Today, we can find artificial intelligence in just about everything. It is on our screens, within our pockets, accessible through social media, and maybe someday, it will be walking right next to us. Artificial Intelligence, without a doubt, is rapidly changing the world around us. In today’s day and age, we can see many futuristic applications of AI and how it is becoming a promising technology for the future.
Back in the days, to make a machine understand and adapt to your surrounding environment is what artificial intelligence was all about. However now, Artificial intelligence has become a monolithic field, an umbrella that covers more than just algorithms making machines adaptive in operation and functioning. People often confuse artificial intelligence with the concepts of deep learning and data collection.
Therefore, I am going to help you identify the distinction between all of them in a profound manner.
Let’s explore what AI, Big Data & Machine Learning is what all about.
Artificial Intelligence is the motherlode of all. It is the big umbrella which includes all the other things. Let’s explain what AI is all about?
So we ask ourselves, what is the purpose of creating a machine? If it isn’t to make processes simpler and more optimized. Artificial Intelligence adds a possibility for the machine to optimize itself in order to deliver more robust solutions then it previously did for a similar problem.
The most simplest example of Artificial Intelligence is the software game Tic-Tac-Toe AI-powered player. By introducing a pre-programmed algorithm at the back-end, this AI-powered bot is capable of training itself to do its best at not losing a game.
Now AI basically doesn’t work on the cognitive learning process. It is more based on solving problems with effective decision making. The algorithm in AI is simply an agent which leads to the optimal solution.
Here the machine simply works on the decision tree logic and that’s what makes Artificial Intelligence different from Machine Learning!
Speaking of Machine Learning, Arthur Samuel introduced the term Machine Learning for the first time back in 1959. He defined the concept as a machine’s ability to learn without being explicitly programmed.
The ability of a machine to parse information, learn from it and then make decisions is defined as Machine Learning. For example, you can consider the website for Redfin or Zillow which easily predicts the price of an owned property.
There are three types of Machine Learning.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
In supervised learning, the data presented to the model basically contains the answer to the problem within the data set. Whereas, in unsupervised learning, similarities in the data set are identified and then judgments are made based on them.
The concept of Reinforced learning is completely different. Here an agent takes action within an environment, which is interpreted into a reward and then fed back into the agent. For example, Artificial Intelligence today is automating the sales process by introducing robust methodologies.
The process of enabling machines to think exactly like human (or in other terms, making machines believe that they are human) is the term coined as Deep Learning.
Deep learning is an inspiration gain by future technologists in order to understand how the human brain works. It leverages the power neurons to interconnect the dots and gaps in information.
When machines are powered by algorithms that mimic the biological structure of the human brain, it is considered a technology that encapsulates the process of Deep Learning.
According to MIT,
“Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected. Most of today’s neural nets are organized into layers of nodes, and they’re “feed-forward,” meaning that data moves through them in only one direction. An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer above it, to which it sends data.”
Many future technologies including social media marketing tools, task management software tools, project management software tools, business analytics tools and what not are now including algorithms which are focused at delivering a more personalized experience to users.
The future in which we are living is dramatically changing. It has introduced many facets which is making our lives a more sophisticated one. We once used to rely on manual methods of performing our day in and day out tasks. However now, we greatly rely on technology to change these bindings permanently for us. And, with the introduction to Artificial Intelligence, Machine Learning and Deep Learning, things are rapidly moving forward.
So, I hope you are now clear about these concepts and will never sub-merge one with other. Hope it was a good read.
Great analysis. Would it be possible to bring in machine learning and the way it serves different purposes in robots and AI?