Tag Archives: Industry
#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.
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#438882 Robotics in the entertainment industry
Mesmer Entertainment Robotics demonstrate some of their humanoid animatronics, as well as their humanoid robot, Owen.
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#439073 There’s a ‘New’ Nirvana Song Out, ...
One of the primary capabilities separating human intelligence from artificial intelligence is our ability to be creative—to use nothing but the world around us, our experiences, and our brains to create art. At present, AI needs to be extensively trained on human-made works of art in order to produce new work, so we’ve still got a leg up. That said, neural networks like OpenAI’s GPT-3 and Russian designer Nikolay Ironov have been able to create content indistinguishable from human-made work.
Now there’s another example of AI artistry that’s hard to tell apart from the real thing, and it’s sure to excite 90s alternative rock fans the world over: a brand-new, never-heard-before Nirvana song. Or, more accurately, a song written by a neural network that was trained on Nirvana’s music.
The song is called “Drowned in the Sun,” and it does have a pretty Nirvana-esque ring to it. The neural network that wrote it is Magenta, which was launched by Google in 2016 with the goal of training machines to create art—or as the tool’s website puts it, exploring the role of machine learning as a tool in the creative process. Magenta was built using TensorFlow, Google’s massive open-source software library focused on deep learning applications.
The song was written as part of an album called Lost Tapes of the 27 Club, a project carried out by a Toronto-based organization called Over the Bridge focused on mental health in the music industry.
Here’s how a computer was able to write a song in the unique style of a deceased musician. Music, 20 to 30 tracks, was fed into Magenta’s neural network in the form of MIDI files. MIDI stands for Musical Instrument Digital Interface, and the format contains the details of a song written in code that represents musical parameters like pitch and tempo. Components of each song, like vocal melody or rhythm guitar, were fed in one at a time.
The neural network found patterns in these different components, and got enough of a handle on them that when given a few notes to start from, it could use those patterns to predict what would come next; in this case, chords and melodies that sound like they could’ve been written by Kurt Cobain.
To be clear, Magenta didn’t spit out a ready-to-go song complete with lyrics. The AI wrote the music, but a different neural network wrote the lyrics (using essentially the same process as Magenta), and the team then sifted through “pages and pages” of output to find lyrics that fit the melodies Magenta created.
Eric Hogan, a singer for a Nirvana tribute band who the Over the Bridge team hired to sing “Drowned in the Sun,” felt that the lyrics were spot-on. “The song is saying, ‘I’m a weirdo, but I like it,’” he said. “That is total Kurt Cobain right there. The sentiment is exactly what he would have said.”
Cobain isn’t the only musician the Lost Tapes project tried to emulate; songs in the styles of Jimi Hendrix, Jim Morrison, and Amy Winehouse were also included. What all these artists have in common is that they died by suicide at the age of 27.
The project is meant to raise awareness around mental health, particularly among music industry professionals. It’s not hard to think of great artists of all persuasions—musicians, painters, writers, actors—whose lives are cut short due to severe depression and other mental health issues for which it can be hard to get help. These issues are sometimes romanticized, as suffering does tend to create art that’s meaningful, relatable, and timeless. But according to the Lost Tapes website, suicide attempts among music industry workers are more than double that of the general population.
How many more hit songs would these artists have written if they were still alive? We’ll never know, but hopefully Lost Tapes of the 27 Club and projects like it will raise awareness of mental health issues, both in the music industry and in general, and help people in need find the right resources. Because no matter how good computers eventually get at creating music, writing, or other art, as Lost Tapes’ website pointedly says, “Even AI will never replace the real thing.”
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