The Ultimate Crash Course On Artificial Intelligence (Part 1)
By: Kevin Liu
Tags: Machine Learning, Technology, Artificial Intelligence, Learning, The Knowledge Society

Introduction

On a bright Monday morning, you’re here sipping your nice cup of java, or otherwise known as coffee, while you’re browsing the latest news on your phone and scrolling through Reddit. Right before you’re about to put your phone away and head to school or work, all the headlines and notifications flood you at once. They flood everyone’s phones.

“GPT-3, an OpenAI initiative that managed to pose as a human, answering questions on Reddit undetected, for over a week…” “AI ‘outperforms’ doctors diagnosing breast cancer — BBC News” “Former Go champion beaten by DeepMind retires after declaring AI invincible” — Verge “Surprising Ways How Driverless Cars Will Change Our Future” — Machine Design “Millions of Americans Have Lost Jobs in the Pandemic — And Robots and AI Are Replacing Them Faster Than Ever” — Time.com

All of a sudden, panic rushes through your veins, and you begin to sweat. You wonder how the world has gotten to a point where technology has revolutionized our everyday lives. Your degree that’s sitting on your wall is starting to shake, knowing that it soon might not be of use. You’re wondering if you’ll be able to keep your job if you’re part of the working class or if you’ll soon be replaced by some super-intelligent robot that doesn’t require a paycheque every two weeks.

However, a tiny voice in the back of your head tells you not to worry. Less than 20 seconds later, your friend calls you up and starts freaking out as well. It turns out that everyone in the world received these notifications at the exact same time.

In the midst of all of this panic, your phone vibrates profusely again, notifying you that there’s something that demands your attention. Sitting there on your screen is a text from someone Anonymous that tells you that there’s nothing to fear. It says that you’ve been invited to join a secret group, a group that will dedicate all of their lives towards research, and education amid these new times. They’ve invited you to be apart of the new 4th Industrial Revolution with technology.

As a naturally curious person, you reply back and agree to join. They instantly send you a link to an article that plans to explain what is all of this Artificial Intelligence, Machine Learning and Deep Learning that’s constantly in the news.

You open it and bam. Here you are!

Let’s get into it. In this article, we’ll be talking about the following concepts broken down into sections!

Get ready for a wild ride and prepare to have your mind blown!

What is Artificial Intelligence (AI)?

Artificial Intelligence is absolutely huge. Here, take a look and see:

This visual encapsulates everything you need to know about Artificial Intelligence, and all of these areas connect with each other to fuel the innovation in this space as we know it today.

The most practical definition that can be used to describe Artificial Intelligence can be as follows:

“The theory and development of computers to be able to perform tasks that normally require human intelligence, i.e. Visual Perception, Speech Recognition, Decision Making, and Translation”

In simple layman terms, this means that the core of AI lies in the principle of machines doing things and performing tasks that usually require a human with some form of intelligence, to complete.

Now you might be wondering… “well, if these computers, programmers, and corporations get sophisticated enough with the technology and innovation, wouldn’t the very need of humans and its existence be in jeopardy?”

To that my friend, I’ll provide some reassurance and evidence to support my claims that we’re very far from that. First, let’s discuss some history and applications of AI.

History of AI

If you’re like most, you typically HATE history with a passion. All you probably remember from your history class is probably what wars our countries have fought, who the leaders of our countries were, and how everything ties back together to where we are today.

However, history is critical since it’s the study of past events, of which usually can be a good indicator of the future.

The very beginnings of AI started in 1950 when Alan Turing published a paper called Computing Machinery and Intelligence in which he outlined the Turing Test. Simply put the Turing Test is the process to determine whether a computer is capable of thinking like a human being.

A few years later in 1956 at the Dartmouth Conference, John McCarthy coined the term “Artificial Intelligence,” and defined it as “the science and engineering of making intelligent machines.” This was the true birth of AI.

Over the course of many years and decades, new advancements were made: the First AI Lab at MIT built-in 1959, the first Chatbot named Eliza created in 1961, IBM Deep Blue beating the world champion at Chess in 1997, IBM Watson defeated the two greatest Jeopardy players and more.

All of this leads us to the present day.

Applications of AI

Today, we have AI at every single corner of our lives, in every single industry. AI can and has transformed healthcare, education, transportation, retail, manufacturing, climate change, and much much more. The applications of AI have changed the world as we know it.

