The term artificial intelligence (AI) is used everywhere. It is used in many verticals, and it is unquestionably powering many sectors such as healthcare, warfare, helping people write books, building resumes and more. However, it is said to be used in things which have no implementation of AI at all. There is hype and bluster revolving around AI. For example, a dental healthcare company claims to have AI in their toothbrush as it gives feedback regarding brushing for the right amount of time in the right places. Simply put, there are sensors involved to find out where the brush is but it cannot be called artificial intelligence.
AI can be tricky for non-experts. It is overly inflated and mistaken as a computer multiple times smarter than a human. Experts call it artificial general intelligence. Creating something similar way in the future is possible. Rather than AI, machine learning is an interesting topic which is a subfield of AI. Machine learning facilitates computers to learn on their own. However, it raises a much bigger question.
As an illustration, a company wants to create a program to recognize cats. It feeds the system with data like a cat has pointy ears and they are furry. Problems arise when a photo of a tiger is presented in front of the program. It is better to let the machine teach itself. The developer can upload a huge collection of cat photos and allow the application to identify the pattern in what it observes. It gets confused at first, but after frequent testing, the program learns what a cat looks like.
This method requires a lot of tinkering and smarter ways to ingest data along the way, but a developer never has to program it. The developer doesn’t have to guide it to look for a particular object. It can spot patterns that humans might miss. As the program needs data which is in binary language, the training of the program is limitless because the modern world is full of data. However, the quality of data is vital. With a slight twitch in the data, the program might pick up a biased view of the world.
According to experts, the research on AI has reached a plateau. There are vast numbers of avenues to explore with the current level of knowledge. With multiple implementations of the technology, it is going to get normalized fast. Every product in the future will have machine learning embedded in it, and it will go unnoticed. Currently, artificial intelligence and machine learning are still new to the masses.
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