AI vs Machine Learning: The Difference You Need to know
Seems like if you are envisioning a life where you can build a sphere around the sun to harness all its power, it might not be very hard to wield. The reason for this is that even researchers cannot fully perceive the power of AI and its future possibilities.
If today we could contact any civilizations akin to the humans living during the end of the last ice age, we might appear as Gods to them. That is because many gadgets used by us even today were thought to be impossible not over 100 years ago. But with the lightning pace of technological progress, we still are positive to cure cancer in the coming years.
One of the major instruments of the advancement we have at our disposal is the sheer explosion of AI applications. From guided missiles in the military to the voice searches on your android phone- these are boons of AI and Machine Learning.
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Since many people use the words artificial intelligence and machine learning interchangeably, its often for people to decipher which is which and what are key differences in their applications.
So, before we are confounded by the immense possibilities of AI and machine learning, let us first see what they are and how are these two concepts different from each other.
What do AI and machine learning mean?
AI or Artificial Intelligence is the umbrella term to define everything that helps a machine attain the consciousness that of a human being. In other words, AI is meant to do activities a human does that but with higher efficiency.
Examples of AI could be as minuscule as taking a city tour in a driverless car through the roads of Phoenix to having CIMON (an AI robot abroad the ISS) play your favorite playlist in outer space.
Machine learning as stated by Tom M. Mitchell from Carnegie Mellon University is- “The study of computer algorithms that improve automatically through experience”. Meaning while AI focuses on the overall aspect of a subject, machine learning narrows it down and focuses on any one of it and over time, improves on it.
Image this, if you process a few hundred images of dogs in a system that is ingrained with machine learning, the algorithm will be highly beneficial in detecting dogs in the future. As in past events made the system take notes on how to identify a dog. However, the system would fail if you’d ask it to recognize a cat instead. It is because the machine learning program now knows how to identify dogs but the same never encountered any system where it had a chance to look at cat images.
Differences between AI and machine learning
The same might not be the case with AI. Since AI is an amalgamation of many machine learning algorithms, it doesn’t necessarily have to stick to one menial task.
If we remove the former words in both AI and Machine learning, we are left with intelligence and learning. So, only after repetitive actions, that organism will comprehend intelligence and perhaps use it elsewhere. So, the more you learn, the greater the intelligence you will possess.
Similarly, the more improvement is given to an algorithm of machine learning, the more AI efficiency it will have. The aim of AI is to create smart machines that will simulate intelligence similar to modern day humans and improve it to solve complex problems. Machine learning teaches itself from existing data to increase performance on the same task.
In that way, they design AI for decision making but machine learning only limits itself to understand new trends from the existing data.
Also, AI goes for an optimal solution for the problem given to it. As in it has the potential to work out all the solutions and then apply the most optimum one.
Take AlphaGo (the epitome of AI created by Google’s subsidiary, Alphabet Inc.) for example which has 10 to the power 80 possible outcomes and because of that, the program could defeat the best Go player from China; Ke Jie. Not one but three times in total before retiring from the game. After every game, it gets better than the previous one.
This enraged China so much that they banned the live-stream of AlphaGo in their country after Ke Jie lost the first match. It was seen as a blow to China’s pride to lose to an American intelligent machine.
This is not the case with machine learning as it will go for the only solution present irrespective of the effectiveness of the outcome.
AI increases the chance of success while machine learning focuses on increasing accuracy. Machine learning doesn’t care about the chances of success as it can only focus on one activity.
Conclusion
Though one is a subset of the other, each of the two has its own unique ways of analyzing and processing data to do their respective activities. But neither can function without the other as they appear to be both sides of the same coin.
As our dependency on these gains of technological progress skyrockets with each passing year, it makes sense that more and more individuals would be drawn to this field. To be a witness of an age where machines possess stronger intelligence than humans is the apotheosis of the technological age.
And the best part? We are yet to herald the birth of new milestones in this field which could have drastic effects on how we live and go about our daily time tables.
In the meantime, if you are intrigued by the concepts of AI and Machine Learning then explore all its concepts in this Learn Machine Learning By Building Projects programme.
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