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You may have heard the terms Artificial Intelligence, Machine Learning, Deep Learning and you maybe trying to figure out what they mean, and whether these terms can be used interchangeably.
I've also had the same questions when I started diving in to the field. And a recent post in the Nvidia Blog brought back the question.
So here’s a simplified explanation on how each of those terms came to be, and how they relate to each other.
Artificial Intelligence
Artificial Intelligence is the idea that machines (or computers) can be built that has intelligence parallel (or greater) to that of a human, giving them capability to perform tasks that requires human intelligence to perform.
The idea of an intelligent machine has been around since 1300 BC, and through 19th century. But the Dartmouth Conferences in 1956 is what’s commonly considered as the starting point of the formal research field of Artificial Intelligence. Since then the field of AI has gone through many ups-and-downs and has branched out into many sub fields. There has been attempts at applying AI for various fields – such as medical, finance, aviation, machinery etc. – with various degrees of success.
Around the late 1990s and early 2000s, the researchers identified a problem in their approach to AI, which was slowing down the success of AI – in order for us to artificially crate a machine with an intelligence, we would first need to understand how intelligence work. But even today, we do not have a complete definition of what we call "intelligence".
In order to tackle the problem, they decided to go ground-up – rather than trying to build an intelligence, we could look in to building a system that can grow its own intelligence. This idea created the new sub-field of AI called Machine Learning.
Machine Learning
Machine Learning is a subset of Artificial Intelligence which aims at providing machines with the ability to learn without explicitly programming. The idea is that such machines (or computer programs) once built will be able to evolve and adapt when they are exposed to new data.
The main idea behind Machine Learning is the ability of a learner to generalize from its experience. The learner (or the program), once given a set of training cases, must be able to build a generalized model upon them, which would allow it to decide upon new cases with sufficient accuracy.
Based on the approach, there are 3 learning methods of Machine Learning systems,
- Supervised Learning – the system is given a set of labelled cases (training set) and asked to create a generalized model on those to act on unseen cases.
- Unsupervised Learning – the system is given a set of cases unlabelled, and asked to find a pattern in them. Good for discovering hidden patterns.
- Reinforcement Learning – the system is asked to take an action, and is given a reward. The system must learn which actions would yield most rewards in certain situations.
By around 2010, few things happened that influenced machine learning further. With the ever advancing technology, more computing power became available, making evaluating more complex machine learning models easier. Data processing and storage became cheaper, so more data became available to machine learning systems to consume. Parallel to these, our understanding of how the natural brain works also increased, allowing us to model new machine learning algorithms around them.
All these findings propelled a new area of Machine Learning called Deep Learning.
Deep Learning
Deep Learning is a subset of Machine Learning which focuses on an area of algorithms which was inspired by our understanding of how the brain works in order to obtain knowledge. It’s also referred to as Deep Structured Learning or Hierarchical Learning.
One of the definitions of Deep Learning is,
"A sub-field within machine learning that is based on algorithms for learning multiple levels of representation in order to model complex relationships among data. Higher-level features and concepts are thus defined in terms of lower-level ones, and such a hierarchy of features is called a deep architecture" - Deep Learning: Methods and Applications
Deep Learning builds upon the idea of Artificial Neural Networks and scales it up to be able to consume large amounts of data by deepening (adding more layers) the networks. By having a large number of layers a deep learning model has the capability of extracting features from raw data and "learn" about those features little-by-little in each layer, building up to the higher-level knowledge of the data. This technique is called Hierarchical Feature Learning, and it allows such system to automatically learn complex features through multiple levels of abstraction with minimal human intervention.
"The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones. If we draw a graph showing how these concepts are built on top of each other, the graph is deep, with many layers. For this reason, we call this approach to AI deep learning." - Deep Learning. MIT Press, Ian Goodfellow and Yoshua Bengio and Aaron Courville.One of the most distinct characteristics of Deep Learning – and one that made it quite popular and practical – is that it scales well, that is, the more data given to it, the better it performs. Unlike many older machine learning algorithms which has a higher bound to the amount of data they can ingest – often called a "plateau in performance" – Deep Learning models has no such limitations (theoretically) and they may be able to go beyond human comprehension, which is evident with the modern deep learning based image processing systems being able to outperform humans.
So how does the areas of Artificial Intelligence, Machine Learning and Deep Learning relate to each other?
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| How Artificial Intelligence, Machine Learning, and Deep Learning relates to each other |
Simply put, Machine Learning is a subset of Artificial Intelligence, and Deep Learning is a subset of Machine Learning, all working towards the common goal of creating an intelligent machine.
With the capabilities demonstrated and the success achieved by Deep Learning, we may be a step closer to the ultimate goal of Artificial Intelligence – building a machine with a human level (or greater) intelligence.
Get your copy now!
Books on Deep Learning:
- Deep Learning (Adaptive Computation and Machine Learning series) - The MIT Press - Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Deep Learning: Methods and Applications (Foundations and Trends(r) in Signal Processing) - Now Publishers - Li Deng, Dong Yu
Related links:
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
http://machinelearningmastery.com/what-is-deep-learning/
Nice post
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