How is Machine Learning Different From Artificial Intelligence?

Man has always wanted to feel superior and our work on trying to mimic our very own nature and experiences artificially are some of the most practical examples of our intentions. Aeroplanes cropped out of our intentions to fly and computers arrived to make our lives easier.

However, there was this unknown element missing in all our innovations and inventions. It was the fact that all these were machines and they all were good at just one thing – processing data. They were devoid of emotions and lacked the ability to think. The man wasn’t satisfied. He wanted to recreate something that would exactly replicate a human and synthesize the emotions and functionalities of himself.

Cut to a few decades and we now have some of the most advanced concepts and tech models. From being buzzwords that were only meant to the access and understanding of the elite, we now get hands-on with these technologies on a daily basis. They are what we call today as artificial intelligence and machine learning.

They are the most disruptive technologies today almost creating an explosion in terms of startups in Silicon Valley. Every company, every idea and every venture intends to have artificial intelligence at its core and that is exactly why there is a huge demand and potential for its growth and that of its aspirants.

According to Adobe, the attraction of investors for startups on artificial intelligence has increased by 6 times in the last two decades. The same report also reveals that the number of jobs requiring qualified artificial intelligence experts has soared by 450%. And this is only in the last 5 years.

These are some of the hottest industries today, shaping our lifestyle, life and our future. Despite this, there tends to be a misconception, or rather confusion, when it comes to understanding the technologies. What most of us believe is that these are the same concepts with different jargon. Partially true, agreed. But there are more distinctions between them than what meets the eye.

This article is all about helping you understand the differences, acknowledge the similarities and make better career choices. To get things started, here are some real-world examples of the two technologies in action.

Artificial Intelligence – What Is It?

So far, our understanding of artificial intelligence has only been based on pop culture concepts and references. Advanced machines, cyborgs, deadly robots and what not! However, we are far from encountering a machine apocalypse. The reality is far more grounded and simple. Artificial intelligence is an all-encompassing term to today’s disruptive technologies.

Artificial intelligence stands as the core concept within which other technologies like machine learning and deep learning are embedded.

If you could draw a Venn diagram in your mind, imagine machine learning to be a subset of artificial intelligence and deep learning to be a subset of machine learning. In the grandeur of artificial intelligence also feature data science and Big Data, which have their own distinct roles and responsibilities.

To precisely explain artificial intelligence, it is the science of giving a computer or a machine the ability to think, process and respond to problems and concerns it is exposed to. Artificial intelligence refers to putting the “smart” in smart devices.

Similar to how a human would, intelligent machines can learn from their own experiences or from what is fed to them via algorithms and keep evolving internally to come up with the best possible solutions.

Another significant aspect of artificial intelligence would be automation, where today, simple pieces of codes or devices are taking care of redundant tasks at work or home. Such a technology minimizes human intervention or even human presence in executing a task.

If you’re wondering about the scale and potential of this idea, realize that Amazon is considering to replace delivery personnel with drones for faster delivery or the fact that it also owns a smart shop that is built completely out of artificial intelligence and the Internet of Things (it does not have a cash counter).

Some of the topics or concepts you would learn under the umbrella of artificial intelligence include:

• Natural language processing
• Machine learning  
• Robotics
• Recommendation systems  
• Image analytics
• Deep learning
• Neural networks and more

Machine Learning – What Is It?

They say all Champagnes are sparkling wines and not all sparkling wines are Champagnes. Similarly, machine learning is a subset of artificial intelligence, where all machine learning tasks are considered artificial intelligence but not all artificial intelligence tasks are considered machine learning.

Machine learning is the perfect example of a set of programs or codes that modify themselves, their behaviour and their learning when exposed to consistent and more data. They are applied, in a broader sense, to minimize errors in an operation, improve its efficiency and keep human intervention at bay.

Its algorithms are responsible for your song recommendations on your music app, responses from Siri and more. When they constantly get exposed to new data, they learn and evolve, understanding your tastes and preferences, becoming more human by the day. Chatbots are products of this mechanism, where you chat to literally machines to get your concerns sorted. This could be as trivial as knowing where your food order to as complex as finding out a solution to a product malfunctioning. Today, there are industries with smart equipment that can detect a probable malfunction and notify the requirement of servicing so that the operation is seamless.

In a report released by PricewaterhouseCoopers, 24% of the participants it surveyed with respect to customer services using chatbots could not tell if they were chatting with a human on the other side or with an algorithm.