Here are some of the most popular ones that you may have heard of:

1. Google

Search Engine

When you type a word into Google’s Search box, you’ll be suggested searches with Google’s autofill. Google’s Predictive Search Engine is based on Natural Language Processing and uses data, browser history, location and other related information to display the best result for you. This is practice is often used among many companies for their search functionalities (Netflix, Youtube, etc.)

Other Implementations

Another application of AI lies in Gmail’s Spam Filtering where Google’s Machine Learning Algorithms and Natural Language Processing views the content in an email and determines whether it’s spam or not.

Google’s AlphaGo had previously beat the world’s best Go player through Reinforcement Learning, a process where the computer teaches itself how to perform certain tasks by trial and error. On top of that, Google’s DeepMind is another subsidiary of Google that focuses on research into the field of AI and advancing the industry as a whole.

Facebook

Facebook uses various Machine Learning and Deep Learning functionalities to do any number of things. For one, it can detect your facial features and tag your friends in the photos you upload before you hit the post button. When Facebook is trying to serve ads to it’s customers, it also uses similar algorithms to classify which ads would be the best for which audiences and it enables the company as a whole to make money off of it’s users.

JPMorgan Chase’s Contract Intelligence (COiN)

JPMorgan’s Machine Learning technology has the ability to review 12,000 annual commercial loan agreements in a mere matter of seconds when the same task would take over 360,000 hours to complete by humans. This single application alone can save JPMorgan, thousands if not millions of dollars.

Netflix

The average Netflix subscriber spends over 10 hours a week on this app alone, and you might be thinking those are rookie numbers in your head. The reason that Netflix can keep eyeballs on their platform for so long is simply due to the fact that their recommendation algorithm is extremely powerful and serves to you what it thinks you’ll fall in love with. Over 75% of what you watch is recommended by Netflix. Since the recommendations are so personalized, there’s a high probability that they’ll keep you glued and loyal to their company, providing them with a solid stream of revenue every month.

And much much more!

Artificial Intelligence has been found to treat and diagnose cancers, used for surveillance, used to power the self-driving cars that exist on the road today and it’s even made music. The solutions that AI can bring to the enormous amount of problems that exist today is endless.

Types of AI

We as humans are always on the lookout for anything that might tip our system and result in massive disruption to our status quo and way of living. By status quo, I mean where humans typically are the rulers of Earth and hold all the power to do anything they so choose. Here’s why you shouldn’t be afraid of AI. It’s only here to help us. For now…

There are two, if not, three types of Artificial Intelligence: the first of which being Artificial Narrow Intelligence, the second of which being Artificial General Intelligence, and the last of which being Artificial Super Intelligence.

Artificial Narrow Intelligence (ANI) is known as weak AI which involves applying AI to only specific tasks. This includes almost all of the current advancements and innovations in this field. Examples include: Facebook using AI to detect facial features and tag friends, your Siri or Google Assistant helping you set timers or turn on lights, or even Tesla with Self Driving Cars; ANI can also help us complete menial and gruesome tasks that no one wants to do, thus resulting in great happiness and satisfaction among humans Artificial General Intelligence (AGI) is known as strong AI, which is where machines possess the ability to perform any intellectual task that a human can do. We are currently still pretty far from reaching this state of AI. Experts predict that we’ll reach AGI around 2050, and even possibly sooner. However, the field will still require a lot of research from scientists and a ton of smart people working on this for us to achieve AGI. Artificial Super Intelligence (ASI) is also known as singularity. The point in time where the capabilities of computers will surpass humans and current the date of this being achieved is unknown. Many predict that it’ll be many decades and potentially even a century before we reach ASI.

The only two that you should pay attention to would probably be AGI and ASI, as these kinds of AI would be the type that could POTENTIALLY take over the world.

Programming Languages Used In Artificial Intelligence

Since Artificial Intelligence is huge, the list of programming languages that can be used to work with AI is also vast. Nonetheless, some work better than others depending on the use case.

Here are some of the most common and most suitable languages for AI:

Python

Python, an Object-Oriented Programming language (OOP) is by far the most popular language for AI. Most developers use Python simply due to its ease of use, readability, and how straightforward it is. Furthermore, many libraries have already built up Artificial Intelligence algorithms that programmers could easily import into their code.

R (statistical programming language, the easiest syntax which greatly resembles English)

R is also a notable choice for Data Science and Machine Learning. As a statistical-oriented programming language, it excels in analyzing and manipulating data. R is also often used in data visualization and can create great-looking publication plots.

