AI vs Machine Learning vs Deep Learning

You hear a lot of different terms bandied about these days when it comes to new data processing techniques. One person says they’re using machine learning, while another calls it artificial intelligence. Still others may claim to be doing deep learning, while “cognitive” is the favored phrase for some. What does it all mean?

While many of these terms are related and can overlap in some ways, there are key differences that can be important, and that could be a barrier to fully understanding what people mean when they use these words (assuming they’re using them correctly).

(AI) Artificial Intelligence

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:

• Speech recognition
• Learning
• Planning
• Problem solving

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

• Knowledge
• Reasoning
• Problem solving
• Perception
• Learning
• Planning
• Ability to manipulate and move objects

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach.

Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

Machine Learning

Machine learning is an artificial intelligence (AI) discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.

Machine learning facilitates the continuous advancement of computing through exposure to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in subsequent (though not identical) situations.

Machine learning is often confused with data mining and knowledge discovery in databases (KDD), which share a similar methodology.

Example :

Today's Facebook News Feed is a perfect example. The News Feed is programmed to display user friendly content. If a user frequently tags or writes on the wall of a particular friend, the News Feed changes its behavior to display more content from that friend.

Other machine learning applications include syntactic pattern recognition, natural language processing, search engines, computer vision and machine perception.

It is difficult to replicate human intuition in a machine, primarily because human beings often learn and execute decisions unconsciously.

Like children, machines require an extended training period when developing broad algorithms geared toward the dictation of future behavior. Training techniques include rote learning, parameter adjustment, macro-operators, chunking, explanation-based learning, clustering, mistake correction, case recording, multiple model management, back propagation, reinforcement learning and genetic algorithms.

Deep Learning

Essentially all of the advancements in image, speech, and text AI made in the last five years have been the result of a class of algorithms known collectively as Deep Learning. These are major advances in the general group of Artificial Neural Net algorithms which have advanced to be able to detect patterns without the prior definition of features or characteristics. They are hybrid supervised learners in that you must still show the NN thousands of pictures of cats, but without the requirement for predefining the characteristics of fur, four legs, tail, etc.

The breakthrough technology here has two parts. First, advances in the types and complexity of Neural Net algorithms. Second hardware, the ability to run these over distributed parallel systems using superfast GPU and FPGA chips on increasingly large networks of processors.

AI versus Deep Learning

It is common today to equate AI and Deep Learning but this would be inaccurate on two counts.

1. AI is broader than just Deep Learning and text, image, and speech processing. In fact AI has been around in many forms for much longer than Deep Learning, albeit in not quite such consumer-friendly forms.

Ten and even 20 years ago AI existed in the form of what was then most commonly called ‘expert systems’. As the name implied, these systems distilled large quantities of domain-specific knowledge such as disease diagnosis or how to select a particularly complex multi-featured device like airbags, and allowed non-experts to reach an ‘expert level’ conclusion. 

Originally these decision tree-like apps were built manually based on the input of large numbers of human experts. More recently they can be evolved more or less automatically using multi-objective decision tree algorithms which are part of legacy data science, not Deep Learning.

2. Deep Learning is broader than AI. Deep Learning as an evolved form of neural nets can be used to solve regular data science problems in the same way that neural net algorithms have always been used. A good example is Amazon’s current major investment in Deep Learning to create better recommenders that enhance shopping. Any segmentation or regression problem from buyer behavior to value prediction is open to the application of Deep Learning.

AI vs. Machine Learning vs. Deep Learning

AI and machine learning are often used interchangeably, especially in the realm of big data. But these aren’t the same thing, and it is important to understand how these can be applied differently.  

Artificial intelligence is a broader concept than machine learning, which addresses the use of computers to mimic the cognitive functions of humans. When machines carry out tasks based on algorithms in an “intelligent” manner, that is AI. Machine learning is a subset of AI and focuses on the ability of machines to receive a set of data and learn for themselves, changing algorithms as they learn more about the information they are processing. 

Training computers to think like humans is achieved partly through the use of neural networks. Neural networks are a series of algorithms modeled after the human brain. Just as the brain can recognize patterns and help us categorize and classify information, neural networks do the same for computers. The brain is constantly trying to make sense of the information it is processing, and to do this, it labels and assigns items to categories. When we encounter something new, we try to compare it to a known item to help us understand and make sense of it. Neural networks do the same for computers. 

Benefits of neural networks:

• Extract meaning from complicated data
• Detect trends and identify patterns too complex for humans to notice
• Learn by example
• Speed advantages

Deep learning goes yet another level deeper and can be considered a subset of machine learning. The concept of deep learning is sometimes just referred to as "deep neural networks," referring to the many layers involved. A neural network may only have a single layer of data, while a deep neural network has two or more. The layers can be seen as a nested hierarchy of related concepts or decision trees. The answer to one question leads to a set of deeper related questions.

Deep learning networks need to see large quantities of items in order to be trained. Instead of being programmed with the edges that define items, the systems learn from exposure to millions of data points. An early example of this is the Google Brain learning to recognize cats after being shown over ten million images. Deep learning networks do not need to be programmed with the criteria that define items; they are able to identify edges through being exposed to large amounts of data.

Going ‘Cognitive’

Machine learning, deep learning, and artificial intelligence all have relatively specific meanings, but are often broadly used to refer to any sort of modern, big-data related processing approach. In this respect, it’s subject to the inevitable hype that accompanies real breakthroughs in data processing, which the industry most certainly is enjoying at the moment.

But some in the industry eschew these phrases almost entirely and use their own set of words. IBM, for instance, refers to its work as cognitive computing. In fact, it went so far as to create a whole new division of the company called Cognitive Systems; its Power Systems division actually lives within Cognitive Systems (which invariably will irritate customers who want nothing but to run their ERP system in peace)

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