At the London Tech Week event in early June, Nvidia CEO Jensen Huang praised the UK as the ‘envy of the world’ when it comes to AI researchers, but he also criticised it as the largest AI ecosystem in the world without its own infrastructure. The criticism is somewhat self-serving: when the UK does get around to building out that infrastructure, it’s certain to consist largely of chips sold by Huang’s company. It’s also unsurprising: Huang has been pitching the idea of ‘sovereign AI’ since at least 2023, conscious that nation states are the next deep pockets to target after the hyperscalers and generously funded model builders. In a world where the only real contenders in the race for AI supremacy are the US and China, we look at how the pursuit of AI sovereignty is playing out across the rest of the planet.

Vaccines have revolutionised the field of medicine, eradicating and controlling many diseases. Recent pandemic vaccine successes have highlighted the accelerated pace of vaccine development and deployment. Leveraging this momentum, attention has shifted to cancer vaccines and personalised cancer vaccines, aimed at targeting individual tumour-specific abnormalities. The UK, now regarded for its vaccine capabilities, is an ideal nation for pioneering cancer vaccine trials. This article convened experts to share insights and approaches to navigate the challenges of cancer vaccine development with personalised or precision cancer vaccines, as well as fixed vaccines. Emphasising partnership and proactive strategies, this article outlines the ambition to harness national and local system capabilities in the UK; to work in collaboration with potential pharmaceutic partners; and to seize the opportunity to deliver the pace for rapid advances in cancer vaccine technology.

It’s less than a year since OpenAI’s board voted to fire Sam Altman as CEO, in a palace coup that lasted just a weekend before Altman was reinstated. That weekend and subsequent events in OpenAI’s storyline provide all the ingredients for a soap opera. So, just in case Netflix is interested, here’s a stab at a synopsis of what might be just the first of many seasons of ‘The Generative AI Wars’.

The technical and mainstream media’s headline coverage of AI invariably centers around the often astounding abilities demonstrated by large language models. That’s hardly surprising, since to all intents and purposes that’s where the newsworthy magic of generative AI lies. But it takes a village to raise a child: behind the scenes, there’s an entire ecosystem that supports the development and deployment of these models and the applications that are built on top of them. Some parts of that ecosystem are dominated by the Big Tech incumbents, but there are also many niches where start-ups are aiming to gain a foothold. We take a look at some components of that ecosystem, with a particular focus on ideas that have led to investment in start-ups over the last year or so.

A lot has happened since OpenAI released ChatGPT to the public in November 2022. We review how things unfolded over the course of the year, tracking significant events and announcements from the tech giants leading the generative AI race and from other players of note; along the way we note the wider impacts of the technology’s progress.

Since the initial publication of A Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals in 2008, the prevention of healthcare-associated infections (HAIs) has continued to be a national priority. Progress in healthcare epidemiology, infection prevention, antimicrobial stewardship, and implementation science research has led to improvements in our understanding of effective strategies for HAI prevention. Despite these advances, HAIs continue to affect ∼1 of every 31 hospitalized patients,1 leading to substantial morbidity, mortality, and excess healthcare expenditures,1 and persistent gaps remain between what is recommended and what is practiced.

The widespread impact of the coronavirus disease 2019 (COVID-19) pandemic on HAI outcomes2 in acute-care hospitals has further highlighted the essential role of infection prevention programs and the critical importance of prioritizing efforts that can be sustained even in the face of resource requirements from COVID-19 and future infectious diseases crises.3

The Compendium: 2022 Updates document provides acute-care hospitals with up-to-date, practical expert guidance to assist in prioritizing and implementing HAI prevention efforts. It is the product of a highly collaborative effort led by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Disease Society of America (IDSA), the Association for Professionals in Infection Control and Epidemiology (APIC), the American Hospital Association (AHA), and The Joint Commission, with major contributions from representatives of organizations and societies with content expertise, including the Centers for Disease Control and Prevention (CDC), the Pediatric Infectious Disease Society (PIDS), the Society for Critical Care Medicine (SCCM), the Society for Hospital Medicine (SHM), the Surgical Infection Society (SIS), and others.

Since the release of ChatGPT at the end of November 2022, generative AI has been talked about endlessly in both the technical press and the mainstream media. Large language model technology has been heralded as many things: the disruption of the search engine, the end of the student essay, the bringer of disinformation … but what does it mean for commercial providers of earlier iterations of natural language generation technology? We look at how the major players in the space are responding, and where things might go in the future.

It’s no secret that the commercial application of NLP technologies has exploded in recent years. From chatbots and virtual assistants to machine translation and sentiment analysis, NLP technologies are now being used in a wide variety of applications across a range of industries. With the increasing demand for technologies that can process human language, investors have been eager to get a piece of the action. In this article, we look at NLP startup funding over the past year, identifying the applications and domains that have received investment.

In the past few years, high-quality automated text-to-speech synthesis has effectively become a commodity, with easy access to cloud-based APIs provided by a number of major players. At the same time, developments in deep learning have broadened the scope of voice synthesis functionalities that can be delivered, leading to a growth in the range of commercially viable use cases. We take a look at the technology features and use cases that have attracted attention and investment in the past few years, identifying the major players and recent start-ups in the space.

Funding for AI start-ups in general is booming, and natural language processing as a subfield has not missed out. We take a closer look at early-stage funding over the last year—just over US$1B in total—for companies that offer solutions that are based on or make significant use of NLP, providing a picture of what funders think is innovative and bankable in this space, and we make some observations on notable trends and developments.

Automated writing assistance – a category that encompasses a variety of computer-based tools that help with writing – has been around in one form or another for 60 years, although it’s always been a relatively minor part of the NLP landscape. But the category has been given a substantial boost from recent advances in deep learning. We review some history, look at where things stand today, and consider where things might be going.

GPT-3 made the mainstream media headlines this year, generating far more interest than we’d normally expect of a technical advance in NLP. People are fascinated by its ability to produce apparently novel text that reads as if it was written by a human. But what kind of practical applications can we expect to see, and can they be trusted?

It took a while, but natural language generation is now an established commercial software category. It’s commented upon frequently in both industry media and the mainstream press, and businesses are willing to pay hard cash to take advantage of the technology. We look at who’s active in the space, the nature of the technology that’s available today and where things might go in the future.

The end of the calendar year always seems like a good time to pause for breath and reflect on what’s been happening over the last 12 months, and that’s as true in the world of commercial NLP as it is in any other domain. In particular, 2019 has been a busy year for voice assistance, thanks to the focus placed on this area by all the major technology players. So, we take this opportunity to review a number of key themes that have defined recent developments in the commercialization of voice technology.