Artificial intelligence AI takes the lead over intelligent automation IA. Intelligent automation is the combination of "'robotic process automation and artificial intelligence to automate processes,'" according to a recent article on the topic in HR Dive, a publication for human resources professionals. Organizations that embrace intelligent automation may experience a return on investment of 200% or more, according to an Everest Group report cited by HR Dive. However, that doesn't mean organizations can expect a reduction in headcount, according to the report. In fact, projections of a reduction in workforce thanks to intelligent automation may be "highly exaggerated," the Everest Group noted.

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.

For instance, a mixture of primary and secondary research has been used to define Artificial Intelligence Software market estimates and forecasts. Sources used for secondary research contain (but not limited to) Paid Data Sources, Technology Journals of 2013-2018, SEC Filings Company Websites, Annual Reports, and various other Artificial Intelligence Software industry publications. Specific details on the methodology used for Artificial Intelligence Software market report can be provided on demand. In addition, It highlights the ability to increase possibilities in the coming years by 2023, also reviewing the marketplace drivers, constraints and restraints, growth signs, challenges, market dynamics. "Global Artificial Intelligence Software Market" gives a region-wise analysis like growth aspects, and revenue, Past, present and future forecast trends, Analysis of emerging market sectors and development opportunities in Artificial Intelligence Software will forecast the market growth. Regional scope: Artificial Intelligence Software market is divided into various regions like North America, Middle-East a and Africa, Asia-Pacific, South America, and Europe. Country scope: Artificial Intelligence Software market is divided into United States, Mexico, Canada, Germany, Singapore, U.K., Italy, Russia, France, Spain, China, India, Japan, South Korea, Australia, Brazil, Colombia, Paraguay, Saudi Arabia, South Africa, Egypt, and UAE, ASEAN countries.

Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Nonnegative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with nonnegative RESCAL, we propose a latent dimension determination method which is based on clustering of the solutions of multiple realizations of nonnegative RESCAL decompositions. We demonstrate the performance of our model selection method on synthetic data and then we apply our method to decompose a network of international trade flows data from International Monetary Fund and validate the resulting features against empirical facts from economic literature.

The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy. In this regard, low-rank tensor decomposition constitutes a powerful tool to compress convolutional neural networks (CNNs) by decomposing the 4-way kernel tensor into multi-stage smaller ones. Building on top of Tucker-2 decomposition, we propose a generalized Higher Order Tucker Articulated Kernels (HOTCAKE) scheme comprising four steps: input channel decomposition, guided Tucker rank selection, higher order Tucker decomposition and fine-tuning. By subjecting each CONV layer to HOTCAKE, a highly compressed CNN model with graceful accuracy trade-off is obtained. Experiments show HOTCAKE can compress even pre-compressed models and produce state-of-the-art lightweight networks.

Chris Smith and Phil Sansom delve into the world of artificial Intelligence (AI) to find out how this emerging technology is changing the way we practise medicine... Mike - I think this is an area where AI stands a really good chance of making quite dramatic improvements to very large numbers of people's lives. Carolyn - Save lives and reduce medical complications. Beth - That's a concern - when machine-learning algorithms learn the wrong things. Andrew - Frankly revolutionary productivity that we are now starting to see from these AI approaches in drug design. Lee - It will replace all manual labor in all research laboratories. And then suddenly everyone can collaborate. Phil - But what was previously sci-fi is now closer to reality. AI technology exists, and there's a brand new frontier where it's being applied to the world of healthcare. Chris - But this isn't the AI you see in the movies. In the words of Kent University computer scientist Colin Johnson, "this is more software than Schwarzeneggar"... Colin - When scientists say AI, they often mean some piece of code that's running on a computer and it's taking some inputs.

More than 1,000 students are vying for places at the world's first dedicated artificial intelligence university in Abu Dhabi. The Mohamed bin Zayed University of Artificial Intelligence will swing open its doors in August, with demand high from those eager to be part of the inaugural class of 2020. The graduate-level institute revealed the bumper number of applicants are currently being put through a stringent vetting process ahead of the landmark opening term. Masters and PhD courses will be held at the forward-thinking seat of learning, which has cast the net far and wide across the globe in search of top talent. World's first artificial intelligence university to open in Abu Dhabi Artificial intelligence isn't coming to the UAE - it is already here During the university's first advisory board meeting, Dr Sultan Al Jaber, Minister of State, said the first wave of students would be at the forefront of a new era of innovation in the country.

Artificial intelligence-powered technologies are increasingly being used to help contain the novel coronavirus outbreak in China, with several tech companies tweaking their facial recognition algorithms to identify people who are wearing masks. Enterprises that use facial recognition for access control and attendance system needed to make the tweaks as traditional algorithms may not be sufficient for recognition. For, masks used by the employees often cover some features of the face. Beijing-based tech company Hanwang Technology Co Ltd has upgraded its core algorithm and introduced a new facial recognition system for those who wear masks. Huang Lei, vice-president of the company, said Hanwang Tech took just a month to develop the upgraded version.

Higher-order Recurrent Neural Networks (RNNs) are effective for long-term forecasting since such architectures can model higher-order correlations and long-term dynamics more effectively. However, higher-order models are expensive and require exponentially more parameters and operations compared with their first-order counterparts. This problem is particularly pronounced in multidimensional data such as videos. To address this issue, we propose Convolutional Tensor-Train Decomposition (CTTD), a novel tensor decomposition with convolutional operations. With CTTD, we construct Convolutional Tensor-Train LSTM (Conv-TT-LSTM) to capture higher-order space-time correlations in videos. We demonstrate that the proposed model outperforms the conventional (first-order) Convolutional LSTM (ConvLSTM) as well as the state-of-the-art ConvLSTM-based approaches in pixel-level video prediction tasks on Moving-MNIST and KTH action datasets, but with much fewer parameters.

Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are "consistent" with the underlying greedy policy class. We introduce a simple penalization scheme that encourages Q-labels used across training batches to remain (jointly) consistent with the expressible policy class. We also propose a search framework that allows multiple Q-approximators to be generated and tracked, thus mitigating the effect of premature (implicit) policy commitments. Experimental results demonstrate that these methods can improve the performance of Q-learning in a variety of Atari games, sometimes dramatically.