Artificial Intelligence or AI and Machine Learning or ML are two very hot keywords right now, and it would often seem to be utilized interchangeably. Still, they aren’t the same, but the perception that they could lead you sometimes leads to confusion. So I thought it would be worth writing a piece for explaining the difference.
Both terms crop up very frequently when the topic would be about Big Data, analytics, as well as the broader waves of technological change that would be sweeping through our world.
The best answer to define the same would be that Artificial Intelligence is the broader concept of machines that would be able to carry out-tasking so that we would consider smart. In contrast, Machine Learning is regarded as a current application of AI-based on the idea that we should be able to give machines access to data and let them do the learning for themselves.
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Early Days
Artificial Intelligence has been around for a long time; the Greek myths would be consisting of the stories regarding the mechanical men designed for mimicking our behavior. Very early European computers would have conceived as logical machines. By reproducing capabilities like performing basic arithmetic and memory, engineers fundamentally observed their job as attempting to create mechanical brains.
As technology and, more importantly, our understanding of how our minds would be working has progressed, our concept of what would constitute AI would have changed. Rather than adding an increment to the complex calculations, working in the field of AI would be concentrated on mimicking human decision-making processes and carrying out tasks in ever more unique ways.
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Artificial Intelligence devices are formulated to act intelligently and are often classified into two fundamental groups, which would be general or applied. Applied AI is a far more common system crafted for performing trade stocks intelligently and shares, or maneuver an autonomous vehicle would be falling into this category.
Neural Networks – Artificial Intelligence And Machine Learning
Generalized AIs systems or devices which could, in theory, handle any task are considered to be less common, but this is where some of the most exciting advancements would be happening today. It is also considered the area which has led to the development of Machine Learning. Often referred to as a subset of AI, it is considered more accurate to think of it as the current high-tech.
The Rise of Machine Learning
Two critical breakthroughs would have led to the emergence of Machine Learning as the vehicle which would be driving AI development forward with the speed it currently has.
One of these was the realization, which was credited to Arthur Samuel in 1959, that rather than teaching computers everything they are required to know regarding the world and how to carry out tasks, it might seem possible for them to teach about learning for themselves.
The second, more recently, would be the emergence of the internet and the massive increase in the amount of digital information being stored, generated, and made available for analysis.
Once these innovations were in place, engineers would be able to realize that rather than teaching computers as well as machines how to do everything, it would be far more efficient for coding them to think like human beings, and later plug them into the internet so that they could have access to all of the information in the world.
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