Artificial intelligence (AI) has become all the rage these days. Technically speaking, AI is not a new field of study. The term was coined back in 1956 when IBM researcher John McCarthy defined it as “the science or engineering of making intelligent machines”. Since then, engineers have been experimenting with different strategies to make computers that can perform tasks normally considered intelligent.
Deep learning is one such strategy. It began around ten years ago when two computer scientists at Stanford University – Yoshua Bengio and Héctor Jarrós Lú-Ampuja– experimented with neural networks for pattern recognition. Neural networks are computational models inspired by how our brains work.
They found that if they stacked several layers of smaller units together, the network would learn increasingly complex patterns through repeated training. This technology quickly became popular among other researchers because it produces impressive results without requiring much human input or supervision.
Today, almost every major tech company uses some form of deep learning. These companies use it to recognize objects, speech, and videos; automate repetitive processes; and augment or replace their existing systems. Some even apply it to tackle more fundamental problems like natural language processing and computer vision.
While there are many ways to implement deep learning algorithms, most share something common: lots of data! That’s why this article will talk about where you can get quality images, video, and text to train your own AI model.
Artificial neural networks (ANNs) are computer programs that use layers of connections to teach computers how to perform tasks or functions. These tasks can be anything, from classifying pictures as being either dog or not-dog to predicting the next move in a game of chess!
In deep learning, there is an overall goal for the system to learn, but individual neurons and neuron groups do not. Individual neurons and groupings of neurons work towards achieving this larger goal independently.
After all, each individual neuron learns on its own what features of the input data mean about the output data. For example, one may learn that vertical lines represent a “wall” while horizontal lines signify a “window.” Once it has learned these correlations, it doesn’t need to know why those patterns exist — it just knows them.
This ability to recognize familiar shapes and patterns without knowing why they’re connected makes ANNs powerful tools in the field of artificial intelligence. They have been successfully applied to speech recognition, image classification, and natural language processing. Some even claim they can outperform humans in certain domains like games.
There you have it! That is your introduction to the basics of how AI works under the hood.
Deep neural networks
Neural networks are one of the most important concepts in modern-day machine learning. Neural networks have been around for quite some time, but it was not until the early 2010s that they regained popularity as the state-of-the-art approach to many classification tasks.
Conceptually, a neural network is similar to how our brains work. The neurons in your brain do not connect directly to other neurons, instead creating connections with nearby cells via an intermediary substance called synapse.
These interconnected groups of neurons form what we refer to as “networks”. We know this because when you lose part of a limb or tissue off of a muscle, the remaining muscles will reorganize to use those connections to fulfill their function.
For example, after someone loses a finger, they may never fully regain full strength in that digit, but the body creates new pathways to make up for it. In doing so, however, the mind often doesn’t recognize the lost functionality as normal anymore. You may also notice changes in self-consciousness, like feeling like you must always be aware of your surroundings due to the missing sensor.
This analogy isn’t perfect, nor does it apply only to humans, but it can help us relate to why neural networks work.
Artificial neural networks
Neural networks are one of the most fundamental concepts in artificial intelligence (AI). Developed by MIT researcher Frank Rosenblatt, they work via what’s called “feedback.” Feedback is when an AI system compares its current state with that of the past. If it finds patterns or similarities, then it adjusts how it behaves based on those lessons to ensure similar results in the future.
The first applications of this concept were physical systems like robots, where feedback comes from sensory data (e.g., sight, touch) to determine if something is working and how to fix it. For example, when a robot needs to learn how to walk, it gets lots of input about legs moving and walking steps, so it can figure out the rest.
Neural networks do not have discrete parts like robots, but their components operate similarly. When you teach a computerized algorithm like Google’s AlphaGo to play games such as Go, for instance, it goes through many cycles of adjusting its internal thinking before achieving success. In these cases, the game master provides the input and the software learns from it.
