Artificial intelligence has become one of the most popular buzzwords in recent years. Technically, AI does not exist yet, but many claim that we are rapidly approaching it. Some even refer to today’s so-called AI as advanced machine learning or deep neural networks.
Deep learning is a type of artificial intelligence (AI) that works by feeding large amounts of data through an algorithm which learns from what it sees. The algorithm then uses this information to produce results for new inputs or situations.
There are some who say that we have already achieved true AI, but this argument is mostly fueled by hype and speculation. Many argue that while technology can now perform certain tasks like speech recognition and facial recognition well, it will never truly understand the meaning of these statements.
Researchers are still working hard to achieve this goal, however. The hope is that one day computers will outperform humans at least slightly in every field, including problem solving, reasoning, and communication.
In this article, you will learn about how computer programs are able to accomplish such complex feats by mimicking the way our brains work. This theory was first proposed over 50 years ago, and several prototypes have been built since then.
Definition of deep learning
Artificial neural networks (ANNs) are computer programs that simulate how neurons in our brain work. ANNs use layers to learn information, just like humans do!
A layer is an internal function within the network that receives input data from a previous layer and produces output data for a later layer. Layers are connected to each other through nodes, which contain some type of processing material such as silicon or fluid.
The more layers and nodes there are, the smarter the system gets! That’s why it’s called “deep” learning. By having lots of layers and nodes, you get very complex systems that perform increasingly abstract tasks.
Deep learning has become the most popular form of machine learning due to its impressive success across various fields. It was first applied successfully to image classification back in 2012 and now it is being used to solve ever-increasing sets of problems, including speech recognition, natural language understanding, and even game playing.
While these applications seem far removed from AI, they all rely on algorithms that take large amounts of data and teach themselves what features signify an object or voice tone. Computers don’t need human experts to tell them what things mean, they can figure it out by figuring out what patterns and relationships between objects and materials they have already seen. This process is referred to as pattern matching.
History of AI
Artificial intelligence (AI) has been around for quite some time now, with the first examples being simple programs that perform specific tasks. These early AIs were designed to be clever, but hardly ever made their program more complex than defining as true or false an assertion.
In fact, many believe that some of today’s most successful “intelligent” systems are simply algorithms that have been trained on large data sets to make assumptions about the world. For example, when you put down a dog, it will assume it is hungry and look for food.
This is what makes so-called deep learning networks interesting. Rather than using pre-defined rules to determine if an object is similar to another one, they find patterns in how the objects relate to each other.
These relationships can be determined through pixels, shapes, colors, etc., which allows the system to learn not only what things look like, but why they look the way they do and how to distinguish between two identical looking items.
History of deep learning
Artificial neural networks (ANNs) have been around since 1950, when mathematician Frank Rosenblatt published his paper introducing them. He called it “neural network” or just “network.” In those days, however, people did not call them that very word ‘deeply-learning’ systems.
It was not until 2008 that Yoshua Bengio coined this term to describe artificial neurons connected in layers rather than chains linearly. This is what makes these networks so powerful – you can add more layers, and even replace one layer with something else, and the system will learn new features!
Since then, there has been an explosion of interest and use for ANNs across almost every field they are applied to. They work particularly well at tasks where humans are already inclined to organize information hierarchically, such as image recognition and language processing.
Deep learning is no longer considered a pure research area, but instead has entered the practical realm. There are many applications for these algorithms beyond computer vision and natural language processing (NLP).
This article will go into some detail about how these systems work and how they can be modified to solve other problems. But first, let us take a look back at their history.
Neural networks
Today, there is a lot of talk about artificial intelligence (AI). Some refer to it as intelligent technology or just AI. What makes something qualify as being an example of AI depends on who you ask!
Some say that anything with learning ability qualifies as having some form of AI. This includes computers, robots, and even plants!
However, this definition of AI is very broad and does not set clear guidelines for what constitutes an “intelligence” system. Is a plant really thinking about complex patterns and linking them together? I would argue no, but then we get into the question of defining thought.
Another common theory of what defines AI comes from mathematician Alan Turing. He described it in his paper titled Intelligent Machines. His idea was to define AI as any machine which can perform tasks which require reasoning and logic.
He also suggested that if such a computer could be made to write a story, then it would have shown signs of reasoning and logical thinking.
This line of thinking inspired people to create so-called neural network systems. These use software and hardware designed to mimic how neurons work in our brains.
Neurons are processing units within our brain that receive information through input signals and process these inputs using simple rules to produce an output. A classic example of this is when your eye receives light and processes this data to determine where an object lies in space.
The layers of a neural network
Layer one is called the input layer, or sometimes referred to as the data layer because that is what it learns about from your training set.
The next layer is called the hidden layer and depends on how many features you want the model to have. A feature is something like gender, age, income, etc., which are all part of making an prediction.
The last layer is determined by what kind of result you’re looking for, such as whether someone has a job or not, or if they will earn more than $50k per year. This is known as the output layer.
Deep learning models typically have several hundred to thousands of neurons in each of these three layers.
What does the network learn?
One of the most important things that deep learning networks do is to take input data and learn how to organize it into patterns or structures.
This is what makes them so powerful. You can give a machine network a lot of raw data, and you will get outputs that are beyond anything possible with only manual methods.
By having the system recognize organized chunks of information, the software can then apply this knowledge to new situations. For example, if we gave it an image of a cat, it could figure out all the features of a typical ‘cat’ picture (spots, whiskers, etc.).
It also learns how to associate these features with being a cat. By doing this over and over, the system comes up with its own rules and logic about what makes a cat picture.
Gradient descent
In deep learning, we are often looking to find optimal solutions by taking small steps towards that solution. This process is called gradient descent, and it works best when there’s an underlying structure or pattern in the data you’re trying to learn.
In computer science, this happens all the time. For example, if you want to determine what word someone else used most frequently in their writing, then you will take lots of documents and apply algorithms to figure out the words themselves and how they relate to each other.
This algorithm was first developed over 100 years ago to solve mathematical equations, but has since been adapted for almost anything. When applied to natural language, researchers have term it statistical linguistics.
With neural networks, however, this isn’t quite as easy. To use a very simple analogy, think about teaching someone how to swim. If you just make them dive into water every day, they won’t know how to float!
Luckily, there’s another way to teach people to swim, which is through imitation. People who can swim understand the basics of moving around in water, so they can imitate the motions. They can also learn from watching others do it.
Neural networks work similarly. At their core, they rely on connections (or imitations) between nodes to achieve their goal. These nodes can be trained together as a group, or individually depending on whether one knows something specific already.
Applying neural networks
Recent developments in artificial intelligence have brought us another powerful tool, one that can perform advanced tasks such as image recognition or natural language processing. This technology is called deep learning.
Deep learning works by using layers of mathematical functions to learn complex patterns. These patterns are then applied to new data to make predictions or categorize information.
Because it uses concepts from neuroscience to function, some refer to this technique as “neural network artificial intelligence” or “artificial neuronal networking.”
Given enough training data, deep learning systems are said to be able to achieve levels of sophistication similar to those performed by human beings.
There are several types of neural networks, but typically they all share two features: 1) lots of neurons and 2) repeated connections between neurons. (A neuron is an element in a layer of the brain that processes input signals.)
The number of neurons you have in your net depends mostly on how many categories you want to classify things into. For example, if you wanted to identify cats and dogs, you would need more neurons for the category ‘animal’ than you would for ‘not animal.’
Once you have determined how many neurons you need, you must decide how many layers you will have in your net. More layers mean bigger nets, which may require more data to train, and possibly higher memory requirements too.
