Is Skynet really the future of Artificial Intelligence?
By: Joey Mach
Tags: Machine Learning, Supervised Learning, Reinforcement Learning, Unsupervised Learning, Artificial Intelligence

Is Skynet really the future of Artificial Intelligence?

Remember how Skynet, the artificial intelligence software developed to command the defense system in the movie Terminator?

Well, Skynet ended up gaining consciousness and betrayed humanity. The crazy part isn’t about Skynet obtaining consciousness, it’s about how humans actually believe the future of “real-life” AI will end up gaining consciousness and eradicate humanity.

Humanity’s negative stigma around technological advances always boggles my mind. Something half the world finds fascinating and revolutionary, the other half of the world thinks it’s detrimental to humanity.

But the real question is, do we really even know what consciousness is? It’s not quantified, and thus not measurable. Artificial intelligence, as the name suggests composes of machines mimicking human intelligence, which of course, makes the intelligence artificial.

If it’s artificial, then my argument is how can it ever gain consciousness. If consciousness is not measurable, then shouldn’t we focus on the measurable aspects of Artificial Intelligence? Does it even matter if we think artificial intelligence will gain consciousness or not when some argue that rocks are conscious?

Because seriously, without technological advances, our lives today will literally be SIGNIFICANTLY more challenging. We don’t realize it because we take it for granted, then all we do is complain and nitpick about the little things while discarding the big picture of its benefits. We literally blame artificial intelligence for taking jobs away, but we never talk about the 133 million jobs AI is set to create by 2022.

According to the World Economic Forum, AI will create 1.8 times more jobs than it will displace. Just to be clear, AI isn’t coming to steal your job, instead, it is going to transform the way you work. AI will open up a broader domain than what was previously termed as possible.

So… What is Artificial Intelligence?

Artificial Intelligence is a powerful tool that allows computers to reason like human beings and mimic our behaviors.

Tasks such as driving, identifying cats, and reading might come very easy for human beings. However, it’s difficult to teach these same tasks to computers. Artificial intelligence unlocks the ability for computers to learn tasks that are normally only exclusive to humans. It’s important to note that behind of scenes of AI isn’t magic, it’s math, but complicated math. Another reason why AI won’t be conscious is that if we all agree that math isn’t conscious, then it doesn’t make sense for AI to suddenly be conscious.

For self-driving cars, AI analyzes tons of data to mimic how humans drive and respond to their environment. The beauty of AI is that it isn’t prone to human flaws like fatigue. Self-driving cars can potentially be much safer than human drivers as approximately 94% of reported fatal car accidents are because of human errors. That’s crazy!

We always think about how AI is going to impact the future, but we rarely realize how AI is already positively impacting the present. Did you watch Youtube or Netflix last night (for those who easily get stuck into Youtube’s rabbit hole, I feel you)? If yes, surprise, you’ve interacted with AI! Tons of AI algorithms are running in the background of video streaming platforms. Ever wonder why something you happened to want to watch showed up in YouTube’s recommendation section? Well, that’s powered by AI. Other common instances of AI include:

Grammarly (Yep! I used Grammarly for this article 😅)

Route optimization in Google Maps

Uber and Lyft’s app

AI autopilot for commercial flights

Spam filters for Gmail

Face recognition on your phone

Google Home, Amazon Alexa, Siri

We often use buzzwords such as AI, machine learning, and deep learning interchangeably, but they’re not exactly the same things. Artificial intelligence refers to the umbrella term that encompasses the different methods that mimic human behaviour.

Machine learning is a subset of artificial intelligence, and within machine learning, there are many more subsets.

Machine Learning

Why are there so many different terms, but aren’t they all basically just a version of AI?

Well, yes, but the approach each one of these versions takes are very different. Since the approach is different, the applications for each one of these versions are also different.

In essence, Machine learning is the process in which computers are taught to analyze data so that it can make intelligent and informed decisions. For humans, the more exposure we experience, the informed our choices ours. This is very similar for the case of machine learning as the more data machines are fed (exposure), the more informed their decisions can be. Data is one of the main factors that drive the accuracy of a machine learning model. A model refers to the algorithm behind all the perceived “magic” of AI.

