reinforcement learning



Google Introduces Neuroevolution for Self-Interpretable Agents

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Good gamers can tune out distractions and unimportant on-screen information and focus their attention on avoiding obstacles and overtaking others in virtual racing games like Mario Kart. However, can machines behave similarly in such vision-based tasks? A possible solution is designing agents that encode and process abstract concepts, and research in this area has focused on learning all abstract information from visual inputs. This however is compute intensive and can even degrade model performance. Now, researchers from Google Brain Tokyo and Google Japan have proposed a novel approach that helps guide reinforcement learning (RL) agents to what's important in vision-based tasks.


When to assume neural networks can solve a problem - LessWrong 2.0

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Note: the original article has been split into two since I think the two points were only vaguely related, I will leave it as is here, since I'd rather not re-post stuff and I think the audience on LW might see the "link" between the two separate ideas presented here. Let's begin with a gentle introduction in to the field of AI risk - possibly unrelated to the broader topic, but it's what motivated me to write about the matter; it's also a worthwhile perspective to start the discussion from. I hope for this article to be part musing on what we should assume machine learning can do and why we'd make those assumptions, part reference guide for "when not to be amazed that a neural network can do something". I've often had a bone to pick against "AI risk" or, as I've referred to it, "AI alarmism". When evaluating AI risk, there are multiple views on the location of the threat and the perceived warning signs. I would call one of these viewpoints the "Bostromian position", which seems to be mainly promoted by MIRI, philosophers like Nick Bostrom and on forums such as AI Alignment.


Microsoft Research Uses Transfer Learning to Train Real-World Autonomous Drones

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Perception-Action loops are at the core of most our daily life activities. Subconsciously, our brains use sensory inputs to trigger specific motor actions in real time and this becomes a continuous activity that in all sorts of activities from playing sports to watching TV. In the context of artificial intelligence(AI), perception-action loops are the cornerstone of autonomous systems such as self-driving vehicles. While disciplines such as imitation learning or reinforcement learning have certainly made progress in this area, the current generation of autonomous systems are still nowhere near human skill in making those decisions directly from visual data. Recently, AI researchers from Microsoft published a paper proposing a transfer learning method to learn perception-action policies from in a simulated environment and apply the knowledge to fly an autonomous drone.


Google's new SEED RL framework reduces AI model training costs by 80% - SiliconANGLE

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Researchers at Google have open-sourced a new framework that can scale up artificial intelligence model training across thousands of machines. It's a promising development because it should enable AI algorithm training to be performed at millions of frames per second while reducing the costs of doing so by as much as 80%, Google noted in a research paper. That kind of reduction could help to level the playing field a bit for startups that previously haven't been able to compete with major players such as Google in AI. Indeed, the cost of training sophisticated machine learning models in the cloud is surprisingly expensive. One recent report by Synced found that the University of Washington racked up $25,000 in costs to train its Grover model, which is used to detect and generate fake news.


6 trends framing the state of AI and ML

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Join Roger Magoulas on March 26 for a live and interactive online session exploring recent O'Reilly AI/ML research. O'Reilly online learning is a trove of information about the trends, topics, and issues tech leaders need to know about to do their jobs. We use it as a data source for our annual platform analysis, and we're using it as the basis for this report, where we take a close look at the most-used and most-searched topics in machine learning (ML) and artificial intelligence (AI) on O'Reilly[1]. Our analysis of ML- and AI-related data from the O'Reilly online learning platform indicates: Get a free trial today and find answers on the fly, or master something new and useful. Engagement with the artificial intelligence topic continues to grow, up 88% in 2018 and 58% in 2019 (see Figure 1), outpacing share growth in the much larger machine learning topic ( 14% in 2018, up 5% in 2019).


Google is using AI to design chips that will accelerate AI

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A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan. Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years.


Uber details Fiber, a framework for distributed AI model training

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A preprint paper coauthored by Uber AI scientists and Jeff Clune, a research team leader at San Francisco startup OpenAI, describes Fiber, an AI development and distributed training platform for methods including reinforcement learning (which spurs AI agents to complete goals via rewards) and population-based learning. The team says that Fiber expands the accessibility of large-scale parallel computation without the need for specialized hardware or equipment, enabling non-experts to reap the benefits of genetic algorithms in which populations of agents evolve rather than individual members. Fiber -- which was developed to power large-scale parallel scientific computation projects like POET -- is available in open source as of this week, on Github. It supports Linux systems running Python 3.6 and up and Kubernetes running on public cloud environments like Google Cloud, and the research team says that it can scale to hundreds or even thousands of machines. As the researchers point out, increasing computation underlies many recent advances in machine learning, with more and more algorithms relying on distributed training for processing an enormous amount of data.


google-research/seed_rl

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This repository contains an implementation of distributed reinforcement learning agent where both training and inference are performed on the learner. However, any reinforcement learning environment using the gym API can be used. For a detailed description of the architecture please read our paper. Please cite the paper if you use the code from this repository in your work. There are a few steps you need to take before playing with SEED.


Three Things to Know About Reinforcement Learning

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If you are following technology news, you have likely already read about how AI programs trained with reinforcement learning beat human players in board games like Go and chess, as well as video games. As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it's the right approach for a given problem. If we simplify the concept, at its foundation, reinforcement learning is a type of machine learning that has the potential to solve tough decision-making problems. Reinforcement learning is a type of machine learning in which a computer learns to perform a task through repeated trial-and-error interactions with a dynamic environment.