Artificial intelligence is becoming more and more important in our lives every year, and everyone can now take advantage of it. Estonia has been a good example of a country excelling in digital transformation. Furthermore, it has surely inspired many others to begin their digital journey. After saying all that, we cannot rest on our laurels – artificial intelligence has come to stay. But what do we really know about it and how can we make it work for us?

IBM began working on its Watson supercomputer then as well. It seems like it's been ages, but these major innovations only happened 12 years ago. And the speed of technological change has only gotten faster, especially with breakthroughs in artificial intelligence (AI). As AI and other digital technologies permeate our workplaces and business practices, employees now have to acquire new skills and evolve just as quickly to keep up with the pace of change. What we find across many enterprises, however, is a growing talent gap, where people want to learn the latest necessary skills, but not enough companies are providing the right reskilling options.

While optimistic about the future, Steve Wozniak is not ready to turn over his identity (nor his Tesla) to artificial intelligence anytime soon. He deleted his Facebook account because of privacy concerns, and he no longer believes that a totally autonomous car will happen in his lifetime. But Wozniak retains the passion and enthusiasm for technology and innovation that made him a household name as Apple's co-founder. When he and Steve Jobs started Apple, they were trying to develop a new kind of computer that would improve the user experience beyond what was available at the time. Today, "The Woz" is a brilliant engineer, who keeps his eye on what is happening in technology, digital transformation and entrepreneurship.

While optimistic about the future, Steve Wozniak is not ready to turn over his identity (nor his Tesla) to artificial intelligence anytime soon. He deleted his Facebook account because of privacy concerns, and he no longer believes that a totally autonomous car will happen in his lifetime. But Wozniak retains the passion and enthusiasm for technology and innovation that made him a household name as Apple's co-founder. When he and Steve Jobs started Apple, they were trying to develop a new kind of computer that would improve the user experience beyond what was available at the time. Today, "The Woz" is a brilliant engineer, who keeps his eye on what is happening in technology, digital transformation and entrepreneurship.

Visit http://TED.com to get our entire library of TED Talks, transcripts, translations, personalized Talk recommendations and more. The danger of artificial intelligence isn't that it's going to rebel against us, but that it's going to do exactly what we ask it to do, says AI researcher Janelle Shane. Sharing the weird, sometimes alarming antics of AI algorithms as they try to solve human problems -- like creating new ice cream flavors or recognizing cars on the road -- Shane shows why AI doesn't yet measure up to real brains. The TED Talks channel features the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and more.

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The AWS Certified Advanced Networking Official Study Guide – Specialty Exam helps to ensure your preparation for the AWS Certified Advanced Networking – Specialty Exam. Expert review of AWS fundamentals align with the exam objectives, and detailed explanations of key exam topics merge with real-world scenarios to help you build the robust knowledge base you need to succeed on the exam--and in the field as an AWS Certified Networking specialist. Coverage includes the design, implementation, and deployment of cloud-based solutions; core AWS services implementation and knowledge of architectural best practices; AWS service architecture design and maintenance; networking automation; and more. You also get one year of free access to Sybex's online interactive learning environment and study tools, which features flashcards, a glossary, chapter tests, practice exams, and a test bank to help you track your progress and gauge your readiness as exam day grows near. The exam assumes existing competency with advanced networking tasks, and assesses your ability to apply deep technical knowledge to the design and implementation of AWS services.

Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for regularization. While deep transfer learning can usually boost the performance with better accuracy and faster convergence, transferring weights from inappropriate networks hurts training procedure and may lead to even lower accuracy. In this paper, we consider deep transfer learning as minimizing a linear combination of empirical loss and regularizer based on pre-trained weights, where the regularizer would restrict the training procedure from lowering the empirical loss, with conflicted descent directions (e.g., derivatives). Following the view, we propose a novel strategy making regularization-based Deep Transfer learning Never Hurt (DTNH) that, for each iteration of training procedure, computes the derivatives of the two terms separately, then re-estimates a new descent direction that does not hurt the empirical loss minimization while preserving the regularization affects from the pre-trained weights. Extensive experiments have been done using common transfer learning regularizers, such as L2-SP and knowledge distillation, on top of a wide range of deep transfer learning benchmarks including Caltech, MIT indoor 67, CIFAR-10 and ImageNet. The empirical results show that the proposed descent direction estimation strategy DTNH can always improve the performance of deep transfer learning tasks based on all above regularizers, even when transferring pre-trained weights from inappropriate networks. All in all, DTNH strategy can improve state-of-the-art regularizers in all cases with 0.1%--7% higher accuracy in all experiments.

--Existing approaches for automatically generating mathematical word problems are deprived of customizability and creativity due to the inherent nature of template-based mechanisms they employ. We present a solution to this problem with the use of deep neural language generation mechanisms. Our approach uses a Character Level Long Short T erm Memory Network (LSTM) to generate word problems, and uses POS (Part of Speech) tags to resolve the constraints found in the generated problems. Our approach is capable of generating Mathematics Word Problems in both English and Sinhala languages with an accuracy over 90%. A Mathematical word problem (MWP) is a mathematical problem expressed in natural language. Unlike other knowledge based question types such as travel or history related questions, MWPs require problem solving ability. In particular, algebraic questions involve sentences to make the questions more deep and inspective. Algebra is a major component of mathematics that is learnt by every student in Ordinary Level (O/L). Simple algebra problems mostly appear in a word format.

Sparse principal component analysis (PCA) is an important technique for dimensionality reduction of high-dimensional data. However, most existing sparse PCA algorithms are based on non-convex optimization, which provide little guarantee on the global convergence. Sparse PCA algorithms based on a convex formulation, for example the Fantope projection and selection (FPS), overcome this difficulty, but are computationally expensive. In this work we study sparse PCA based on the convex FPS formulation, and propose a new algorithm that is computationally efficient and applicable to large and high-dimensional data sets. Nonasymptotic and explicit bounds are derived for both the optimization error and the statistical accuracy, which can be used for testing and inference problems. We also extend our algorithm to online learning problems, where data are obtained in a streaming fashion. The proposed algorithm is applied to high-dimensional gene expression data for the detection of functional gene groups.