Finland is offering a free-of-charge online course in artificial intelligence for European Union citizens in their own language, officials said Tuesday. The Nordic nation, headed by the world's youngest head of government, 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 5 million people -- through a basic online course by the end of 2021. 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 nearly $2 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 digitalization and the possibilities AI offers to the job market and elsewhere.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

Data Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, etc. For beginners, it's only natural to raise the following question: What skills do I need to become a data scientist? This article will discuss 10 essential skills that are necessary for practicing data scientists. These skills could be grouped into 2 categories, namely, technological skills (Math & Statistics, Coding Skills, Data Wrangling & Preprocessing Skills, Data Visualization Skills, Machine Learning Skills,and Real World Project Skills) and soft skills (Communication Skills, Lifelong Learning Skills, Team Player Skills and Ethical Skills). Data science is a field that is ever-evolving, however mastering the foundations of data science will provide you with the necessary background that you need to pursue advance concepts such as deep learning, artificial intelligence, etc.

With a long history, Web Summit has been held once a year since 2009. After ten years of development, it has become a world-renowned and large-scale technology event, and the 2019 Summit has attracted attention from all walks of life. The event not only brought together more than 70,000 leaders of technology enterprises, founders of start-ups and policy makers from more than 160 countries, but also invited more than 2,600 media from all over the world to attend the summit, which has a powerful influence in the world. Although the concept of artificial intelligence is hot, the specific empowerment of artificial intelligence in all walks of life cannot be accomplished at one stroke. At the "Davos Forum of Tech-geeks", many guests shared wonderful perspectives, and expressed their opinions around transportation technology, artificial intelligence, financial technology, earth technology, future technology, wearable devices, big data, front-end design, content creation, fashion and music industry technology and other fields.

Artificial Intelligence has had many breakthroughs in 2019. In fact, we can go as far as to say that it has trickled down to every single facet of modern life. With its intervention in our daily life, it is imperative that everyone knows about how it is affecting our lives, bringing about change in it, the threats and possible solutions. While there are some people who still think AI is only robots and chatbots, it is important that they know of the advancements in the field. There are many online courses and books on artificial intelligence that give a comprehensive understanding to the reader whether it is a professional or an AI enthusiast.

Organisations across the world are witnessing talent shortage in AI and are struggling to hire competent employees in this ever-changing landscape. Since every company is striving for a data-driven approach, there is a rise in the integration of technologies such as artificial intelligence, data science, among others, to achieve business objectives. However, the absence of superior talent in the market is impeding the growth of firms. In fact, research tells us that 85% of AI projects fail due to risk, confusion and lack of upskilling in the employees. "It is very challenging to get excellent developers in the space even though AI and Data Science has been the most sought-after skill," says Gaurav Mehrotra, vice president and head of business data solutions at Innoviti Payment Solutions. A recent report published by noted online learning platform Coursera states that out of the 45 million learners on the platform, two million enrolled in AI-based content in 2019.

Once a user logs into an online shopping website, knowing whether the person will make a purchase or not holds a massive economical value. A lot of current research is focused on real-time revenue predictors for these shopping websites. In this article, we will start building a revenue predictor for one such website. In this Data Science Machine Learning project, we will create a Real-time prediction of online shoppers' purchasing intention Project using Apache Spark Machine Learning Models using Logistic Regression, one of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.

US semiconductor giant Intel has acquired Israeli startup Habana Labs, a developer of artificial intelligence processors, for $2 billion, the company announced on Monday. Founded in 2016, Habana Labs develops processor platforms that are optimized for training deep neural networks and for inference deployment in production environments. The company is headquartered in Tel Aviv and has offices in California, Poland, and China. Intel led a $75 million investment in Habana Labs in 2018. That year, Habana unveiled its Goya inference processor which it says is ideally suited for the most demanding AI applications in the industry, including private and cloud data centers, autonomous vehicles, factory and warehouse automation robots, and high-end drones.

In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously. We first cast the graph structure learning problem as a similarity metric learning problem and leverage an adapted graph regularization for controlling smoothness, connectivity and sparsity of the generated graph. We further propose a novel iterative method for searching for a hidden graph structure that augments the initial graph structure. Our iterative method dynamically stops when the learned graph structure approaches close enough to the optimal graph. Our extensive experiments demonstrate that the proposed DIAL-GNN model can consistently outperform or match state-of-the-art baselines in terms of both downstream task performance and computational time. The proposed approach can cope with both transductive learning and inductive learning.

The main goal of this thesis is to point out that the bias-variance tradeoff is not always true (e.g. in neural networks). We advocate for this lack of universality to be acknowledged in textbooks and taught in introductory courses that cover the tradeoff. We first review the history of the bias-variance tradeoff, its prevalence in textbooks, and some of the main claims made about the bias-variance tradeoff. Through extensive experiments and analysis, we show a lack of a bias-variance tradeoff in neural networks when increasing network width. Our findings seem to contradict the claims of the landmark work by Geman et al. (1992). Motivated by this contradiction, we revisit the experimental measurements in Geman et al. (1992). We discuss that there was never strong evidence for a tradeoff in neural networks when varying the number of parameters. We observe a similar phenomenon beyond supervised learning, with a set of deep reinforcement learning experiments. We argue that textbook and lecture revisions are in order to convey this nuanced modern understanding of the bias-variance tradeoff.