#reinforced learning

[xponentialdesign]

Reinforced Yearning

[invisiblebuddha]

Los Angeles Times (@latimes.com)

The robot puppeteers of Silicon Valley teaching humanoids how to make your morning coffee https://www.latimes.com/business/story/2026-05-31/

Robots are being trained now for the future.

[edujournalblogs]

Types of Machine Learning problems

Types of Machine Learning problems:

Supervised

Un-Supervised

Reinforcement Learning

Supervised Learning: Here, we want to make certain predictions for the future. Hence, we want the machine to learn the previous historical data and forccast for the future for instance, temperature for today. We provide both the input value and output (labels) of historical data such as climate details, humidity etc and along with that output like temperature. Hence, the model can find the derivatives between input and output and generate an equation. eg regression model.

Unsupervised Learning : USL is to simply find out different patterns in data and categorize something Or group something or segment something. Here, we only provide input values not output values. We will only provide the relative features of the class but will not label them. For instance, for identifying a dog, we give long tail, sharp teeth, sharp claws, makes a boow noise etc. But we will not give the label for it. We only expect the machine to make a segregation based on the underlying features. Therefore, Unsupervised Learning does not make any predictions for the future but only makes segregation.

Reinforcement Learning: it is known as reward-based learning. For instance, we have a robot that is learning how to walk. We train the robot that walk straight and if you strike anything on your way like wall or table etc, then turn left or right and move forward or come back. Each time he strikes somewhere, we would point out his mistake and tell him where he is going wrong. Reward him in such a way that if we does not strike anywhere, he provide a rewarded system such that the robot does not commit the same mistake. So this is what Reinforced Learning is all about.

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URL: www.edujournal.com

[nextgeninvent]

Machine Learning – Supervised, Unsupervised, & Reinforced Learning

Learn about the different types of machine learning used in artificial intelligence, including supervised, unsupervised, and reinforced tech

[cheshirecat-rabbit]

Now any business can access the same type of AI that powered AlphaGo

A startup called CogitAI has developed a platform that lets companies use reinforcement learning, the technique that gave AlphaGo mastery of the board game Go.

A startup called CogitAI has developed a platform that lets companies use reinforcement learning, the technique that gave AlphaGo mastery of the board game Go.

Gaining experience: AlphaGo, an AI program developed by DeepMind, taught itself to play Go by practicing. It’s practically impossible for a programmer to manually code in the best strategies for winning. Instead, reinforcement learning let the program figure out how to defeat the world’s best human players on its own.

Drug delivery: Reinforcement learning is still an experimental technology, but it is gaining a foothold in industry. Amazon recently launched a reinforcement-learning platform, but it is aimed more at researchers and academics. CogitAI’s first commercial customers include those working in robotics for drug manufacturing. Its platform lets the robot figure out the optimal way to process drug orders.

Brain trust: CogitAI was founded by several smart AI experts, including Peter Stone, a professor at the University of Texas. Rich Sutton, one of the fathers of reinforcement learning, is an advisor.

Learn for life: Stone says CogitAI’s platform is also the first to incorporate the ability to apply what it has learned in one situation to a new one, a first step toward “lifelong learning” for AI programs. “The platform has all of the cutting-edge RL algorithms and then some of our steps toward continual learning,” he says.

[futurehalfbloodprince]

How Do Computers learn?

Do you believe in magic? Well, you should cause you are just about to witness some. Allow me....

How do Computers learn?

The simple answer: Machine Learning. Machine Learning is application of Artificial Intelligence that gives computer systems the ability to learn and become intelligent without being explicitly programmed. That’s right, no more old school ‘hard-coding’ a computer to do stuff. Instead, they learn and do it themselves. I am going to demonstrate this idea with one branch of Machine Learning known as Reinforcement Learning.

Reinforcement Learning allows computers to have goals. And this goals are what pushes the computer to get better and better. Let’s think of this goals in form of points, where making the right choices leads to the highest points and the computer obviously aims to get the most points it can get. The algorithm interacts with its environment and corrects and adjusts itself based on its results of interaction; that is, it aims to get better after each interaction. It learns from its mistakes. Just like us in schools, after each fail or pass in a CAT or exam, we adjust ourselves accordingly, sometimes we study more, we go on with the method we’ve been studying with or change the study method altogether.

I’m going to give you two real world examples where machines learned and became really good, even better than humans.

1. Learning to play Atari games.

Remember the early Atari Games, specifically Atari Breakout? Where you had to move a bar of some kind hitting a ball that hits the ceiling that awards you points?

Well, Google’s Deepmind was put to the test, of learning to play the game. The AI is given sensory input and ordered to maximize the points on the screen. Note that no domain knowledge is used. This means that the AI doesn’t know what the controls do or the concept of the ball.

After around 10 minutes, the AI the algorithm tries to hit the ball, but its not yet perfect and it frequently misses the ball.

Two hours later, it’s really good and it gets returns good points, which is amazing but most humans can do this too.

After around four hours, is where the real magic happens. The AI algorithm learns a strategy where it can maximize its points by drilling a whole in the ceiling and letting the ball bounce on the upper red and orange side first since it returns most points. I know, blew my mind too. Here’s an algorithm that had never played Breakout before and had no knowledge of the controls and ball, but it discovers a brilliant way of playing the game that no human had ever thought of, IN JUST FOUR HOURS!!!!!! Dzaaaayuum.

2. Chess and Go

The other example is where AlphaZero AI developed by Google was able to learn Chess in four hours without human help by just playing itself, then proceeded to beat the reigning computer chess program in a 100-game tournament: 28 wins, 72 draws and no losses. If that’s not scary enough, it used moves and tactics never seen before, in the 1500 years history of the game.

Google’s AlphaGo also beat the world champion Lee Sedol, 4-1, again by moves and tactics never seen before. The most interesting fact about Go is that in this game, after the first two moves, there are 10^170 possible moves (please correct me if I’m wrong) more than the number of atoms in the planet, 10^80.

I know, it gets more and more interesting.

Computers learning to play computer games and board games doesn’t sound that magical.

In truth, by use of Reinforcement Learning plus the other forms of Machine Learning: Supervised Learning and Unsupervised Learning, computers are learning to do awesome stuff.

From Virtual Personal Assistants like Siri and Cortana, to Email Spam and Malware Filtering, to Medical Diagnosis like Cancer detection, to Product Recommendation in shopping websites, and Friend Recommendation in social media sites....Artificial Intelligence is really impacting our lives positively.

And as I always say, there is more, much much more.

This is the property of the HalfBloodPrince.