Regulatory Challenges and the New AI Standard ISO 42001

Tony Porter, former Surveillance Camera Commissioner for the UK Home Office, provided insights into regulatory challenges surrounding AI transparency. He highlighted the significance of ISO 42001, the international standard for AI management systems which offers a framework for responsible AI governance. “Regulations are evolving rapidly, but standards like ISO 42001 provide organisations with a structured approach to balancing innovation with accountability,” Porter said.

Chamelio: Transforming Legal Decision-Making with Explainable AI

Alex Zilberman from Chamelio, a legal intelligence platform exclusively built for in-house legal teams, addressed the role of AI in corporate legal operations. Chamelio changes how in-house legal teams operate through an AI agent that learns and uses the legal knowledge stored in its repository of contracts, policies, compliance documents, corporate records, regulatory filings, and other business-important legal documents.

“Trust is the number one requirement to build a system that professionals can use,” Zilberman said. “This trust is achieved by providing as much transparency as possible. Our solution allows users to understand where each recommendation comes from, ensuring they can confirm and verify every insight.”

Chamelio avoids the ‘black box’ model by letting legal professionals trace the reasoning behind AI-generated recommendations. For example, when the system encounters areas of a contract that it doesn’t recognise, instead of guessing, it flags the uncertainty and requests human input. This approach helps legal professionals control important decisions, particularly in unprecedented scenarios like clauses with no precedent or conflicting legal terms.

Pini Usha from Buffers.ai shared insights on AI-driven inventory optimisation, an important application in retail. Buffers.ai serves medium to large retail and manufacturing brands, including H&M, P&G, and Toshiba, helping retailers – particularly in the fashion industry – tackle inventory optimisation challenges like forecasting, replenishment, and assortment planning.

Buffers.ai offers a full-SaaS ERP plugin that integrates with systems like SAP and Priority, providing ROI in months. “Transparency is key. If businesses cannot understand how AI predicts demand fluctuations or supply chain risks, they will be hesitant to rely on it,” Usha said.

Buffers.ai integrates explainability tools that allow clients to visualise and adjust AI-driven forecasts, helping ensure alignment with real-time business operations and market trends. For example, when placing a new product with no historical data, the system analyses similar product trends, store characteristics, and local demand signals. If a branch has historically shown strong demand for comparable items, the system might recommend a higher quantity without any existing data for the new product.

Corsight AI: Facial Recognition in Retail and Law Enforcement

Matan Noga from Corsight AI discussed the role of explainability in facial recognition technology, which is used increasingly for security and customer experience enhancement in retail. Corsight AI specialises in real-world facial recognition, and provides its solutions to law enforcement, airports, malls, and retailers.

The company’s technology is used for applications like watchlist alerting, locating missing persons, and forensic investigations. Corsight AI differentiates itself by focusing on high-speed, and real-time recognition in ways compliant with evolving privacy laws and ethical AI guidelines. The company works with government and its commercial clients to promote responsible AI adoption, emphasising the importance of explainability in building trust and ensuring ethical use.

ImiSight: AI-Powered Image Intelligence

Daphne Tapia from ImiSight highlighted the importance of explainability in AI-powered image intelligence, particularly in high-stakes applications like border security and environmental monitoring. ImiSight specialises in multi-sensor integration and analysis, utilising AI/ML algorithms to detect changes, anomalies, and objects in sectors like land encroachment, environmental monitoring, and infrastructure maintenance.

“AI explainability means understanding why a specific object or change was detected. We prioritise traceability and transparency to ensure users can trust our system’s outputs,” Tapia said. ImiSight continuously refines its models based on real-world data and user feedback. The company collaborates with regulatory agencies to ensure its AI meets international compliance standards.

Conclusion

The panel underscored the important role of AI explainability in fostering trust, accountability, and ethical use of AI technologies, particularly in retail and other high-stakes industries. By prioritising transparency and human oversight, organisations can ensure AI systems are both effective and trustworthy, aligning with evolving regulatory standards and public expectations.

FAQs

Q: What is ISO 42001?

A: ISO 42001 is the international standard for AI management systems, offering a framework for responsible AI governance.

Q: What is the role of explainability in AI-driven decision-making?

A: Explainability is crucial in AI-driven decision-making, as it enables users to understand the reasoning behind AI-generated recommendations, fostering trust, accountability, and ethical use of AI technologies.

Q: How can AI systems be made more transparent and explainable?

A: AI systems can be made more transparent and explainable by integrating features that provide users with insights into the reasoning behind AI-generated recommendations, such as traceability and transparency.

Andrew Barto and Rich Sutton Awarded Turing Award for Pioneering Work in Reinforcement Learning

A Revolution in Artificial Intelligence

In the 1980s, Andrew Barto and Rich Sutton were considered eccentric devotees to an elegant but ultimately doomed idea—having machines learn, as humans and animals do, from experience. Decades on, with the technique they pioneered now increasingly critical to modern artificial intelligence and programs like ChatGPT, Barto and Sutton have been awarded the Turing Award, the highest honor in the field of computer science.

Trailblazing a New Approach

Barto, a professor emeritus at the University of Massachusetts Amherst, and Sutton, a professor at the University of Alberta, trailblazed a technique known as reinforcement learning, which involves coaxing a computer to perform tasks through experimentation combined with either positive or negative feedback.

A Technique Born from Unfashionable Ideas

“When this work started for me, it was extremely unfashionable,” Barto recalls with a smile, speaking over Zoom from his home in Massachusetts. “It’s been remarkable that [it has] achieved some influence and some attention,” Barto adds.

