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AIhub monthly digest: May 2023 – mitigating biases, ICLR invited talks, and Eurovision fun

Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
30 May 2023, by

Mitigating biases in machine learning

Max Springer examines the notion of fairness in hierarchical clustering.
17 May 2023, by

The Machine Ethics Podcast: featuring Marc Steen

In this episode, Ben chats to Marc Steen about AI as tools, the ethics of business models, writing "Ethics for People Who Work in Tech", and more.
06 June 2023, by

On privacy and personalization in federated learning: a retrospective on the US/UK PETs challenge

Studying the use of differential privacy in personalized, cross-silo federated learning.
05 June 2023, by


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VISION AI Open Day: Trustworthy AI

Watch the roundtable discussion on trustworthy AI, with a focus on generative models, from the AI Open Day held in Prague.
02 June 2023, by

PeSTo: an AI tool for predicting protein interactions

The model can predict the binding interfaces of proteins when they bind other proteins, nucleic acids, lipids, ions, and small molecules.
01 June 2023, by

Tetris reveals how people respond to an unfair AI algorithm

An experiment in which two people play a modified version of Tetris revealed that players who get fewer turns perceive the other player as less likeable, regardless of whether a person or an algorithm allocates the turns.
31 May 2023, by

Latest AI announcements from the US Government include updated strategic plan

Find out more about the latest initiatives pertaining to responsible AI in the USA.
26 May 2023, by

Interview with Haotian Xue: learning intuitive physics from videos

A framework for learning 3D-grounded visual intuitive physics models from videos of complex scenes.
25 May 2023, by

Using engineered bacteria and AI to sense and record environmental signals

Synthetic biologists engineer bacterial swarm patterns to visibly record environment and use deep learning to decode patterns.

Writing with AI help can shift your opinions

A study investigates whether a language-model-powered writing assistant that generates some opinions more often than others impacts what users write – and what they think.
23 May 2023, by

AI is helping astronomers make new discoveries and learn about the universe faster than ever before

As the technology has become more powerful, AI algorithms have begun helping astronomers tame massive data sets and discover more about the universe.
22 May 2023, by

The Good Robot Podcast: live from the AI Anarchies conference in Berlin

In this episode, hosts Eleanor Drage and Kerry Mackereth chat to Christina Lu and Grace Turtle.
19 May 2023, by

A list of resources, articles, and opinion pieces relating to large language models

We've updated our list to include the latest LLM resources.
18 May 2023, by and

Researchers create a tool for accurately simulating complex systems

The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.
16 May 2023, by

The Machine Ethics Podcast: featuring Marie Oldfield

In this episode, Ben chats to Marie Oldfield about explainable models, ethics in education, problems with audits and legislation and more.

Engineering molecular interactions with machine learning

By using deep learning-generated "fingerprints" to characterize millions of protein fragments, researchers have computationally designed novel protein binders that attach to key targets.
12 May 2023, by

#ICLR2023 invited talk: Data, history and equality with Elaine Nsoesie

Elaine Nsoesie talked about how the neighbourhood in which you live impacts your health outcomes.
11 May 2023, by

Forthcoming machine learning and AI seminars: May 2023 edition

A list of free-to-attend AI-related seminars that are scheduled to take place between 10 May and 30 June 2023.
10 May 2023, by

Four ways that AI can help students

As artificial intelligence systems play a bigger role in everyday life, they’re changing the world of education, too.
09 May 2023, by

Machine learning helps researchers separate compostable from conventional plastic waste

Scientists combined imaging techniques and machine learning methods to identify compostable plastics among conventional types.
05 May 2023, by

Understanding the impact of misspecification in inverse reinforcement learning

Our work provides a framework for reasoning about the question of misspecification in inverse reinforcement learning.
04 May 2023, by and

#ICLR2023 invited talks: exploring artificial biodiversity, and systematic deviations for trustworthy AI

We give a flavour of the first two invited talks at ICLR 2023, which is taking place in Kigali.
03 May 2023, by

Interview with Fanglan Chen: Exploring tradeoffs in automated school redistricting

Fanglan Chen tells us about work exploring the feasibility of automatically generating school redistricting plans.
02 May 2023, by

TIDEE: An embodied agent that tidies up novel rooms using commonsense priors

We introduce a new benchmark to test agents in their ability to clean up messy scenes without any human instruction.
28 April 2023, by

AIhub monthly digest: April 2023 – addressing class imbalance, personalized reward functions, and ad hoc teamwork

Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
27 April 2023, by

“Computing and Technology Ethics: Engaging Through Science Fiction” – an interview with the authors

Emanuelle Burton, Judy Goldsmith, Nicholas Mattei, Cory Siler and Sara-Jo Swiatek tell us about their new book.
26 April 2023, by

Back to the future: towards a reasoning and learning architecture for ad hoc teamwork

Our architecture formulates ad hoc teamwork as a joint reasoning and learning problem.
25 April 2023, by

AI in health care challenges us to define what better, people-centred care looks like

Catherine Burns on thinking about future healthcare in the age of big data and AI.
24 April 2023, by

Understanding AI-generated misinformation and evaluating algorithmic and human solutions

Current ML models designed for human-written content have significant performance discrepancies in detecting paired human-generated misinformation and misinformation generated by algorithms.







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