Engineers at the company DeepMind built a machine-learning system based on … Christopher Intagliata: Artificial intelligence systems have bested …

Netflix's Resident Evil series has been canceled a little over a month after its initial debut, according to a report from Deadline. The streamer reportedly decided against renewing the series due to lackluster ratings and viewership. Like the many other Resident Evil adaptations, Netflix's live-action series attempts to put yet another spin on the video game franchise it's based on. The show, which was helmed by Supernatural's Andrew Dabb, flashes between two different timelines, centering around Umbrella executive Albert Wesker (Lance Reddick) and his two daughters (played by Tamara Smart and Siena Agudong). In our review of the series, my colleague Charles Pulliam-Moore notes that it "keeps things feeling fresh up to a point," but it's dragged down by a "predictable plot that ultimately suffers from being such a late entry in the modern-day zombie craze." Resident Evil made Netflix's top 10 list of its most-watched shows the week it debuted and the two weeks that followed, but largely fell off the map after that.

Context: The number of TV series offered nowadays is very high. Due to its large amount, many series are canceled due to a lack of originality that generates a low audience. Problem: Having a decision support system that can show why some shows are a huge success or not would facilitate the choices of renewing or starting a show. Solution: We studied the case of the series Arrow broadcasted by CW Network and used descriptive and predictive modeling techniques to predict the IMDb rating. We assumed that the theme of the episode would affect its evaluation by users, so the dataset is composed only by the director of the episode, the number of reviews that episode got, the percentual of each theme extracted by the Latent Dirichlet Allocation (LDA) model of an episode, the number of viewers from Wikipedia and the rating from IMDb. The LDA model is a generative probabilistic model of a collection of documents made up of words. Method: In this prescriptive research, the case study method was used, and its results were analyzed using a quantitative approach. Summary of Results: With the features of each episode, the model that performed the best to predict the rating was Catboost due to a similar mean squared error of the KNN model but a better standard deviation during the test phase. It was possible to predict IMDb ratings with an acceptable root mean squared error of 0.55.

At the age of big data, recommender systems have shown remarkable success as a key means of information filtering in our daily life. Recent years have witnessed the technical development of recommender systems, from perception learning to cognition reasoning which intuitively build the task of recommendation as the procedure of logical reasoning and have achieve significant improvement. However, the logical statement in reasoning implicitly admits irrelevance of ordering, even does not consider time information which plays an important role in many recommendation tasks. Furthermore, recommendation model incorporated with temporal context would tend to be self-attentive, i.e., automatically focus more (less) on the relevance (irrelevance), respectively. To address these issues, in this paper, we propose a Time-aware Self-Attention with Neural Collaborative Reasoning (TiSANCR) based recommendation model, which integrates temporal patterns and self-attention mechanism into reasoning-based recommendation. Specially, temporal patterns represented by relative time, provide context and auxiliary information to characterize the user's preference in recommendation, while self-attention is leveraged to distill informative patterns and suppress irrelevances. Therefore, the fusion of self-attentive temporal information provides deeper representation of user's preference. Extensive experiments on benchmark datasets demonstrate that the proposed TiSANCR achieves significant improvement and consistently outperforms the state-of-the-art recommendation methods.

Dominant Algorithms to Evaluate Artificial Intelligence: From the view of Throughput Model is an informative reference for all professionals and scholars who are working on AI projects to solve a range of business and technical problems. The six AI algorithmic pathways represent. As AI is increasingly employed for applications where decisions require explanations, the Throughput Model offers business professionals the means to look under the hood of AI and comprehend how those decisions are attained by organizations. Finally, The Throughput Model provides the first steps towards building architectures that combine the strengths of the symbolic approaches that can be adapted for machine learning/ deep learning, and to develop better techniques for extracting and generalizing abstract knowledge from large, often noisy data sets. As AI is employed more and more for applications where decisions require explanations, the Throughput Model offers the means to look under the hood of AI and comprehend how those decisions are attained by organizations.

Most people older than 30 probably remember doing research with good old-fashioned encyclopedias. You'd pull a heavy volume from the shelf, check the index for your topic of interest, then flip to the appropriate page and start reading. It wasn't as easy as typing a few words into the Google search bar, but on the plus side, you knew that the information you found in the pages of the Britannica or the World Book was accurate and true. The overwhelming multitude of sources was confusing enough, but add the proliferation of misinformation and it's a wonder any of us believe a word we read online. Wikipedia is a case in point.

Friends may already be familiar with the term. Machine learning is currently popular and is applied in various fields. Machine learning is a branch of Artificial Intelligence (AI) which is also part of data science that is able to learn by itself without the need to be reprogrammed regularly. Machine learning will learn from the given data and provide the appropriate output. An easy-to-find example of machine learning is the Netflix recommendation system.

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Sign up for the daily Marketplace newsletter to make sense of the most important business and economic news. Algorithms play a huge role in the content people watch. Netflix, for example, has said that approximately 80% of subscribers trust the platform's recommendations. But as artificial intelligence technology advances, it may play an increasingly important role in what films and TV shows are made. Bloomberg columnist Trung Phan recently wrote about AI's potential in evaluating film and television projects' commercial viability.