With all the excitement and hype about AI that's "just around the corner"--self-driving cars, instant machine translation, etc.--it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you're already using--right now? In the process of navigating to these words on your screen, you almost certainly used AI. You've also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases. We distinguish between AI and machine learning (ML) throughout this article when appropriate. At Emerj, we've developed concrete definitions of both artificial intelligence and machine learning based on a panel of expert feedback. To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI.

Link: The Complete Python 3 Course: Beginner to Advanced his course is designed to fully immerse you in the Python language, so it is great for both beginners and veteran programmers! This diploma in C and Python programming course is a great way to get started in programming. It covers the study of the C and Python group of languages used to build most of the world's object oriented systems. The course is for interested students with a good level of computer literacy who wish to acquire programming skills. It is also ideal for those who wish to move to a developer role or areas such as software engineering.

Education is the premise of progress, in every society, in every family"- Kofi Annan Education is the backbone of every society and education must be made available to everyone. To ensure this we have to modify the traditional method of learning and adopt new technologies in this field. Digitization is turning as an aid in accomplishing the same, it had made learning electronic and widen its availability to 24 7. Anybody with internet access can learn anything at any time, the boundaries to education have been destructed. Technologies in the education sector are changing the methodology for both educators and modern-day learners. People are adopting new methods other than just bookish learning and prefer a more realistic learning experience. Digitization has brought drastic changes in the education sector nationally and globally. Let us see what various personalities have viewpoints on the same. "Yes to some extent digitization can help today's industrial age education industry in reaching the unreachable population of the country, that said, reform is only possible when the education system is rebooted to focus on the needs of the 21st century and beyond.

Inspur has announced the open-source release of TF2, an FPGA-based efficient AI computing framework. The inference engine of this framework employs the world's first DNN shift computing technology, combined with a number of the latest optimization techniques, to achieve FPGA-based high-performance low-latency deployment of universal deep learning models. This is also the world's first open-sourced FPGA-based AI framework that contains comprehensive solutions ranging from model pruning, compression, quantization, and a general DNN inference computing architecture based on FPGA. The open source project can be found at https://github.com/TF2-Engine/TF2. Many companies and research institutions, such as Kuaishou, Shanghai University, and MGI, are said to have joined the TF2 open source community, which will jointly promote open-source cooperation and the development of AI technology based on customizable FPGAs, reducing the barriers to high-performance AI computing technology, and shortening development cycles for AI users and developers.

This paper introduces the modulated Hebbian plus Q network architecture (MOHQA) for solving challenging partially observable Markov decision processes (POMDPs) deep reinforcement learning problems with sparse rewards and confounding observations. The proposed architecture combines a deep Q-network (DQN), and a modulated Hebbian network with neural eligibility traces (MOHN). Bio-inspired neural traces are used to bridge temporal delays between actions and rewards. The purpose is to discover distal cause-effect relationships where confounding observations and sparse rewards cause standard RL algorithms to fail. Each of the two modules of the network (DQN and MOHN) is responsible for different aspects of learning. DQN learns low level features and control, while MOHN contributes to the high-level decisions by bridging rewards with past actions. The strength of the approach is to support a DQN standard framework when temporal difference errors are difficult to compute due to non-observable states. The system is tested on a set of generalized decision making problems encoded as decision tree graphs that deliver delayed rewards after key decision points and confounding observations. The simulations show that the proposed approach helps solve problems that are currently challenging for state-of-the-art deep reinforcement learning algorithms.

Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment. Human knowledge of how to solve these tasks can be incorporated using imitation learning, where the agent learns to imitate human demonstrated decisions. However, human guidance is not limited to the demonstrations. Other types of guidance could be more suitable for certain tasks and require less human effort. This survey provides a high-level overview of five recent learning frameworks that primarily rely on human guidance other than conventional, step-by-step action demonstrations. We review the motivation, assumption, and implementation of each framework. We then discuss possible future research directions.

This paper extends the idea of Universum learning [1, 2] to single-class learning problems. We propose Single Class Universum-SVM setting that incorporates a priori knowledge (in the form of additional data samples) into the single class estimation problem. These additional data samples or Universum belong to the same application domain as (positive) data samples from a single class (of interest), but they follow a different distribution. Proposed methodology for single class U-SVM is based on the known connection between binary classification and single class learning formulations [3]. Several empirical comparisons are presented to illustrate the utility of the proposed approach.

Despite the development of numerous adaptive optimizers, tuning the learning rate of stochastic gradient methods remains a major roadblock to obtaining good practical performance in machine learning. Rather than changing the learning rate at each iteration, we propose an approach that automates the most common hand-tuning heuristic: use a constant learning rate until "progress stops," then drop. We design an explicit statistical test that determines when the dynamics of stochastic gradient descent reach a stationary distribution. This test can be performed easily during training, and when it fires, we decrease the learning rate by a constant multiplicative factor. Our experiments on several deep learning tasks demonstrate that this statistical adaptive stochastic approximation (SASA) method can automatically find good learning rate schedules and match the performance of hand-tuned methods using default settings of its parameters. The statistical testing helps to control the variance of this procedure and improves its robustness.

The pace of technological change is rendering many job activities -- and the skills they require -- obsolete. Research by McKinsey suggests that globally more than 50% of the workforce is at risk of losing their jobs to automation, and a survey by the World Economic Forum suggests that 42% of the core job skills required today will change substantially by 2022. In this landscape of constant disruption, individuals, companies, and governments are fighting to ensure they have the skills to remain competitive. To shed light on the global skills landscape, Coursera recently released the first edition of our Global Skills Index (GSI) report. As the world's largest platform for higher education, Coursera brings together 40 million learners around the world with over 3,000 courses from leading universities and companies.

In the digital-first world, the value of artificial intelligence (AI) is more evident than ever, and many CEOs and business leaders are witnessing the positive impact it's having on their organizations. So it's no surprise that enterprises plan to double their number of AI projects within the next year. But despite the clear advantages of AI, businesses are still struggling to find the right talent to successfully implement and fully utilize these technologies. What's more, the disparity between AI optimism across the C-suite, and trust at the employee level, adds yet another barrier. A recent study we conducted at EY of U.S. CEOs and business leaders shows that, while a majority (84%) of CEOs realize the value of AI and its importance to their company's success, nearly one in three (31%) view a lack of skilled talent as a top barrier to AI adoption.