The Difference Between AI and Machine Learning

How ML differs from AI, and the role it plays in your day-to-day life.

In my last article, I introduced you to ML with 6 Steps to Apply Machine Learning in Your Business. Having briefly introduced the six steps, I also promised to expand on each one in separate articles.

So here we are: expanding on Step 1: Understanding the Difference Between AI and Machine Learning.

Today, we’ll delve into what Artificial Intelligence and Machine Learning are, discuss their differences, and finally take a look at how Machine Learning can help you in your business as well as day-to-day life.

Understanding The Difference Between AI and ML

Artificial Intelligence and Machine Learning are pretty hot buzzwords these days, and often used interchangeably. So is there a difference between the two?

Yes and no, because one is part of the other.

Machine Learning is a subset of Artificial Intelligence and one of the techniques available for realizing AI.

Artificial Intelligence encompasses several fields, such as Natural Language Processing, Deep Learning, Computer Vision, Speech Recognition, and more — all of which have the common goal of making machines even more intelligent than humans.

To further explore the differences and similarities between AI and ML, let’s expand our understanding of each term.

What is Artificial Intelligence?

When people hear of AI, they usually have different views of what it is and what it’s capable of — most of which have been influenced by movies, TV Shows, video games and books.

Some see a Dystopian future where the machines take over (Terminator, The Matrix); others see the possibility of creating advanced personal assistants that cater to your every need (Iron Man, Her, Detroit: Become Human) or the possibility of having robotic children that you don’t have to raise (A.I. Artificial Intelligence, Detroit: Become Human).

Depending on how you look at it, these ideas may sound very cool or very scary. Regardless, we are not yet at the level of Artificial General Intelligence required to build such technology.

But hold on, I’m getting ahead of myself. I can’t discuss Artificial General Intelligence without having more clearly defined what Artificial Intelligence is.

Artificial Intelligence (AI) is the ability of a machine to perform tasks commonly associated with intelligent beings. Artificial Intelligence is the intelligence demonstrated by machines while Natural Intelligence is displayed by living beings. As mentioned previously, it is an interdisciplinary science comprised of different fields, all with the aim of creating machines capable of learning, solving problems and performing tasks in a human-like manner.

AI In Business

Back in 2011, Marc Andreessen (of venture capital firm Andreessen-Horowitz) penned his famous “Why Software Is Eating the World” essay in The Wall Street Journal. He spoke of how major businesses and industries were being run by software and how internet companies were building high-growth, high-margin, and highly defensible businesses.

A few years down the line, Jensen Huang, co-founder and CEO of Nvidia, took this a step further and declared: “Software Is Eating the World, but AI Is Going to Eat Software”. This statement could not be more true in recent years, as more and more companies continue to invest heavily in AI and we get to watch the growth of AI-enabled products.

What is Machine Learning?

Now that we have a basic but solid understanding of AI, let’s expand on Machine Learning.

Machine Learning (ML), technically speaking, is a predefined programming model or algorithm, trained by a huge amount of data to make predictions or suggestions. It is based on the idea that systems can be programmed to learn automatically from their experience. By analyzing data and identifying patterns, machines can improve and make better predictions or decisions with minimal human intervention.

Machine Learning can help you automate a lot of processes that humans otherwise have to repeat on a daily basis. Additionally, it can make decisions that are based on statistics and probability and may in some cases, be better than human decisions that are affected by irrationality or bias.

How ML Can Help You in Your Daily Life

Unless you’ve been living in a cave for the past few years and isolated from technology, I can guarantee that you’ve used at least one product supported by AI/ML.

Examples of everyday ML use include email spam filtering, language translation, biometric technology (including fingerprint and facial recognition), product recommendation in e-commerce, voice recognition in personal assistants such as Siri, Alexa, Cortana, and Google Assistant, auto-pilot in transportation, chatbots in customer support, and the list goes on.

Particularly in business, Machine Learning has been gaining a lot of traction and is being applied in many industries.

In healthcare, we’ve seen systems that are better than doctors at detecting certain types of cancer. In manufacturing, we’ve seen increased adoption of robots. In transportation, we have self-driving cars.

The image below gives an idea of just how much this technology is invading various industries.

Source: CB Insights

How Can ML Help My Business?

Machine Learning can be a game-changer for your business. It can improve and streamline business processes and add to your company’s bottom line.

Today, Machine Learning is more mature and easier to deploy than ever before. You can create and train your own models if you wish, but you can also take advantage of ready-to-use Machine Learning APIs that are available on the market for quick integration of AI in your business.

Companies such as Google, IBM, Microsoft, Amazon, Alibaba, and Tencent have a bunch of ready-to-use Machine Learning APIs that provide a variety of features such as face recognition and speech-to-text.

Wrapping Up

This brings us to the end of our discussion of AI and ML. Next in the series, we’ll study business processes and I’ll help you identify which ones of yours can be ML-enabled. Stay tuned!

If you have comments or questions about this post, you can leave a response here or reach out to me via email or LinkedIn.

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