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The 2011 AAAI Classic Paper Award was given to the authors of the most influential papers from the Tenth National Conference on Artificial Intelligence, held in 1992 in San Jose, California. The award was presented to Mitchell received his BSc in cognitive process. The winning papers were selected Hector Levesque, David Mitchell, and science and artificial intelligence at by the program chairs with the Bart Selman for their two papers, Hard the University of Toronto, his MSc in help of area chairs and members of the and Easy Distribution of SAT Problems computing science from Simon Fraser senior program committee. Honors and A New Method for Solving Hard University, and his PhD in computer went to Jessica Davies (University of Satisfiability Problems. Paris Sud 11), Nina Narodytska to the area of automated Bart Selman is a professor of computer (NICTA and University of New South reasoning via methods and analyses science at Cornell University.


Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities

arXiv.org Machine Learning

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.



A review of machine learning applications in wildfire science and management

arXiv.org Machine Learning

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.


Top 100 Artificial Intelligence Companies 2020

#artificialintelligence

As artificial intelligence has become a growing force in business, today's top AI companies are leaders in this emerging technology. Often leveraging cloud computing, AI companies mix and match myriad technologies. Foremost among these is machine learning, but today's AI leading firms tech ranging from predictive analytics to business intelligence to data warehouse tools to deep learning. Entire industries are being reshaped by AI. RPA companies have completely shifted their platforms. AI in healthcare is changing patient care in numerous – and major – ways. AI companies are attracting massive investment from venture capitalist firms and giant firms like Microsoft and Google. Academic AI research is growing, as are AI job openings across a multitude of industries. All of this is documented in the AI Index, produced by Stanford University's Human-Centered AI Institute. Consulting giant Accenture believes AI has the potential to boost rates of profitability by an average of 38 percentage points and could lead to an economic boost of $14 trillion in additional gross value added (GVA) by 2035. In truth, artificial intelligence holds not just possibilities, but a plethora of risks. "It will have a huge economic impact but also change society, and it's hard to make strong predictions, but clearly job markets will be affected," said Yoshua Bengio, a professor at the University of Montreal, and head of the Montreal Institute for Learning Algorithms. To keep up with the AI market, we have updated our list of top AI companies playing a key role in shaping the future of AI. We feature artificial intelligence companies that are commercially successful as well as those that have invested significantly in artificial intelligence. AI companies in the years ahead are forecast to see exponential growth in deep learning, machine learning and natural language processing.