AI vs. ML. It seems that these two are connected, especially when it comes to their application in computer science and software development. But is one a subset of the other? Do we use the same tools to construct the system for both of them? What are the differences between AI and ML and why are they important? Let’s try to get to the bottom of the issue together.
We are pretty sure that there was a time when you tried to understand all the acronyms surrounding Artificial Intelligence, be it ML, DL, or AI as such. Don’t be ashamed to accept that you failed to do that. In our previous article, we made an effort to draw the dividing line between Deep Learning and Artificial Intelligence. This time we’ll focus on the difference between AI and ML.
AI, or Not AI: That Is the Question!
Artificial Intelligence is a broad term that refers to advanced machine intelligence. At the Dartmouth Summer Research Project on Artificial Intelligence back in 1956, this technology was described as follows: “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”.
Artificial intelligence can refer to anything from computer programs for playing chess to speech-recognition systems such as the Amazon Alexa voice assistant. If taken in a broader and more general sense, AI systems can be divided into three groups: limited artificial intelligence (Narrow AI), artificial general intelligence (AGI), and superintelligent AI.
IBM’s Deep Blue program, which beat Garry Kasparov at the chess game in 1996, or Google’s DeepMind’s AlphaGo program, which beat Go world champion Lee Sedol in 2016, are examples of narrow AI capable of solving one particular problem. This is its main difference from AGI), which is on a par with human intelligence and can perform many different tasks.
Superintelligent artificial intelligence is one step ahead of humans. Nick Bostrom describes it this way: it is “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills”. In other words, this is when machines outsmart us.
Let’s Watch the Machine Learning!
Machine Learning is one of the branches of artificial intelligence. The basic principle is that machines receive data and “learn” from it. It is currently the most promising AI-powered business tool. Machine learning systems quickly apply knowledge gained from training on large datasets, which enables them to excel in tasks such as face recognition, speech recognition, object recognition, translation, and many others. Unlike programs with hand-coded instructions for performing specific tasks, machine learning allows the system to learn to recognize patterns and make predictions on its own.
While both Deep Blue and DeepMind are examples of the use of artificial intelligence, Deep Blue was built on a pre-programmed set of rules so that it has nothing to do with machine learning. DeepMind, on the other hand, is an example of machine learning: the program beat the Go world champion by training itself on a large dataset of moves made by experienced players.
Is your business interested in integrating machine learning into its strategy? Amazon, Baidu, Google, IBM, Microsoft, and others already offer machine learning platforms that enterprises can use to the best of their abilities.
But What is the Essential Difference between AI and ML after All?
Artificial Intelligence (AI) is an umbrella that spans over other fields such as image processing, cognitive science, neural networks, and more. Machine learning is also part of this umbrella. This is a subset of artificial intelligence.
AI deals with more general problems of system automation. This automation can be done using any field as mentioned above, which includes image processing, neural networks, machine learning, or more. AI is about building smart machines, systems, and other devices, giving them the ability to think and perform tasks like we, humans, do.
Machine learning enables your computer to learn from the external environment. This external environment can be in the form of sensors, electrical components, external storage devices, and many others. What ML does, based on user input or user request, the system checks if it is present in the knowledge base or not. If present, it will return the result to the user associated with that query, but if it was not saved initially, the machine will examine the user input and improve its knowledge base to provide better value for the end-user.
ML and AI are completely different. AI is more general, while ML is more specific, which deals with classification, clustering, forecasting, and other operations. AI deals with images, multimedia data, neural networks, expert systems, and other fields that work depending on the type of data you have and what the end result should be.
Additionally, machine learning is a field of artificial intelligence that deals with the creation of software to predict results more accurately without explicit programming. Various algorithms are used on a dataset to predict the future. Machine learning develops from the study of pattern recognition theory and computer theory of artificial intelligence.
For its part, AI is a computer program that is able to effectively solve tasks assigned to it without a clear algorithm, using the “knowledge” and “experience” obtained during training or as a result of solving previous tasks. Artificial intelligence can be based on neural network technology or on some other mathematical/logical algorithms.
Below is a brief summary of key differences between ML and AI. Enjoy!
Want to know more about AI or ML and the difference between the two? Contact us and we’ll be glad to discuss your future projects and share some ideas.