Every once in a while, a revolutionary product comes along that changes everything.

"Mitchell knows what she's talking about. Artificial Intelligence has significantly improved my knowledge when it comes to automation technology, [but] the greater benefit is that it has also enhanced my appreciation for the complexity and ineffability of human cognition."―John "Without shying away from technical details, this survey provides an accessible course in neural networks, computer vision, and natural-language processing, and asks whether the quest to produce an abstracted, general intelligence is worrisome . . . Mitchell's view is a reassuring one." "In Mitchell's telling, artificial intelligence (AI) raises extraordinary issues that have disquieting implications for humanity. AI isn't for the faint of heart, and neither is this book for nonscientists . . . "Artificial intelligence can trounce you at chess, but will mistake a school bus for an ostrich or make bizarre connections between birds and hydrants.

Highlights: We will give an overview of the most common types of noise that is present in images. We will show how we can generate these types of noise and add them to clean images. Then, we will show how we can filter these images using a simple median filter. In this post, we will assume that we "know" how the noise looks like in our experiments and then it will be easier for us to find an optimal way how to remove that noise. Different kind of imaging systems might give us different noise.

--V ehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it - a key ability for evaluating safety from a planning and verification perspective. In this work, we devise a novel approach for generating realistic and diverse vehicle trajectories. We extend the generative adversarial network (GAN) framework with a low-dimensional approximate semantic space, and shape that space to capture semantics such as merging and turning. We sample from this space in a way that mimics the predicted distribution, but allows us to control coverage of semantically distinct outcomes. We validate our approach on a publicly available dataset and show results that achieve state of the art prediction performance, while providing improved coverage of the space of predicted trajectory semantics. V ehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing literature relates to improving the accuracy of prediction [1]-[5], the diversity of the predicted trajectories [6], [7] must be explored.

'Tis the season for looking ahead, and one of the most exciting technologies on the horizon is AVs (autonomous vehicles), including both personal AVs and transportation services, like autonomous taxis or "robotaxis." New research delves into the projected robotaxi market, providing a glimpse into the potential future of shared transportation services in a connected, autonomous world. The road ahead isn't free of obstacles, but the industry is already looking for ways to commercialize this transformative technology. Autonomous mobility offers the promise of safety and efficiency. When AI (artificial intelligence) rules the roadways, roads won't be subject to the consequences of human mistakes and human distraction.

At the end of the week I had a chance to give a guest input at the kick-off meeting of the new Erasmus Plus project "Artificial Intelligence (AI) and Vocational Education and Training (VET)". The partners come from Greece, Italy, Lithuania and the United Kingdom/Wales. All partners are known to us from previous European cooperation activities, so the project team was in a good position to have a rapid start. My role as a visitor was to give an overview of some predecessor projects and their recent/ongoing work. In addition I had a surprise input to deliver on top of my presentation.

We present, FT-SWRL, a fuzzy temporal extension to the Semantic Web Rule Language (SWRL), which combines fuzzy theories based on the valid-time temporal model to provide a standard approach for modeling imprecise temporal domain knowledge in OWL ontologies. The proposal introduces a fuzzy temporal model for the semantic web, which is syntactically defined as a fuzzy temporal SWRL ontology (SWRL-FTO) with a new set of fuzzy temporal SWRL built-ins for defining their semantics. The SWRL-FTO hierarchically defines the necessary linguistic terminologies and variables for the fuzzy temporal model. An example model demonstrating the usefulness of the fuzzy temporal SWRL built-ins to model imprecise temporal information is also represented. Fuzzification process of interval-based temporal logic is further discussed as a reasoning paradigm for our FT-SWRL rules, with the aim of achieving a complete OWL-based fuzzy temporal reasoning. Literature review on fuzzy temporal representation approaches, both within and without the use of ontologies, led to the conclusion that the FT-SWRL model can authoritatively serve as a formal specification for handling imprecise temporal expressions on the semantic web.

Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized to be non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificial datasets, often with bias or contaminated discriminating content. Through their increased distribution, decision-making algorithms can contribute promoting prejudge and unfairness which is not easy to notice due to lack of transparency. Hence, scientists developed several so-called explanators or explainers which try to point out the connection between input and output to represent in a simplified way the inner structure of machine learning black boxes. In this survey we differ the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks. We give a comprehensive overview about taxonomy of related studies and compare several survey papers that deal with explainability in general. We work out the drawbacks and gaps and summarize further research ideas.

AI is not part of their current plans (36%), more than quarter (27%) are assessing and planning to deploy AI in the future and 23% are advanced and proficient with AI Top 3 benefits of AI: Improving efficiency, workflow and accuracy Top priority for AI: Using EMR data to reliably predict risk Top challenge of AI: Lack of financial resources AI holds the greatest promise in: Diabetes, neurological disease and heart disease Almost two-thirds of their organizations expect to add 1-10 AI apps in the next 18 months About half report their organizations will spend $1million to $10 million on AI this year About half will spend $1 million to $10 million this year on infrastructure Optimism reigns supreme among CIOs, with most of them reporting that their organization has a full understanding of data governance and privacy, uses data to effectively support AI efforts and senior leadership demonstrates ownership and commitment to AI initiatives 59% work for an organization that is already using 1-10 AI-based apps in clinical practice 53% have a data governance policy CIOs aren't yet sure (44%) if they want to share de-identified data with other healthcare organizations to improve AI methods, although 38% will share it for a fee. While about half are shy on collaborating with research organizations to develop AI apps trained on local patient data reporting they have no plans to collaborate. Some 38% are planning to collaborate. CIOs aren't yet sure (44%) if they want to share de-identified data with other healthcare organizations to improve AI methods, although 38% will share it for a fee. While about half are shy on collaborating with research organizations to develop AI apps trained on local patient data reporting they have no plans to collaborate.

In'The Terminator' series of action films starring Arnold Schwarzenegger, a cybernetic organism (cyborg) is programmed from the future to go back in time and kill the mother of the scientist who leads the fight against Skynet, an artificial intelligence system that will cause a nuclear holocaust. Terrifying and at times comical ("I'll be back", "Make my day") The Terminator cyborg was among the first presentations of artificial intelligence (AI) to a global audience. While numerous facets of AI have been developed over the past couple of decades, all with positive outcomes, the fear of AI being programmed to do something devastating to the human race, of computers "going rogue", continues to persist. On the other hand, AI holds tremendous potential for benefiting humanity in ways we are only just starting to recognize. This article gives an overview of artificial intelligence including some of its most interesting manifestations. The first step is defining what we mean by artificial intelligence. One definition of AI is "the simulation of human intelligence processes by machines, especially computers." Such processes include learning by acquiring information, understanding the rules around using that information, employing reasoning to reach conclusions, and self-correcting.