The term artificial intelligence was first coined decades ago in the year 1956 by John McCarty at the Dartmouth conference. He defined AI as the science and engineering of making intelligent machines. In a sense, AI is a technique of getting machines to work and behave like humans.
In the recent past, AI has been able to accomplish this by creating machines and robots that are being used in a wide range of fields including healthcare, robotics, marketing, business analytics, and many more. However, many AI applications are not perceived as AI. It is because we often tend to think of it as robots doing our daily course. But the truth is AI found its way into our daily lives. It has become so general that we don’t realize we use it all the time. For instance, have you ever wondered how Google can give you such accurate search results or how your Facebook feed always gives you content based on your interest? The answer to these questions is AI.
Difference between AI, machine learning, and deep learning
Now before moving further, clear a very common misconception. People often tend to think that AI, machine learning, and deep learning are the same since they have common applications. For example, SIRI is an application of AI, machine learning, and deep learning. But how are these technologies different?
AI is the science of getting machines to mimic the behavior of humans. Machine learning is a subset of AI that focuses on getting machines to make decisions by feeding them data. On the other hand, deep learning is a subset of machine learning that uses the concept of neural networks to solve complex problems. So to sum it up, artificial intelligence, machine learning, and deep learning are interconnected fields. Machine learning and deep learning aids AI by providing a set of algorithms and neural networks to solve data-driven problems. However, AI is not restricted to only machine learning and deep learning. It covers a vast domain of fields including natural language, processing, object detection, computer vision, robotics, expert systems, and so on.
Stages/Types of AI
Now AI can be structured along three evolutionary stages or you can say that there are three different types of AI.
1. Artificial narrow intelligence
Artificial narrow intelligence which is also known as weak AI involves applying AI only to specific tasks. Now many currently existing systems that claim to use artificial intelligence are operating as weak AI focused on a narrowly defined specific problem. Now Alexa is a very good example of narrow intelligence. It operates within a limited predefined range of functions. There is no genuine intelligence or no self-awareness despite being a sophisticated example of weak AI. Other examples of weak AI include the face verification that you see in your iPhone, the autopilot feature at Tesla, the social humanoid Sophia which is built at Hanson robotics, and finally we have Google Maps. All of these applications are based on weak AI or artificial narrow intelligence.
2. Artificial general intelligence
Now let’s take a look at artificial general intelligence. It is also known as strong AI. It involves machines that possess the ability to perform any intellectual task that a human being can. You see machines don’t possess human-like abilities. We have a strong processing unit that can perform high-level computations but they are not yet capable of thinking and reasoning like a human. There are many experts who doubt that artificial general intelligence will ever be possible and there are also many who question whether it should be desirable? I am sure all of you have heard of Stephen Hawking’s. Now he warned us that strong AI would take off on its own and redesign itself at an ever-increasing rate. Humans who are limited by slow biological evolution couldn’t compete and would be superseded.
3. Artificial superintelligence
This is a term that refers to the time when the capabilities of computers will surpass human beings. Artificial superintelligence is presently seen as a hypothetical situation as depicted in movies and science-fiction books where machines will take over the world. However, tech masterminds like Elon Musk believe that artificial superintelligence will take over the world by the year 2040.
Applications of artificial intelligence
Now that you know the different types of AI let’s take a look at how AI is used in the real world. From spotting an eight planet solar system that is 2,500 light-years away to composing sonnets and poems, the applications of AI have covered all possible domains in the market.
In the finance sector, JP Morgan’s G’s contract intelligent platform uses AI, machine learning, and image recognition software to analyze legal documents and extract important data points and clauses in a matter of seconds. Now, manually reviewing 12,000 agreements takes over $36,000 but AI was able to do this in a matter of seconds.
Coming to healthcare, IBM is one of the pioneers that has developed AI software perfectly for medicine. More than 230 healthcare organizations worldwide use IBM Watson technology. In 2016 IBM Watson AI technology was able to cross-reference 20 million oncology records and correctly diagnose a rare leukemia condition in a patient.
Coming to the next application. Google’s AI eye doctor is another initiative taken by google where they are working with an Indian IKEA chain to develop an AI system that can examine retina scans and identify a condition called diabetic retinopathy which causes blindness
Social media platforms
Coming to social media platforms like Facebook, AI is used for face verification. Whereas, machine learning and deep learning concepts are used to detect facial features and tag your friends. Another such example is Twitter’s AI which is being used to identify hate speech and terroristic languages in tweets. It makes use of machine learning, deep learning, and natural language processing to filter out offensive content. The company discovered and banned three hundred thousand terrorist-linked accounts, ninety-five percent of which were found by non-human artificially intelligent machines.
Google predictive search
The Google predictive search is one of the most famous AI applications. When you begin typing a search term, Google makes recommendations for you to choose from. That is AI in action. Predictive searches are based on data that Google collects about you such as your location, your age, and other personal details. By using AI, the search engine attempts to guess what you might be trying to find.
Virtual assistance like SIRI, Alexa, and Cortana are examples of AI. A newly released Google’s virtual assistant called Google duplex has astonished millions of people. It only cannot respond to calls and book appointments for you, but also it adds a human touch.
Another famous application of AI is self-driving cars. AI implements computer vision, image detection, and deep learning to build cars. These cars can automatically detect objects and drive around without human intervention. Elon Musk talks a ton about how AI is implemented in Tesla’s self-driving cars and autopilot features. Tesla had fully self-driving cars ready by the end of the year 2019 and there will be a Robotaxi version, one that can ferry passengers without anyone behind the wheel.
The bottom line
So I can go on and on about the various Artificial intelligence applications. Since the emergence of AI in the 1950s we have seen exponential growth in its potential. AI covers domains such as machine learning, deep learning, neural networks, natural language, processing, knowledge base export systems, and so on. It has also made its way into computer vision and image processing. As AI is branching out into every aspect of our lives, is it possible that one day AI might take over our lives. And if it is possible then how long will this take? Well, it may be sooner than you think. It is estimated that AI will take over the world within the next thirty years. By then I hope we develop some sort of teleportation machine that helps us escape our very own creation.
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