What's the difference between A.I., machine learning, and robotics?

There's a lot of confusion as to what AI, machine learning, and robotics do. Sometimes, they can all be used together.


Artificial intelligence is everywhere. On your screens, in your pockets and one day may even be walking to a home near you. The headlines tend to group together this vast and diverse field into one subject. Robots emerging from the labs, algorithms playing ancient games and winning, AI and its promises are becoming a part of our everyday lives. While all of these instances have some relationship to AI, this is not a monolithic field, but one that has many separate and distinct disciplines.

A lot of the times we use the term Artificial intelligence as an all-encompassing umbrella term that covers everything. That’s not exactly the case. A.I., machine learning, deep learning, and robotics are all fascinating and separate topics. They all serve as an integral piece of the greater future of our tech. Many of these categories tend to overlap and complement one another.

The broader AI field of study is an extensive place where you have a lot to study and choose from. Understanding the difference between these four areas are foundational to getting a grasp and seeing the whole picture of the field.  


Blade Runner 2049 depicts a world over-run... and populated heavily... with robots. 

Artificial intelligence

At the root of AI technology is the ability for machines to be able to perform tasks characteristic of human intelligence. These types of things include planning, pattern recognizing, understanding natural language, learning and solving problems.  

There are two main types of AI: general and narrow. Our current technological capabilities fall under the latter. Narrow AI exhibits a sliver of some kind of intelligence – be it reminiscent of an animal or a human. This machine’s expertise is as the name would suggest, is narrow in scope. Usually, this type of AI will only be able to do one thing extremely well, like recognize images or search through databases at lightning speed.  

General intelligence would be able to perform everything equally or better than humans can. This is the goal of many AI researchers, but it is a ways down the road.  

Current AI technology is responsible for a lot of amazing things. These algorithms help Amazon give you personalized recommendations and makes sure your Google searches are relevant to what you’re looking for. Mostly any technologically literate person uses this type of tech every day.

One of the main differentiators between AI and conventional programming is the fact that non-AI programs are carried out by a set of defined instructions. AI on the other hand learns without being explicitly programmed.    

Here is when the confusion starts to take place. Often times – but not all the time – AI utilizes machine learning, which is a subset of the AI field. If we go a little deeper, we get deep learning, which is a way to implement machine learning from scratch.  

Furthermore, when we think about robotics we tend to think that robots and AI are interchangeable terms. AI algorithms are usually only one part of a larger technological matrix of hardware, electronics and non-AI code inside of a robot.

Ex Machina, A24

Robot... or artificially intelligent robot?  

Robotics is a branch of technology that concerns itself strictly with robots. A robot is a programmable machine that carries out a set of tasks autonomously in some way. They’re not computers nor are they strictly artificially intelligent.

Many experts cannot agree on what exactly constitutes a robot. But for our purposes, we’ll consider that it has a physical presence, is programmable and has some level of autonomy. Here are a few different examples of some robots we have today:

  • Roomba (Vacuum Cleaning Robot)

  • Automobile Assembly Line Arm

  • Surgery Robots

  • Atlas (Humanoid Robot)    

Some of these robots, for example, the assembly line robot or surgery bot are explicitly programmed to do a job. They do not learn. Therefore we could not consider them artificially intelligent.

These are robots that are controlled by inbuilt AI programs. This is a recent development, as most industrial robots were only programmed to carry out repetitive tasks without thinking.  Self-learning bots with machine learning logic inside of them would be considered AI. They need this in order to perform increasingly more complex tasks.


"I'm sorry, Dave..." — Hal 9000 from Stanley Kubrick's 2001: A Space Odyssey

What’s the difference between Artificial Intelligence and Machine Learning?

At its foundation, machine learning is a subset and way of achieving true AI. It was a term coined by Arthur Samuel in 1959, where he stated: “The ability to learn without being explicitly programmed.” 

The idea is to get the algorithm to learn or be trained to do something without being specifically hardcoded with a set of particular directions. It is the machine learning that paves way for artificial intelligence.

Arthur Samuel wanted to create a computer program that could enable his computer to beat him in checkers. Rather than create a detailed and long-winding program that could do it, he thought of a different idea. The algorithm that he created gave his computer the ability to learn as it played thousands of games against itself. This has been the crux of the idea ever since. By the early 1960s, this program was able to beat champions in the game.  

Over the years, machine learning developed into a number of different methods. Those being:

  1. Supervised

  2. Semi-supervised

  3. Unsupervised

  4. Reinforcement  

In a supervised setting, a computer program would be given labeled data and then be asked to assign a sorting parameter to them. This could be pictures of different animals and then it would guess and learn accordingly while it trained. Semi-supervised would only label a few of the images. After that, the computer program would have to use its algorithm to figure out the unlabeled images by using its past data.   

Unsupervised machine learning doesn’t involve any preliminary labeled data. It would be thrown into the database and have to sort for itself different classes of animals. It could do this based on grouping similar objects together due to how they look and then creating rules on the similarities it finds along the way.

Reinforcement learning is a little bit different than all of these subsets of machine learning. A great example would be the game of Chess. It knows a set amount of rules and bases its progress on the end result of either winning or losing.  


A.I., 2001, Stephen Speilberg

Deep learning

For an even deeper subset of machine learning comes deep learning. It’s tasked with far greater types of problems than just rudimentary sorting. It works in the realm of vasts amounts of data and comes to its conclusion with absolutely no previous knowledge.

If it was to differentiate between two different animals, it would distinguish them in a different way compared to regular machine learning. First, all pictures of the animals would be scanned, pixel by pixel. Once that was completed, it would then parse through the different edges and shapes, ranking them in a differential order to determine the difference.  

