Asthma is a chronic respiratory condition that affects millions of people worldwide. While this condition can be managed by administering controller medications through handheld inhalers, clinical studies have shown low adherence to the correct inhaler usage technique. Consequently, many patients may not receive the full benefit of their medication. Automated classification of inhaler sounds has recently been studied to assess medication adherence. However, the existing classification models were typically trained using data from specific inhaler types, and their ability to generalize to sounds from different inhalers remains unexplored. In this study, we adapted the wav2vec 2.0 self-supervised learning model for inhaler sound classification by pre-training and fine-tuning this model on inhaler sounds. The proposed model shows a balanced accuracy of 98% on a dataset collected using a dry powder inhaler and smartwatch device. The results also demonstrate that re-finetuning this model on minimal data from a target inhaler is a promising approach to adapting a generic inhaler sound classification model to a different inhaler device and audio capture hardware. This is the first study in the field to demonstrate the potential of smartwatches as assistive technologies for the personalized monitoring of inhaler adherence using machine learning models.

As owner of GitHub and lead investor in OpenAI, the developer of the GPT-x series of large language models (LLMs), it did not take long for Microsoft to see the potential for collaboration between the two. Three years ago, GitHub partnered with OpenAI to develop Codex as an automated assistant for programmers, quickly followed by the Copilot code-completion tool. The public release of ChatGPT by OpenAI toward the end of 2022 made the technology even more widely available to software developers and people learning to program, with other vendors joining in the effort to automate the job of writing software using LLMs. Rapid scaling has enabled major improvements in the ability of artificial intelligence (AI) to turn natural-language requests into working code. Workplace studies have claimed LLMs boost productivity on real-world projects.

Analysis While the legal and ethical implications of assistive AI models like GitHub's Copilot continue to be sorted out, computer scientists continue to find uses for large language models and urge educators to adapt. Brett A. Becker, assistant professor at University College Dublin in Ireland, provided The Register with pre-publication copies of two research papers exploring the educational risks and opportunities of AI tools for generating programming code. The papers have been accepted at the 2023 SIGCSE Technical Symposium on Computer Science Education, to be held March 15 to 18 in Toronto, Canada. In June, GitHub Copilot, a machine learning tool that automatically suggests programming code in response to contextual prompts, emerged from a year long technical preview, just as concerns about the way its OpenAI Codex model was trained and the implications of AI models for society coalesced into focused opposition. In "Programming Is Hard – Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation" [PDF], Becker and co-authors Paul Denny (University of Auckland, New Zealand), James Finnie-Ansley (University of Auckland), Andrew Luxton-Reilly (University of Auckland), James Prather (Abilene Christian University, USA), and Eddie Antonio Santos (University College Dublin) argue that the educational community needs to deal with the immediate opportunities and challenges presented by AI-driven code generation tools.

But last week every human face included in ImageNet suddenly disappeared--after the researchers who manage the data set decided to blur them. Russakovsky says the ImageNet team wanted to determine if it would be possible to blur faces in the data set without changing how well it recognizes objects. In a research paper, posted along with the update to ImageNet, the team behind the data set explains that it blurred the faces using Amazon's AI service Rekognition; then, they paid Mechanical Turk workers to confirm selections and adjust them. Blurring the faces did not affect the performance of several object-recognition algorithms trained on ImageNet, the researchers say. In July 2020 Vinay Prabhu, a machine learning scientist at UnifyID and Abeba Birhane, a PhD candidate at University College Dublin in Ireland, published research showing they could identify individuals, including computer science researchers, in the data set. But last week every human face included in ImageNet suddenly disappeared--after the researchers who manage the data set decided to blur them.

As part of our SLAS Europe 2022 coverage, we speak to Professor Patricia Maguire from the University College Dublin about their AI_PREMie technology and how it can help to save mothers and babies lives. My name is Patricia Maguire, and I am a professor of biochemistry at University College, Dublin (UCD). Four years ago, I was appointed director of the UCD Institute for Discovery, a major university research institute in UCD, and our focus is cultivating interdisciplinary research. In that role, I first became excited by the possibilities of integrating AI into my research. I think there are two major obstacles to adopting AI in healthcare.

Barely a day goes by without a company talking up their green credentials -- how they're aligning themselves with global climate goals, cutting waste and upping their recycling. With all this corporate happy-talk about saving the planet on the rise, so are concerns about greenwashing. Investors and regulators are increasingly sounding the alarm about companies that exaggerate or misrepresent their environmental bona fides. That's what prompted academics at University College Dublin to develop algorithms to help the financial services sector detect and quantify greenwashing. Greenwashing encompasses everything from slightly disingenuous claims of being environmentally friendly to outright falsehoods.

In recent decades, AI has become a pervasive technology, affecting companies across industries and throughout the world. These innovations arise from research, and the research objectives in the AI field are influenced by many factors. Together, these factors shape patterns in what the research accomplishes, as well as who benefits from it -- and who doesn't. In an effort to document the factors influencing AI research, researchers at Stanford, the University of California, Berkeley, the University of Washington, and University College Dublin & Lero surveyed 100 highly cited studies submitted to two prominent AI conferences, NeurIPS and ICML. They claim that in the papers they analyzed, which were published in 2008, 2009, 2018, and 2019, the dominant values were operationalized in ways that centralize power, disproportionally benefiting corporations while neglecting society's least advantaged.

Last week saw the virtual running of the 12th Asian Conference on Machine Learning (ACML). The event had been due to be held in Thailand, but instead went online and the organisers decided to make all content freely available. You can watch all of the invited talks, tutorials, workshops, and video presentations of the contributed papers. Also, find out who won the conference awards. There were four invited speakers: Suriya Gunasekar (Microsoft Research, USA) Rethinking the role of optimization in learning This talk presented an overview of recent results towards understanding how we learn large capacity machine learning models.

A UCD student's research has resulted in the withdrawal of an 80-million image library used to train artificial intelligence systems. The research by PhD student Abeba Birhane found that hundreds of millions of images in academic datasets that are used to develop AI systems and applications are partly based on racist and misogynistic labels and slurs, according to the Irish Software Research Centre (Lero) and University College Dublin's Complex Software Lab. "Already, MIT has deleted its much-cited '80 Million Tiny Images' dataset, asking researchers and developers to cease using the library to train AI and ML system," said the software research centre in a statement. "MIT's decision came as a direct result of the research carried out by University College Dublin based Lero researcher Abeba Birhane and Vinay Prabhu, chief scientist at UnifyID, a privacy start-up in Silicon Valley." In the course of the work, the Lero statement says, Ms Birhane found the MIT database contained thousands of images labelled with racist and misogynistic insults and derogatory terms.

Stephen Howell is the Academic Evangelist and Accessibility Lead for Microsoft Ireland. In this role he is a researcher, guest lecturer, and evangelist on many topics including Cloud engineering, teacher education, Computational Thinking, and applied Machine Learning. As Accessibility Lead, he is an advocate for greater awareness of accessibility and disability issues, particularly Autism, ADHD, and related neurodiversity areas. Formerly, he was a software engineer who discovered a passion for teaching. He has lectured on Software Engineering topics since 1999 in Irish and Northern Irish universities, and a visiting professorship in Japan.