GENEVA – Researchers from around the world have embarked on an effort to try to build a system allowing humanity to anticipate violent conflicts before they erupt -- and thus potentially prevent them. They will examine dramatic advances in artificial intelligence and how the decisions taken by the world's leaders could be swayed at a time when war in Ukraine has reshaped reality for tens of millions of people. "We are living in a crisis society … and different kinds of nondesirable futures exist," said Sirkka Heinonen, a professor of future studies at Finland's Turku University. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

The largest prostate cancer biopsy dataset – involving over 95,000 images – has been created by researchers in Sweden to ensure AI can be trained to diagnose and grade prostate cancer for real world clinical applications. The researchers will call today, at the European Association of Urology annual congress (EAU22), for large-scale clinical trials of artificial intelligence (AI) algorithms and greater global coordination to ensure that AI enhanced diagnostics, prognostication, and treatment selection can help save lives. There is a shortage of pathologists around the world, both generalists and those specialised in urology. AI can help in detecting prostate cancer at an early stage, but because of the vast differences in the way clinics prepare samples, scan images and in the diverse patient populations they serve, many algorithms do not have universal application. The team, from Karolinska Institutet, worked with colleagues from Radboud University Medical Center in the Netherlands, University of Turku in Finland and Google Health in the US to run an AI competition involving nearly 1,300 developers from around the world.

In this paper, we approach the problem of semantic search by framing the search task as paraphrase span detection, i.e. given a segment of text as a query phrase, the task is to identify its paraphrase in a given document, the same modelling setup as typically used in extractive question answering. On the Turku Paraphrase Corpus of 100,000 manually extracted Finnish paraphrase pairs including their original document context, we find that our paraphrase span detection model outperforms two strong retrieval baselines (lexical similarity and BERT sentence embeddings) by 31.9pp and 22.4pp respectively in terms of exact match, and by 22.3pp and 12.9pp in terms of token-level F-score. This demonstrates a strong advantage of modelling the task in terms of span retrieval, rather than sentence similarity. Additionally, we introduce a method for creating artificial paraphrase data through back-translation, suitable for languages where manually annotated paraphrase resources for training the span detection model are not available.

Music can spark emotion on the listener's face, but scientists discovered they can'see' the type of melody being played when looking at the individual's brain. Using machine learning and functional magnetic resonance imaging, researchers at the University of Turku found that the auditory and motor cortex were activated when happy or sad music is played. The auditory cortex processes the acoustic elements, such as rhythm and melody, and the motor cortex could be related to the fact that music inspires feelings of movement. The study also looked at music that induces fear, revealing it correlates with subcortical structures involved with memory, emotion and pleasure. 'Music can induce strong subjective experience of emotions, but it is debated whether these responses engage the same neural circuits as emotions elicited by biologically significant events,' researchers shared in the study published in Oxford Academic.

The accurate detection of disease outcomes still remains a challenging obstacle for physicians. As a result, machine learning (ML) has emerged as a popular tool for researchers. It can aid in discovering and identifying patterns and relationships from complex datasets, while predicting future outcomes. Now, researchers at Aalto University, the University of Helsinki, and the University of Turku in Finland report they have developed a machine learning model that can predict how combinations of different cancer drugs kill various types of cancer cells. The new AI model was trained with a large set of data obtained from previous studies, which had investigated the association between drugs and cancer cells.

Espoo: A team of researchers have developed a machine learning model that accurately predicts how combinations of different cancer drugs kill various types of cancer cells. The new AI model was trained with a large set of data obtained from previous studies, which had investigated the association between drugs and cancer cells. 'The model learned by the machine is actually a polynomial function familiar from school mathematics, but a very complex one,' says Professor Juho Rousu from Aalto University. The study was led by researchers at Aalto University, the University of Helsinki, and the University of Turku in Finland. The research results were published in the prestigious journal Nature Communications.

In a collaboration project between Helsinki University Hospital (HUS), Kuopio University Hospital and Turku University Hospital (all Finland), a team of researchers have presented the first artificial intelligence (AI) based algorithm that has the potential to assist in treating patients with severe TBI in intensive care units (ICUs). Patients with the most severe cases of TBI are usually treated in ICUs, however, despite the high-quality care, recent observational studies have reported mortality rates of approximately 30%. Patients who suffer from severe TBI are unconscious, therefore, it is a challenge to accurately monitor their condition. In ICUs many tens of variables, such as intercranial pressure and mean arterial pressure, are continuously monitored to assess the patient's condition. One variable alone could yield hundreds of thousands of data points per day, making it impossible for ICU staff to fully analyze.

As scientists continue to toil away at creating machine learning algorithms that will one day enslave humanity save us all, artificial intelligence researchers have discovered that computers are outpacing human doctors in a number of important areas. We've already seen the ability of AI to spot things like cancer, and a new study reveals that a digital brain may also be better at predicting overall mortality and specific conditions such as heart attack with greater accuracy than a trained individual. The research, which was presented at the International Conference on Nuclear Cardiology and Cardiac CT, suggests that we may be fast approaching a day when artificial intelligence works hand-in-hand with medical professionals to anticipate life-threatening problems before they occur. The researchers, led by Dr. Luis Eduardo Juarez-Orozco of the Turku PET Centre in Finland, trained a machine learning algorithm on a data set of nearly 1,000 patients. The data, which spanned six years for each patient, included dozens of variables that the computer had to digest in order to draw correlations between instances of death and heart attack with data on various heart and blood flow readings.

Participants were given a mall as a context and then use machine learning to simulate an aspect of the mall. The idea in the workshop is to open both tech and non-technical people for understanding and apply machine learning.

IMAGE: This is Ion Petre, Professor of Computer Science at Abo Akademi University. The ongoing research on artificial intelligence methods for precision cancer medicine at the Computational Biomodeling (Combio) Laboratory of Åbo Akademi University and Turku Centre for Computer Science (TUCS) got a major boost with renewed funding from Business Finland. The concept of this project is that a patient's own molecular data can be used to identify, with the use of artificial intelligence methods, the best combinatorial multi-drug therapy for that patient. Network modelling plays a major role in this line of work, integrating genome-scale patient data into detailed interaction networks, that can be analysed by Combio's recently developed algorithms to identify combinations of drugs and inhibitors that are likely to be therapeutically effective. The project focuses on individual patients with the goal to dynamically adapt their therapeutical strategies to avoid the onset of drug resistance.