AI has been a big focus and a booming concept for quite some time now and there seems to have been a recognizable revolution that took place in the IT world around 2010.
IT companies either started using AI, ML in their own processes for operational efficiency or they are using AI/ML to power their products. Another interesting observation is that people or organizations have started using AI (Artificial Intelligence) and ML (Machine Learning) interchangeably or synonymously in their presentation; this is the topic that we are investigating through this blog, “are they really the same?”
“According to the survey from London venture capital firm MMC, 40 percent of European start-ups that are classified as AI companies don’t actually use artificial intelligence in a way that is “material” to their businesses.”
AI has evolved but it took a long time to mature into where it stands today. The Core concepts are still the same, but it has matured immensely. I can recollect using LISP as a coding language from my college days and how similar results are achieved using “python” or “R” today. Before the AI industry started to take shape, the field of instrumentation was what had bought automation into the industry then.
So, is there a link of automation with AI?
The primary objective of AI is to bring in automation in actions and decision making. For a machine to be able to take a decision it must think like a human and to think like human it has infer a conclusive decision and to do that it has to learn like humans do from experience. Thinking of how complex a human mind is, it is amazing to see that a machine can make decisions like a human.
There is a wonderful article about AI titled “Why AI is Harder Than We Think,” where Mitchell articulated common fallacies about AI that cause misunderstandings not only among the public and the media, but also among experts.
Back to the topic – Are AI and ML the same thing? What is machine learning?
According to Tom Mitchell,
Machine learning is for computer programs to be able to improve themselves through their own experience.
ML is prevalent anywhere AI exists, but it has some specific use cases with which we may already be familiar with like predictions or automating the tasks in terms of decision making. ML has evolved from the study of pattern recognition from the ingested data as input and output as it learns through the algorithms through interactions and iterations. Let’s try to understand this as a simple scenario.
You are planning for an event based on certain data or information that you have, and you would want to know if it is going to rain or not on that day. ML will help you predict based on historical data available for last 5 to 10 years. ML can learn and infer a prediction based on the pattern it can deduct from the ingested data.
What is Artificial Intelligence?
According to Andrew Moore, Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.”
AI is a science, and its scope is vast. In a single sentence to best articulate is make machines work, think, act and decision making like human, which is the objective of the generalized AI. According to Mitchell, common sense is the human factor missing from the state-of-art AI system. The lack of common sense alternatively requires huge amounts data as fundamentals on which the AI system is based.
In conclusion ML and AI are not the same thing but they are correlated. ML is one of the means to achieve AI (narrowed AI) objective. Hope this article helps one to understand the right context of the application of ML or AI in the solution scope. AI has evolved greatly. AI has evolved in Narrowed AI than in General AI. Narrow AI is basically a focused area of AI application and an example of it can be “disease detection” based on images in the health sector, also specialized in games like chess and so forth. We see remarkable accuracy in computer visions, which is AI driven by image detection, but it is still far from general AI. Natural Language processing has also progressed tremendously. AI is still researching how to use common sense like a human; once that is solved machines will be able to have open-ended conversations with a human. This is tough but the research so far is exciting.
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