Non-Technical Introduction to Artificial Intelligence

Photo by Franck V. on Unsplash

Have you ever feared super scary robots taking over the whole world, causing mass destruction? Maybe you’ve also feared of dystopias like one’s in the Matrix, where superintelligent AI becomes a reality.

Unless you’ve been living completely under a rock, and watching Tiktok’s all day every day, you’ve most definitely heard about Artificial Intelligence. It’s almost impossible for you to not have heard of this term at all, since it’s been thrown around in the news, by big companies, or even your local know-it-all at school!

Artificial Intelligence can be something super cool, but also something super intimidating and scary: it impacts, it’s applications (it involves lots of math and programming), and what it will do to our everyday life.

Lucky for you, I decided to write this article to share my learnings in the past few weeks in the field of Artificial Intelligence. After reading this article, you’ll have a pretty basic understanding of what artificial intelligence is, why it matters (it’s use cases), it’s barriers to entry, and if you want, how you and I can get started diving deeper into this TODAY! You’ll be better equipped with this new knowledge and you’ll be able to sleep better at night knowing there are things you can do.

Yes! You, you’re super-duper smart, and I know it you’re the right person to be reading this article. Get ready for a quick non-technical rollercoaster ride into the world of AI! (a more in-depth technical version is coming soon!)

1. Introduction to: Artificial Intelligence

Artificial Intelligence (a.k.a. “AI”) is such a broad term. There are so many other technologies that exist under the artificial intelligence umbrella. Here, take a look at this diagram and see for yourself:

See? Saying you’re interested in Artificial Intelligence is like saying I’m interested in tech, or finance without being specific on the area you’re talking about.

For the purposes of this article though, in simple terms:

Artificial Intelligence is the training of a computer or machine to act or behave like a human, either in one specific task or in broad intelligence. This is done through a variety of techniques like Machine Learning (Neural Networks, Logistic Regression, Supervised Learning) or Deep Learning or more. Generally, it requires the developer to feed the computer a large amount of data (called a dataset) so that the machine can be trained. It’s use cases range from healthcare, transportation, education, and more. Every industry can be impacted positively (and negatively) by Artificial Intelligence.

2. Clearing Up Confusion

You might be really concerned about AI, computers, machines and robots taking over the whole world. However, here’s an explanation of why you shouldn’t freak out.

Artificial Intelligence is divided into two categories: Artificial Narrow Intelligence (ANI), and Artificial General Intelligence (AGI).

  1. Artificial Narrow Intelligence (ANI): This refers to computers that can learn and can be trained to do certain individual tasks that typically are done by humans (in some cases, better than a human could). Examples include Virtual Assistants (Google Assistant/Siri), spam filter for your email, diagnosing cancer-based off of images, an algorithm to predict what you want to watch next on Netflix or what you want to see next on Tiktok etc. Artificial Narrow Intelligence (ANI) already exists in today’s world and will be the main category of Artificial Intelligence development for the next several years, or even decades. We have only made progress in ANI at the moment, contrary to very flashy headlines in the news that lead people to believe otherwise.
  2. Artificial General Intelligence (AGI): This, on the other hand, refers to computers/machines that can learn and can be trained to do practically anything/everything a human can. This is where the computer is as smart or even smarter than humans. This is what everyone fears in terms of the dystopian future where robots roam the streets and do everything that we would do. In a sense, Artificial General Intelligence (AGI) is the goal for researchers and those working in the field of AI. However, given its capabilities, and what’s required to develop AGI, achieving this will take many many years or decades. We are still very far from reaching AGI.

TL;DR — Calm down and sleep better knowing that the sci-fi dystopian, Matrix/Terminator-like future is coming but you may or may not be around when it does become a reality. It will be many years/decades until we reach breakthroughs for AGI.

3. Why It Matters

Artificial Intelligence matters so much because of its capabilities, and it’s power. It has so many use cases and it can do so much. There’s a reason why so many companies are pouring billions and billions of dollars into AI research and hiring the team that can get the job done. Notable companies in the AI field include Google, Apple, Microsoft, Facebook, Amazon, Tesla, OpenAI and many more.

4. Applications and Existing Solutions

Applications with this technology include:

  • Healthcare: Diagnosing Cancer in Patients With Machine Learning
  • Transportation & Autonomous Vehicles: Tesla and other manufacturers using Object Recognition, and other sensors to operate their self-driving cars
  • Search Engines and Ads: Google/Facebook/Instagram serving us extremely targeted ads that we’ll click on
  • Virtual Assistants and Speech Recognition: Google Assistant, or Siri
  • Retail & E-Commerce: Amazon predicting what we’re interested in purchasing
  • Finance and Business: Predicting stock market prices or fraud prevention (at major banks)
  • Online automated chatbots: using AI and Machine Learning to answer questions from consumers

As you can see there are an infinite amount of applications and problems that can be solved, with AI.

5. Barriers to Entry & Problems With This Technology

The key barriers to entry for companies to get into the field are primarily the cost associated with developing and integrating Artificial Intelligence into the organization. Hiring talent, data collection, and research take a lot of time, and resources. However, with the benefits of artificial intelligence (customer satisfaction, personalized customer experiences, increase revenue), adopting Artificial Intelligence is definitely a worthwhile investment.

The key barrier to entry for those that want to learn about Artificial Intelligence is simply that the learning curve is steep. When I’m talking about steep, I mean very steep for those in high school (ah, someone like me!). Generally, it requires background knowledge in programming, mathematics (calculus, linear algebra, statistics, abstract thinking, algorithms, & more), and a ton of grit + work. This leads me to the next section, where I’m going to talk about what I’m going to do!

6. Next Steps I’m Taking

I feel incredibly lucky and blessed to live in a world with a tremendous amount of resources that are available for anybody willing to put in the time and effort. If you think about it, we can learn and figure anything out on our own online with resources like Khan Academy, Coursera, edX, Udemy, Udacity, Youtube, freeCodeCamp and more. This is why I’m dedicating the next few months to improving my knowledge of mathematics (Khan Academy + Coursera), my knowledge of programming languages (freeCodeCamp + Udacity + Udemy), as well as consistent effort and grind, at least 4 hours of consistent learning every day. In roughly 2 months, I’ll push out a more in-depth technical explanation of Artificial Intelligence with all the math behind it, and I also expect to build a project (I’ll document the process, don’t worry!).

Furthermore, I also plan on reaching out to existing professionals in the AI field already and learning more from them. If you know anyone that’d be open to that and they’re a big brain in the field of Artificial Intelligence, please feel free to refer them to me, and send my email their way: contact@kevinliuofficial.com.

6.1 Resources

Here are some (mostly free but high-quality) resources that I’m using and that you can also check out if you want to get into this field:

AI For Everyone by Andrew Ng: https://www.coursera.org/learn/ai-for-everyone

Machine Learning by Andrew Ng: https://www.coursera.org/learn/machine-learning

Udacity Machine Learning: https://www.udacity.com/course/intro-to-machine-learning--ud120

IBM Data Science: https://www.coursera.org/professional-certificates/ibm-data-science

Python for Applied Data Science: https://www.coursera.org/learn/python-for-applied-data-science-ai

Mathematics for Machine Learning Specialization: https://www.coursera.org/specializations/mathematics-machine-learning

Khan Academy: https://www.khanacademy.org

freeCodeCamp: https://www.freecodecamp.org

Bonus — Self Driving Cars Specialization: https://www.coursera.org/specializations/self-driving-cars

7. Takeaways (and a little advice)

Artificial Intelligence is a huge realm of tech that is about to change the world on a whole other level. However, there’s no need to be afraid of it. