It is common for many people to use the terms Artificial Intelligence (AI) and Machine Learning (ML) indistinctly, without considering that they are actually different. The confusion occurs probably because ML is a specific type of AI, that is, ML is a subset of AI. The illustration below shows the relation between them.
ML is contained within the universe of AI. In other words, talking about ML means indirectly talking about AI. However, talking about AI does not necessarily imply talking about ML.
Although both have many similarities, they are not the same. In the world of app development it is important to learn to differentiate them correctly in order to communicate properly (especially if you don’t want to confuse developers) and to understand how they can help improve your app. This post addresses some of the main differences between AI and ML so that you can understand the characteristics and functionalities of each.
The Ability to Learn
Both AI and ML are used to help solve complex problems. AI is mostly used to perform tasks or make decisions. Meanwhile, ML is substantially used to maximize the performance of a given task. More important than the problems they solve is how they solve them; this is where ML’s ability to learn stands as a major differentiator. On one hand, AI solves problems by simulating human intelligence through a set of rules. On the other hand, ML seeks to learn from data in order to make its own rules and solve problems. It’s best to understand this difference by imagining a computer scientist saying “instead of telling the computer what to do (AI), I will teach it how to think (ML).”
AI is “Bigger” than ML
There are various ways in which AI can emulate human intelligence. One of the ways to do this is through ML, but it is not the only alternative. Some types of AI are not capable of learning and are therefore not referred to as ML. AI, at its core, consists of an algorithm that emulates human intelligence based on a set of rules predefined by the code. These rules don’t only use ML methods; other alternatives like Markov decision processes and heuristics exist.
ML takes a different approach to some AI techniques while still being a part of the whole. As I’ve mentioned, it doesn’t just follow the rules of an algorithm. Instead, it creates its own algorithm and rules through the ability to learn. In practical terms, ML is a particular AI technique in which the algorithm is able to learn in order to emulate human intelligence rather than just follow rules.
Similar Origins, Different Paths
This is a minor difference between AI and ML, but it is worth mentioning. Both concepts were coined around the same time by computer scientists experimenting with new developments during the 40s and 50s. Since then, AI has evolved considerably, although only in recent decades has it become more powerful and accessible. The idea of ML, although not new, has only come to fruition more recently than AI as a result of the availability of powerful data analysis tools as well as to large amounts of data, known as Big Data, that have become available thanks to the Internet).
Intelligence vs. Knowledge
There is an important difference that often goes unnoticed even by the most experienced developers, and the reason is that it is outside the domain of computer science. It is the fact that AI pursues intelligence, while ML pursues knowledge. This is a purely philosophical problem, and as you might have expected of a philosophical conundrum, there is no consensus as to what intelligence and knowledge mean. In an attempt to define them, knowledge can be understood in a simplistic way as justified-true-belief. Intelligence can be defined as the ability to make use of knowledge. As intelligence contains knowledge, AI contains ML.
Success vs. Accuracy
AI is associated with success, while ML to accuracy. Success refers to getting the job done, while accuracy to how a measurement relates to a specific value (do not confuse with precision). An AI algorithm that works without ML can be said to be successful in terms of how it achieves a given task. An AI algorithm that works with ML can be said to be successful and accurate. Accuracy in ML can be improved depending on the quality of the data.
Newness vs. Past
One last difference worth mentioning is that AI focuses on how to solve old and new problems. Because AI algorithms seek to emulate human intelligence, they can target problems for which there is no data. ML, on the contrary, focuses exclusively on problems that have already occurred, or for which data is available. This is due to its dependence on data in order to modify its algorithm. Although more complex AI and ML algorithms are constantly being developed, it is still unclear up to what extent a computer will be able to learn about that which hasn’t occurred.
I’ve discussed various differences between AI and ML in the hope of making clear that, although they have similarities, both are different. The most important of these differences is probably that ML, as a subset of AI, focuses on solving problems strictly through learning from the available data, while AI, in general, does not necessarily depend on data.
With this crash course on the differences between AI and ML you can now contact your quality development partner and start working on integrating the latest HiTech technologies to your app. At Koombea we’d be more than happy to help! Contact us for a free consultation.