Recommender Systems and Deep Learning in Python 4.6 (1,635 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. They are why Google is the most successful technology company today.

Artificial intelligence (AI) and machine learning offer new ways to boost productivity, develop talent, and drive organizational change by enhancing managers' ability to make the right calls in complex situations. Augmented intelligence tools have already made an impact for many companies, but the next revolution will happen when every aspect of a business, from top to bottom, is designed with AI in mind. Call this new construct the intelligent enterprise. Like other major revolutions in management, it's poised to transform industries and organizations for decades to come. To prepare for this next phase, leaders will need to harness machine intelligence for decision-making across the business, assemble the right talent, and recognize the benefits and limitations of AI to shape organizational strategy.

The Pixel 4a is a return to form for Google's smartphone efforts: a lower-cost, mid-range phone that is high quality, long-lasting and fairly small, with a great camera. The £349 Pixel 4a is very much an attempt to boil down a smartphone to only the essentials and then make them all work really well. It slots under the higher-priced £669-and-up Pixel 4 series, replacing last year's Pixel 3a and 3a XL with only one size of phone, taking the design of the 3a and expanding the screen to fill the front of the device. A 5G version is coming later in the year but for now the Pixel 4a is 4G only. The body is high-quality, soft-touch polycarbonate and feels surprisingly nice.

In the face of ever evolving and increasingly sophisticated cyber crime, integrating automation, AI and Machine Learning into financial crime …

The first in a series, this white paper provides an overview of Artificial Intelligence (AI) and its common methods.

"The AI & Machine Learning Imperative" offers new insights from leading academics and practitioners in data science and artificial intelligence.

Artificial Intelligence in Marketing Industry 2020 Global Market Analysis report gives the In-depth analysis of historical data along with size, share, …

Psychological semantics is the study of how people represent the meanings of words and then build sentence meaning out of those representations. People use language dozens of time a day--to have conversations and give instructions, to read and write, to label objects and teach. A theory of psychological semantics must provide the basis for how people do all those things, choosing which words to use and understanding the words they read or hear. In this article we focus on the mental representation of word meaning. Human language is still the gold standard for a communication system, but artificial intelligence (AI) systems have made important progress in language use. Research on Natural Language Processing (NLP) develops systems that understand language to the degree that computers can carry out useful tasks. As described below, such systems use vast text corpora to learn about words, using neural networks and other statistical models. The recent explosion of research in NLP, driven largely by advances in neural networks (also called deep learning), has resulted in continuously improving performance on various benchmarks that require interpreting words and sentences. Systems are now used in interfaces with customers to make sales or solve problems.

Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate that even though online users mainly disfavor AI and robot rights, they are supportive of protecting electronic agents from cruelty (i.e., favor the right against cruel treatment). Furthermore, people's perceptions became more positive when given information about rights-bearing non-human entities or myth-refuting statements. The style used to introduce AI and robot rights significantly affected how the participants perceived the proposal, similar to the way metaphors function in creating laws. For robustness, we repeated the experiment over a more representative sample of U.S. residents (N=164) and found that perceptions gathered from online users and those by the general population are similar.

To understand the black-box characteristics of deep networks, counterfactual explanation that deduces not only the important features of an input space but also how those features should be modified to classify input as a target class has gained an increasing interest. The patterns that deep networks have learned from a training dataset can be grasped by observing the feature variation among various classes. However, current approaches perform the feature modification to increase the classification probability for the target class irrespective of the internal characteristics of deep networks. This often leads to unclear explanations that deviate from real-world data distributions. To address this problem, we propose a counterfactual explanation method that exploits the statistics learned from a training dataset. Especially, we gradually construct an explanation by iterating over masking and composition steps. The masking step aims to select an important feature from the input data to be classified as a target class. Meanwhile, the composition step aims to optimize the previously selected feature by ensuring that its output score is close to the logit space of the training data that are classified as the target class. Experimental results show that our method produces human-friendly interpretations on various classification datasets and verify that such interpretations can be achieved with fewer feature modification.