EU presidency extends access to free AI course across EU Jan Petter Myklebust 13 December 2019 European Union employment ministers have endorsed a proposal from Finland's Presidency of the Council of the EU to provide European citizens with free access to a successful online course on basic artificial intelligence (AI), developed and run by the University of Helsinki in partnership with private firm Reaktor. To achieve this, the course on "Elements of AI" will be made available in all official EU languages. The Finnish government will fund the project with €1.7 million (US$1.9 million) from Finland's Ministry of Economic Affairs and Employment as part of the EU Council presidency's effort to democratise awareness of AI and develop people's skills for jobs of the future. At the launch of the initiative in Brussels on 10 December, Finland's Minister of Employment Timo Harakka said: "Our investment has three goals: we want to equip EU citizens with digital skills for the future; we wish to increase practical understanding of what artificial intelligence is; and by doing so, we want to give a boost to the digital leadership of Europe." "As our presidency ends, we want to offer something concrete. It's about one of the most pressing challenges facing Europe and Finland today: how to develop our digital literacy," Harakka said.

Dr. Iain Brown is the Head of Data Science for SAS UK&I and Adjunct Professor of Marketing Analytics at the University of Southampton. For the last decade he has been working closely with the financial services sector, providing thought leadership on the topics of risk, AI and machine learning. During his time at SAS he has been involved in driving innovation in AI and the corresponding fields of machine learning, computer vision and natural language understanding through the delivery of numerous projects. He is also a contributor to SAS' blog and an active member of the AI community on Twitter. In a wide-ranging conversation about the applications of machine learning in the financial services sector, Iain offered some helpful advice around the integration of AI into business models.

The Nordic nation, headed by the world's youngest head of government, Sanna Marin, will mark the end of its rotating presidency of the EU with a highly ambitious goal. Finland is aiming to give practical understanding of AI to 1% of EU citizens -- or about five million people -- through a basic online course by the end of 2021. As its presidency gift to Europeans, the Finnish state provides the free online course by us and @ReaktorNow, translated to all official EU languages. It is teaming up with the University of Helsinki, Finland's largest and oldest academic institution, and the Finland-based tech consultancy Reaktor. Teemu Roos, a University of Helsinki associate professor in the department of computer science, described the near £1.5 million project as "Finland's gift to Europe" and "a civics course in AI" for every EU citizen to cope with the society's ever-increasing digitalisation and the possibilities AI offers to the job market and elsewhere.

Java How to Program (Early Objects), Tenth Edition is intended for use in the Java programming course. It also serves as a useful reference and self-study tutorial to Java programming. The Deitels' groundbreaking How to Program series offers unparalleled breadth and depth of object-oriented programming concepts and intermediate-level topics for further study. Java How to Program (Early Objects), Tenth Edition, teaches programming by presenting the concepts in the context of full working programs and takes an early-objects approach( MyProgrammingLab for Java How to Program (Early Objects) is a total learning package. MyProgrammingLab is an online homework, tutorial, and assessment program that truly engages students in learning. It helps students better prepare for class, quizzes, and exams–resulting in better performance in the course–and provides educators a dynamic set of tools for gauging individual and class progress.

Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to address the challenges of machine learning safety such as interpretability, verification, and performance limitations. In this paper, we review and organize practical machine learning safety techniques that can complement engineering safety for machine learning based software in autonomous vehicles. Our organization maps safety strategies to state-of-the-art machine learning techniques in order to enhance dependability and safety of machine learning algorithms. We also discuss security limitations and user experience aspects of machine learning components in autonomous vehicles.

