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DeepMind's XLearn trains AI agents to complete complex tasks

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All the sessions from Transform 2021 are available on-demand now. DeepMind today detailed its latest efforts to create AI systems capable of completing a range of different, unique tasks. By designing a virtual environment called XLand, the Alphabet-backed lab says that it managed to train systems with the ability to succeed at problems and games including hide and seek, capture the flag, and finding objects, some of which they didn't encounter during training. The AI technique known as reinforcement learning has shown remarkable potential, enabling systems to learn to play games like chess, shogi, Go, and StarCraft II through a repetitive process of trial and error. But a lack of training data has been one of the major factors limiting reinforcement learning–trained systems' behavior being general enough to apply across diverse games.


Two-legged robot called Cassie makes history by completing 5K run in 53 minutes

Daily Mail - Science & tech

Cassie has made history as the first bipedal robot to complete a five-kilometer (5K) run, having done so in just over 53 minutes. Developed by Oregon State University, the two-legged machine with knees that bend like those of an ostrich, taught itself how to run through a deep reinforcement learning algorithm. Yesh Godse, an undergraduate in the lab, said in a statement: 'Deep reinforcement learning is a powerful method in AI that opens up skills like running, skipping and walking up and down stairs.' Cassie's total time of 53 minutes, three seconds, included about six and a half minutes of resets following two falls. Cassie first stumbled when its computer overheated and the other came after it took a turn at too high of a speed. The robot's makers foresee it eventually delivering packages, managing warehouse tasks and helping people in their homes.


Introduction to Machine Learning

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Humans learn from their experiences and gain expertise. Machine Learning is concerned with computer programs that imitate this process. It is a field of study that gives computers the capability to learn without being explicitly programmed. Can you imagine how Netflix makes those recommendations? The program learns from your past activities and tries to gain expertise in predicting your behavior.



Watch Cassie the bipedal robot run a 5K

Engadget

Cassie, a bipedal robot that's all legs, has successfully ran five kilometers without having a tether and on a single charge. The machine serves as the basis for Agility Robotics' delivery robot Digit, as TechCrunch notes, though you may also remember it for "blindly" navigating a set of stairs. Oregon State University engineers were able to train Cassie in a simulator to give it the capability to go up and down a flight of stairs without the use of cameras or LIDAR. Now, engineers from the same team were able to train Cassie to run using a deep reinforcement learning algorithm. According to the team, Cassie teaching itself using the technique gave it the capability to stay upright without a tether by shifting its balance while running.


Cassie the bipedal robot uses machine learning to complete a 5km jog

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Four years is a long time in robotics, especially so for a bipedal robot developed at Oregon State University (OSU) named Cassie. Dreamt up as an agile machine to carry packages from delivery vans to doorsteps, Cassie has recently developed an ability to run, something its developers have now shown off by having it complete what they say is the first 5-km (3.1-mi) jog by a bipedal robot. We first took a look at Cassie the bipedal robot back in 2017, when OSU researchers revealed an ostrich-like machine capable of waddling along at a steady pace. It is based on the team's previously developed Atrias bipedal robot, but featured steering feet and sealed electronics in order to function in the rain and snow and navigate outdoor terrain. The team has since used machine learning to equip Cassie with an impressive new skill: the ability to run. This involved what they call a deep reinforcement learning algorithm, which Cassie combines with its unique biomechanics and knees that bend like an ostrich to make fine adjustments to keep itself upright when on the move.


Deep Learning: GANs and Variational Autoencoders

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Free Coupon Discount - Deep Learning: GANs and Variational Autoencoders, Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow Created by Lazy Programmer Inc. Students also bought Natural Language Processing with Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Ensemble Machine Learning in Python: Random Forest, AdaBoost Cutting-Edge AI: Deep Reinforcement Learning in Python Deep Learning: Advanced NLP and RNNs Preview this Udemy Course GET COUPON CODE Description Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently. Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs. GAN stands for generative adversarial network, where 2 neural networks compete with each other. Unsupervised learning means we're not trying to map input data to targets, we're just trying to learn the structure of that input data. Once we've learned that structure, we can do some pretty cool things.


Three Popular Machine Learning Methods

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Let's move onto the different types of machine learning. The first type of machine learning we will talk about is supervised learning. In this method, you take a sample from the larger data set. This sample is used to represent the correlation and relationships that can be inferred from the data. Basically, it will try to summarize different cases in order to learn what predictions can be made or how to classify data.


Reinforcement Learning for a Better Tomorrow

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Artificial Intelligence (AI) has had the power of ruling the technologically dominated world for quite some time now. Today, we have reached a stage wherein advanced artificial intelligence has become one of the most sought after techniques to bring about innovation and solve complex business problems. Over the last few years, the technology has matured to the extent that it has become highly scalable. In the midst of all this, what has grabbed eyeballs from everywhere across is reinforcement learning – training the machine learning models to be able to make the best possible decisions. Reinforcement learning makes use of algorithms that do not rely only on historical data sets, to learn to make a prediction or perform a task.


Three Popular Machine Learning Methods

#artificialintelligence

Let's move onto the different types of machine learning. The first type of machine learning we will talk about is supervised learning. In this method, you take a sample from the larger data set. This sample is used to represent the correlation and relationships that can be inferred from the data. Basically, it will try to summarize different cases in order to learn what predictions can be made or how to classify data.