Java (known for their awesome Graphical User Interfaces, GUI’s)

Remember the language that you learnt in your high school CS class? That’s right, you can even use it for AI development. Due to its ease of use, debugging capabilities, and packages that make Java UI friendly, it’s no wonder why Java is another popular language for AI.

Lisp (on the older end, invented by John McCarthy)

Created by the father of AI, John McCarthy, it’s the oldest and the most suited language for AI development. It’s able to process symbolic information which can prove to be useful in many situations. It’s also easy to use and great for prototyping. While it is a good language, there are other languages that are more effective to choose from.

Other Languages

While I’ve mentioned 4 of the most popular languages, there are also other ones that you can use for AI, however, I won’t go in-depth, since their main purpose usually isn’t for AI.

C++ SASS Javascript MATLAB/Octave Julia

Why You Should Care and Why AI is Important

Now, you might be thinking, “dude, this sounds cool and all but, why should I care?”

Well for starters, the AI and Data Science field is growing at an exponential rate since it’s a very rewarding career path for many people. Average salaries for an AI developer ranges from $100,000 USD to $150,000 USD (nearly $200,000 in CAD)! This is a huge contributing factor as to why so many are interested in learning about it. Almost 15–20% of jobs at the biggest companies lie in the realm of AI, which makes getting a job a lot easier. In a time where job stability is at an all-time low and where up to 800 million jobs could be lost due to AI by 2030, this is a great game to get into. Furthermore, in 2016, the global market value for AI was only $4 billion, but by 2025, it;’s expected to be at $169 billion! That’s seriously rapid growth.

We also create and generate over 2.5 quintillion bytes of data every single day. That amount of data is insane. Imagine if we needed to have humans manually tag and label every single photo on Facebook for example. By the time that someone has successfully tagged 100 photos, they’ll have thousands if not millions more to go through. This is where AI excels, as its strength lies in its ability to process, analyze astronomical amounts of data and find correlations in a matter of seconds.

Other applications in various realms have also already been mentioned and with all of that combined, it should be evident how impactful and important AI will be in our daily lives and in the years to come.

What is Machine Learning (ML)?

Let’s now dive into Machine Learning. A computer scientist, Arthur Samuel first coined the term Machine Learning in 1959 and here was his definition:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if it’s performance at tasks in T, as measured by P, improves with the experience E.

Trust me, it’s okay, when I first heard of this, I was just confused as you are right now. In simplified terms, it’s essentially a subarea of Artificial Intelligence which lets machines learn automatically and improve from experience, without it being explicitly programmed to do so.

Difference Between Machine Learning and AI

The difference between Machine Learning and Artificial Intelligence, is simply that Machine Learning is a SUBSET of AI and ML is USED in AI. Machine Learning by definition is simply the process of feeding computers a ton of data to make them learn and draw conclusions & insights.

The Significance of Machine Learning

Just in case you haven’t understood how powerful this technology is, I’m going to continue proving why it’s critical. Here are three main reasons that Machine Learning can power many industries and it can help with various implications. Due to the increase in Data Generation, Machine Learning can improve decision making, uncover patterns & trends in data and be used to solve complex problems.

Increase In Data Generation

We literally generate over 2.5 quintillion bytes of data every day and it’s only going to grow from there. By 2020, it’s estimated that 1.7 MB of data will be created every second for every person on Earth. That’s astronomical. To put that in perspective, that’s 2,500,000 terabytes, and assuming we break that down and store it 1TB hard drives (the average size of disks for personal computers). That’s 2,500,000 hard drives of data that we’re creating every single day. That’s insane. You could probably fill multiple football fields will all of the disks that you’d have filled with data.

Improved Decision Making

Since we have so much data to work through, and so many algorithms to choose from, we can leverage Machine Learning to make better decisions in all facets of life. For example, many companies have a ton of data that they collect from their customers that simply haven’t been used. The insights that can be drawn from this data that’s just sitting there can produce thousands, if not millions in additional revenue depending on the organization.

Data Analysis

Using Machine Learning, we can once again plow through all of the enormous amounts of data that we generate daily. Theoretically, yes, we can have humans analyze these 2.5 quintillion bytes of data every day, but I’m sure all of these people have better things to do with their time, like binge Netflix and generate more data. Instead, we can just use Machine Learning to plow through the data and bam, problem solved.