Artificial intelligence (AI) is a branch of computer science which deals with building machines that are smart and capable of performing tasks that require human-like intelligence. Most AI systems rely heavily on machine learning, deep learning, and natural language processing (NLP). John McCarthy coined the term artificial intelligence in 1956. AI has become more popular today because of advanced algorithms and improvements in computing power, storage, and availability of large volumes of data to train and test the algorithms. The problem is that people are extrapolating many unrealistic expectations from the initial successes of AI, without recognizing many constraints surrounding their achievement. Human intelligence existed since the dawn of humanity and is further honed by millions of years of evolution. AI is merely 70 years old and is a product of human intelligence. So, this essay will argue that AI may not equal to human experts. It is too early for the human to go down in defeat to AI.
Today, people are misled by AI because of a phenomenon known as the Eliza effect. Eliza is an early NLP program created in 1966 at MIT. Eliza responded to people’s typed statements in the manner of a Rogerian psychotherapist. The computer running Eliza program was executing some very simple logic. But the people interacting with it ascribed emotional intelligence and empathy to all Eliza’s replies. The same phenomenon is happening today in our reactions to the successes of AI. People overestimate the achievement of AI and underestimate human performance because we do not take a second to think about how much humans already know.
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All aspects of the human brain cannot be simulated by a machine. From Buddhism perspective also, it is believed that human possesses an extraordinary knowledge and wisdom which cannot be matched by anything in the universe. Human knowledge is infinite like a universe; every second, new knowledge is acquired. Unlike a human, AI systems can only do things effectively, efficiently, and precisely in what it is programmed to do. Nothing more, nothing less. Human expertise that is compared with an AI machine one second ago is not the same after that second. He would have learned new thing in that split second. AI machine is the same; if a new learning mechanism is to be added into AI, it has to be redesigned, coded, and implemented in the algorithm. Consider a hypothetical example of an AI robot preparing Emadatshi rice (the unofficial national dish of Bhutan) menu. Let us compare an AI robot and Bhutanese top chef who is curious to learn new things in cooking. If we make AI robot prepare Emadatshi rice 100 times, it is going to prepare the same menu with the same taste and flavour. On the other hand, the menu prepared by the human chef today will be the same as yesterday, but the taste and flavour will be different. He would have learned on how to improve the menu just like all chefs do. An AI robot is simply going to prepare the menu as per the algorithm programmed without putting his heart, soul, and emotions. However, a human chef would do what AI robot lacks. Unlike human, an AI robot does not have a self-learning capability. It has to depend on the developer or it is already programmed to prepare in different ways.
In the paper, “A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis”, the authors suggest AI equal human expert in medical diagnosis. To claim that AI equal human which is as good as saying human is outwitted by AI, full real-world testing in every field is required. Even if AI is able to diagnose better than a human expert, the dynamics are different. For example, a student can learn a complete subject from YouTube, but learning from a dedicated teacher is different. The same can be applied to a patient and a human expert. Today people live longer, stay in different geographical locations, and are going to experience different types of diseases that were not earlier detected and programmed into an AI system. New detection can be easily read by human experts and accordingly carry out the treatment with presence of mind and experiences gained from working with the prevailing diseases. Experts have also warned that the latest finding is based on a small number of studies and consists of methodological deficiencies.
Humans are optimized for learning unlimited patterns and then selecting the patterns we need to apply to deal with whatever situation we find ourselves in. AI system is better at matching only known patterns. For example, Booking.com , a travel e-commerce company used machine learning to autonomously tag images. But the problem Booking.com encountered was that those services don’t tag images in a way that’s useful in the Booking.com context. They may identify attributes such as ‘ocean’, ‘nature’, ‘apartment’, but Booking.com needs to know whether there’s a sea view, is there a bed in the room, and so on. The back-end developers had to train the machines to work with a more detailed set of tags that matches their specific context. If the task was given to image tagging human expert, it would have tagged the images flawlessly as per the requirement.
So, with the aforementioned discussions, it is best to argue that nothing can take over the human brain. Human expert has a success story against AI. For example, Harish Natarajan, a champion debater beat IBM’s AI debating system dubbed ‘Miss Debater’ and restored everyone’s faith in humanity. One of the reasons Harish was able to outwit the AI in the 25 minutes debate was the emphasis he gave to ‘emotion’ during his arguments. He said, “Emotion elevates the importance of what you’re saying. There were moments when even the machine was trying to evoke emotion. But I did have an edge because, when I talk about experiences, it comes across as more genuine partly because… well, I’m not a machine.” People should not be carried away by the initial success of AI. An AI can be compared to a human expert, but may not equal a human expert.
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