Deep learning tends to require much more hardware power. These machines that run this are usually housed away in large data centers. Programs that use deep learning are essentially starting from scratch.

Of all the AI disciplines, deep learning is the most promising for one day creating a generalized artificial intelligence. Some current applications that deep learning has spurned have been the many chatbots we see today. Alexa, Siri and Microsoft’s Cortana can thank their brains because of this nifty tech.   

A new cohesive approach

There have been many seismic shifts in the tech world this past century. From the computing age to the internet and to the world of mobile devices. These different categories of tech will pave the way for a new future. Or as Google CEO Sundar Pichai put it quite nicely:

“Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you through your day. We will move from mobile first to an A.I. first world.”

Artificial intelligence in all of its many forms combined together will take us on our next technological leap forward.

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A map of America’s most famous – and infamous

The 'People Map of the United States' zooms in on America's obsession with celebrity

Image: The Pudding
Strange Maps
  • Replace city names with those of their most famous residents
  • And you get a peculiar map of America's obsession with celebrity
  • If you seek fame, become an actor, musician or athlete rather than a politician, entrepreneur or scientist

Chicagoland is Obamaland

Image: The Pudding

Chicagoland's celebrity constellation: dominated by Barack, but with plenty of room for the Belushis, Brandos and Capones of this world.

Seen from among the satellites, this map of the United States is populated by a remarkably diverse bunch of athletes, entertainers, entrepreneurs and other persons of repute (and disrepute).

The multitalented Dwayne Johnson, boxing legend Muhammad Ali and Apple co-founder Steve Jobs dominate the West Coast. Right down the middle, we find actors Chris Pratt and Jason Momoa, singer Elvis Presley and basketball player Shaquille O'Neal. The East Coast crew include wrestler John Cena, whistle-blower Edward Snowden, mass murderer Ted Bundy… and Dwayne Johnson, again.

The Rock pops up in both Hayward, CA and Southwest Ranches, FL, but he's not the only one to appear twice on the map. Wild West legend Wyatt Earp makes an appearance in both Deadwood, SD and Dodge City, KS.

How is that? This 'People's Map of the United States' replaces the names of cities with those of "their most Wikipedia'ed resident: people born in, lived in, or connected to a place."

‘Cincinnati, Birthplace of Charles Manson'

Image: The Pudding

Keys to the city, or lock 'em up and throw away the key? A city's most famous sons and daughters of a city aren't always the most favoured ones.

That definition allows people to appear in more than one locality. Dwayne Johnson was born in Hayward, has one of his houses in Southwest Ranches, and is famous enough to be the 'most Wikipedia'ed resident' for both localities.

Wyatt Earp was born in Monmouth, IL, but his reputation is closely associated with both Deadwood and Dodge City – although he's most famous for the Gunfight at the O.K. Corral, which took place in Tombstone, AZ. And yes, if you zoom in on that town in southern Arizona, there's Mr Earp again.

The data for this map was collected via the Wikipedia API (application programming interface) from the English-language Wikipedia for the period from July 2015 to May 2019.

The thousands of 'Notable People' sections in Wikipedia entries for cities and other places in the U.S. were scrubbed for the person with the most pageviews. No distinction was made between places of birth, residence or death. As the developers note, "people can 'be from' multiple places".

Pageviews are an impartial indicator of interest – it doesn't matter whether your claim to fame is horrific or honorific. As a result, this map provides a non-judgmental overview of America's obsession with celebrity.

Royals and (other) mortals

Image: The Pudding

There's also a UK version of the People Map – filled with last names like Neeson, Sheeran, Darwin and Churchill – and a few first names of monarchs.

Celebrity, it is often argued, is our age's version of the Greek pantheon, populated by dozens of major gods and thousands of minor ones, each an example of behaviours to emulate or avoid. This constellation of stars, famous and infamous, is more than a map of names. It's a window into America's soul.

But don't let that put you off. Zooming in on the map is entertaining enough: celebrities floating around in the ether are suddenly tied down to a pedestrian level, and to real geography. And it's fun to see the famous and the infamous rub shoulders, as it were.

Barack Obama owns Chicago, but the suburbs to the west of the city are dotted with a panoply of personalities, ranging from the criminal (Al Capone, Cicero) and the musical (John Prine, Maywood) to figures literary (Jonathan Franzen, Western Springs) and painterly (Ivan Albright, Warrenville), actorial (Harrison Ford, Park Ridge) and political (Eugene V. Debs, Elmhurst).

Freaks and angels

Image: Dorothy

The People Map of the U.S. was inspired by the U.S.A. Song Map, substituting song titles for place names.

It would be interesting to compare 'the most Wikipedia'ed' sons and daughters of America's cities with the ones advertised at the city limits. When you're entering Aberdeen, WA, a sign invites you to 'come as you are', in homage to its most famous son, Kurt Cobain. It's a safe bet that Indian Hill, OH will make sure you know Neil Armstrong, first man on the moon, was one of theirs. But it's highly unlikely that Cincinnati, a bit further south, will make any noise about Charles Manson, local boy done bad.

Inevitably, the map also reveals some bitterly ironic neighbours, such as Ishi, the last of the Yahi tribe, captured near Oroville, CA. He died in 1916 as "the last wild Indian in North America". The most 'pageviewed' resident of nearby Colusa, CA is Byron de la Beckwith, Jr., the white supremacist convicted for the murder of Civil Rights activist Medgar Evers.

As a sampling of America's interests, this map teaches that those aiming for fame would do better to become actors, musicians or athletes rather than politicians, entrepreneurs or scientists. But also that celebrity is not limited to the big city lights of LA or New York. Even in deepest Dakota or flattest Kansas, the footlights of fame will find you. Whether that's good or bad? The pageviews don't judge...

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