Enabling a neural network to sequentially learn multiple tasks is of great significance for expanding the applicability of neural networks in realistic human application scenarios. However, as the task sequence increases, the model quickly forgets previously learned skills; we refer to this loss of memory of long sequences as long-term catastrophic forgetting. There are two main reasons for the long-term forgetting: first, as the tasks increase, the intersection of the low-error parameter subspace satisfying these tasks will become smaller and smaller or even non-existent; The second is the cumulative error in the process of protecting the knowledge of previous tasks. This paper, we propose a confrontation mechanism in which neural pruning and synaptic consolidation are used to overcome long-term catastrophic forgetting. This mechanism distills task-related knowledge into a small number of parameters, and retains the old knowledge by consolidating a small number of parameters, while sparing most parameters to learn the follow-up tasks, which not only avoids forgetting but also can learn a large number of tasks. Specifically, the neural pruning iteratively relaxes the parameter conditions of the current task to expand the common parameter subspace of tasks; The modified synaptic consolidation strategy is comprised of two components, a novel network structure information considered measurement is proposed to calculate the parameter importance, and a element-wise parameter updating strategy that is designed to prevent significant parameters being overridden in subsequent learning. We verified the method on image classification, and the results showed that our proposed ANPSC approach outperforms the state-of-the-art methods. The hyperparametric sensitivity test further demonstrates the robustness of our proposed approach.

The thesis contributes in several important ways to the research area of 3D object category learning and recognition. To cope with the mentioned limitations, we look at human cognition, in particular at the fact that human beings learn to recognize object categories ceaselessly over time. This ability to refine knowledge from the set of accumulated experiences facilitates the adaptation to new environments. Inspired by this capability, we seek to create a cognitive object perception and perceptual learning architecture that can learn 3D object categories in an open-ended fashion. In this context, ``open-ended'' implies that the set of categories to be learned is not known in advance, and the training instances are extracted from actual experiences of a robot, and thus become gradually available, rather than being available since the beginning of the learning process. In particular, this architecture provides perception capabilities that will allow robots to incrementally learn object categories from the set of accumulated experiences and reason about how to perform complex tasks. This framework integrates detection, tracking, teaching, learning, and recognition of objects. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks. The contributions presented in this thesis have been fully implemented and evaluated on different standard object and scene datasets and empirically evaluated on different robotic platforms.

Last year, Finland launched a free online crash course in artificial intelligence with the aim of educating its citizens about the new technology. Now, as a Christmas present to the world, the European nation is making the six week program available for anyone to take. Finland is relinquishing the EU's rotating presidency at the end of the year, and decided to translate its course into every EU language as a gift to citizens. But there aren't any geographical restrictions as to who can take the course, so really it's to the world's benefit. The course certainly proved itself in Finland, with more than 1 percent of the Nordic nation's 5.5 million citizens signing up. The course, named Elements of AI, is currently available in English, Swedish, Estonian, Finnish, and German.

Generative adversarial networks (GANs) -- two-part AI systems consisting of generators that create samples and discriminators that attempt to distinguish between the generated samples and real-world samples -- have countless uses, and one of them is producing synthetic data. Researchers at Uber recently leveraged this in a paper titled "Accelerating Neural Architecture Search by Learning," which proposes a tailored GAN -- Generative Teaching Networks (GTNs) -- that generates data or training environments from which a model learns before being tested on a target task. They say that they speed up searches for algorithms by up to nine times compared with approaches that use real data alone, and that GTNs are competitive with state-of-the-art architectures that achieve top performance while using "orders of magnitude" less computation. As the contributing authors explain in a blog post, most model searches require "substantial" resources because they evaluate models by training them on a data set until their performance no longer improves. This process might be repeated for thousands or more model architectures in a single cycle, which is both expensive in terms of computation and incredibly time-consuming.

Machine learning models are trained by learning a mapping between a set of input features and an output target. Typically, this mapping is learned by optimizing some cost function to minimize prediction error. Once the optimal model is found, it's released out into the wild with the goal of generating accurate predictions on future unseen data. Depending on the problem, these new data examples may be generated from user interactions, scheduled processes, or requests from other software systems. Ideally, we hope that our models predict these future instances as accurately as the data used during the training process.