The market report from Technavio states that the cognitive EW market, which includes artificial intelligence and machine learning algorithms in …

Tableau Functions gives extra capabilities to business intelligence … writing technical blogs on machine learning and data science technologies.

Reinforcement learning entails data scientists training ML to fulfil tasks.

The team began by training the device's machine–learning (ML) algorithm … "Integration of artificial intelligence with wearable electronics is …

Dublin, Aug. 09, 2022 (GLOBE NEWSWIRE) -- The "Asia Pacific Artificial Intelligence In Fintech Market Size, Share & Industry Trends Analysis Report By Component (Solutions and Services), By Deployment (On-premise and Cloud), By Application, By Country and Growth Forecast, 2022 - 2028" report has been added to ResearchAndMarkets.com's offering. The Asia Pacific Artificial Intelligence In Fintech Market is expected to witness market growth of 17.7% CAGR during the forecast period (2022-2028). Artificial intelligence enhances outcomes by employing approaches derived from human intellect but applied at a scale that is not human. Fintech firms have been transformed in recent years as a result of the computational arms race. Additionally, near-endless volumes of data are altering AI to unprecedented heights, and smart contracts may simply be a continuation of the current market trend.

The use of machine learning in artistic music generation leads to controversial discussions of the quality of art, for which objective quantification is nonsensical. We therefore consider a music-generating algorithm as a counterpart to a human musician, in a setting where reciprocal interplay is to lead to new experiences, both for the musician and the audience. To obtain this behaviour, we resort to the framework of recurrent Variational Auto-Encoders (VAE) and learn to generate music, seeded by a human musician. In the learned model, we generate novel musical sequences by interpolation in latent space. Standard VAEs however do not guarantee any form of smoothness in their latent representation. This translates into abrupt changes in the generated music sequences. To overcome these limitations, we regularise the decoder and endow the latent space with a flat Riemannian manifold, i.e., a manifold that is isometric to the Euclidean space. As a result, linearly interpolating in the latent space yields realistic and smooth musical changes that fit the type of machine--musician interactions we aim for. We provide empirical evidence for our method via a set of experiments on music datasets and we deploy our model for an interactive jam session with a professional drummer. The live performance provides qualitative evidence that the latent representation can be intuitively interpreted and exploited by the drummer to drive the interplay. Beyond the musical application, our approach showcases an instance of human-centred design of machine-learning models, driven by interpretability and the interaction with the end user.

This dissertation explores the linguistic and computational aspects of the meaning relations that can hold between two or more complex linguistic expressions (phrases, clauses, sentences, paragraphs). In particular, it focuses on Paraphrasing, Textual Entailment, Contradiction, and Semantic Similarity. In Part I: "Similarity at the Level of Words and Phrases", I study the Distributional Hypothesis (DH) and explore several different methodologies for quantifying semantic similarity at the levels of words and short phrases. In Part II: "Paraphrase Typology and Paraphrase Identification", I focus on the meaning relation of paraphrasing and the empirical task of automated Paraphrase Identification (PI). In Part III: "Paraphrasing, Textual Entailment, and Semantic Similarity", I present a novel direction in the research on textual meaning relations, resulting from joint research carried out on on paraphrasing, textual entailment, contradiction, and semantic similarity.

Recent work demonstrates a bias in the GPT-3 model towards generating violent text completions when prompted about Muslims, compared with Christians and Hindus. Two pre-registered replication attempts, one exact and one approximate, found only the weakest bias in the more recent Instruct Series version of GPT-3, fine-tuned to eliminate biased and toxic outputs. Few violent completions were observed. Additional pre-registered experiments, however, showed that using common names associated with the religions in prompts yields a highly significant increase in violent completions, also revealing a stronger second-order bias against Muslims. Names of Muslim celebrities from non-violent domains resulted in relatively fewer violent completions, suggesting that access to individualized information can steer the model away from using stereotypes. Nonetheless, content analysis revealed religion-specific violent themes containing highly offensive ideas regardless of prompt format. Our results show the need for additional debiasing of large language models to address higher-order schemas and associations.

Amid flamboyant performances, flashy costumes and pyrotechnics, the Eurovision Song Contest is probably one of the weirdest, most peculiar shows ever to grace our screens. It's the kind of event that an alien species or an emotionless android would have a hard time understanding, were they watching Europe tune into the annual celebration of kitsch, patriotism and unity. But could machines understand - and reproduce - such a uniquely human experience? The answer could be found at the AI Song Contest, a Eurovision-inspired music competition where all the songs are written by artificial intelligence. Since its creation in 2020, the song contest has been hosted every year by the Belgian city of Liège, where teams of data scientists, programmers, and musicians from all over the world participate with the compositions they have created with the help of AI.

Orchvate – A study in Diversity, Equity & Inclusion in Artificial Intelligence. The Beginning. The idea of facilitating neurodiversity in the …