reinforcement learning


Debunking The Myths And Reality Of Artificial Intelligence

#artificialintelligence

Intelligence should be "distributed" where "knowledge" is created and "decisions" are made A few years ago, it was hard to find anyone to have a serious discussion about Artificial Intelligence (AI) outside academic institutions. Like any new major technology trend, the new wave of making AI and intelligent systems a reality is creating curiosity and enthusiasm. People are jumping on its bandwagon adding not only great ideas but also in many cases a lot of false promises and sometimes misleading opinions. Built by giant thinkers and academic researchers, AI adoption by industries and further development in academia around the globe is progressing at a faster rate than anyone had excepted. Accelerated by the strong belief that our biological limitations are increasingly becoming a major obstacle towards creating smart systems and machines that work with us to better use our biological cognitive capabilities to achieve higher goals. This is driving an overwhelming wave of demands and investments across industries to apply AI technologies to solve real-world problems and create smarter machines and new businesses.


Debunking The Myths And Reality Of Artificial Intelligence

#artificialintelligence

Intelligence should be "distributed" where "knowledge" is created and "decisions" are made A few years ago, it was hard to find anyone to have a serious discussion about Artificial Intelligence (AI) outside academic institutions. Like any new major technology trend, the new wave of making AI and intelligent systems a reality is creating curiosity and enthusiasm. People are jumping on its bandwagon adding not only great ideas but also in many cases a lot of false promises and sometimes misleading opinions. Built by giant thinkers and academic researchers, AI adoption by industries and further development in academia around the globe is progressing at a faster rate than anyone had excepted. Accelerated by the strong belief that our biological limitations are increasingly becoming a major obstacle towards creating smart systems and machines that work with us to better use our biological cognitive capabilities to achieve higher goals. This is driving an overwhelming wave of demands and investments across industries to apply AI technologies to solve real-world problems and create smarter machines and new businesses.


Learn about the Types of Machine Learning Algorithms

#artificialintelligence

Isn't it true that we are living in a digitalized world that has eliminated tons of human work by positioning automation?. In fact, it is the most defined period as Google's self-driving car has been invented. But, this period is not in its final stages instead is multiplying to create many more awesome things to surface in the near future. The most exciting concept that sits beside all these major transformations is Machine Learning, which is nothing but allowing computers to learn on their own to arrive at useful insights. Supervised learning is similar to a teacher teaching his students with examples and after sufficient practice, the teacher stops supervising and let the students derive at their own solution.


Introduction to Deep Q-Learning for Reinforcement Learning (in Python)

#artificialintelligence

I have always been fascinated with games. The seemingly infinite options available to perform an action under a tight timeline – it's a thrilling experience. So when I read about the incredible algorithms DeepMind was coming up with (like AlphaGo and AlphaStar), I was hooked. I wanted to learn how to make these systems on my own machine. And that led me into the world of deep reinforcement learning (Deep RL).


r/deeplearning - Learning to paint: A Painting AI

#artificialintelligence

Abstract: We show how to teach machines to paint like human painters, who can use a few strokes to create fantastic paintings. By combining the neural renderer and model-based Deep Reinforcement Learning (DRL), our agent can decompose texture-rich images into strokes and make long-term plans. For each stroke, the agent directly determines the position and color of the stroke. Excellent visual effect can be achieved using hundreds of strokes. The training process does not require experience of human painting or stroke tracking data.


Video Friday: Soft Robots, and More

IEEE Spectrum Robotics Channel

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. The 2019 IEEE International Conference on Soft Robotics (RoboSoft) takes place in Seoul, South Korea, next week, and the organizers put together this preview video stuffed full of--what else?--soft robots. Single-stream recycling is currently an extremely labor intensive process due to the need for manual object sorting.


Amir Barati Farimani: Creative Robots with Deep Reinforcement Learning CMU RI Seminar

Robohub

"Recent advances in Deep Reinforcement Learning (DRL) algorithms provided us with the possibility of adding intelligence to robots. Recently, we have been applying a variety of DRL algorithms to the tasks that modern control theory may not be able to solve. We observed intriguing creativity from robots when they are constrained in reaching a certain goal. To introduce the topic, I will talk about some of the experiments that are being done to show the capabilities and limitations of modern Deep Reinforcement Learning approaches, including those of sparse rewards and continuous observations and action spaces. An in depth explanation of how Hindsight Experience Replay (HER) has been used to obtain dense results from sparse environments when using Deep Deterministic Policy Gradient (DDPG) agents will be given. I will then show how we have modified some of these experiments to have a deeper understanding of the intelligence we are developing, and what are the baseline environmental characteristics that make the robots achieve higher levels of creativity during their problem solving scenarios."


Reinforcement Learning Demystified: Markov Decision Processes (Part 1)

#artificialintelligence

In the previous blog post we talked about reinforcement learning and its characteristics. We mentioned the process of the agent observing the environment output consisting of a reward and the next state, and then acting upon that. This whole process is a Markov Decision Process or an MDP for short. This blog post is a bit mathy. Grab your coffee and a comfortable chair, and just dive in.


Andrea Thomaz: Robots Learning from Human Teachers CMU RI Seminar

Robohub

Abstract: "In this talk I will cover some of the recent work out of the Socially Intelligent Machines Lab at UT Austin (http://sim.ece.utexas.edu/research.html). The vision of our research is to enable robots to function in dynamic human environments by allowing them to flexibly adapt their skill set via learning interactions with end-users. We explore the ways in which Machine Learning agents can exploit principles of human social learning, and breakdown assumptions about what "data" will be like, when the source of that data is an average human teacher. I will cover our work on interactive reinforcement learning algorithms that model the attention of the teacher; coupling learning from demonstration with simulation to make the best use of valuable interactions with people; and algorithms for re-using previously learned tasks in new contexts with the help of a teacher's hints and corrections. In the latter part of the talk, I will put on my other hat, as co-founder and CEO of Diligent Robotics (http://diligentrobots.com/about) to tell you about how we are translating our research on adapting to human environments into a commercial product. Our first product, Moxi, is a robot assistant that works alongside and supports clinical care teams in hospitals. Moxi was launched into beta trials late last year, and has been deployed in four hospitals across Texas to date."


What is AI? Everything you need to know about Artificial Intelligence ZDNet

#artificialintelligence

Video: Getting started with artificial intelligence and machine learning It depends who you ask. Back in the 1950s, the fathers of the field Minsky andMcCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to recognise what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. At a very high level artificial intelligence can be split into two broad types: narrow AI and general AI. Narrow AI is what we see all around us in computers today: intelligent systems that have been taught or learned how to carry out specific tasks without being explicitly programmed how to do so.