What is an Artificial intelligence for everyone
The easiest way to think about artificial intelligence is in the context of the human. After all, humans are the most intelligent creatures we know off.
AI: AI is a broad branch of computer science, the goal of an AI is to create systems that can function intelligently and independently
speech recognition: 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’s called Statistical Learning.
Text recognition: Humans can write and read a text in a language, this is the field of Natural language processing -NLP
computer vision: Humans can see with their eyes and process what they can see, this is the field of computer vision, falls under the branch of symbolic learning for computers to process information. recently there’s been another way which you can see it later.
Object recognition: Humans recognize the scenes around them through their eyes which create images of that world, this field of image processing, which even though is not directly related to the branch of an AI is required for computer vision.
Robotics: Humans can understand their environment and move around fluidly, this is the field of robotics under the branch of symbolic learning.
Pattern recognition: Humans have the ability to see patterns such as grouping of like objects, this is the field of pattern recognition.
Machines Learning: Machines are even better at pattern recognition because they can use more data and dimensions of data, this is the field of Machine learning
Neural networks: Human brain is the network of neurons and we use these to learn things if we replicate the structure and the function of the human brain we might be able to get cognitive capabilities in machines, this is the field of Neural networks
Deep learning: if neural 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
Convolution Neural Network: if we get the network to scan images from left to right and top to bottom, it’s a convolution neural network-CNN is used to recognize objects in a scene, this is how computer vision fits in an object recognition is accomplished through AI
Recurrent Neural Network: 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 field of recurrent neural network.
Machine Learning
As you see there are two ways an AI works and the one is symbolically based (symbolic learning) and another one is databased (machine learning) For the database side we need to feed machine with lots of data before it can learn
example: if you had lots of data for sales vs advertising spend, you can plot the 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 two or even three dimensions is easy for humans to understand and learn, machines can learn in many more dimensions like even hundred or thousands, that's why machines can look at lots of high dimensional data and determine patterns, once it learns these patterns it can make predictions that humans can't even come close.
we can use all these machine learning techniques to do one of two things is classification or prediction as an example when you use some info about customers to assign new customers to a group like young adults, then you are classifying their customer. if you use data to predict if they’re likely to defect to a competitor then your making a prediction.
There's another way to think about learning algorithms used for AI .
Supervised learning: if you train an algorithm with data that also contain the answer or solution. For example, when you train a machine to recognize your friends by name you’ll need to identify them for computer
Unsupervised Learning: if you train an algorithm with data where you want the machine to figure out the patterns. For example, you might want to feed the data about celestial objects in the universe and expect the machine to come up with patterns.
Reinforcement Learning: In data by itself if you give any algorithm a goal and expect the machine through trials and errors to achieve that goal. For example, robots attempt to climb over the wall until it succeeds.
That's all, I have summarized the knowledge I acquired. I would be happy if it helps some to kick-start the learning of Artificial intelligence. It is no more a Buzzword. It is happening. please share your thoughts and hit that clap button if you feel good about this-thanks!
