Sarvapali (Gopal) Ramchurn is a Professor of Artificial Intelligence, Turing Fellow, and Fellow of the Institution of Engineering and Technology. He is the Director of the UKRI Trustworthy Autonomous Systems hub and Co-Director of the Shell-Southampton Centre for Maritime Futures. He is also a Co-CEO of Empati Ltd, an AI startup working on decentralised green hydrogen technologies. His research is about the design of Responsible Artificial Intelligence for socio-technical applications including energy systems and disaster management. Ferdinando Rodriguez y Baena is Professor of Medical Robotics in the Department of Mechanical Engineering at Imperial College, where he leads the Mechatronics in Medicine Laboratory and the Applied Mechanics Division. He has been the Engineering Co-Director of the Hamlyn Centre, which is part of the Institute of Global Health Innovation, since July 2020.

Image-based abuse – when someone takes, shares or threatens to share nude, semi-nude or sexual images or video without consent – has become a growing issue, experienced by 1 in 3 Australians surveyed in 2019. Lead researcher behind the creation of'Umibot', Professor Nicola Henry from RMIT's Social and Global Studies Centre, said'deepfake' content (fake videos or images generated using AI), incidents where people are pressured into creating sexual content and being sent unsolicited sexual images or videos also count as image-based abuse. "It's a huge violation of trust that's designed to shame, punish or humiliate. It's often a way for perpetrators to exert power and control over others," said Henry, who is an Australian Research Council Future Fellow. "A lot of victim-survivors we talked to just want the issue to go away and the content to be taken down or removed but often they don't know where to go for help."

Faculty mentors will be expected to play an active role in helping Fellows plan and achieve their research and professional goals, providing assessments of the novelty and research quality throughout the fellowship term. The STEM mentor will serve as the Fellow's primary mentor and will be expected to provide intellectual leadership, guidance, and support while overseeing the Fellow's research agenda in their home department. The AI mentor will be expected to co-advise the Fellow on their use of AI methods. Both STEM and AI co-mentors will help guide Fellows toward independent research careers, and will be expected to provide a nominal amount of time and funds to the professional development of the postdoctoral scholars. Mentors should also assist Fellows in securing opportunities to participate in national and international research meetings and in facilitating connections among scholars in their fields.

It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks. Machine learning, in particular deep learning (DL), is expected to be one of the key technological enablers of 6G by offering a new paradigm for the design and optimization of networks with a high level of intelligence. In this article, we introduce an emerging DL architecture, known as the transformer, and discuss its potential impact on 6G network design. We first discuss the differences between the transformer and classical DL architectures, and emphasize the transformer's self-attention mechanism and strong representation capabilities, which make it particularly appealing for tackling various challenges in wireless network design. Specifically, we propose transformer-based solutions for various massive multiple-input multiple-output (MIMO) and semantic communication problems, and show their superiority compared to other architectures. Finally, we discuss key challenges and open issues in transformer-based solutions, and identify future research directions for their deployment in intelligent 6G networks.

Professor Yang-Hui He is a Fellow of the London Institute for Mathematical Sciences, professor of mathematics at City, University of London, Lecturer in mathematics at Merton College, Oxford, and Chang-Jiang Chair of physics at Nankai University in China. He obtained his BA at Princeton (summa cum laude, Shenstone Prize and Kusaka Prize), MA at Cambridge (Distinction, Tripos), and PhD at MIT. After a postdoc at the University of Pennsylvania, he joined Oxford as the FitzJames Fellow and an STFC Advanced Fellow. He works at the interface of string theory, algebraic and combinatorial geometry, and machine learning. Professor He is the Editor-in-Chief of the International Journal of Data Science in the Mathematical Sciences (World Scientific), and has over 200 journal publications and invited chapters.

HAI Fellowship Programs offer opportunities to explore topics, conduct research, and collaborate across disciplines related to AI technologies, applications, or impact. The Institute for Human-Centered Artificial Intelligence (HAI) offers a 2-quarter program for Stanford Graduate Students. The goal of this program is to encourage interdisciplinary research conversations, facilitate new collaborations, and grow the HAI community of graduate scholars who are working in the area of AI, broadly defined. HAI is seeking graduate students to participate in this program. We would like to ensure the cohort is well-rounded across disciplines.

Professor Chad Jenkins has been elected a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). Jenkins specializes in mobile manipulation robots and human-robot interaction. His research explores how to enable robots to learn from human demonstration in complex environments. His work has been supported through a number of prestigious awards, including a PECASE award, an NSF CAREER Award, an ONR Young Investigator Award, and a Sloan Research Fellowship. Jenkins is also devoted to ensuring that the fields of robotics and AI are accessible to everyone.

In some ways it's a redux of the early days – in 1956, to be precise – immediately following the coining of the term "AI", at Dartmouth College in New Hampshire, USA. Back then, AI investments among competing countries created an effective AI "arms race". There were controversies, with early applications being developed in gaming, robotics, and autonomous vehicles. Well, innovation is either something new, or something nobody remembers. But today's modern AI is a mixture of both, and HPE and Intel have the something new part.

Hai Zhuge is an ACM (Association of Computer Machinery) Distinguished Scientist and a Fellow of the British Computer Society. He has made a systematic contribution to semantics and knowledge modelling through fundamental research on the Semantic Link Network and the Resource Space Model based on multi-dimensional methodology. He is leading research on cyber-physical-social intelligence using methodological, theoretical and technical innovations. As an ACM Distinguished Speaker, he has delivered 20 keynotes at international conferences and invited lectures at universities in various countries. As a Professor, he is head of an international research network consisting of the Guangzhou University, the Key Laboratory of Intelligent Information Processing at the Institute of Computing Technology in Chinese Academy of Sciences, the University of Chinese Academy of Sciences, and the System Analytics Research Institute at Aston University.

But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture. "Current methods create a situation where each worker has expensive computational cost; GD-SEC is relatively cheap where only one GD step is needed at each round," says Blum. Professor Blum's collaborators on this project include his former student Yicheng Chen '19G '21PhD, now a software engineer with LinkedIn; Martin Takác, an associate professor at the Mohamed bin Zayed University of Artificial Intelligence; and Brian M. Sadler, a Life Fellow of the IEEE, U.S. Army Senior Scientist for Intelligent Systems, and Fellow of the Army Research Laboratory. But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture.