Intelligence is the single biggest differentiator that separates humans from any other species. In a real-world context, your intelligence is defined by your capacity to acquire, retain, and use of the knowledge in identifying and solving problems of general and complex nature.
Your intelligence requires your mental capacity to apply logic, reasoning, planning and problem-solving. You might be the master of one subject or domain, but intelligence is little more than that domain-specific knowledge. The speed of information processing and finding a pattern in the information flow through the application of logic and reasoning gave birth to the idea of artificial intelligence. The field of AI boomed exponentially since the coining of the term in 1956.
What is Artificial Intelligence?
If we see it in a contemporary context AI is all about handling the Big Data volumes using advanced algorithms and finding a pattern to mimic and repeat the activity to improve the work efficiency of the related system. Starting from the basics of neural network it moved towards machine learning and currently the whole focus is on the deep learning, where simulation is the key.
In simple words, AI is all about replicating the basic human learning process by machines and applies logical reasoning to define the process and to derive conclusions and use it at maximum scale, which even humans cannot do. Some practical real-world examples are speech recognition, machine vision, and assistant support. These all are just the primary application of AI tech, the deep AI is all about cognitive simulation of human intelligence, which we will see in advanced robotics in the future.
AI Technology Examples
Just to get the feel of how AI is slowly coming into the mainstream, here are some of the examples to help you understand and visualize the future:
As a human, you might hate monotony, but your AI-driven robotic process automation (RPA) makes it sure that your process runs smoothly without any human intervention. Yor RPA can be programmed to do a repetitive task 24×7. This is different from the simple IT automation as this can easily adapt according to the demand of the situation.
- Machine learning
This is little forward-looking as you train your system to start thinking according to the data inputs and take corrective measures when required. Deep learning is a subset of machine learning based on the concept of predictive analytics. You just control the flow of the data and your system is trained enough to process it and act according to the predictive learning.
- Machine vision
This is a miracle in the happening as it is little beyond the human biology of visual field capture and analysis? Machine vision is all about training the computer to have the capacity to see and interpret visual data to take requisite action. It is more powerful as it can be programmed to see more than a human eye by enabling UV/IR, X-ray or other analyzers. It is likely to revolutionize the medical world with inputs of mobile robotics and human communication.
- Natural language processing (NLP)
It is a bit farfetched, but the primary application is showing amazing results at least in identifying patterned linguistic of spam mails. It is moving slowly away from the machine learning approach, but it might take a few more years to reach the practical application. This is likely to eliminate the linguistic barrier between a robot and humans. Currently, you see the application of NLP in text translations, speech recognition, and sentiment analysis.
It is just an amalgamation of all to make it suitable for human use. Imagine any situation and you can have a robotic operation in place.
- Self-driving cars
This is also one of the best-combined applications of visual computing, image identification and deep learning to build automated motoring skill. Once fully developed, this can disrupt the automotive space.
It is just a begging of AI, the scale of intelligence will be shocking for humans to see and digest. Be ready to embrace the miracle in the happening.