With Siry you will learn:
what is artificial intelligence and machine learning
what kind of tasks robots already have in the world, in different fields and in school
what it’s like to live in a smart home
how bots and artificial intelligence affect everyday life
what kind of information a smart house or robot needs to collect in order to serve its owner
Siry’s house is a place that is always felt by its residents movements and states of being.
For example heating and air condition start and adjust themselves when they notice that a lot of guests have arrived in Siry today or if it is especially warm outside. The light from Siry's workstation, in turn, dims and brightens according to how much natural light comes out of the window.
In the smart house, the lamps also turn off and light up automatically depending on where Siry and her cat Miuku-Mauku move.
Siry thinks the most comfortable thing is that the house helps her in everyday life, even when she gets old. If he regularly monitors his heart rate, for example with a heart rate monitor, that information can be automatically transferred to the doctor treating him. This way, professionals in the field can see that Siry is all right.
It is important for Siry that the house produces as much as possible low emissions and low energy consumption. The house works with solar energy and has been added to it wind turbine to grab energy from the wind for house use. So the house produces energy for its own needs. Also part from Siry surplus energy stored in an electric car found in Siry's yard, for example, and the rest of Siry can be sold to others for use in the electricity grid.
I would need an even smarter cleaning robot. My current robot just bumps into furniture.
When a professional designs the robot, he takes into account:
mechatronics – robotic equipment components (sensors, actuators, their control, power supply, materials, telecommunications)
system development - including methods and tools for the design, modeling and development of robotic systems
human-robot interaction - including human-machine interaction, cooperation and safety
environmental observation – including the processing of sensor data and its interpretation for understanding
movement - including robot location, mapping and route planning
intelligence – including data modeling, reasoning, and learning.
Perhaps the most famous artificial intelligence developed to beat its opponent has been developed to beat the popular GO board game in Asia AlphaGO. It was developed by Google DeepMind. It is a computer program that plays the board game Go. It became the first computer-to-go program to beat a professional player on a 19 × 19 gaming board. In March 2016, it won the first three games in a five-game match against professional player Lee Sedol and thus the entire match series. As a whole, the series ended 4-1. This is the first time the go program has beaten a 9-dan level professional player without leveling stones.
AlphaGo's algorithm is a combination of machine learning and woodworking as well as training against human and machine players. It uses the Monte Carlo tree search, which is guided by an “evaluation network”. value network) and the ‘business model network’ policy network). The operating model network recommends different game modes, from which the following game situations are evaluated by the evaluation network. In the end, AlphaGo chooses the game mode that is most successful in its simulation
Wood search means the ability of a program to use a variety of proven routes to select the right solution. Pictured is a tree search chain.
Pictured is a GO game board. The game conquers areas. Have you ever played?
A professional Go player named Lee Sedol fights AlphaGO.
Deep learning based on artificial nerves that form a multilayer neural network. Nerves, or neurons, calculate a numerical result from one or more numerical nerve stimuli from other neurons or inputs, based on intrinsic weight.
The goal of deep learning is to create algorithm with the help of a neural network that could solve the problems given to it. In-depth learning is used in particular to solve problems where solutions made by traditional methods would require very complex rules. In-depth learning is used, for example, to identify or process speech, images and text.
strong artificial intelligence
weak artificial intelligence
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