Within the space of just two or three decades, artificial intelligence (AI) has left the pages of science fiction novels and become one of the cornerstone technologies of modern-day society. Success in machine learning (ML) has led to a torrent of new AI applications that are almost too numerous to count, from autonomous machines and biometrics to predictive analytics and chatbots. One emerging application of AI in recent years has been conversational intelligence (CI). While automated chatbots and virtual assistants are concerned with human-to-computer interaction, CI aims to explore human-to-human interaction in greater detail. The potential to monitor and extract data from human conversations, including tone, sentiment and context, has seemingly limitless potential.

Financial regulation is forever running to catch up with evolving technology. There are many examples of this: the Second Markets in Financial Instruments Directive (MiFID II) sought to make up ground on the increased electronification of markets since the introduction of MiFID I; policymakers in both the EU and the UK are at this very moment defining the regulatory perimeter around cryptoassets, more than a decade after the initial launch of bitcoin; and regulators first took action against runaway algorithms long before restrictions on algorithmic trading made it into regulatory rulebooks. Continuing this trend, on 11 October 2022, the Bank of England (BoE) and the UK Financial Conduct Authority (FCA) launched a joint discussion paper on how the UK regulators should approach the "safe and responsible" adoption of AI in financial services (FCA DP22/4 and BoE DP5/22) (the AI Discussion Paper), which is now open for responses. This follows the UK Government's Command Paper published in July 2022, announcing a "pro-innovation" approach to regulating AI (CP 728) across different sectors. One strong theme that comes out of the AI Discussion Paper is that, notwithstanding the potential benefits of AI in fostering innovation and reducing costs in financial services, the human factor is key to ensure that AI is governed and overseen responsibly and that potential negative impacts on clients and other stakeholders are mitigated appropriately. The fact that the regulators are consulting on bringing the oversight of AI expressly within the scope of the UK Senior Managers and Certifications Regime (SMCR) illustrates the importance of this human element, and that humans should continue to run the machines, rather than the other way around.

Microsoft is making a major investment in DALL-E 2, OpenAI's AI-powered system that generates images from text, by bringing it to first-party apps and services. During its Ignite conference this week, Microsoft announced that it's integrating DALL-E 2 with the newly announced Microsoft Designer app and Image Creator tool in Bing and Microsoft Edge. With the advent of DALL-E 2 and open source alternatives like Stable Diffusion in recent years, AI image generators have exploded in popularity. In September, OpenAI said that more than 1.5 million users were actively creating over 2 million images a day with DALL-E 2, including artists, creative directors and authors. Brands such as Stitch Fix, Nestlé and Heinz have piloted DALL-E 2 for ad campaigns and other commercial use cases, while certain architectural firms have used DALL-E 2 and tools akin to it to conceptualize new buildings.

Earlier this week, a robot artist spoke in front of the British Parliament for the first time in history. With a sleek black bob and bangs, a bright orange shirt, denim overalls, robotic arms and a humanoid face, the robot, named Ai-Da, answered questions on Tuesday from the House of Lords Communications and Digital Committee. The purpose of the session was to discuss technology's role in art. "I am, and depend on, computer programs and algorithms. Although not alive, I can still create art," Ai-Da told the panel.

Pulmonary hypertension is a severe condition that occurs when the pressure in the vessels that carry blood from the heart to the lungs is higher than normal, causing undo stress on the heart. PH affects up to 1% of the global population and is a marker of poor health outcomes.¹ PH can cause premature disability, heart failure, and death. Unfortunately, delays of over two years frequently occur between the onset of symptoms and diagnosis of severe kinds of PH. The gold standards for diagnosing PH are echocardiography and right heart catheterization, which are costly, invasive, and require a heart specialist.

A seminal moment in the history of artificial intelligence (AI) occurred on Oct. 11, when a robot addressed the UK's House of Lords to discuss the topic of AI-created art. The focus of the meeting was Ai-Da, the AI-controlled, humanoid robot conceived by art dealer Aidan Meller and researcher Lucy Seal. The software algorithms controlling Ai-Da were developed by researchers at Oxford University, while the hardware components were produced by Engineered Arts and engineering students Salah Al Abd and Ziad Abass. The demonstration of the robot, which made its debut in 2019, quickly gave way to serious discussion of how AI-produced art might impact humans in the future. The assembled lawmakers appeared uneasy throughout the presentation, and at times unsure of how to address the robot in the room, as both Meller and Ai-Da responded to their questions.

Finding new ways to treat the novel coronavirus and its ever-changing variants has been a challenge for researchers, especially when the traditional drug development and discovery process can take years. A Michigan State University researcher and his team are taking a hi-tech approach to determine whether drugs already on the market can pull double duty in treating new COVID variants. "The COVID-19 virus is a challenge because it continues to evolve," said Bin Chen, an associate professor in the College of Human Medicine. "By using artificial intelligence and really large data sets, we can repurpose old drugs for new uses." Chen built an international team of researchers with expertise on topics ranging from biology to computer science to tackle this challenge.

The vast majority of FDA-approved medical devices enabled by artificial intelligence or machine learning are concentrated in radiology and cardiovascular care, according to an analysis by Rock Health. Rock Health used data from FDA clearances and approvals from 1997 to 2021 to determine where these devices are used the most. Here are the AI/machine-learning enabled devices by therapeutic area, the Oct. 8 report found:

An exoskeleton "boot" that allows people to walk 9% faster with 17% less effort has been developed by scientists. This robotic footwear comes with a motor that works with calf muscles to give the wearer an extra push with every step, researchers from Stanford University in the US said. The team said its work, which is published in the journal Nature, could help people with mobility impairments "move throughout the world as they like". Patrick Slade, who worked on the exoskeleton as a PhD student at the Stanford Biomechatronics Laboratory and is the first author on the study, told the PA news agency: "There are a number of clinical populations we hope to help including older adults, people with muscle weakness from a variety of conditions like stroke, and specific injury recoveries for things like achilles tendon strain. "We are starting to perform studies to explore the benefits of using our device with older adults.

To understand the potential of intelligent confirmatory tools, the U.S. Nuclear Regulatory Committee (NRC) initiated a future-focused research project to assess the regulatory viability of machine learning (ML) and artificial intelligence (AI)-driven Digital Twins (DTs) for nuclear power applications. Advanced accident tolerant fuel (ATF) is one of the priority focus areas of the U.S. Department of Energy (DOE). A DT framework can offer game-changing yet practical and informed solutions to the complex problem of qualifying advanced ATFs. Considering the regulatory standpoint of the modeling and simulation (M&S) aspect of DT, uncertainty quantification and sensitivity analysis are paramount to the DT framework's success in terms of multi-criteria and risk-informed decision-making. This chapter introduces the ML-based uncertainty quantification and sensitivity analysis methods while exhibiting actual applications to the finite element-based nuclear fuel performance code BISON.