But unlike chess engines or other programs designed to solve specific problems, advanced algorithms like deep learning ones aren’t programmed into anything at all. They develop on their own, picking up new skills along the way without anyone telling them what to do! This makes them very powerful, sometimes even surpassing humans in some areas.
Convolutional neural networks
CNNs are one of the most important concepts in deep learning. They play an integral part in many applications, including computer vision, natural language processing (NLP), and speech recognition.
CNNs work by taking input images or audio files and looking for patterns and relationships. For instance, if you have pictures of alligators, then you can use a CNN to identify whether or not there is an alligator in a new picture.
There are three main components that make up a CNN. These components are usually referred to as convolutions, pooling, and fully-connected layers.
Recurrent neural networks
Recent developments in deep learning are due to recurrent neural networks (RNNs). RNNs were first proposed in 1985 by psychologist Geoffrey Hinton at University of Toronto, then developed more thoroughly by other researchers. They work by using feedback loops or internal connections within the network that allow it to learn how information is connected together over time.
By having these loops, the system can recognize patterns not just one time, but repeatedly throughout the whole sequence. This makes them ideal for applications like speech recognition and natural language processing.
Deep neural nets with lots of layers have become the standard approach in many areas because they can achieve very complex functions with their architecture. However, there’s still some value in older architectures like convolutional neural networks (CNNs) which only look at small chunks of data at once rather than all at once like an RNN.
That said, CNNs are much faster to train than RNNs so most research has focused on developing efficient ways to use CNNs instead of investing significant effort into creating new types of NNs.
One of the most important components in AI is long term memory or learning. This component allows an algorithm to remember something for longer than just a few seconds.
Machine learning algorithms use this long term memory to learn how to perform specific tasks. For instance, there are many computer programs that can recognize handwriting now.
The software learns how to do this by looking at lots of examples of written words and diagrams and then combining these pieces into one code that it uses to identify handwritten letters and shapes.
In recent years, researchers have been able to apply deep neural networks — sets of nodes connected together like a net — to this task.
These types of nets work because they create their own internal representation of information. The network will continually compare its new input with the information stored in its long term memory to see if any patterns emerge. If so, the network updates its understanding of the input data and stores this knowledge away for future reference.
By having more and bigger memories, AI systems get better and faster at performing specific tasks over time.
Recent developments in AI are built around something called deep learning. Supervised neural networks use what’s known as short term memory to perform tasks. This is an interesting concept because it implies that, instead of having a computer program rely only on long-term memories for information, it can learn by feeding it examples and references.
A great example of this was when Google made its famous “self driving car” videos. They would show cars doing all sorts of things like changing lanes or making left turns, then they would erase everything but the car moving forward before adding some special effects and putting the narrative together at the end.
This way, the system learns how to do these things on its own without being explicitly told. It uses past experiences to make predictions about the future!
By having short term memory, computers have more room to process new information. And since most of today’s state-of-the-art AI systems employ lots of different algorithms working together, there’s also the potential for parallel processing to happen automatically.
Deep learning isn’t the only technique used in AI, but it has become one of the most promising ones. We’ll talk more about it in another article! For now though, just know that you don’t need to be a data scientist to apply it to your own projects.
Restricted Boltzmann Machines
In deep learning, one of the most important concepts is that of a restricted boltzman machine (RBM). An RBM is similar to a binary neural network, except there are no fully connected layers between your input data and the output.
In RBMs, we have some hidden neurons which take inputs from both the visible layer and the previous hidden layer. This way, the model can learn complex patterns by combining low-level features with higher level representations.
The key difference between an ordinary feedforward NN and an RBM is that in the latter case, you’re not giving the same weight to every connection. Some are set as strong weights, while others are weak.
These strong connections form what’s called a “bias” for the neuron. The strength of this bias determines whether the neuron will be activated or deactivated, depending on how much it wants the next unit to activate.
By having different strengths, the units together create a hierarchy of information that the system uses to make decisions.