Machine Learning Algorithms

When we are understanding machine learning better, it’s also essential to understand the algorithms and tools involved. As far as algorithms are concerned, they are categorized into three:

• Supervised Learning – It is where a target is fixed and a set of predictors are used to forecast the outcome. Examples include logistic regression, KNN, decision tree, regress and more.
• Unsupervised Learning – There are no target variable to estimate in unsupervised learning. It is rather used to cluster (categories) data sets into specific segments. Examples of unsupervised learning include K-means, Apriori algorithm and more.
• Reinforcement Learning – Reinforcement algorithm refers to training a machine to make significant specific forecasts or decisions. In this system, the algorithm trains by itself by means of trial-and-error mechanisms. Examples include Markov decision process.

Deep Learning

Otherwise known as deep neural networks, deep learning is a sub-set of machine learning, where advanced networks of neurons are capable of learning autonomously. One of the major factors that sets deep learning apart is that this can learn from data sets that are both structured and unstructured.

A practical reflection of deep learning would be the incident when Facebook had to shut down its artificial intelligence wing after it started developing its own language and communicating amongst themselves.

The name has more meaning to it than a fancy appeal. Deep refers to the amount/number of layers a specific neural network has. These deep neural networks learn from a feature hierarchy and evolve as they move from one layer to another. One of the signatures of deep learning is computational insensitivity and the reason why graphical processing units are hot today to get deep learning models trained.

Some of the practical applications of deep learning include natural language processing, speech recognition, image recognition and more.

The Technologies in Action

Autonomous Cars

Well, who isn’t talking about this? The very fact that these cars have turned science fiction movie concepts into a reality is a testimony to our evolution and what we could achieve with our collective intelligence. Today, autonomous cars are being tested by some of the most prominent market players.

These make use of sensors, devices, cloud architecture and algorithms – together they form an ecosystem called the Internet of Things – to sense road conditions, pedestrian traffic, incoming vehicle, weather conditions, traffic signal and more to quickly analyse the existing scenario and use algorithms to take the most effective driving decision. While we have to wait a little more before we could actually get into one of them, we are still progressing in the right direction.

Online Shopping Websites

Want a simpler example?

Well, your preferred online shopping website is loaded with artificial intelligence concepts and machine learning algorithms. When you browse, you generate data and that gets processed by these technologies and comes back to you in the name of customer experience. Do you get reminded of a perishable product you need to buy just before its actual lifespan ends? Do you get deals or discounts once you add an item to your cart and not check out? Artificial Intelligence at play!

Entertainment

We have something more interesting!

If you have an account on any of the popular video streaming websites, you would have probably noticed this. When you finish watching a series or a movie, you get to see a fresh set of recommendations that are almost 100% based on your taste and preferences right? Artificial intelligence again!

As a side note, you would be surprised to know that the entire show called House of Cards was produced by Netflix as a result of artificial intelligence and data science.

Why Are These Technologies Important To Us?

Apart from the fact that these disruptive technologies are making our lives easier, they are also working on an aspect we have hardly realized. Or to be precise, realized wrongly! We say this is the age of automation and that it is taking up jobs. Like we saw how machine learning can keep humans away from being at work, there is a lot of misconceptions spreading that these technologies have been influential in causing layoffs across companies.

They are false.

These technologies, in reality, are offering new job roles and opportunities that did not exist before. It has created a demand for niche experts like machine learning specialists, natural language processing experts, image analysts, cloud architects, data scientists, Big Data specialists and more. We hardly knew of such job roles before automation kicked in.

Artificial intelligence and machine learning technologies are driving ahead the point that we need to upskill and stay relevant in the job market to progress faster in our career and get more lucrative paychecks. Redundant skill sets are bound to be done away with algorithms but we need to be prepared to take up higher and more specific responsibilities.

The Way Ahead

We are now in the phase similar to when spreadsheets dominated manual data entry and those who worked on MSExcel were considered to be elite. Today, most of us can seamlessly work on Excel. Did spreadsheets take up jobs or make us smarter?

The answer is simple. If you are analytical and need proof in numbers, understand that there are more than 50k jobs open in this field, waiting for qualified professionals to take up. With the supply of them in single digits, this is the perfect time to learn applied AI and machine learning for a great career shift. Industries like healthcare, BFSI, logistics, entertainment, e-commerce and more are looking for professionals and if you switch now, you could be the one to look back and smile with contentment years from now.

Good luck!