Why do people focus on deep learning when it comes to artificial intelligence?

6Jun - by aiuniverse - 0 - In Deep Learning

Source: optocrypto.com

There are other areas of AI that look promising, such as Deep Learning. Remember that top companies like Google’s DeepMind and OpenAI are already working on this approach. Making the breakthrough.

So what is Deep Learning? Well, interestingly, it’s not new. “Deep learning is a classic behavioral phenomenon widely known in the psychological literature since the early 1950s,” says Matt D., says Matt Johnson, Ph. He is a professor of psychology at Haught International Business School and author of Blindsight: the ( Mostly ) Hidden Ways Marketing) author. Transforming our brain. “In its simplest form, the frequency of behavior will rise or fall depending on the immediate consequences of that behavior. This applies to both animal and human behavior.

Future is AI & Deep Learning

However, some of the key principles of Deep learning have been applied to AI models. Fiddler’s head of data science Ankur Taly says: “Deep learning requires action, a stimulus and a payoff”. “An agent, such as a robot or a character, interacts with its environment, observes certain activities and reacts accordingly in order to achieve useful or desirable results”. Deep learning follows a specific approach and determines the best way to achieve the best results. This is very similar to the structure in which we play video games, where the agent makes a series of attempts to obtain the highest score or maximum reward. After many iterations, it learns to maximize its cumulative rewards”.

Machine Learning is changing the world

In fact, some of the most interesting applications for Deep learning can be complex games. Consider the case of DeepMind’s AlphaGo. The system quickly learned how to play Go through Deep learning and beat world champion Lee Sedol 2016 (the game has more action potential than the number of atoms in the universe!)

But of course, there are other applications for the technology than just games. This is why Reinforcement Learning is particularly useful for robotics. OpenAI has, for example, using the technology for a robot arm that is able to solve the magic cube.

Here are some other areas where Deep learning can have an impact:

Entertainment: “The future will consist of the free-form environment that the next generation of ‘movie lovers’. AI-driven characters will work together to adapt to generate detailed storylines, and consumers will no longer rely on fixed conversations rather than rigid interactions between player characters.

Healthcare: “ImagineAI trying to teach doctors on how to treat medical patients through deep learning. Also, AI doctors can try drugs almost at random to see how they work, and over time they should develop patterns and understand which drugs work best in which situations. But we obviously can’t get AI doctors to do these experiments on real patients, and the physiology is too complex to construct Suitable human-computer simulations to conduct virtual experiments.

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