Reinforcement Learning in Modern AI

Reinforcement learning was perhaps most famously used by Google DeepMind in 2016 to build AlphaGo, a program that learned for itself how to play the incredibly complex and subtle board game of Go to an expert level. This demonstration sparked new interest in the technique, which has gone on to be used in advertising, optimizing data-center energy use, finance, and chip design. The approach also has a long history in robotics, where it can help machines learn to perform physical tasks through trial and error.

Guiding Large Language Models

More recently, reinforcement learning has been crucial to guiding the output of large language models (LLMs) and producing extraordinarily capable chatbot programs. The same method is also being used to train AI models to mimic human reasoning, and to build more capable AI agents.

A Debate on Machine Learning

Sutton notes, however, that the methods used to guide LLMs involve humans providing goals rather than an algorithm learning purely through its own exploration. He says having machines learn entirely on their own may ultimately be more fruitful. “The big division is whether [AI is] learning from people or whether it’s learning from its own experience,” he says.

Praise for the Pioneers

Barto and Sutton’s “work has been a lynchpin of progress in AI over the last several decades,” Jeff Dean, a senior vice president at Google, said in a statement released by the Association for Computing Machinery (ACM) which hands out the Turing Award. “The tools they developed remain a central pillar of the AI boom and have rendered major advances.”

A Long and Checkered History

Reinforcement has a long and checkered history within AI. It was there at the dawn of the field, when Alan Turing suggested that machines could learn through experience and feedback in his famous 1950 paper “Computing Machinery and Intelligence,” which examines the notion that a machine might someday think like a human. Arthur Samuel, an AI pioneer, used reinforcement learning to build one of the first machine learning programs, a system capable of playing checkers, in 1955.

Conclusion

Andrew Barto and Rich Sutton’s pioneering work in reinforcement learning has led to a revolution in artificial intelligence, with far-reaching implications for fields such as language processing, robotics, and more. Their award is a testament to the importance of their contributions and the impact they have had on the development of modern AI.

Frequently Asked Questions

Q: What is Reinforcement Learning?

A: Reinforcement learning involves coaxing a computer to perform tasks through experimentation combined with either positive or negative feedback.

Q: What is the Turing Award?

A: The Turing Award is the highest honor in the field of computer science, awarded to individuals who have made significant contributions to the field.

Q: How is Reinforcement Learning Used in AI?

A: Reinforcement learning is used in AI to train programs to perform tasks through trial and error, with applications in areas such as language processing, robotics, and more.

Malaysia’s Rise as a Chip Manufacturing Hub

Malaysia Becomes a Hotspot for Chip Manufacturing

Malaysia has become a hotspot for chip manufacturing due to increasing tensions between the U.S. and China, especially around the development of semiconductors used for AI applications. Predictably, tech firms around the world have been looking to diversify their source of chips, and ARM Holdings is now hoping to make the most of this opportunity.

ARM Partners with Malaysian Government

The SoftBank-backed chipmaker said on Wednesday that it has signed an agreement with the Malaysian government to bolster the country’s chip design ecosystem. As part of the deal, Malaysia will pay ARM $250 million over 10 years to buy its chip designs and technology for local manufacturers. Specifically, the country will buy IP, including seven of ARM’s chip design blueprints, according to Reuters.

Training and Investment

ARM said the partnership will also see the company training 10,000 engineers in Malaysia on its technology. ARM declined to comment on the $250 investment from the Malaysian government, and a representative of the Malaysian government did not respond to a request for comment ahead of publication.

Malaysia’s National Semiconductor Strategy

The move is the latest of Malaysia’s ongoing efforts to become a hub for chip manufacturing within the next decade. Earlier this year, Malaysia said it planned to manufacture its own GPUs to meet demand for AI and data centers. The Malaysian government said last May that it would set aside at least $5.3 billion in financial support and train 60,000 engineers for its National Semiconductor Strategy (NSS), under which the country will seek to enhance its current infrastructure, develop an advanced chip supply chain, and attract top global clients.

History of Chip Manufacturing in Malaysia

Malaysia has been involved in the chip industry for over 50 years, and currently provides about 13% of global chip testing, assembly, and packing services, according to a report by TrendForce that cited the Malaysian Investment Development Authority (MIDA). In 1972, Intel established its first production facility outside the U.S. in Penang, Malaysia, investing $16 million in an assembly site. The U.S. chip giant in December 2021 said it would invest more than $7 billion to establish a chip assembly and testing factory in the country, and it’s also building its largest 3D chip packaging facility in Malaysia. GlobalFoundries, the U.S. chip company, also opened a new hub facility in Penang, Malaysia, in 2023, and Neways, a Dutch chip equipment maker, plans to build a new plant in Malaysia.

Recent Investments in Malaysia

In addition, a number of tech giants, including Google, Microsoft, and Nvidia, have announced billions of dollars in investments in Malaysia since 2023, primarily for data centers, AI development projects, and cloud services.

Conclusion

Malaysia’s emergence as a key player in the chip manufacturing industry is a testament to its commitment to innovation and its strategic location in the heart of Asia. With its favorable business environment, skilled workforce, and government support, Malaysia is well-positioned to become a major player in the global chip manufacturing industry.

FAQs

Q: What is the significance of ARM’s partnership with the Malaysian government?
A: The partnership will help bolster Malaysia’s chip design ecosystem and provide a boost to the country’s efforts to become a hub for chip manufacturing.

Q: How much will Malaysia pay ARM for its chip designs and technology?
A: Malaysia will pay ARM $250 million over 10 years to buy its chip designs and technology for local manufacturers.

Q: How many engineers will ARM train in Malaysia on its technology?
A: ARM will train 10,000 engineers in Malaysia on its technology as part of the partnership.

Q: What is Malaysia’s National Semiconductor Strategy?
A: The National Semiconductor Strategy is a government initiative aimed at enhancing Malaysia’s current infrastructure, developing an advanced chip supply chain, and attracting top global clients.