Technology is evolving faster nowadays, and Artificial Intelligence or AI is considered the next big thing in the future. The term AI was first referred back to in 1956 by John McCarthy, which involved many mainstream things ranging from robotics to process automation over a time period. Lately, AI is becoming a huge thing among the larger enterprises, owing to the huge volume of data businesses get to deal with.
There is an increased demand to process and understand these big data patterns, which further expedite the growth and application of AI. Artificial Intelligence-based tools and processes are now becoming more efficient and accurate in terms of identifying the underlying patterns of data, which can be largely beneficial for companies to make proper decision making. To understand AI in a better way, let us consider two types of AI as
- Weak AI
- Strong AI
What Is Weak AI?
This is also called Narrow AI, which is a system set up to accomplish a specific task. Weak Ai can help solve the given problem at hand with the help of data. Weak AI uniquely works based on the set rules and cannot go beyond it on its own. As the name suggests, it will only focus on the narrow tasks assigned and do the job at best than humans can do. Weak AI can take up all the cognitive functions, but you cannot define it as general intelligence. Narrow AI is designed intelligently towards accomplishing the narrow tasks which they are specifically assigned for.
One of the real-time examples of Narrow AI is the personal assistant of Apple named Siri. It is governed by the data available over the internet. It seems to be an intelligent application as being able to interact with humans. Siri is also able to make some witty comments, but it actually acts in a predefined way. You can only witness the “narrowness” of Siri on getting engaged in conversations to which it is not designed to respond accurately too.
This is the same in the case of the robotic process automation used by the manufacturing units. These can answer the queries and respond to situations aptly for which they are trained for. Narrow AI is fully capable of handling situations that are even complex. However, the intelligence level of weak AI applications is limited to giving solutions to such problems that that system is programmed for.
Weak AI works on the backdrop of the data availed to it. To implement Weak AI systems to meet a specific purpose, it is also important to back it up with a comprehensive database. RemoteDBA.com offers database management support for business AI systems to ensure optimum performance and minimum downtime.
What is Strong AI?
Strong AI is alternatively called Full AI, which is more powerful and functional than Narrow Ai. This is the seamless application of artificial intelligence for handling situations with wider capabilities. In a sense, Strong AI can mimic the process of the human brain. When trained properly, it can become so powerful that it can respond with actions exactly similar to how human beings are taking actions. Full AI also has the power of understanding things logically and consciousness.
Fundamentally, Strong AI is the process that functions the same as the human brain. AI, in all sense, can have extraordinary capacities in situational decision-making and can understand everything as a human being does. The cognitive states, beliefs, and perceptions which can be found in humans can also be programmed to the same degree in strong AI applications.
However, the challenge is when it comes to accurately defining the level of intelligence. In the case of AI-based applications, it is highly difficult or almost impossible to determine success or to set the boundaries of artificial intelligence as we consider the strong AI. So, in many typical business cases, weak AI is preferred over Strong AI.
The primary reason behind it is that Weak AI can accomplish the major tasks assigned and offer optimum efficiency. Even though Weak AI cannot fully encompass intelligent processes, it can complete the particular task at hand with perfection.
Role of AI
The primary thing to understand while dealing with artificial intelligence is that it is a mechanical intelligence shown by machines. The devices and applications working on AI use the artificial cognitive senses to identify various aspects of a problem and solve it.
Still, in many cases, humans can solve the same in a better manner by also considering the social and emotional context of the problem. AI can also be successfully managed to create a bigger impact by doing what is thought to be impossible.
One such industry, which is largely benefited from AI-based applications, is the financial industry. Many AI-based applications are being used in this sector now, like cognitive computing applications, personal assistants, chatbots, machine learning methodologies, etc., which all are peripherals of AI.
Many financial organizations like banks and trading houses have been largely investing in AI research for many years now and are pumping in more to bring up operational AI systems.
Artificial intelligence is gaining popularity lately due to artificial augmented reality, big data, cloud-based services, hyper-processing systems, etc. AI is also predicted to efficiently replace humans in the near future as enterprises are looking for more features from machine learning applications, personal financial advertisers, and digital labor to replace human efforts.
However, the biggest challenges in the growth path of AI still are the biases, lack of trust, regulatory concerns, etc. So, the companies now prefer to bring forth a reliable option in the form of augmented intelligence, which is primarily meant to assist humans in their tasks.
In any case, artificial intelligence is used abundantly in many processes, including financial and auditing transactions. It can also be expected that companies make their key business decisions also making by relying on AI in the near future. AI already has the capability to understand how customers may react to various situations.
With more advanced artificial intelligence solutions, people and businesses can now make smarter decisions at a quicker pace. However, the key to success in AI adoption is to hit the right balance between machines and humans.
