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monthly digest
AIhub monthly digest: June 2023 – combining learning and reasoning, physics from videos, and the EU AI act
Lucy Smith
28 Jun 2023
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
GPT-4 + Stable-Diffusion = ?: Enhancing prompt understanding of text-to-image diffusion models with large language models
BAIR blog
26 Jun 2023
Our LLM-grounded model delivers improved prompt understanding in cases including negation, numeracy, and spatial relationships.
Algorithms can be useful in detecting fake news, stopping its spread and countering misinformation
The Conversation
23 Jun 2023
Laks V.S. Lakshmananand colleagues have developed algorithms for detecting dense structures from communication networks, information which can be used for identifying instances of misinformation campaigns.
Researchers use AI to identify similar materials in images
MIT News
14 Jun 2023
This machine-learning method could assist with robotic scene understanding, image editing, or online recommendation systems.
Bridging the gap between learning and reasoning
Marianne Defresne
07 Jun 2023
Combining deep learning with automated reasoning to solve real-world problems.
On privacy and personalization in federated learning: a retrospective on the US/UK PETs challenge
ML@CMU
05 Jun 2023
Studying the use of differential privacy in personalized, cross-silo federated learning.
PeSTo: an AI tool for predicting protein interactions
EPFL
01 Jun 2023
The model can predict the binding interfaces of proteins when they bind other proteins, nucleic acids, lipids, ions, and small molecules.
Tetris reveals how people respond to an unfair AI algorithm
Cornell University
31 May 2023
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.
monthly digest
AIhub monthly digest: May 2023 – mitigating biases, ICLR invited talks, and Eurovision fun
Lucy Smith
30 May 2023
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
Using engineered bacteria and AI to sense and record environmental signals
Columbia University in the City of New York
24 May 2023
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
Cornell University
23 May 2023
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.
AI is helping astronomers make new discoveries and learn about the universe faster than ever before
The Conversation
22 May 2023
As the technology has become more powerful, AI algorithms have begun helping astronomers tame massive data sets and discover more about the universe.
Mitigating biases in machine learning
Max Springer
17 May 2023
Max Springer examines the notion of fairness in hierarchical clustering.
Researchers create a tool for accurately simulating complex systems
MIT News
16 May 2023
The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.
Engineering molecular interactions with machine learning
EPFL
12 May 2023
By using deep learning-generated "fingerprints" to characterize millions of protein fragments, researchers have computationally designed novel protein binders that attach to key targets.
#ICLR2023 invited talk: Data, history and equality with Elaine Nsoesie
Lucy Smith
11 May 2023
Elaine Nsoesie talked about how the neighbourhood in which you live impacts your health outcomes.
Four ways that AI can help students
The Conversation
09 May 2023
As artificial intelligence systems play a bigger role in everyday life, they’re changing the world of education, too.
Machine learning helps researchers separate compostable from conventional plastic waste
Frontiers Science News
05 May 2023
Scientists combined imaging techniques and machine learning methods to identify compostable plastics among conventional types.
Understanding the impact of misspecification in inverse reinforcement learning
Joar Skalse and Alessandro Abate
04 May 2023
Our work provides a framework for reasoning about the question of misspecification in inverse reinforcement learning.
#ICLR2023 invited talks: exploring artificial biodiversity, and systematic deviations for trustworthy AI
Lucy Smith
03 May 2023
We give a flavour of the first two invited talks at ICLR 2023, which is taking place in Kigali.
TIDEE: An embodied agent that tidies up novel rooms using commonsense priors
ML@CMU
28 Apr 2023
We introduce a new benchmark to test agents in their ability to clean up messy scenes without any human instruction.
monthly digest
AIhub monthly digest: April 2023 – addressing class imbalance, personalized reward functions, and ad hoc teamwork
Lucy Smith
27 Apr 2023
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
Back to the future: towards a reasoning and learning architecture for ad hoc teamwork
Hasra Dodampegama
25 Apr 2023
Our architecture formulates ad hoc teamwork as a joint reasoning and learning problem.
ChatGPT: what the law says about who owns the copyright of AI-generated content
The Conversation
20 Apr 2023
The capabilities of recent language models raise important questions about ownership of the content produced.
Koala: A dialogue model for academic research
BAIR blog
18 Apr 2023
In this post, we introduce Koala, a chatbot trained by fine-tuning Meta’s LLaMA on dialogue data gathered from the web.
Benefits and control influence the acceptance of automated decision-making
Radboud University
17 Apr 2023
Research indicates that users accept or reject decisions and decisional agents based on their predicted benefits and the ability to exercise control over the decision.
Are model explanations useful in practice? Rethinking how to support human-ML interactions
ML@CMU
14 Apr 2023
This post describes a workflow for evaluating XAI methods, how this workflow was instantiated in two domains, and insights from these efforts.
#AAAI2023 workshops round-up 3: Reinforcement learning ready for production
AIhub
13 Apr 2023
The organisers of the AAAI2023 workshop on RL for production tell us their key takeaways from their event.
Learning personalized reward functions with Interaction-Grounded Learning (IGL)
Jessica Maghakian and Akanksha Saran
04 Apr 2023
Find out about research using Interaction-Grounded Learning (IGL) to infer reward functions that capture the intent of an end-user.
Advancing data justice research and practice project
Lucy Smith
31 Mar 2023
Find out more about this project from The Alan Turing Institute and the Global Partnership on AI.
Methods for addressing class imbalance in deep learning-based natural language processing
Sophie Henning and Annemarie Friedrich
30 Mar 2023
This blogpost gives an overview of class imbalance in NLP and surveys methods for addressing this.
monthly digest
AIhub monthly digest: March 2023 – plant disease diagnosis, logic for trustworthy AI, and neurosymbolic approaches
Lucy Smith
28 Mar 2023
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
Neurosymbolic AI for graphs: a crime scene analogy
Ramon Fernández Mir and Lauren Nicole DeLong
23 Mar 2023
Find out more about neurosymbolic approaches and how these can be used for reasoning on graph structures.
#AAAI2023 workshops round-up 2: health intelligence and privacy-preserving AI
AIhub
21 Mar 2023
The organisers of two of the AAAI2023 workshops tell us their key takeaways from their respective events.
Is there a way to pay content creators whose work is used to train AI? Yes, but it’s not foolproof
The Conversation
17 Mar 2023
Is imitation the sincerest form of flattery, or theft? Perhaps it comes down to the imitator.
Improving the understanding of metal-organic frameworks
EPFL
15 Mar 2023
Researchers at EPFL and KAIST have developed a new AI model that improves the understanding of MOFs.
