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Artificial Intelligence and Machine Learning are two terms related to the world of computer science that can be heard a lot these days. These technologies are helping to bring about a considerable change in different fields today. Be it medical sciences, meteorology, robotics, understanding customer perspectives or scientific developments; these fields are offering an excellent way to move forward without letting technology stagnate.
However, many of us get confused about these terms. Machine Learning is about making informed decisions and isn’t that a form of intelligence, or rather, Artificial Intelligence? Then why do they have different fancy names? Let us find out what these terms mean and how they differ from each other.
Let us first start with the way they are defined to make an educated guess about how they might differ from each other.
- Artificial Intelligence – The term is comprised of two words – ‘Artificial’ and ‘Intelligence’ both of which make complete sense in themselves. While ‘artificial’ can be deduced as something that cannot be found in Nature or has been designed by human beings from natural sources, ‘intelligence’ refers to the general ability of thinking or the capacity to reason. Natural intelligence is something that is found in human beings and other animals in their capacity to interact with the environment. Some people misinterpret the fact that Artificial Intelligence is a system. But in fact, it is not a system. Artificial Intelligence is integrated into the system. One way to define Artificial Intelligence could be top say that it is the study of how the computers and various systems can be trained to perform actions that can be done by the human beings better. Thus it means adding human capabilities to the machine in the sense of intelligence.
- Machine Learning – ML is something where the machine learns by itself. It does not need to be explicitly programmed every time it receives a different output. It should be able to learn from past experiences and act on its own. It is an application of AI, and this is the reason why people get confused at times. It automatically learns and improves from the system. A program can be generated by integrating only the inputs and outputs.
It can be said in a way that Artificial Intelligence is the broadest way one could think about advanced intelligence in computers. Anything and everything that the machines could do would be counted in artificial intelligence. At an Artificial Intelligence Conference that was held in Dartmouth, 1956, it was described that every aspect of learning or any feature of intelligence that can be simulated in a machine could be described as Artificial Intelligence.
If you take instances, Artificial Intelligence could refer to the ability of a computer program to play a game of chess. It could even be a voice recognition system like Alexa from Amazon, which interprets and responds to speech. This technology is sometimes classified into three different groups – Narrow AI, artificial general intelligence (AGI) and superintelligent AI. There has been AIs like Deep Blue from IBMK and Alpha Go from google Deepmind that has been successful at defeating the most expert players at their games. These AIs are skilled at just one particular task.
General Artificial Intelligence would be at the same level as human beings and perform a lot of different tasks.
Superintelligent AIs can take things to a different level. They would be smarter than the best human beings in every possible field and would act as a superior mind. It would have social skills, scientific creativity, and general wisdom as well. It would only happen when the machines could outsmart us.
Machine Learning is just a subset of AI, where the core notion is that the machine would be able to take data and learn for them without human intervention. It is currently the most poignant weapon in the AI toolkit for most businesses. The ML systems would be quickly able to apply the knowledge from the large training datasets to speech or facial recognition, translation, object recognition and other similar tasks. This is unlike the hand-coded system which has a set of specific instructions. Machine Learning would allow a system to learn to recognize the patterns and make the predictions on its own.
Deep Blue and DeepMind were both types of Artificial Intelligence. Deep Blue was based on rules and was dependent on programming and was hence not a form of Machine Learning. DeepMind, on the other hand, beat the world champion in Go just by training on large datasets. The large companies today machine learning platforms for other businesses today and they can benefit from these.
Let us look at some of the key differences between the two:
- Artificial Intelligence aims to increase the chances of success and not accuracy, while Machine Learning focuses on increasing efficiency and does not care about success.
- The goal of AI is to simulate natural intelligence in solving complex problems, while the goal of ML is to learn from data to maximize machine performance.
- Artificial Intelligence is about making decisions, while Machine Learning allows the systems to learn new things from data.
- Artificial Intelligence would make a system to mimic human beings and respond and behave accordingly. Machine Learning, on the other hand, is involved in creating self-learning algorithms.
- AI would be going for the optimal solution in a case, while ML would not care about the optimality.
- While Artificial Intelligence would lead to wisdom or knowledge, Machine Learning would lead to knowledge.
This clears the confusion most people seem to have between Artificial Intelligence and Machine Learning. Machine Learning is just a subset of Artificial Intelligence in the same way that knowledge leads to wisdom and intelligence.
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