Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms' decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs' perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV's algorithms and of policies and regulations to fully realise AVs' benefits for smart and sustainable cities.

We propose a heuristically modified FP-Tree for ontology learning from text. Unlike previous research, for concept extraction, we use a regular expression parser approach widely adopted in compiler construction, i.e., deterministic finite automata (DFA). Thus, the concepts are extracted from unstructured documents. For ontology learning, we use a frequent pattern mining approach and employ a rule mining heuristic function to enhance its quality. This process does not rely on predefined lexico-syntactic patterns, thus, it is applicable for different subjects. We employ the ontology in a question-answering system for students' content-related questions. For validation, we used textbook questions/answers and questions from online course forums. Subject experts rated the quality of the system's answers on a subset of questions and their ratings were used to identify the most appropriate automatic semantic text similarity metric to use as a validation metric for all answers. The Latent Semantic Analysis was identified as the closest to the experts' ratings. We compared the use of our ontology with the use of Text2Onto for the question-answering system and found that with our ontology 80% of the questions were answered, while with Text2Onto only 28.4% were answered, thanks to the finer grained hierarchy our approach is able to produce.

We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability. In this framework, we present asymptotically unbiased estimators for generic observables, including those that explicitly depend on the partition function such as free energy or entropy, and derive corresponding variance estimators. We demonstrate their practical applicability by numerical experiments for the 2d Ising model which highlight the superiority over existing methods. Our approach greatly enhances the applicability of generative neural samplers to real-world physical systems.

One of the most recognized applications of deep learning is chatbots. Chatbots are often used to provide sales assistance or customer support, the most common being the latter. These chatbots use workflows and deep learning to answer customer queries. As the service is used more and more and the machine gathers more data, deep learning enables a near-human conversation.

Report Scope: The scope includes the analysis of the AI hardware market based on technology type, computation type, end use industries and regional markets. For each of these market segments, revenue forecasts for 2018 through 2024 are provided at the global level. PRN The AI hardware market is segmented into the following categories - - Technology: machine learning, Computer vision, Natural Language Processing, Expert Systems. This report covers analyses of the global market trends, with data from 2018 to 2024 and projections of CAGR during 2019 to 2024 .The estimated values used are based on manufacturers' total revenues. Projected and forecasted revenue values are in constant U.S. dollars that have not been adjusted for inflation.

Report Scope: The scope includes the analysis of the AI hardware market based on technology type, computation type, end use industries and regional markets. For each of these market segments, revenue forecasts for 2018 through 2024 are provided at the global level. PRN The AI hardware market is segmented into the following categories - - Technology: machine learning, Computer vision, Natural Language Processing, Expert Systems. This report covers analyses of the global market trends, with data from 2018 to 2024 and projections of CAGR during 2019 to 2024 .The estimated values used are based on manufacturers' total revenues. Projected and forecasted revenue values are in constant U.S. dollars that have not been adjusted for inflation.

Choosing the right metric is crucial while evaluating machine learning (ML) models. Various metrics are proposed to evaluate ML models in different applications, and I thought it may be helpful to provide a summary of popular metrics in a here, for better understanding of each metric and the applications they can be used for. In some applications looking at a single metric may not give you the whole picture of the problem you are solving, and you may want to use a subset of the metrics discussed in this post to have a concrete evaluation of your models. Here, I provide a summary of 20 metrics used for evaluating machine learning models. There is no need to mention that there are various other metrics used in some applications (FDR, FOR, hit@k, etc.), which I am skipping here.

Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations. In addition to the structured information KGs help in facilitating interconnectivity and interoperability between different resources represented in the Linked Data Cloud. KGs have been used in a variety of applications such as entity linking, question answering, recommender systems, etc. However, KG applications suffer from high computational and storage costs. Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information. This paper conducts a survey of KG embedding models which not only consider the structured information contained in the form of entities and relations in a KG but also the unstructured information represented as literals such as text, numerical values, images, etc. Along with a theoretical analysis and comparison of the methods proposed so far for generating KG embeddings with literals, an empirical evaluation of the different methods under identical settings has been performed for the general task of link prediction.

SAP is renowned for its enterprise software, providing solutions across finance, supply chain and more. Another side of its business, however, lies in advising customers on the adoption of innovative technology. Frank Wilde is a Vice President for SAP's Global Center of Excellence (COE), which serves to provide this advice and expertise. "The Global COE is designed to be an incubator to support the sales motion and create a linkage to our product organization," he explains. "We help introduce new innovations and showcase the latest aspects of our portfolio to drive new customer conversations. A core component lies in making it easier for our sales teams to learn about new aspects of our portfolio, and then turn those into customer driven conversations. We're fundamentally changing the relationship with customers to be much more customer focused and much more agile as a result."

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high‐dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data‐driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning.