NeuroEvolution is one of the most competitive evolutionary learning frameworks for designing novel neural networks for use in specific tasks, such as logic circuit design and digital gaming. However, the application of benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT) remains a challenge, in terms of their computational cost and search time inefficiency. This paper advances a method which incorporates a type of topological edge coding, named Reverse Encoding Tree (RET), for evolving scalable neural networks efficiently. Using RET, two types of approaches -- NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) -- have been designed to solve problems in benchmark continuous learning environments such as logic gates, Cartpole, and Lunar Lander, and tested against classical NEAT and FS-NEAT as baselines. Additionally, we conduct a robustness test to evaluate the resilience of the proposed NEAT algorithms. The results show that the two proposed strategies deliver an improved performance, characterized by (1) a higher accumulated reward within a finite number of time steps; (2) using fewer episodes to solve problems in targeted environments, and (3) maintaining adaptive robustness under noisy perturbations, which outperform the baselines in all tested cases. Our analysis also demonstrates that RET expends potential future research directions in dynamic environments. Code is available from https://github.com/HaolingZHANG/ReverseEncodingTree.

The WORKSHOPS will facilitate demonstrations of inclusive AI-based solutions, digital innovations, programmes or research that are aligned with the MLW 2020 subthemes. The INNOVATIONS will be presented by the winners of the MLW 2020 Call for Innovations and consist of a demonstration and presentation of developed AI applications and digital innovations for the advancement of inclusion and equity in education. The SYMPOSIUM will feature plenary panel discussions with experts in the field of inclusion in education, AI and education, and keynote addresses from thought leaders working at the intersection of inclusion, learning and AI and digital technologies. The UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education recognizes innovative approaches in leveraging new technologies to expand educational and lifelong learning opportunities for all, in line with the 2030 Agenda for Sustainable Development and its Goal 4 on education. During the continued SYMPOSIUM sessions, UNESCO will gather participants from around the world to share experiences and plan joint actions with a view to harnessing digital innovations to achieve Sustainable Development Goal 4. The POLICY FORUM will offer a unique space to discuss the key policy components for advancing digital technologies and inclusion in education to ensure the achievement of SDG 4 with specific regard to AI and inclusion.

Superintelligence: What happens in a world with AI that is hundreds or thousands of times smarter than humans? In this episode, we chat with research scientist Roman Yampolskiy. He's a professor at the University of Louisville, and his most recent book is Artificial Superintelligence: A Futuristic Approach. Subscribe wherever you find podcasts: If you listen to podcasts, here's where you can subscribe to future39 and here more interviews like this on the future. What happens in a world with AI that's hundreds or thousands of times smarter than we are? He's a professor at the University of Louisville, and his most recent book is Artificial Superintelligence: A Futuristic Approach. John Koetsier: Thank you so much for coming on the show. You have an amazing background there, I love it.

Are you planing to build your career in Data Science in This Year? Do you the the Average Salary of a Data Scientist is $100,000/yr? Do you know over 10 Million New Job will be created for the Data Science Filed in Just Next 3 years?? If you are a Student / a Job Holder/ a Job Seeker then it is the Right time for you to go for Data Science! Do you Ever Wonder that Data Science is the "Hottest" Job Globally in 2018 - 2019!

Modern technology has observed an immense evolution. Self-driven cars, instant translation, and cell phones that will do anything we need with a simple verbal order, are some examples of artificial intelligence (AI). We have discussed it with Gianluca Mauro, energy engineer, co-author and co-founder together with Nicolò Valigi of the AI Academy, a consulting firm of tech experts with the mission to help business leaders to "understand AI and what to do with it through trainings and coaching, and build successful AI projects with tailored consulting." Gianluca is a Roman, young entrepreneur and speaker, who has the ambitious plan to spread awareness about AI. Considering the many hardships of being young professionals -- especially in Italy -- we talked with him about the academy, the main issues in the Italian educational and professional fields, and prejudices against AI.

This article by Geoff Johnson raises the interesting question of where AI can be helpful to educators, and where it may fall short. Certainly, AI has its advantages--as Johnson points out, teachers can spend more time actually teaching, and less time grading, setting short answer tests, keeping attendance records, organizing syllabi, and the like. And it is also possible that, as we learn more about students' strengths and challenges, we can tailor educational software to the learner's profiles. I have termed this possibility "individuation." But it's more difficult to envision how an AI program can establish a personal relationship with a student, one in which the student's needs and aspirations are taken into account. And most important, as educators, at our best, we provide a model--the most salient model other than parents--of how one deals with the various challenges and opportunities that life affords.

In this article, I will present my favorite top-10 Machine Learning YouTube Channels to follow in order to keep up with the current trends. Jeremy Howard is an Australian data scientist and entrepreneur. He is a founding researcher at fast.ai, a research institute dedicated to make Deep Learning more accessible. Prior to it, Howard was the President and Chief Scientist at Kaggle. Another useful YouTube Channel is that of Rachel Thomas, co-founder of fast.ai.

Artificial intelligence and machine learning are becoming common in research and everyday life, raising concerns about how these algorithms work and the predictions they make. For example, when Apple released its credit card over the summer, there were claims that women were given a lower credit limit than otherwise identical men were. In response, Sen. Elizabeth Warren warned that women "might have been discriminated against, on an unknown algorithm." On its face, her statement appears to contradict the way algorithms work. Algorithms are logical mathematical functions and processes, so how can they discriminate against a person or a certain demographic?

Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field). In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.

Deep learning has a growing history of successes, but heavy algorithms running on large graphical processing units are far from ideal. A relatively new family of deep learning methods called quantized neural networks have appeared in answer to this discrepancy. In Leapmind R&D, we are working on quantization methods, among others, for enabling efficient high-performance deep learning computation on small devices. Neural networks are composed of multiple layers of parameters, each layer transforms the input image, separating and contracting [0] the feature space, resulting in the separation of input images to their various classes. Perhaps the most notable of deep learning problems are image classification, object detection, and segmentation.