Artificial intelligence(AI)-for people in a hurry. The easiest way to think about artificial intelligence is in the context of the human. After al Humans are the most intelligent creatures we know of.
What is AI?
AI is a broad branch of computer science. The goal of AI is to create systems that can function intelligently and independently. Humans can speak and listen to communicate through language. This is the field of speech recognition. Much of speech recognition is statistically based. Hence It called statistical learning. Humans can write and read text in a language. This is the field of NLP or natural language processing. Humans can see with their eyes and process what they see. This is a field of computer vision. Computer vision falls under the symbolic way for computers to process information. Recently there has been another way, which I’ll come to later.
Humans, recognize the scene around them through their eyes which create images of that world.
This field of image processing which even though it’s not directly related to AI . It’s related to computer vision. Humans can understand their environment and move around fluidly. This is the field of robotics. Humans have the ability to see patterns such as a grouping of light objects. This is the field of pattern recognition.
“Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and simulate their actions. The term may also be applied to any machine that exhibits traits associated with a human brain such as memorizing and problem-solving.”
Human Brain vs AI:
Machines are even better at pattern recognition because they can use more data and dimensions of data. This is the field of machine learning. Now let’s talk about the human brain. The human brain is a network of neurons and we use these to learn things. If we can replicate the structure and the function of the human brain. We might be able to get cognitive capabilities in machines.
This is a field of neural networks. Get these networks are more complex and deeper and we use those to learn complex things. That is the field of deep learning. There are different types of deep learning and machines which are essentially different techniques to replicate what the human brain does. If we get the network to scan images from left to right top to bottom. It’s a convolution neural network.This is how computer vision fits in an object recognition is accomplished through AI.
How does It Work?
Humans can remember the past like what you had for dinner last night. Well, at least most of you. We can get a neural network to remember a limited past. This is a recurrent neural network. As you see there are two ways AI works one is symbolic based and another is data-based. For the database side called machine learning. We need to feed the machine lots of data before it can learn. For example, if you had lots of data for sales versus advertising spend you can plot that data to see some kind of a pattern.
If the machine can learn this pattern then it can make predictions based on what it has learned. While one or two or even three dimensions is easy for humans to understand and learn. Machines can learn in many more dimensions like even hundreds or thousands. That’s why machines can look at lots of high-dimensional data and determine patterns.
Once it learns these patterns we can make predictions that humans can’t even come close to. We can use all these machine learning techniques to do one of two things classification or prediction. As an example when you use some information about customers to assign new customers to a group like young adults, then you are classifying that customer. If you use data to predict if there are likely to defect to a competitor, then you’re making a prediction.
There’s another way to think about learning algorithms used for AI. If you train an algorithm with data that also contains the answer then it’s called supervised learning. For example, when you train a machine to recognize your friends by name, you’ll need to identify them for the computer. If you train and an algorithm with data where you want the machine to figure out the patterns, then it’s unsupervised learning.
For example, you might want to feed the data about celestial objects in the universe and expect the machine to come up with patterns in that data by itself. If you give any algorithm a goal and expect the machine through trial and error to achieve that goal then it’s called reinforcement learning. A robot attempt to climb over the wall until it succeeds is an example of that.