Artificial Intelligence or AI is an approach to making a computer think, similar to how smart a human thinks. To be more precise, AI is the in-depth study of how the human brain thinks, learns, decides, and works to solve real-world problems. And finally, this study leads to the creation of intelligent software systems. The aim of AI is to improve computer functions that are related to human knowledge, for example, reasoning, learning, and problem-solving. Now here’s a question. How can we make a computer learn these intelligent skills? We provide machines the ability to examine examples and create machine learning models based on the inputs and desired outputs. There are 3 ways in which machines can learn:
1) Supervised Learning
2) Unsupervised Learning
3) Reinforcement learning.
In simple words, training a model on examples of input and output data is called Supervised Learning whereas training a model on examples of input and output data and in addition giving allowance to it to look for some interesting patterns in a given new data is called unsupervised learning. Despite these two, my favorite one is Reinforcement Learning. Reinforcement learning algorithms rely on providing rules and constraints (restrictions) and letting the model learn how to achieve the desired results by trying different combinations of actions that could be performed within those constraints (restrictions). They are given a reward or punishment depending on whether the decision was a good one or not. Reinforcement learning is able to make independent decisions. Based on strength, breadth, and applications, AI can be described in different ways:
1) Weak or Narrow AI: applied only to a specific domain. Narrow AI is AI that is programmed to perform a single task — whether it's checking the weather, being able to play chess, or any other domain-specific real-world tasks. Even Siri, Google Assistant, and Alexa are good examples of Narrow AI.
2) Strong AI: They are able to interact and operate with a wide variety of independent and unrelated tasks. It can also be defined as a combination of many AI strategies that learn from human experience and can perform at the human level of intelligence.
3) Super or Conscious AI: Super AI is AI that surpasses human intelligence and ability. It's also known as artificial superintelligence (ASI) or superintelligence. It's the best at everything— mathematics, science, medicine, hobbies, you name it. Even the brightest human minds cannot come close to the abilities of super AI.
According to “Investopedia”, experts predict that Strong AI may be developed by2030 or 2045.
Now, Artificial Intelligence is not something independent. It is dependent on several other specialized learning which include machine learning, Deep Learning, and neural networks.
Machine Learning: is actually a subset of AI where machines or computers are provided with data and it’s their responsibility to learn from the provided data themselves. It’s very similar to how we are given books that contain data and it’s our responsibility to learn and prepare every chapter precisely!
Deep Learning: In order to make it absolutely simple, deep learning in short is simply defined as the work done by human brains. Taking a real-life example, when you read in class 5, you may find some questions to be difficult to understand and try to mug them up. When you are in class 6, if a question of class 5 is given to you, it will be comparatively easy to solve it as compared to questions of class 6 (maybe). Similarly, when you are in class 7, questions from class 6 will be easy! This is the essence of Deep Learning in simple words.
Neural Networks: In Terminology, as the name suggests, it’s all about the input and output processed by a vast connection of neurons, similar to biological neurons of our brain which allows us to do tasks with the help of coordination!
Let’s say your family goes out for a dinner to some restaurant, which serves some really good soup, your family tastes it and loves it. On coming back home they tell you about the soup and on hearing this Gordon Ramsay in you wakes up and you want to replicate the soup at your home. Now you start and you have 10 chances to come up with that soup, and you start preparing and every time you try the combination of the recipe, you give it to your parents to taste it and start taking their feedback and make changes each time, and finally, you come with that perfect Soup that you wanted to make, that’s it, your neural network is made. The ingredients that you used are the input to your neural networks, the weights of the neural network are analogous to the right quantity of salt, spice, temperature, duration, etc., the 10 chances are your iterations, and the feedback that your parents give are the losses and if you are any good you would see that their feedback improves over time. [1]
Now, let’s discuss the applications of AI in the present scenario:
1) AI-enabled chatbots are being used in healthcare to question patients and run basic diagnoses like real doctors!
2) Extensively used in Oil and Gas industries where AI is helping companies analyze and classify thousands of rock samples to help identify the best locations to drill oil.
3) AI can be used for gaming purposes. The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible outcomes.
4) We are currently using some AI-based applications in our daily life with some entertainment services such as Netflix or Amazon. With the help of AI algorithms, these services show the recommendations for programs or shows.
5) AI can also be used for detecting fake news.
6) Social Media sites such as Facebook, Twitter, and Snapchat contain billions of user profiles, which need to be stored and managed in a very efficient way. AI can organize and manage massive amounts of data. AI can analyze lots of data to identify the latest trends, hashtags, and requirements of different users.
And there are many more such vast applications!
To sum up, the objectives of AI research are reasoning, knowledge representation, planning, learning, natural language processing, realization, and the ability to move and manipulate objects. We are yet to see further evolution in the field of AI!
[1] – abridged from a Medium Article.
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