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.
More and more, it is becoming necessary to consider working collaboratively, not only for questions regarding skills or because of the very quick evolution of engineering skills, and devices in particular, but also because working alone in a studio or a laboratory will be less and less viable. Interlocution is also essential in artistic practice. After having developed most of my projects alone for a long time, I understand how sharing this experience and competences gives meaning to this activity. You have worked in Brazil for many years. Is it a good place for new media art?
In Part 3 of our series on how utilities are using artificial intelligence, we look at how AI amplifies analytics for grid operations. Duke Energy saved some $130 million in avoided costs by using predictive data analytics to identify problems before they caused equipment failures. A utility in Brazil estimates savings in the range of $420,000 USD each month through better, analytics-based theft detection. Because, as an article published by Forbes notes, "Machine learning is a continuation of the concepts around predictive analytics, with one key difference: The AI system is able to make assumptions, test and learn autonomously." With these enhancements, data science will become more powerful than ever, and utilities stand to gain.
On Jan. 21, 2019, Michael Casuga drove his new Tesla Model 3 southbound on Santiago Canyon Road, a two-lane highway that twists through hilly woodlands east of Santa Ana. He wasn't alone, in one sense: Tesla's semiautonomous driver-assist system, known as Autopilot -- which can steer, brake and change lanes -- was activated. Suddenly and without warning, Casuga claims in a Superior Court of California lawsuit, Autopilot yanked the car left. The Tesla crossed a double yellow line, and without braking, drove through the oncoming lane and crashed into a ditch, all before Casuga was able to retake control. Tesla confirmed Autopilot was engaged, according to the suit, but said the driver was to blame, not the technology.
Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using some underlying parametric form to capture historical dependencies, or on non-parametric models that focus primarily on tasks such as prediction. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. We use a novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval. We evaluate our method against state-of-the-art baselines on model fitting tasks as gauged by log-likelihood. Through experiments on both synthetic and real-world datasets, we find that our proposed approach outperforms existing baselines on most of the datasets studied.
The dream of endowing computers with causal reasoning drew Bareinboim from Brazil to the United States in 2008, after he completed a master's in computer science at the Federal University of Rio de Janeiro. He jumped at an opportunity to study under Judea Pearl, a computer scientist and statistician at UCLA. Pearl, 83, is a giant--the giant--of causal inference, and his career helps illustrate why it's hard to create AI that understands causality.
In this work we propose a novel self-attention mechanism model to address electricity theft detection on an imbalanced realistic dataset that presents a daily electricity consumption provided by State Grid Corporation of China. Our key contribution is the introduction of a multi-head self-attention mechanism concatenated with dilated convolutions and unified by a convolution of kernel size $1$. Moreover, we introduce a binary input channel (Binary Mask) to identify the position of the missing values, allowing the network to learn how to deal with these values. Our model achieves an AUC of $0.926$ which is an improvement in more than $17\%$ with respect to previous baseline work. The code is available on GitHub at https://github.com/neuralmind-ai/electricity-theft-detection-with-self-attention.
Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive when compared to conventional methods. However, some challenges in EDXRF spectral data analysis still demand more efficient methods capable of providing accurate outcomes. Using Multi-target Regression (MTR) methods, multiple parameters can be predicted, and also taking advantage of inter-correlated parameters the overall predictive performance can be improved. In this study, we proposed the Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on learning from different regressors arranged in stacking structure for a boosted outcome. We compared MTSG and 5 MTR methods for predicting 10 parameters of soil fertility. Random Forest and Support Vector Machine (with linear and radial kernels) were used as learning algorithms embedded into each MTR method. Results showed the superiority of MTR methods over the Single-target Regression (the traditional ML method), reducing the predictive error for 5 parameters. Particularly, MTSG obtained the lowest error for phosphorus, total organic carbon and cation exchange capacity. When observing the relative performance of Support Vector Machine with a radial kernel, the prediction of base saturation percentage was improved in 19%. Finally, the proposed method was able to reduce the average error from 0.67 (single-target) to 0.64 analysing all targets, representing a global improvement of 4.48%.
Accenture today opened a new Innovation Hub in Hyderabad, where clients can co-innovate with Accenture by ideating, rapidly prototyping and then scaling disruptive products and services for the digital economy. The latest addition to Accenture's global innovation network, the Hyderabad Innovation Hub is spread over 300,000 square feet where clients can co-innovate and co-create solutions with more than 2,000 Accenture professionals with expertise across multiple industries and advanced technologies such as artificial intelligence, security, extended reality, automation and blockchain. "Our research shows that organizations are struggling to achieve their innovation goals, due to the lack of an enterprise-wide strategy for technology investments and adoption," said Bhaskar Ghosh, group chief executive, Accenture Technology Services. "Through our leading advanced technology capabilities, we help clients scale their technology investments and bridge the innovation achievement gap. Our Innovation Hub in Hyderabad has the pieces our clients require to accelerate value creation through enterprise-wide, game-changing innovation."