Notes About Artificial Intelligence (AI) From Dr. A S Ramasastri, Director, IDRBT

Intelligence is one of the defining characteristics of being human and it is required to perform various functions including linguistic, spatial, mathematical and emotional. From a purely practical point of view, intelligence can be defined as the ability to absorb and learn from experiences; it is the ability to cope with and deal with difficulties and adapt to new situations.

Learning is the process of acquiring new knowledge, knowledge, skills, values, attitudes and preferences. The ability to learn is possessed by plant life in a very limited way, animals to a greater extent, and humans to a greater extent. Humans’ ability to acquire knowledge stopped at birth (may have started earlier) and continues to be due to continuous interaction with humans and the environment. Some knowledge is instant to acquire, given with the resource of the use of a single event (like burning due to fire), but the knowledge of a kind of one-of-a-kind acquirement comes from repeated reviews.

The brain is considered the seat of knowledge acquisition among humans. It is the most complex organ with the human body; composed of about 86 billion neurons that communicate in trillions of connections, referred to as synapses; with the help of which it reviews and learns the field. Thoughts receive input, sort, and store data, analyze, create indexes and links, and also retrieve it in favor of all associated information. Despite the growth in neurosciences, the serious adder of intelligence and knowledge acquisition performance with the resource of the use of thoughts, remains with unknown limits.

Artificial Intelligence (AI), in the estimation of natural intelligence displayed with the resource of the use of thoughts, is intelligence with the resource of the use of machines. The term AI is used to provide an explanation for machines that attempt to mimic cognitive functions with the resources humans use, as well as knowledge and troubleshooting. Yet it is hard to define what exactly AI is.

The AI ​​effect highlights the trouble at the forefront of defining AI. In keeping with the famous Tesler’s theorem, “AI is not something talked about”. AI is a changing goalpost. There exist plenty of examples of the AI ​​effect in action. For example, optical character popularity has emerged as a simple age and is often left out of AI discussions.

This quickly modified the concept as a device that can beat a grandmaster at chess is the epitome of AI. Deep Blue accomplished this feat in a 1997 competition with chess grandmaster Garry Kasparov. Then the goalposts moved and the game AI was defeated to play Go. (And it did so in 2016 at the same time that AlphaGo beat Lee Sedol in four out of five games.)

The chatbot that appeared to be Eleven Eleven was modified to make it easier to talk to you. But it is no longer considered so because it does not recognize the purpose behind your messages. The fact that an AI sometimes accomplishes a modern achievement, this achievement is no longer a benchmark. Current AI capabilities can encompass areas like strategic games, self-maintaining cars, and military simulations. And possibly some important areas in banks like fraud detection, risk management, and customer behavior.

Banks have been on the leading edge of adopting more modern technology for the last few decades. Many of the simple gaming tasks have already been handed over to machines. There have been more unique enhancements in character interfaces, price channels, internal controls, and useful dashboards. They have also used AI related systems like robotic technology automation, robot receptionists, chatbots, and tools to gain knowledge of technologies. But such adoptions can meet their own personal questions on ethics.

The ethics of developing artificial beings that have the intelligence of humans have been on the minds of humans for quite some time now. Some humans view AI, if it advances steadily, as a threat to humanity. Some fear that AI, in the assessment of previous technological revolutions, may need to risk mass unemployment. Thus far events in human information have sufficient proof to allay such fears. Humans have consistently been a success in paying attention to temporary difficulties.

However, at the same time as absorbing AI in banking, it is vital to take top adequate precautions to ensure the security of the system. Even more so, it may be very essential to assemble internal controls to ascertain liability and responsibility in case of massive errors creeping into various AI managed functions of banking. The entire environment wants to deal with issues related to AI in banks.

The ecosystem for the banking era includes the government, regulators, directive institutions, most important era companies, rising financial era companies (FinTech), further the banks themselves. The need for close coordination between academia, IT industry, and banks is currently the biggest challenge in AI adoption in banking worldwide. The e-book in your hand is a testament to such collaboration.

The companies of the banks, Microsoft, and IDRBT, who put forth their high-quality efforts to deliver this primer on AI, all deserve praise. I am sure the primer will provide a remarkable reference for all banks in their AI journey.

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