Maja Matarić Receives ACM Athena Lecturer Award

ACM has named Maja Matarić as the the 2023-2024 ACM Athena Lecturer. Matarić is recognised for pioneering the field of socially assistive robotics, including groundbreaking research, evaluation and technology transfer, and pioneering work in multi-robot coordination and human-robot interaction. Matarić is the Chan Soon-Shiong Chair and Distinguished Professor of Computer Science at the University of Southern California, where she is the founding director of the USC Robotics and Autonomous Systems Center. Matarić is also a Principal Scientist at Google DeepMind.

The ACM Athena Lecturer Award celebrates women researchers who have made fundamental contributions to computer science. It includes a $25,000 honorarium provided by Two Sigma. The Athena Lecturer is invited to present a lecture at an ACM event. Each year, the Athena Lecturer honors a preeminent woman computer scientist who the gives an invited talk at a major ACM conference of her choice.

Read the ACM news release.

ACM ByteCast: Yoshua Bengio 

In this episode of ACM ByteCast, Rashmi Mohan hosts ACM A.M. Turing Award laureate Yoshua Bengio, Professor at the University of Montreal and Founder and Scientific Director of MILA (Montreal Institute for Learning Algorithms) at the Quebec AI Institute. Yoshua shared the 2018 Turing Award with Geoffrey Hinton and Yann LeCun for their work on deep learning. He is also a published author and the most cited scientist in Computer Science. He is a Fellow of ACM, the Royal Society, the Royal Society of Canada, Officer of the Order of Canada and recipient of the Killam Prize, Marie-Victorin Quebec Prize and Princess of Asturias Award. Yoshua also serves on the United Nations Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology and as a Canada CIFAR AI Chair.

Here, Yoshua traces his path in computing, defines deep learning and talks about knowledge as the relationship between symbols, talks at length about artificial general intelligence (AGI) and the major risks it will present, offers advice for researchers, shares what he is most excited about with the future of AI and more.

ACM ByteCast: Francesca Rossi

In this episode of ACM ByteCast, Bruke Kifle hosts Francesca Rossi, IBM Fellow and AI Ethics Global Leader and current President of the Association for the Advancement of Artificial Intelligence (AAAI). Rossi works at the Thomas J. Watson IBM Research Lab in New York. Her research interests focus on artificial intelligence, especially constraint reasoning, preferences, multi-agent systems, computational social choice and collective decision making. She is also interested in ethical issues in the development and behaviour of AI systems.

Here, Francesca shares how experiences with multidisciplinary work in computer science drew her to AI and ethics, and the challenges of synchronising with people from a variety of different backgrounds at IBM. She also talks about her involvement in the development of AI ethics guidelines in Europe. She also walks through some of her concerns around building ethical and responsible AI, such as bias, lack of availability, transparency of AI developers, data privacy and the accuracy of generated content, and more. 

ACM ByteCast: Partha Talukdar

In this episode of ACM ByteCast, Bruke Kifle hosts Partha Talukdar, Senior Staff Research Scientist at Google Research India, where he leads a group focused on natural language processing and Associate Professor at the Indian Institute of Science, Bangalore. He shares how exposure to language processing drew him to languages with limited resources and NLP, discusses the role of language in machine learning and more.

Here, Partha shares how exposure to language processing drew him to languages with limited resources and NLP. He and Bruke discuss the role of language in machine learning and whether current AI systems are merely memorising and reproducing data or are actually capable of understanding, about his recent focus on inclusive and equitable language technology development through multilingual-multimodal Large Language Modeling, including Project Bindi. And they discuss current limitations in machine learning in a world with more than 7,000 languages, data scarcity, how knowledge graphs can mitigate this issue, and more. 

ACM TechTalk: Sebastian Raschka

Register now for the next free ACM TechTalk, “Understanding the LLM Development Cycle: Building, Training, and Finetuning,” presented on Wednesday, June 5 at 1:00 pm ET/5:00 pm UTC by Sebastian Raschka, Staff Research Engineer at Lightning AI. 

This talk will guide attendees through the key stages of developing large language models (LLMs), from initial coding to deployment. It will start by explaining how these models are built, including the coding of their architectures, and then discuss the processes of pre-training and finetuning, showing what these stages involve and why they are important. Throughout the talk, real examples will be provided and questions will be encourage, making this a practical and interactive session for anyone interested in how LLMs are created and used.

Featured ACM Member: Nesime Tatbul

Nesime Tatbul is a Senior Research Scientist at Intel’s Parallel Computing Lab (PCL) and MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). Her research interests are broadly in large-scale data management systems and modern data-intensive applications, with a recent focus on learned data systems, time series analytics, and observability data management. She is most known for her contributions to stream processing, which include the Aurora/Borealis Systems (now TIBCO StreamBase) and the S-Store System.

Among her honours, she was a co-recipient of an ACM SIGMOD Research Highlight Award (2022), an ACM SIGMOD Best Paper Award (2021), and two ACM SIGMOD Best Demonstration Awards (2005 and 2019). She was recently named an ACM Distinguished Member for foundational scientific contributions in streaming data systems and time series analytics.

In her inteview, she discusses large-scale data management systems, stream processing, working in both industry and academia, and more

Read Tatbul’s interview here.

Featured ACM Member: Zvi Galil

Zvi Galil is the Frederick G. Storey Chair and Executive Advisor to Online Programs at the Georgia Institute of Technology (Georgia Tech). His earlier positions have included Dean of Georgia Tech’s College of Computing, President of Tel Aviv University, Chair of Columbia University’s Computer Science Department, and Dean of Columbia University’s School of Engineering and Applied Sciences.

Galil’s research areas are design and analysis of algorithms, complexity, cryptography, and experimental design. He has written over 200 scientific papers and edited five books. At Georgia Tech, Galil led the faculty in creating its Online Master of Science program in Computer Science, which is the largest Master’s Degree program in the US and has been featured in numerous articles as an example of a successful online computer science education initiative.

In his inteview, he discusses his Online Master of Science program in Computer Science, his accomplishments as Dean of Columbia University’s School of Engineering and Applied Sciences, and more.

Read Galil’s interview here.