Machine learning and Artificial Intelligence: Same or different?
Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion.
Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world.
In short, the best answer is that Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.
And, Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
Artificial Intelligence: The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non-natural thing and Intelligence means the ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system. There can be so many definitions of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better. ”Therefore It is an intelligence where we want to add all the capabilities to a machine that humans contain.
Machine Learning: Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provides a systemwith the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learner’s performance at the task in the class as measured by P improves with experiences.”
The key difference between AI and ML are:
| ARTIFICIAL INTELLIGENCE | MACHINE LEARNING |
| AI stands for Artificial intelligence, where intelligence is defined as the acquisition of knowledge, ability to acquire and apply knowledge | ML stands for Machine Learning which is defined as the acquisition of knowledge or skill |
| The aim is to increase the chance of success and not accuracy | The aim is to increase accuracy, but it does not care about the success |
| It works as a computer program that does smart work | It is a simple concept where a machine takes data and learns from it |
| The goal is to simulate natural intelligence to solve complex problems | The goal is to learn from data on certain task to maximize the performance of machine on this task. |
| AI is decision making. | ML allows system to learn new things from data. |
| It leads to development of a system to mimic humans to respond & behave similar to them in any situation | It involves creating self-learning algorithms, and does not aim to mimic human behaviour |
| AI will go for finding the optimal solution. | ML will go for any solution whether it is optimal or not. |
| AI leads to intelligence or wisdom. | ML leads to knowledge. |
Artificial Intelligences – devices designed to act intelligently – are often classified into one of two fundamental groups – applied or general. Applied AI is far more common – systems designed to intelligently trade stocks and shares, or manoeuvre an autonomous vehicle would fall into this category.
The Rise of Machine Learning
Two important breakthroughs led to the emergence of Machine Learning as the vehicle which is driving AI development forward with the speed it currently has.
One of these was the realization – credited to Arthur Samuel in 1959 – that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves.
The second, more recently, was the emergence of the internet, and the huge increase in the amount of digital information being generated, stored and made available for analysis.
Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings, and then plug them into the internet to give them access to all of the information in the world.
Neural Networks
The development of neural networks has been key to teaching computers to think and understand the world in the way we do, while retaining the innate advantages they hold over us such as speed, accuracy and lack of bias.
A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain.
Essentially it works on a system of probability – based on data fed to it, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” – by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.
Machine Learning applications can read text and work out whether the person who wrote it is making a complaint or offering congratulations. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood. In some cases, they can even compose their own music expressing the same themes, or which they know is likely to be appreciated by the admirers of the original piece.
These are all possibilities offered by systems based around ML and neural networks. Thanks in no small part to science fiction, the idea has also emerged that we should be able to communicate and interact with electronic devices and digital information, as naturally as we would with another human being. To this end, another field of AI – Natural Language Processing (NLP) – has become a source of hugely exciting innovation in recent years, and one which is heavily reliant on ML.
NLP applications attempt to understand natural human communication, either written or spoken, and communicate in return with us using similar, natural language. ML is used here to help machines understand the vast nuances in human language, and to learn to respond in a way that a particular audience is likely to comprehend.
