Artificial intelligence has to date been enlisted as a bogeyman in cultural circles: Software will take the jobs of writers and translators, and AI-generated images ring the death toll for illustrators and graphic designers. Yet there's a corner of high culture where AI is taking on a starring role as hero, not displacing the traditional protagonists--art experts and conservators--but adding a powerful, compelling weapon to their arsenal when it comes to fighting forgeries and misattributions. AI is already exceptionally good at recognizing and authenticating an artist's work, based on the analysis of a digital image of a painting alone. AI's objective analysis has thrown a wrench into this traditional hierarchy. If an algorithm can determine the authorship of an artwork with statistical probability, where does that leave the old-guard art historians whose reputations have been built on their subjective expertise?

The introduction of transformer architecture was a turning point in Natural Language Processing (NLP). Models based on the transformer architecture such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT) have gained widespread popularity in various applications such as software development and education. The availability of Large Language Models (LLMs) such as ChatGPT and Bard to the general public has showcased the tremendous potential of these models and encouraged their integration into various domains such as software development for tasks such as code generation, debugging, and documentation generation. In this study, opinions from 11 experts regarding their experience with LLMs for software development have been gathered and analysed to draw insights that can guide successful and responsible integration. The overall opinion of the experts is positive, with the experts identifying advantages such as increase in productivity and reduced coding time. Potential concerns and challenges such as risk of over-dependence and ethical considerations have also been highlighted.

I am honoured to be chairing the delivery of the inaugural International Scientific Report on Advanced AI Safety. I am proud to publish this interim report which is the culmination of huge efforts by many experts over the six months since the work was commissioned at the Bletchley Park AI Safety Summit in November 2023. We know that advanced AI is developing very rapidly, and that there is considerable uncertainty over how these advanced AI systems might affect how we live and work in the future. AI has tremendous potential to change our lives for the better, but it also poses risks of harm. That is why having this thorough analysis of the available scientific literature and expert opinion is essential. The more we know, the better equipped we are to shape our collective destiny.

The problem setting would be a good contribution to the literature on crowdsourcing. However, I am not sure that paper is ready for publication for the following reasons: 1) the theoretical part looks not solid, 2) the proposed algorithm (HA) looks not grounded, 3) the results of experiments are not significant. These points are supported below. Lemmas 3,4 are reasonable, however, they cover only very special cases. Specifically, Lemma 3 considers only one agent and Lemma 4 assumes that all agents have the same amount of information (they observed exactly n samples).

Factors which attract customers and persuade them to buy new car are various regarding different consumer tastes. There are some methods to extract pattern form mass data. In this case we firstly asked passenger car marketing experts to rank more important factors which affect customer decision making behavior using fuzzy Delphi technique, then we provided a sample set from questionnaires and tried to apply a useful artificial neural network method called selforganizing map (SOM) to find out which factors have more effect on Iranian customer's buying decision making. Fuzzy tools were applied to adjust the study to be more real. MATLAB software was used for developing and training network. Results report four factors are more important rather than the others. Results are rather different from marketing expert rankings. Such results would help manufacturers to focus on more important factors and increase company sales level.

Abstract-- Machine Learning systems have achieved outstanding performance in different domains. In this paper machine learning methods have been applied to classification task to classify music genre. The code shows how to extract features from audio files and classify them using supervised learning into 2 genres namely classical and metal. Machine Learning is used to classify the audio files into 2 genres classical and metal. A total of 20 audio files, 10 for each genre respectively are taken.

This article describes an approach to incorporate expert opinion on observable quantities through the use of a loss function which updates a prior belief as opposed to specifying parameters on the priors. Eliciting information on observable quantities allows experts to provide meaningful information on a quantity familiar to them, in contrast to elicitation on model parameters, which may be subject to interactions with other parameters or non-linear transformations before obtaining an observable quantity. The approach to incorporating expert opinion described in this paper is distinctive in that we do not specify a prior to match an expert's opinion on observed quantity, rather we obtain a posterior by updating the model parameters through a loss function. This loss function contains the observable quantity, expressed a function of the parameters, and is related to the expert's opinion which is typically operationalized as a statistical distribution. Parameters which generate observable quantities which are further from the expert's opinion incur a higher loss, allowing for the model parameters to be estimated based on their fidelity to both the data and expert opinion, with the relative strength determined by the number of observations and precision of the elicited belief. Including expert opinion in this fashion allows for a flexible specification of the opinion and in many situations is straightforward to implement with commonly used probabilistic programming software. We highlight this using three worked examples of varying model complexity including survival models, a multivariate normal distribution and a regression problem.

Artificial intelligence's (AI) applicability in cardiac imaging is rapidly growing and was a major topic of discussion at this year's EuroPCR 2022 meeting. Many session speakers discussed how they are using AI tools in their day-to-day practice and in their research to improve decision-making and patient/research outcomes. It's no secret, however, that AI tools are only as good as the data sets and the thousands of expert opinions used to power them. Implementing AI applications in our day-to-day practice, from an operations standpoint, could mean adjusting clinician workflows and setting aside time to set up and train on the new systems. And from an efficacy standpoint, it leaves clinicians wary of result accuracy, especially if they are unsure how good the data used to power the technology really is.

The increasing collection of health data coupled with continuous improvements in information processing and analysis have enabled precision medicine. Traditionally, dentistry has lagged behind medicine in the adoption and seamless integration of new technologies, so what is the status quo of precision dentistry and data science that can improve oral health outcomes? Big Data has played an essential role in the progression of medicine in the 21st century. Digital health data is collected specifically, in the context of examinations and treatments by healthcare providers, but also non-specifically through personal health apps, social media, and other devices. Due to the rapid progress in information technology, completely new approaches in dental medicine are feasible today. A key technology in the future of healthcare is artificial intelligence (AI) with the disruptive potential to influence all dental disciplines.The objective of this Research Topic is to provide an update on the current knowledge with state-of-the-art theory and practical information on precision dentistry and e-health data science in oral healthcare focusing on (1) telemedicine; (2) digital therapeutics; and (3) care navigation.Emphasis is placed on identifying future research needs to harness the power of data to improve oral health outcomes. So far, the continuous increase in digital health data has not resulted in dramatic improvements in diagnostic and treatment as we have not yet accomplished their ...

In 1997, an AI beat a human world chess champion for the first time in history (it was IBM's Deep Blue playing Garry Kasparov). Fourteen years later, in 2011, IBM's Watson beat two winners of Jeopardy! In late 2017, DeepMind's AlphaZero reached superhuman levels of play in three board games (chess, go and shogi) in just 24 hours of self-learning without any human intervention, i.e. it just played itself. Some of the people who have played against it say that the creativity of its moves make it seem more like an alien that a computer program. But despite all that, in 2019 nobody has yet designed anything that can go into a strange kitchen and fry an egg. Are our machines truly intelligent? The fact is that today AI can solve ever more complex specific problems with a level of reliability and speed beyond our reach at an unbeatable cost, but it fails spectacularly in the face of any challenge for which it has not been programmed.