Artificial intelligence (AI), machine learning and deep learning are
three terms often used interchangeably to describe software that behaves
intelligently. However, it is useful to understand the key distinctions among
them.
Machine learning can lead to a
variety of automated tasks. It affects virtually every industry — from IT
security malware search, to weather forecasting, to stockbrokers looking for
optimal trades. Machine learning requires complex math and a lot of coding to achieve the
desired functions and results. Machine learning also incorporates
classical algorithms for various kinds of tasks such as
clustering, regression or classification. We have to train these
algorithms on large amounts of data.
Unlike machine learning, deep learning is
a young subfield of artificial intelligence based on artificial neural
networks.
Since deep learning algorithms also require
data in order to learn and solve problems, we can also call it a subfield of
machine learning. The terms machine learning and deep learning are often
treated as synonymous. However, these systems have different capabilities.
Deep learning uses a multi-layered
structure of algorithms called the neural network.
All recent advances in intelligence are due
to deep learning. Without deep learning we would not have self-driving cars,
chatbots or personal assistants like Alexa and Siri. Google Translate would
remain primitive and Netflix would have no idea which movies or TV series to
suggest.
Artificial intelligence describes when a
machine mimics cognitive functions that humans associate with other human
minds, such as learning and problem solving. On an even more elementary level,
AI can merely be a programmed rule that tells the machine to behave in a
specific way in certain situations.
Artificial Intelligence: a program that can
sense, reason, act and adapt.
Machine Learning: algorithms whose
performance improve as they are exposed to more data over time.
Deep Learning: subset of machine learning in which multilayered neural networks learn from vast amounts of data.
Artificial Intelligence is the broader
umbrella under which Machine Learning and Deep Learning come.
Some of the examples of Artificial
Intelligence from our day to day life are Apple’s Siri, the chess-playing computer,
tesla’s self-driving car and many more. These examples are based on deep
learning and natural language processing.
Machine Learning is a subset of artificial intelligence. It allows the machines to learn and make predictions based on its experience(data).
The field of deep learning is a special kind of machine learning which is inspired by the functionality of our brain cells called artificial neural network. It simply takes data connections between all artificial neurons and adjusts them according to the data pattern. More neurons are needed if the size of the data is large. It automatically features learning at multiple levels of abstraction thereby allowing a system to learn complex functions mapping without depending on any specific algorithm.
Types of Artificial Intelligence
Reactive Machines – These are systems that only react. These systems don’t form memories, and they don’t use any past experiences for making new decisions.
Limited Memory – These systems reference the past, and information is added over a period of time. The referenced information is short-lived.
Theory of Mind – This covers systems that are able to understand human emotions and how they affect decision making. They are trained to adjust their behavior accordingly.
Self-awareness – These systems are designed and created to be aware of themselves. They understand their own internal states, predict other people’s feelings, and act appropriately.
Types of Machine Learning
Machine learning algorithms are classified into three main categories:
1. Supervised Learning
In supervised learning, the data is already labeled, which means you know the target variable. Using this method of learning, systems can predict future outcomes based on past data. It requires that at least an input and output variable be given to the model for it to be trained.
2. Unsupervised Learning
Unsupervised learning algorithms employ unlabeled data to discover patterns from the data on their own. The systems are able to identify hidden features from the input data provided. Once the data is more readable, the patterns and similarities become more evident
3. Reinforcement Learning
The goal of reinforcement learning is to train an agent to complete a task within an uncertain environment. The agent receives observations and a reward from the environment and sends actions to the environment. The reward measures how successful action is with respect to completing the task goal.
Types of Deep Neural Networks
Convolutional Neural Network (CNN) – CNN is a class of deep neural networks most commonly used for image analysis.
Recurrent Neural Network (RNN) – RNN uses sequential information to build a model. It often works better for models that have to memorize past data.
Generative Adversarial Network (GAN) – GAN are algorithmic architectures that use two neural networks to create new, synthetic instances of data that pass for real data. A GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers.
Deep Belief Network (DBN) – DBN is a generative graphical model that is composed of multiple layers of latent variables called hidden units. Each layer is interconnected, but the units are not.
Conclusion
So AI refers to devices exhibiting human-like intelligence in some way. There are many techniques for AI, but one subset of that bigger list is machine learning – let the algorithms learn from the data. Finally, deep learning is a subset of machine learning, using many-layered neural networks to solve the hardest (for computers) problems.