Machine learning is a very broad concept and can be broken down further into different approaches. There are three main branches of machine learning:

Supervised Learning Unsupervised Learning Reinforcement Learning

Artificial intelligence is the overarching concept that encompasses machine learning and deep learning

Supervised Learning

Imagine you were a programmer tasked to write code that explicitly programs computers to distinguish between cats and dogs.

Front view of a cat and a dog

There really isn’t an efficient way to explicitly program computers to detect animals in an image. It shouldn’t be a surprise if you end up writing countless lines of code, but yet, your code isn’t even close to functional. Let's say your code ends up working, and it can pretty accurately tell the difference between the image of the cat and the dog above. But wait, what if you had a picture of a dog that looked something like the one below?

Side view of a dog

Your code most likely won’t work for an image that portrays the side view of a dog. The code will likely return some random answer or an error because it was explicitly programmed to be personalized for images that portray the front view of cats and dogs, not side views with crowded backgrounds. This means that you will have to start from scratch again.

Luckily, supervised machine learning exists so that you won’t have to write tons of code!

Machine learning works by learning on its own, which means you don’t have to explicitly program it to learn. Supervised machine learning is the approach of teaching machines to learn on its own from labeled data. Labeled datasets are tagged, while unlabelled datasets are not. In the instance of a labeled dataset for images of cats and dogs, each image will be tagged with the corresponding animal.

Think of labeled data as being color-coded, while unlabeled data are not

The training process for supervised learning looks something like this:

The model takes an input, in the case of the dogs vs cats classifier, the input is an image The input is passed through the model The first time the model trains, it guesses whether an image is a cat or dog and outputs that answer The model compares its guess to the labels and learns from its mistakes Refines the model based on its mistakes Step 1–5 is repeated in a more structured format

The model doesn’t know the labels when the data is passed in as inputs. After the model outputs an answer, it compared its predictions with the input’s label. The model then refines itself slightly and becomes smarter the next time inputs are passed through it as it learns from its mistakes.

Unsupervised learning

Don’t panic if you have data without labels, because unsupervised learning is to the rescue. Instead of learning from its mistakes, unsupervised learning works by identifying patterns in unlabeled data.

A common approach of unsupervised learning is by grouping raw data based on their similarities.

I really like giraffes, but I don’t like lions. Let's say I have a list of descriptive features of giraffes and lions. I want to separate giraffes from lions, but it’ll take too long to do all of that manually. So, I decided to employ an unsupervised machine learning model to separate giraffes from lions by looking at two of their features, their height, and weight. The unsupervised machine learning model is able to separate the list into two groups based on the patterns it has identified.

Yikes! That’s chaotic!

Graphing the data, it becomes clear that there are two distinct groups. The first group weights more and is taller (giraffes), and the second group weights less and is shorter (lions). Unsupervised learning is able to identify and separate data into different groups.

The group of data points at the bottom left are lions, and those a the top right are giraffes

Reinforcement Learning

When I think of reinforcement learning, I like to think about two-year-olds who are trying to learn about societal rules through experimenting. Two years old do things that adults normally won’t do, like crying in public because their parents decided to not get them the Frozen Elsa playset. Little kids react this way because they are still experimenting and trying to figure out what they have to do to get them what they want. Soon, the two-year-old becomes a ten-year-old. They now understand that crying in public just because they want a toy isn’t acceptable because they have experienced the punishment of doing so at least several times before.

Reinforcement learning is similar to the two-year-old in that it also learns through experimenting, instead of learning from data like supervised learning and unsupervised learning. Through experience, reinforcement learning can optimize its decisions so that they maximize reward, and minimize punishment.

Teaching machines how to play games is a common application of reinforcement learning. Humans get better at games through experience. Since AI reflects human behavior, it also learns to play games through experience.

Reinforcement learning in progress

To train a reinforcement learning model to play piano tiles, the machine will experiment with different scenarios. It will then only repeat the things that generated rewards, in this case, game points. As it experiments and finds sets of actions that ends up rewarding it with game points, it will continue to execute these specific set of actions while refraining itself from repeating actions that don’t lead to game points.

You’ve probably heard of how AlphaGo, a reinforcement machine learning model that was named the world’s best Go player after triumphing against the world’s human championship in Go. AlphGo was then repurposed to play chess and went on to defeat the world’s best AI chess program, Stockfish8.