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I am getting in contact about one of our authors, Anthony Elliott. He has recently written a publication titled: The Culture of AI https://www.routledge.com/product/isbn/9781138230057?source=igodigital and is extremely keen to get out to speak to…

An algorithm that teaches robot agents how to exchange advice to complete a task helps them learn faster.

Researchers pinpoint the “neurons” in machine-learning systems that capture specific linguistic features during language-processing tasks.

Algorithm could help autonomous underwater vehicles explore risky but scientifically-rewarding environments.

A new database of images could pave a path for algorithmic models that ensure accurate diagnoses of conditions like pneumonia.

Study uncovers language patterns that AI models link to factual and false articles; underscores need for further testing.

We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy ϵ is O~(1/ϵ), where each iteration operates on a...

We study the label complexity of pool-based active learning in the agnostic PAC model. Specifically, we derive general bounds on the number of label requests made by the A2 algorithm proposed by Balcan, Beygelzimer & Langford (Balcan et al., 2006). This represents the first nontrivial general...

A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science—such as abstraction and modular design—are used to...

When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Models of agent responsiveness termed "strategic manipulation," analyze the interaction between a learner and agents in a world where all...

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the...

As machine learning expands into climate modeling, EAPS Associate Professor Paul O’Gorman answers what that looks like and why it's important now.

New system of “strain engineering” can change a material’s optical, electrical, and thermal properties.

The rapid pace of research in Deep Reinforcement Learning has been driven by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games to classic home console games to modern strategy games. We propose a...

In this paper we revisit the method of off-policy corrections for reinforcement learning (COP-TD) pioneered by Hallak et al. (2017). Under this method, online updates to the value function are reweighted to avoid divergence issues typical of off-policy learning. While Hallak et al.'s solution is...

Autonomous AI systems will be entering human society in the near future to provide services and work alongside humans. For those systems to be accepted and trusted, the users should be able to understand the reasoning process of the system, i.e. the system should be transparent. System transparency...

The rhetoric of the race for strategic advantage is increasingly being used with regard to the development of artificial intelligence (AI), sometimes in a military context, but also more broadly. This rhetoric also reflects real shifts in strategy, as industry research groups compete for a limited...

Currently, there is no standard way to identify how a dataset was created, and what characteristics, motivations, and potential skews it represents. To begin to address this issue, we propose the concept of a datasheet for datasets, a short document to accompany public datasets, commercial APIs, and...

Feedback loops in algorithms amplify chosen content, to the exclusion of others.