Future is AI -(Artificial intelligence )

Dnyaneshwar Wakshe
5 min readJan 29, 2022

If you are techno-savvy then you have listened to the AI (Artificial intelligence) concept but if not then no problem I will let know that all.

History of AI?

This concept comes into the picture at the end of World War II when the German Air Force used Enigma enciphering machine for sending messages securely, at that time mathematician Alan Turing was responsible personally there who worked on the successful decoding of that more complex German encrypted message. He changed history a second time by raising the question “Can machines think? ”

Turing’s paper “Computing Machinery and Intelligence” (1950), and its Turing Test, opened new paths for the World, he established the fundamental goal and vision of artificial intelligence.

What is AI?

Artificial intelligence is an intelligent system, which learns to solve problems as normal humans do. This system with the capability to process the visual, audio, and text data input as we do, but in a more advanced way than us according to its training.


Yes, training, you read correctly, as we learned from observation and by trying likewise that system has to learn from to somewhere. We can say

“ Artificial intelligence is a set of algorithms and intelligence to try to minc human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques.”

In today's

So on the basis of learning, there are types of AI systems :

Reactive Machines

Reactive machines are the simplest level of robot. They cannot create memories or use information learned to influence future decisions — they are only able to react to presently existing situations.

IBM’s Deep Blue, a machine designed to play chess against a human, is an example of this.

Limited Memory

A limited memory machine, as the name might suggest, is able to retain some information learned from observing previous events or data. It can build knowledge using that memory in conjunction with pre-programmed data. Self-driving cars for instance store pre-programmed data — i.e. lane markings and maps, alongside observing surrounding information such as the speed and direction of nearby cars, or the movement of nearby pedestrians.

These vehicles can evaluate the environment around them and adjust their driving as necessary. As technology evolves, machine reaction times to make judgments have also become enhanced — an invaluable asset in technology as potentially dangerous as self-driving cars. Improvements in machine learning also help autonomous vehicles to continue to learn how to drive in a similar way to humans — through experience over time.

Theory of Mind

Human beings have thoughts and feelings, memories, or other brain patterns that drive and influence their behavior. It is based on this psychology that theory of mind researchers work, hoping to develop computers that are able to imitate human mental models. That is — machines that are able to understand that people and animals have thoughts and feelings that can affect their own behavior.

It is this theory of mind that allows humans to have social interactions and form societies. Theory of mind machines would be required to use the information derived from people and learn from it, which would then inform how the machine communicates in or reacts to a different situation.

A famous but still very primitive example of this technology is Sophia, the world-famous robot developed by Hanson Robotics, who often goes on press tours as an ever-evolving example to the public of what robots are capable of doing. Whilst Sophia is not natively able to determine or understand human emotion, she can hold a basic conversation and has image recognition and an ability to respond to interactions with humans with the appropriate facial expression, as well as an incredibly human-like appearance.

Researchers have yet to truly develop a theory of mind technology, however, with criticisms of Sophia for instance being that she is simply “a chatbot with a face”.


Self-awareness AI machines are the most complex that we might ever be able to envision and are described by some as the ultimate goal of AI.

These are machines that have human-level consciousness and understand their existence in the world. They don’t just ask for something they need, they understand that they need something; ‘I want a glass of water is a very different statement to ‘I know I want a glass of water.

As a conscious being, this machine would not just know of its own internal state but be able to predict the feelings of others around it. For instance, as humans, if someone yells at us we assume that that person is angry because we understand that is how we feel when we yell. Without a theory of mind, we would not be able to make these inferences from other humans.

Obviously, self-aware machines are, at present, a work of science fiction and not something that exist — and in fact, may never exist. As it is, we’re probably best focusing on the development of machine learning in our AI. A machine that has a memory, that can learn from events in its memory and then can take that learning and apply it to future decisions is the baseline of evolution in Artificial Intelligence. Developing this will lead to AI innovation that could turn society on its head, enhance how we live in the day to day exponentially, and even save lives.

The Future is Now AI’s Impact is Everywhere

There’s virtually no major industry modern AI — more specifically, “narrow AI,” which performs objective functions using data-trained models and often falls into the categories of deep learning or machine learning — hasn’t already affected. That’s especially true in the past few years, as data collection and analysis have ramped up considerably thanks to robust IoT connectivity, the proliferation of connected devices, and ever-speedier computer processing.

Some sectors are at the start of their AI journey, others are veteran travelers. Both have a long way to go. Regardless, the impact artificial intelligence is having on our present-day lives is hard to ignore:

Transportation: Although it could take a decade or more to perfect them, nowadays self-driving cars have lunch by Tesla and it is just one step in the revolution of the transportation industry.

Manufacturing: AI-powered robots work alongside humans to perform a limited range of tasks like assembly and stacking, and predictive analysis sensors keep equipment running smoothly.

Healthcare: In the comparatively AI-nascent field of healthcare, diseases are more quickly and accurately diagnosed, drug discovery is sped up and streamlined, virtual nursing assistants monitor patients and big data analysis helps to create a more personalized patient experience.

Education: Textbooks are digitized with the help of AI, early-stage virtual tutors assist human instructors, and facial analysis gauges the emotions of students to help determine who’s struggling or bored and better tailor the experience to their individual needs.

Media: Journalism is harnessing AI, too, and will continue to benefit from it. Bloomberg uses Cyborg technology to help make quick sense of complex financial reports. The Associated Press employs the natural language abilities of Automated Insights to produce 3,700 earning reports stories per year — nearly four times more than in the recent past.

Customer Service: Last but hardly least, Google is working on an AI assistant that can place human-like calls to make appointments at, say, your neighborhood hair salon. In addition to words, the system understands context and nuance.