What You Should Know About Artificial Intelligence
Artificial intelligence (AI) has been making headlines lately, with companies like Google and Facebook using AI to power their products and services. However, most people are still confused about what exactly AI is, how it works and why it matters to them personally. In this article we’ll give you an overview of what artificial intelligence is, how it works in the real world and some of the different applications it has in business and other areas of life.
The History of AI
The term artificial intelligence was coined in 1956 by computer scientist John McCarthy. He and his colleagues at Dartmouth College believed that computers could be programmed to do tasks that are commonly considered intelligent, such as proving mathematical theorems or diagnosing illnesses. At the time, most researchers believed that these tasks required some degree of human intelligence.
The first AI-based product was a chess-playing program named Deep Blue. It was developed by IBM and became world champion in 1996 when it defeated Garry Kasparov, who is considered the greatest chess player of all time. Deep Blue used an algorithm called brute force which means it simply found every possible solution until it found one that matched its opponent's move.
Artificial General Intelligence
The term artificial intelligence or AI is usually used to describe software that mimics a human's ability to learn, understand, and make decisions. AI has been used in many industries, such as video games, virtual assistants (e.g., Siri) and robotics. But what does AI really mean?
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. The field of AI research deals with the construction of intelligent machines that work and react like humans do. One way to simulate human intelligence processes is through deep learning and machine learning algorithms. Deep learning is an artificial neural network-based machine learning technique that uses multiple layers of computational models to make sense out of complex data sets by processing them as raw input data.
Machine Learning
Another type of machine-learning algorithm is unsupervised machine-learning algorithms, which does not require training sets because it does not need to predict future input data. Supervised machine-learning algorithms are a type of supervised learning and are trained on a set of input data with the desired output values (labelled targets). The labels in the training set help the model learn what outputs should be generated for new inputs. The more training examples provided, the better the model will become at predicting output values for new inputs. When applied to speech recognition or object recognition problems, supervised learning uses a large number of example images (labelled target classes) in order to teach an AI system how different objects look like and sound like so that it can classify new images correctly.
Deep Learning
The first step in using deep neural networks is to feed them data from which they will extract patterns and form representations (features). A key benefit of this technique is that it allows us to extract features automatically without manual labor; we only need to provide enough information for the machine to learn what is important.
The Future of AI
Artificial intelligence has come a long way in the past decade. It's gone from being just a term to something that actually influences our daily lives. As we get deeper into this new world of intelligent machines, it's important to be knowledgeable about what they are and what they do. In particular, it's important to know that artificial intelligence is more than just an idea or a concept; it's become an integral part of modern society.
Challenges and Considerations
1. The machine is only as good as the data it has been trained on.
2. If a company doesn’t have enough data to train an AI, they can always purchase or steal it from somewhere else, but you should never do this because it will just lead to a bunch of people with AI that are doing the same thing and not enough progress in the world.
3. It’s hard for humans to understand how an AI is thinking because we don’t know how our own brains work so we can’t fully understand how another brain works either, especially one that was created by someone else who also doesn’t understand how their own brain works.
