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Artificial Intelligence in Drug Discovery Market Expected to Witness High Growth over the Forecast …


Global Artificial Intelligence in Drug Discovery Market Forecast to 2024 is a new research released at Market Study Report and provides information …

Pfizer, PwC, and more: The top panels from the Transform 2020 Technology and Automation Summit


Reinforcement Learning (RL) is a machine learning technique that solves large and complex problems in situations where labeled datasets are not …

Patient Safety, Data Privacy Key for Use of AI-Powered Chatbots


… mention of CAs in the US Food and Drug Administration’s (FDA) proposed regulatory framework for AI or machine learning for software as a medical …

GPT-3 Creative Fiction


What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.

Artificial Intelligence (AI) in Drug Discovery Market to Raise at 40.5% CAGR till 2027


Artificial Intelligence (AI) in Drug Discovery Market Research report is a professional and in-depth study on the market size, growth, share, trends, as well …

Artificial Intelligence for Drug Discovery Market Outlook, Opportunity, Demand Analysis And …


With the classified Artificial Intelligence for Drug Discovery market research based on various growing regions, this report provides leading players …

Massive Growth in Artificial Intelligence (AI) in Drug Discovery Market Set to Witness Huge Growth …


"Artificial Intelligence (AI) in Drug Discovery Market is growing at a High CAGR during the forecast period 2020-2026.

Artificial Intelligence in Drug Discovery Market Size by Top Key Players, Growth Opportunities …


Global Artificial Intelligence in Drug Discovery Market 2020: This is a latest report, covering the current COVID-19 impact analysis on the market.

Interactions in information spread: quantification and interpretation using stochastic block models Machine Learning

In most real-world applications, it is seldom the case that a given observable evolves independently of its environment. In social networks, users' behavior results from the people they interact with, news in their feed, or trending topics. In natural language, the meaning of phrases emerges from the combination of words. In general medicine, a diagnosis is established on the basis of the interaction of symptoms. Here, we propose a new model, the Interactive Mixed Membership Stochastic Block Model (IMMSBM), which investigates the role of interactions between entities (hashtags, words, memes, etc.) and quantifies their importance within the aforementioned corpora. We find that interactions play an important role in those corpora. In inference tasks, taking them into account leads to average relative changes with respect to non-interactive models of up to 150\% in the probability of an outcome. Furthermore, their role greatly improves the predictive power of the model. Our findings suggest that neglecting interactions when modeling real-world phenomena might lead to incorrect conclusions being drawn.

Automatically Assessing Quality of Online Health Articles Machine Learning

The information ecosystem today is overwhelmed by an unprecedented quantity of data on versatile topics are with varied quality. However, the quality of information disseminated in the field of medicine has been questioned as the negative health consequences of health misinformation can be life-threatening. There is currently no generic automated tool for evaluating the quality of online health information spanned over a broad range. To address this gap, in this paper, we applied a data mining approach to automatically assess the quality of online health articles based on 10 quality criteria. We have prepared a labeled dataset with 53012 features and applied different feature selection methods to identify the best feature subset with which our trained classifier achieved an accuracy of 84%-90% varied over 10 criteria. Our semantic analysis of features shows the underpinning associations between the selected features & assessment criteria and further rationalize our assessment approach. Our findings will help in identifying high-quality health articles and thus aiding users in shaping their opinion to make the right choice while picking health-related help from online.