In their paper FAIR-FATE: Fair Federated Learning with Momentum, Teresa Salazar, Miguel Fernandes, Helder Araujo, and Pedro Henriques Abreu develop a fairness-aware federated learning algorithm which aims to achieve group fairness while maintaining classification performance. Here, Teresa tells us more about their work. With the widespread use of machine learning algorithms to make decisions which impact people's lives, the area of fairness-aware machine learning has been receiving increasing attention. Fairness-aware machine learning algorithms ensure that predictions do not prejudice unprivileged groups of the population with respect to sensitive attributes such as race or gender. However, the focus has been on centralized machine learning, with decentralized methods receiving little attention.

There's an age-old adage in biology: structure determines function. In order to understand the function of the myriad proteins that perform vital jobs in a healthy body--or malfunction in a diseased one--scientists have to first determine these proteins' molecular structure. But this is no easy feat: protein molecules consist of long, twisty chains of up to thousands of amino acids, chemical compounds that can interact with one another in many ways to take on an enormous number of possible three-dimensional shapes. Figuring out a single protein's structure, or solving the "protein-folding problem, can take years of finicky experiments. But earlier this year an artificial intelligence program called AlphaFold, developed by the Google-owned company DeepMind, predicted the 3-D structures of almost every known protein--about 200 million in all. DeepMind CEO Demis Hassabis and senior staff research scientist John Jumper were jointly awarded this year's $3-million Breakthrough Prize in Life ...

You know that text autocomplete function that makes your smartphone so convenient -- and occasionally frustrating -- to use? Well, now tools based on the same idea have progressed to the point that they are helping researchers to analyse and write scientific papers, generate code and brainstorm ideas. The tools come from natural language processing (NLP), an area of artificial intelligence aimed at helping computers to'understand' and even produce human-readable text. Called large language models (LLMs), these tools have evolved to become not only objects of study but also assistants in research. LLMs are neural networks that have been trained on massive bodies of text to process and, in particular, generate language.

Australian surf lifesavers are increasingly using drones to spot sharks at the beach before they get too close to swimmers. But just how reliable are they? Discerning whether that dark splodge in the water is a shark or just, say, seaweed isn't always straightforward and, in reasonable conditions, drone pilots generally make the right call only 60% of the time. While this has implications for public safety, it can also lead to unnecessary beach closures and public alarm. Engineers are trying to boost the accuracy of these shark-spotting drones with artificial intelligence (AI).

How similar are bears and bulls? If you ask a biologist, she might say that they are pretty similar, since they are both four-legged mammals found in North America. However, if you ask an economist, he might say they are polar opposites, since they are used to describe distinct stock market conditions. The unique way in which individuals organize their semantic knowledge, or general information gained through life experiences, could cause two people to judge the similarity between two animals in very different ways. Scientists have been trying to understand the structure of semantic knowledge for a long time, in large part because it may lead to deeper insights about human behavior.

Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores. These pores give MOFs record-breaking internal surface areas, which make them extremely versatile for a number of applications: separating petrochemicals and gases, mimicking DNA, producing hydrogen, and removing heavy metals, fluoride anions, and even gold from water are just a few examples. MOFs are the focus of Professor Berend Smit's research at EPFL School of Basic Sciences, where his group employs machine learning in the discovery, design, and even categorization of the ever-increasing MOFs that currently flood chemical databases. In a new study, Smit and his colleagues have developed a machine-learning model that predicts the heat capacity of MOFs. "This is about very classical thermodynamics," says Smit. "How much energy is needed to heat up a material by one degree? Until now, all engineering calculations have assumed that all MOFs have the same heat capacity, for the simple reason that there is hardly any data available."

Self-driving car developer Argo AI suddenly announced that it was closing its doors this week. Some of its 1,800-odd employees, winnowed already by summer layoffs, are to be offered jobs to "work on automated technology with either Ford or Volkswagen," Catherine Johnsmeyer, an Argo spokesperson, said in a statement. The two auto giants had sunk some $3.6 billion into Argo and owned most of it. Now, they had decided to pull the plug. The end of Argo is just the latest sign that the global effort to get cars to drive themselves is in trouble--or at least more complex than once thought.

Only after she spoke with a therapist did she understand the problem: she was burnt out. She ended up taking medical leave because of stress. Mitchell, who now works as an AI researcher and chief ethics scientist at the AI startup Hugging Face, is far from alone in her experience. Burnout is becoming increasingly common in responsible-AI teams, says Abhishek Gupta, the founder of the Montreal AI Ethics Institute and a responsible-AI consultant at Boston Consulting Group. Companies are under increasing pressure from regulators and activists to ensure that their AI products are developed in a way that mitigates any potential harms before they are released.

There's little question artificial intelligence and machine learning are playing an increased role in making business decisions. A 2022 survey of senior data and technology executives by NewVantage Partners found that 92% of large companies reported achieving returns on their data and AI investments -- an increase from 48% in 2017. But as these technologies enter the mainstream, new issues arise: How will they change the nature of workflow and workplace connection? Will they be ethically harnessed? Here's what to consider as AI and machine learning become omnipresent, according to MIT Sloan researchers, visiting scholars, and industry experts.

Sarah Guo, founder of venture capital firm Conviction, organized a buzzy salon at a posh bar in San Francisco last week that drew an animated crowd of engineers, entrepreneurs, and financiers. Guo's event was just one of several held last week in San Francisco by investors and technologists excited by the commercial potential of what has been dubbed "generative AI." Her guests included AI engineers from large tech companies, fellow investors, and entrepreneurs building businesses powered by recent advances in algorithms that generate text or images. One of the guests of honor was Clement Delangue, CEO of Hugging Face, a company that hosts a number of open source generative AI projects, including one that recently sparked a frenzy of AI memes. He answered questions from engineers thinking about jumping onto the bandwagon with generative AI startups of their own. "It's just the hottest area from a fundraising perspective right now," Guo says.