This post contains a list of the AI-related seminars that are scheduled to take place between 10 May and 30 June 2023. All events detailed here are free and open for anyone to attend virtually. Natural Language Generation Problems and Challenges Speaker: Konstantinos Diamantaras Organised by: Chalmers AI Research Centre Zoom link is here. Exhaustive Symbolic Regression (or how to find the best function for your data) Speaker: Harry Desmond (University of Portsmouth) Organised by: University of Lisbon Register here. Multi-Fidelity Bayesian Optimization with Unreliable Information Sources Speakers: Julien Martinelli (Aalto University) Organised by: Finnish Center for Artificial Intelligence Zoom link is here.

Generative AI has hit the public imagination in full force during 2022. Perhaps the biggest splash was made by OpenAI's launch of the text-to-image generator DALL-E 2 with its stunning illustrations. Under the guise of generative AI art, code-completion programs are boosting developer productivity by automating repetitive and mundane programming tasks. The world's largest source code host GitHub released its code-completion tool called Copilot in June 2022. It is trained on 45 terabytes of coding data from the GitHub code repository and runs on OpenAI's Codex model.

The use of Agent-Based and Activity-Based modeling in transportation is rising due to the capability of addressing complex applications such as disruptive trends (e.g., remote working and automation) or the design and assessment of disaggregated management strategies. Still, the broad adoption of large-scale disaggregate models is not materializing due to the inherently high complexity and computational needs. Activity-based models focused on behavioral theory, for example, may involve hundreds of parameters that need to be calibrated to match the detailed socio-economical characteristics of the population for any case study. This paper tackles this issue by proposing a novel Bayesian Optimization approach incorporating a surrogate model in the form of an improved Random Forest, designed to automate the calibration process of the behavioral parameters. The proposed method is tested on a case study for the city of Tallinn, Estonia, where the model to be calibrated consists of 477 behavioral parameters, using the SimMobility MT software. Satisfactory performance is achieved in the major indicators defined for the calibration process: the error for the overall number of trips is equal to 4% and the average error in the OD matrix is 15.92 vehicles per day.

Researchers at Aalto University and the University of Luxembourg report they have developed a new machine learning model that will help identify small molecules, with applications in medicine, drug discovery, and environmental chemistry. Their findings, "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data," were published in the journal Nature Machine Intelligence. "Structural annotation of small molecules in biological samples remains a key bottleneck in untargeted metabolomics, despite rapid progress in predictive methods and tools during the past decade," wrote the researchers. "Liquid chromatography–tandem mass spectrometry, one of the most widely used analysis platforms, can detect thousands of molecules in a sample, the vast majority of which remain unidentified even with best-of-class methods. "Even the best methods can't identify more than 40% of the molecules in samples without making some additional assumptions about the candidate molecules," explained Juho Rousu, PhD, professor of computer science at Aalto University.

A new machine learning model will help scientists identify small molecules, with applications in medicine, drug discovery and environmental chemistry. Developed by researchers at Aalto University and the University of Luxembourg, the model was trained with data from dozens of laboratories to become one of the most accurate tools for identifying small molecules. Thousands of different small molecules, known as metabolites, transport energy and transmit cellular information throughout the human body. Because they are so small, metabolites are difficult to distinguish from each other in a blood sample analysis – but identifying these molecules is important to understand how exercise, nutrition, alcohol use and metabolic disorders affect wellbeing. Metabolites are normally identified by analysing their mass and retention time with a separation technique called liquid chromatography followed by mass spectrometry.

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.

An international team of researchers have designed a miniaturised spectrometer with high resolution, employing machine learning methodology as one of their tools. The results are reported in the journal Science. Traditionally, spectrometers rely on bulky components to filter and disperse light. In addition, these traditional spectrometers are heavy and large, which limits their application in portable and mobile devices. Modern approaches simplify these components to shrink footprints, but tend to suffer from limited resolution and bandwidth.

CEST researchers developed a new machine learning approach based on a low-energy latent space (LOLS) and density functional theory (DFT) to search for molecular conformers. Molecular conformer search is a topic of great importance in computational chemistry, drug design and material science. The challenge is to identify low-energy conformers in the first place. This difficulty arises from the high complexity of search spaces, as well as the computational cost associated with accurate quantum chemical methods. In the past, conformer search would take up considerable time and computational resources.

MRSA is an antibiotic-resistant staph infection that can be deadly for those in hospital care or with weakened immune systems.

Stethoscopes are among doctors' most important instruments, yet there have not been any essential improvements to the device since the 1960s. Now, researchers at Aalto University have developed a device that analyzes a broad range of bodily functions and offers physicians a probable diagnosis as well as suggestions for appropriate further examinations. The researchers believe that the new device could eventually replace the stethoscope and enable quicker and more precise diagnoses. A startup called Vital Signs is taking the device to the market. The researchers are currently testing the device in a clinical pilot trial.