AI is a huge hype nowadays and people benefit from it every day from Netflix to music recommender systems, Google maps, Zomato, and many more applications that are being powered with AI. It is really funny because not many people actually know what it means or they try to tell people that what they have created is not artificial intelligence but it actually is.
On the other hand, some tech organizations are deceiving customers by proclaiming to use machine learning (ML) and artificial intelligence (AI) on their technologies while not being clear about their products’ limits. Not long ago, TechTalks, also stumbled upon misuse by companies claiming to use machine learning and advanced artificial intelligence to gather and examine thousands of users’ data to enhance user experience in their products and services[1].
Sadly, there’s still much confusion regarding what actually is artificial intelligence and what exactly is machine learning. Usually these terms are in use interchangeably.
It’s time to Educate ourselves. Let’s go through some of the main difference of ARTIFICIAL INTELLIGENCE VS MACHINE LEARNING.
ARTIFICIAL INTELLIGENCE VS MACHINE LEARNING
ARTIFICIAL INTELLIGENCE
Now the formal definition of Artificial Intelligence is “The effort to automate intellectual tasks normally performed by humans”.
Now that’s a fairly big definition, what is considered as an intellectual task and this doesn’t help much. So, let’s start from the very beginning to understand what actually artificial intelligence is and how it evolve. Around the 1950’s there was a question ask by scientists and researchers “Can computers think?”, “can we get them to figure things out?”, “can computers do their own thing?” and so on.
Artificial intelligence requires the belief that the process of human thought can be mechanize. thus, AI was just predefine set of rules where programmers feed the computers with all the bulk of instructions. E.g., a game of chess.
history of aI
AI was born at The Dartmouth Conference of 1956 organize by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. The proposal for the conference assertion: “every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it”.
At the conference Allen Newell and Simon debuted the “Logic Theorist” and McCarthy persuaded the attendees to accept “Artificial Intelligence”. The 1956 Dartmouth conference was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI[2].
Artificial intelligence (AI) has become the overarching discipline that covers anything related to making machines smart. Whether it’s a robot, a fitting room, a car, or an application, if you are making them intelligent, then it’s AI. Machine Learning (ML) is commonly used along with Artificial Intelligence but they are not the same thing.
MACHINE LEARNING
ML is a subset of Artificial Intelligence. Machine Learning refers to systems that can learn by themselves. Systems that get smarter and smarter over time without any human intervention. Most of the AI work involves ML because intelligence requires knowledge, and learning is the best way to get it.
Deep Learning
Deep learning is a class of mL algorithms that use complex multi-layered neural networks, where the level of abstraction increases gradually by non-linear transformations of input data.
An Artificial Neural Network (ANN) that is build up of more than three layers.
an input layer, an output layer and multiple hidden layers – called a ‘deep neural network’,.
this is what underpins deep learning. A deep learning system is self-teaching, learning as it goes by filtering information through multiple hidden layers. The image below captures the relationship between AI, ML, and Neural Networks and Deep Learning[3].
The core principle of Machine learning is that machines-computers take in data and predict the required output by “learning” for themselves. It is a method of training algorithms such that they can learn how to make accurate decisions mostly. Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information so as to predict with greater accuracy.
Machine Learning is basically build up of 3 things:
- Datasets. They are thus simply the collection of different types of data to train our systems on.
- Features. They are the key to our solution. They tell our machine on what they need to focus on.
- Algorithm. They define the accuracy or speed of getting the results. Sometimes in order to achieve better performance, we combine different algorithms, like in ensemble learning.
though, Tom Mitchell in his book Machine Learning[4] provides a classic definition of ML:
“The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.”
conclusion
In statistical terms “Machine Learning is to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”
So, again, AI is the learning, understanding, and using the knowledge learn to carry out one or more tasks or goals. ML is thus the process by which machines learn from data in order to be able to do things like make predictions or recommendations.
so, I hope this was helpful to understand the difference between ARTIFICIAL INTELLIGENCE VS MACHINE LEARNING.
Resources:
[1] Why the difference between AI and machine learning matter – Ben Dickson,TechTalks https://bdtechtalks.com/2018/10/08/artificial-intelligence-vs-machine-learning/
[2] History of artificial intelligence – From Wikipedia, the free encyclopedia- https://en.wikipedia.org/wiki/History_of_artificial_intelligence
[3] How Do Deep Neural Networks Work? https://hackernoon.com/how-do-deep-neural-networks-work-gg183xp8
[4] Machine Learning by Tom Mitchell, McGraw Hill, 1997.
written by: Aishwarya Singh
Reviewed By: Krishna Heroor
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