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The Different Types Of Hardware AI Accelerators


An AI accelerator is a kind of specialised hardware accelerator or computer system created to accelerate artificial intelligence apps, particularly artificial neural networks, machine learning, robotics, and other data-intensive or sensor-driven tasks. They usually have novel designs and typically focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As deep learning and artificial intelligence workloads grew in prominence in the last decade, specialised hardware units were designed or adapted from existing products to accelerate these tasks, and to have parallel high-throughput systems for workstations targeted at various applications, including neural network simulations. As of 2018, a typical AI integrated circuit chip contains billions of MOSFET transistors. Hardware acceleration has many advantages, the main being speed. Accelerators can greatly decrease the amount of time it takes to train and execute an AI model, and can also be used to execute special AI-based tasks that cannot be conducted on a CPU.

Future of AI Part 5: The Cutting Edge of AI


Edmond de Belamy is a Generative Adversarial Network portrait painting constructed in 2018 by Paris-based arts-collective Obvious and sold for $432,500 in Southebys in October 2018.

An Introduction to Reinforcement Learning - Lex Fridman, MIT


We were delighted to be joined by Lex Fridman at the San Francisco edition of the Deep Learning Summit, taking part in both a'Deep Dive' session, allowing for a great amount of attendee interaction and collaboration, alongside a fireside chat with OpenAI Co-Founder & Chief Scientist, Ilya Sutskever. The MIT Researcher shared his thoughts on recent developments in AI and its current standing, highlighting its growth in recent years. Lex then referenced, Lee Sedol, the South Korean 9th Dan GO player, whom at this time is the only human to ever beat AI at a video game, which has since become somewhat of an impossible task, describing this feat as a seminal moment and one which changed the course of not only deep learning but also reinforcement learning, increasing the social belief in the subsection of AI. Since then, of course, we have seen video games and tactically based games, including Starcraft become imperative in the development of AI. The comparison of Reinforcement Learning to Human Learning is something which we often come across, referenced by Lex as something which needed addressing, with humans seemingly learning through "very few examples" as opposed to the heavy data sets needed in AI, but why is that?

Artificial Intelligence vs. Machine Learning vs. Deep Learning: What's the Difference


In 2020, people benefit from artificial intelligence every day: music recommender systems, Google maps, Uber, and many more applications are powered with AI. One of popular Google search requests goes as follows: "are artificial intelligence and machine learning the same thing?". Let's clear things up: artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three different things. The term artificial intelligence was first used in 1956, at a computer science conference in Dartmouth. AI described an attempt to model how the human brain works and, based on this knowledge, create more advanced computers. The scientists expected that to understand how the human mind works and digitalize it shouldn't take too long.

Artificial Intelligence: Reinforcement Learning in Python


Online Courses Udemy Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications Created by Lazy Programmer Team, Lazy Programmer Inc. English [Auto-generated], French [Auto-generated], 4 more Students also bought Bayesian Machine Learning in Python: A/B Testing Ensemble Machine Learning in Python: Random Forest, AdaBoost Machine Learning A-Z: Hands-On Python & R In Data Science Complete Python Developer in 2020: Zero to Mastery Natural Language Processing with Deep Learning in Python Preview this course GET COUPON CODE Description When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

Advanced AI: Deep Reinforcement Learning in Python


Online Courses Udemy Advanced AI: Deep Reinforcement Learning in Python, The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks Created by Lazy Programmer Team, Lazy Programmer Inc. English [Auto-generated], Indonesian [Auto-generated], 5 more Students also bought Deep Learning: Convolutional Neural Networks in Python Deep Learning: Recurrent Neural Networks in Python Unsupervised Machine Learning Hidden Markov Models in Python Bayesian Machine Learning in Python: A/B Testing Data Science: Supervised Machine Learning in Python Preview this course GET COUPON CODE Description This course is all about the application of deep learning and neural networks to reinforcement learning. If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace.

Working towards explainable and data-efficient machine learning models via symbolic reasoning


In recent years, we have witnessed the success of modern machine learning (ML) models. Many of them have led to unprecedented breakthroughs in a wide range of applications, such as AlphaGo beating a world champion human player or the introduction of autonomous vehicles. There has been continuous effort, both from industry and academia, to extend such advances to solving real-life problems. However, converting a successful ML model into a real-world product is still a nontrivial task. Firstly, modern ML methods are known for being data-hungry and inefficient.

CoCoPIE: A software solution for putting real artificial intelligence in smaller spaces


Bit by bit, byte by byte, artificial intelligence has been working its way into public consciousness and into everyday computer use. Artificial intelligence and deep learning have been deeply woven into more and more aspects of end-user computing. Smartphones and other mobile devices use AI as well. Up until now, the artificial intelligence work has been done in the cloud, but a new approach to software design aims to arm mobile devices with real artificial-intelligence capability. "A mobile device is very resource-constrained," explained William & Mary computer scientist Bin Ren.

Self-supervised learning is the future of AI


Despite the huge contributions of deep learning to the field of artificial intelligence, there's something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn't emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data. Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers. In his keynote speech at the AAAI conference, computer scientist Yann LeCun discussed the limits of current deep learning techniques and presented the blueprint for "self-supervised learning," his roadmap to solve deep learning's data problem.