Hello everyone! In this blog is about robot.. 

Robots :- A robot is a mechanical device that moves under its own power and is guided by programs rather than human operators. Robots perform repetitive tasks according to a predetermined set of instructions (i.e., a program). Since they are predictable, robots are useful for performing tasks where people might get hurt, like moving dangerous materials around a factory.

Robots have been built since ancient times. However, the first recorded robot was the invention of Archytas of Tarentum, a Greek philosopher, statesman, and mathematician from 460 BC. He wrote a book called On planes and spheres titled "On Mechanics." In it he described his steam powered war machine. He called it a “flying artillery piece.”

The word robot derives from the Czech word robota meaning forced labor. In Russia, the word robot means worker, laborer, or slave. In Russian folklore, a robot is an undead creature created when a person dies by evil magic.

How do robot learn to do things 

Learning Algorithms:

Learning algorithms are mathematical models that predict future behavior based on previous experience. There are two broad categories of learning algorithms: supervised learning and unsupervised learning. Supervised learning requires some labeled data (data with known values) to train the model. Unsupervised learning does not require any labeled training data. Instead, it learns from observations.

Neural Networks:

Neural networks are models inspired by the way neurons work together in the brain. A neural network uses layers of nodes to represent different concepts. These nodes are connected either directly or indirectly to other nodes. Each node takes inputs from its neighbors and then produces outputs based on these inputs. Nodes are able to adapt their connections over time.

Reinforcement Learning:

Reinforcement learning is the discipline of designing systems that automatically acquire skills by experiencing rewards and punishments without explicit instructions. Examples of reinforcement learning include teaching computers how to play games and robots learning how to drive cars. Reinforcement learning works best when the agent's environment changes constantly.

Evolutionary Algorithms:

Evolutionary algorithms use natural selection to find optimal solutions. Genetic algorithms are a specific type of evolutionary algorithm. They start with a population of random individuals and apply selective pressures (like natural selection) until they converge on a solution.

Deep Learning:

Deep learning is a subset of machine learning that involves connecting many simple elements (layers) together. Different types of deep learning have been developed including convolutional neural networks, recurrent neural networks, and restricted Boltzmann machines.

Metaheuristics:

Metaheuristic algorithms combine aspects of both optimization and heuristics. One example of a metaheuristic method is Simulated Annealing, which starts with the worst possible solution and gradually becomes better over time.

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