In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. This new second edition improves with the addition of Spark―a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code. Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud.

As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a human interpretable manner. It has recently become apparent that a high-fidelity explanation of a black box ML model may not accurately reflect the biases in the black box. As a consequence, explanations have the potential to mislead human users into trusting a problematic black box. In this work, we rigorously explore the notion of misleading explanations and how they influence user trust in black box models. More specifically, we propose a novel theoretical framework for understanding and generating misleading explanations, and carry out a user study with domain experts to demonstrate how these explanations can be used to mislead users. Our work is the first to empirically establish how user trust in black box models can be manipulated via misleading explanations.

Alan Turing (1950) was one of the founders of modern computers and AI. The "Turing test" was based on the fact that the intelligent behavior of a computer is the ability to achieve human level performance in cognition related tasks.[1] The 1980s and 1990s saw a surge in interest in AI. Artificial intelligent techniques such as fuzzy expert systems, Bayesian networks, artificial neural networks, and hybrid intelligent systems were used in different clinical settings in health care. In 2016, the biggest chunk of investments in AI research were in healthcare applications compared with other sectors.[2] AI in medicine can be dichotomized into two subtypes: Virtual and physical.[3]

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction (AKBC). While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to our knowledge to evaluate social biases in NRE systems. We create WikiGenderBias, a distantly supervised dataset with a human annotated test set. WikiGenderBias has sentences specifically curated to analyze gender bias in relation extraction systems. We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE. We also analyze how name anonymization, hard debiasing for word embeddings, and counterfactual data augmentation affect gender bias in predictions and performance.

The "Innovations in Cancer Diagnosis and Treatment, Micro-LEDs, Renewable Energy Generation and Storage, and Fault Detection" report has been added to ResearchAndMarkets.com's offering. The edition also provides insights on the role of macropinocytosis in pancreatic cancer. The TOE covers use of ceramic electrodes for doubling energy density and a biosensor for earlier diagnosis of tumors.

This is most apparent when auto-encoders are trained, where a network is trained to map the input data upon itself but is forced to project them into a lower-dimensional embedding space on the way (Vincent et al., 2010). In addition to the conventional fully connected layers, there are various special types of network connections. For example, in computer vision, convolu-tional layers are commonly used, which train multiple sliding windows that move over the image data and process just a part of the image at a time, thereby learning to recognize local features. These layers are subsequently abstracted into more and more complex visual patterns (Krizhevsky et al., 2017). For temporal data, one can use recurrent neural networks, which do not make predictions for individual input vectors, but for a sequence of input vectors. To do so, they allow feeding abstracted information from previous data points forward to the next layers.

Normative expert systems have not become commonplace because they have been difficult to build and use. Over the past decade, however, researchers have developed the influence diagram, a graphical representation of a decision maker's beliefs, alternatives, and preferences that serves as the knowledge base of a normative expert system. Most people who have seen the representation find it intuitive and easy to use. Consequently, the influence diagram has overcome significantly the barriers to constructing normative expert systems. Nevertheless, building influence diagrams is not practical for extremely large and complex domains. In this book, I address the difficulties associated with the construction of the probabilistic portion of an influence diagram, called a knowledge map, belief network, or Bayesian network. I introduce two representations that facilitate the generation of large knowledge maps. In particular, I introduce the similarity network, a tool for building the network structure of a knowledge map, and the partition, a tool for assessing the probabilities associated with a knowledge map. I then use these representations to build Pathfinder, a large normative expert system for the diagnosis of lymph-node diseases (the domain contains over 60 diseases and over 100 disease findings). In an early version of the system, I encoded the knowledge of the expert using an erroneous assumption that all disease findings were independent, given each disease. When the expert and I attempted to build a more accurate knowledge map for the domain that would capture the dependencies among the disease findings, we failed. Using a similarity network, however, we built the knowledge-map structure for the entire domain in approximately 40 hours. Furthermore, the partition representation reduced the number of probability assessments required by the expert from 75,000 to 14,000.

There is no shortage of training content for employees. However, quick access to the right information is the challenge. Traditionally, the L&D departments spend significant time on instructor-led training and aggregating and buying third-party training content. Other learning avenues, like on-the-job training, personalized training, micro-learning, and data or event-driven training programs are equally important. Employees today learn from content spread across internal and external systems including intranets, MooC platforms, LMS, social media platforms, external training content providers, document management systems, collaboration platforms, and even forums, Q&A portals, email and messenger/ chat platforms.

Artificial intelligence (AI) has permeated enterprise operations to the point that it now determines an organization's success, including in the area of project management. In a report, Project Management Institute (PMI) examines how six AI technologies are affecting today's project managers and will affect project management operations in the future. PMI's AI Innovators: Cracking the Code on Project Performance (2019) found that in the next three years, project professionals expect overall AI usage to jump from 23% to 37% and the majority of respondents (81%) said their organizations are currently being affected by AI technologies. SEE: The ethical challenges of AI: A leader's guide (free PDF) (TechRepublic) "Project leaders are in the earliest stages of adopting AI to streamline--and improve--project work. AI technologies are already contributing to higher productivity and better quality," said Mark Broome, chief data officer at PMI. "For example, technology is decreasing the amount of time project managers need to spend on activities like monitoring progress and managing documentation--they can rely on AI for these more administrative tasks. The time saved can then be repurposed to more strategic and creative tasks and planning."

This is crucial for the use of large Knowledge bases in many downstream applications. However explaining the predictions given by a KBC algorithm is quite important for several real world use cases. For example in rec-ommender systems, a knowledge graph of users, items and their interactions are used to recommend an item to a user based on the users interactions on several items. The ability to explain and reason on the decision is of critical importance to add knowledge to recommender systems. Similarly in a knowledge graph consisting human biological data such as genes, drugs, symptoms and diseases, it is crucial to know which gene and symptoms were involved in predicting a drug for a disease. This requires automatic extraction and ranking of multi-hop paths between a given source and a target entity from a knowledge graph. Previous work has focused on using path information in knowledge graphs for KBC known as path-based inference (Lao et al., 2011; Gardner et al., 2014; Neelakantan et al., 2015; Das et al., 2017b), in which a model is trained to predict missing links between a given pair of entities taking as input several paths that existed between them. Paths are ranked according to a scoring method and used as features to train the model. Embedding-based inference models (Bordes et al., 2013; Lin et al., 2015; Nickel et al., 2011; Socher et al., 2013; Trouillon et al., 2016) for KBC learn entity and relation embeddings by solving an optimization problem that maximises the plausibility of known facts in the knowledge graph.