial and error, it learned to move in a simulated world. Then its handlers sent it strolling through a minefield of real-world tests to see how it’d fare.
And, as it turns out, it fared pretty damn well. With no further fine-tuning, the robot—which is basically just a pair of legs—was able to walk in all directions, squat down while walking, right itself when pushed off balance, and adjust to different kinds of surfaces.
It’s the first time a machine learning approach known as reinforcement learning has been so successfully applied in two-legged robots.
This likely isn’t the first robot video you’ve seen, nor the most polished.
For years, the internet has been enthralled by videos of robots doing far more than walking and regaining their balance. All that is table stakes these days. Boston Dynamics, the heavyweight champ of robot videos, regularly releases mind-blowing footage of robots doing parkour, back flips, and complex dance routines. At times, it can seem the world of iRobot is just around the corner.
This sense of awe is well-earned. Boston Dynamics is one of the world’s top makers of advanced robots.
But they still have to meticulously hand program and choreograph the movements of the robots in their videos. This is a powerful approach, and the Boston Dynamics team has done incredible things with it.
In real-world situations, however, robots need to be robust and resilient. They need to regularly deal with the unexpected, and no amount of choreography will do. Which is how, it’s hoped, machine learning can help.
Reinforcement learning has been most famously exploited by Alphabet’s DeepMind to train algorithms that thrash humans at some the most difficult games. Simplistically, it’s modeled on the way we learn. Touch the stove, get burned, don’t touch the damn thing again; say please, get a jelly bean, politely ask for another.
In Cassie’s case, the Berkeley team used reinforcement learning to train an algorithm to walk in a simulation. It’s not the first AI to learn to walk in this manner. But going from simulation to the real world doesn’t always translate.
Subtle differences between the two can (literally) trip up a fledgling robot as it tries out its sim skills for the first time.
To overcome this challenge, the researchers used two simulations instead of one. The first simulation, an open source training environment called MuJoCo, was where the algorithm drew upon a large library of possible movements and, through trial and error, learned to apply them. The second simulation, called Matlab SimMechanics, served as a low-stakes testing ground that more precisely matched real-world conditions.
Once the algorithm was good enough, it graduated to Cassie.
And amazingly, it didn’t need further polishing. Said another way, when it was born into the physical world—it knew how to walk just fine. In addition, it was also quite robust. The researchers write that two motors in Cassie’s knee malfunctioned during the experiment, but the robot was able to adjust and keep on trucking.
Other labs have been hard at work applying machine learning to robotics.
Last year Google used reinforcement learning to train a (simpler) four-legged robot. And OpenAI has used it with robotic arms. Boston Dynamics, too, will likely explore ways to augment their robots with machine learning. New approaches—like this one aimed at training multi-skilled robots or this one offering continuous learning beyond training—may also move the dial. It’s early yet, however, and there’s no telling when machine learning will exceed more traditional methods.
And in the meantime, Boston Dynamics bots are testing the commercial waters.
Still, robotics researchers, who were not part of the Berkeley team, think the approach is promising. Edward Johns, head of Imperial College London’s Robot Learning Lab, told MIT Technology Review, “This is one of the most successful examples I have seen.”
The Berkeley team hopes to build on that success by trying out “more dynamic and agile behaviors.” So, might a self-taught parkour-Cassie be headed our way? We’ll see.
Image Credit: University of California Berkeley Hybrid Robotics via YouTube Continue reading
#439081 Classify This Robot-Woven Sneaker With ...
For athletes trying to run fast, the right shoe can be essential to achieving peak performance. For athletes trying to run fast as humanly possible, a runner’s shoe can also become a work of individually customized engineering.
This is why Adidas has married 3D printing with robotic automation in a mass-market footwear project it’s called Futurecraft.Strung, expected to be available for purchase as soon as later this year. Using a customized, 3D-printed sole, a Futurecraft.Strung manufacturing robot can place some 2,000 threads from up to 10 different sneaker yarns in one upper section of the shoe.
Skylar Tibbits, founder and co-director of the Self-Assembly Lab and associate professor in MIT's Department of Architecture, says that because of its small scale, footwear has been an area of focus for 3D printing and additive manufacturing, which involves adding material bit by bit.
“There are really interesting complex geometry problems,” he says. “It’s pretty well suited.”
Photo: Adidas
Beginning with a 3D-printed sole, Adidas robots weave together some 2000 threads from up to 10 different sneaker yarns to make one Futurecraft.Strung shoe—expected on the marketplace later this year or sometime in 2022.
Adidas began working on the Futurecraft.Strung project in 2016. Then two years later, Adidas Futurecraft, the company’s innovation incubator, began collaborating with digital design studio Kram/Weisshaar. In less than a year the team built the software and hardware for the upper part of the shoe, called Strung uppers.
“Most 3D printing in the footwear space has been focused on the midsole or outsole, like the bottom of the shoe,” Tibbits explains. But now, he says, Adidas is bringing robotics and a threaded design to the upper part of the shoe. The company bases its Futurecraft.Strung design on high-resolution scans of how runners’ feet move as they travel.
This more flexible design can benefit athletes in multiple sports, according to an Adidas blog post. It will be able to use motion capture of an athlete’s foot and feedback from the athlete to make the design specific to the athlete’s specific gait. Adidas customizes the weaving of the shoe’s “fabric” (really more like an elaborate woven string figure, a cat’s cradle to fit the foot) to achieve a close and comfortable fit, the company says.
What they call their “4D sole” consists of a design combining 3D printing with materials that can change their shape and properties over time. In fact, Tibbits coined the term 4D printing to describe this process in 2013. The company takes customized data from the Adidas Athlete Intelligent Engine to make the shoe, according to Kram/Weisshaar’s website.
Photo: Adidas
Closeup of the weaving process behind a Futurecraft.Strung shoe
“With Strung for the first time, we can program single threads in any direction, where each thread has a different property or strength,” Fionn Corcoran-Tadd, an innovation designer at Adidas’ Futurecraft lab, said in a company video. Each thread serves a purpose, the video noted. “This is like customized string art for your feet,” Tibbits says.
Although the robotics technology the company uses has been around for many years, what Adidas’s robotic weavers can achieve with thread is a matter of elaborate geometry. “It’s more just like a really elegant way to build up material combining robotics and the fibers and yarns into these intricate and complex patterns,” he says.
Robots can of course create patterns with more precision than if someone wound it by hand, as well as rapidly and reliably changing the yarn and color of the fabric pattern. Adidas says it can make a single upper in 45 minutes and a pair of sneakers in 1 hour and 30 minutes. It plans to reduce this time down to minutes in the months ahead, the company said.
An Adidas spokesperson says sneakers incorporating the Futurecraft.Strung uppers design are a prototype, but the company plans to bring a Strung shoe to market in late 2021 or 2022. However, Adidas Futurecraft sneakers are currently available with a 3D-printed midsole.
Adidas plans to continue gathering data from athletes to customize the uppers of sneakers. “We’re building up a library of knowledge and it will get more interesting as we aggregate data of testing and from different athletes and sports,” the Adidas Futurecraft team writes in a blog post. “The more we understand about how data can become design code, the more we can take that and apply it to new Strung textiles. It’s a continuous evolution.” Continue reading