Genomics is playing an important role in transforming healthcare. Genetic data, however, is being produced at a rate that far outpaces Moore's Law. Many efforts have been made to accelerate genomics kernels on modern commodity hardware, such as CPUs and GPUs, as well as custom accelerators (ASICs) for specific genomics kernels. While ASICs provide higher performance and energy efficiency than general-purpose hardware, they incur a high hardware-design cost. Moreover, to extract the best performance, ASICs tend to have significantly different architectures for different kernels.

The current U.S. administration has launched a wara on academia. Indirect costs, or, more accurately, facility and administration expenses, support research but cannot be directly attributed to a specific project, such as lab infrastructure, utilities, and administrative support. These are real costs; the limit, which has since been suspended by courts, is a severe blow to biomedical research in the U.S. Beyond expanding this limit to other agencies, such as the National Science Foundation (NSF), the administration is also reportedly considering slashing NSF's annual budget from approximately US 9 billion down to about US 3– 4 billion. This would deal a devastating blow to academic U.S. research, especially computing research. As statedc by the Computing Research Association (CRA), "NSF budget cuts would put the future of U.S. innovation and security at risk."

In recent years, large language model (LLM)–based agents have garnered widespread attention across various fields. Their impressive capabilities, such as natural language communication,21,23 instruction following,26,28 and task execution,22,38 have the potential to expand both the format of information carriers and the way in which information is exchanged. LLM-based agents can now evolve into domain experts, becoming novel information carriers with domain-specific knowledge.1,28 For example, a Travel Agent can retain travel-related information within its parameters. LLM-based agents are also showcasing a new form of information exchange, facilitating more intuitive and natural interactions with users through dialogue and task execution.24,34 Figure 1 shows an example of these capabilities, in which users engage in dialogue with a Travel Agent to obtain information and complete their travel plans.

Robin K. Hill Is Computer Science a Profession? We computer scientists--many of us--like to think of ourselves as professionals, as do doctors and lawyers, and police officers, and accountants. But there are definitions of "profession," with criteria and expectations, that we fail to meet. Are we ready, collectively, to confront the criteria? Do we want to be card-carrying members of a learned institution of service?

In its successful understanding of phenomena, science allows us to solve problems by concentrating on some of their characteristics to find optimal solutions along these chosen characteristics. It provides analytical and precise dedicated solutions along a particular set of dominant attributes of interest where all other criteria are set aside. The adoption of such dedicated one-sided solutions that can maximize the "profit" in one area of interest can lead to pollution, namely a cumulative adverse effect on other aspects of the problem that are collaterally related to the focus of the dedicated solution. We therefore have environmental pollution by maximizing the rate of industrial production or social pollution of poverty in some parts of the population due to a sterile maximization of economic growth. These causes of pollution emerge out of a scientific understanding of the world (physical or socioeconomic) through which it is possible to find and engineer solutions dedicated to a particular area of interest.

AI safety is a contentious topic. While some prominent figures of the AI community have argued that destructive general artificial intelligence (AI) is on the horizon, others derided their warning as a marketing stunt to sell large language models (LLMs). "If the call for'AI safety' is couched in terms of protecting humanity from rogue AIs, it very conveniently displaces accountability away from the corporations scaling harm in the name of profits," tweeted Emily Bender, a professor of computational linguistics at the University of Washington. Focusing on potential future harm from ever more powerful AI systems distracts from harm that is already happening today. Most of us do not set out to make software that is actively harmful.

Today's text-entry tools offer a plethora of interface technologies to support users in a variety of situations and with a range of different input methods and devices.16 Recent hardware developments have enabled remarkable innovations, such as virtual keyboards that allow users to type in thin air, or to use their body as a surface for text entry. Similarly, advances in machine learning and natural language processing have enabled high-quality text generation for various purposes, such as summarizing, expanding, and co-authoring. As these technologies rapidly develop, there has been a rush to incorporate them into existing systems, often with little thought for the interactivity problems this may cause. The use of large language models (LLMs) to speed up text generation and improve prediction or completion models is becoming increasingly commonplace, with enormous theoretical efficiency savings;29 however, the implementation of these LLMs into text-entry interfaces is crucial to realizing their potential.

This is undoubtedly the case for scientific and statistical databases, which have largely replaced traditional reference works. Database and Web technologies have led to an explosion in the number of databases that support scientific research, for obvious reasons: Databases provide faster communication of knowledge, hold larger volumes of data, are more easily searched, and are both human- and machine-readable. Moreover, they can be developed rapidly and collaboratively by a mixture of researchers and curators. For example, more than 1,500 curated databases are relevant to molecular biology alone.10 The value of these databases lies not only in the data they present but also in how they organize that data.

Generative artificial intelligence (GAI) started making waves a few years ago with the release of systems such as ChatGPT and DALL-E. They are able to produce sophisticated and human-like text, code, or images after the models powering them are trained on large quantities of data. However, it soon became apparent that the specific phrasing of a question or statement input by a user, known as a prompt, had an impact on the quality of the resulting output. "It's a way of unlocking different capabilities from these models," says Andrei Muresanu, an AI researcher at Vector Institute in Toronto, Canada. "If you tell ChatGPT to pretend that it's a professor of mathematics, it will do better on math questions than if you just say, 'answer this question' or'pretend you're a student'." Coming up with prompts that steer a model towards a desired output has emerged as a relatively new profession, called prompt engineering, to help achieve more relevant and accurate results.

Adopting and implementing digital automation technologies, including artificial intelligence (AI) models such as ChatGPT, robotic process automation (RPA), and other emerging AI technologies, will revolutionize many industries and business models. It is forecasted that the rise of AI will impact a wide range of job functions and roles. White-collar positions such as administrative, customer service, and back-office roles will all be impacted by AI-fueled digital automation. The adoption of digital workers is currently positioned in the early adopter phase of the product lifecycle.1 AI-driven digital workers are expected to substantially alter many white-collar tasks, including finance, customer support, human resources, sales, and marketing.42 A study from Oxford University and Deloitte predicts AI is a significant risk to the white-collar workforce.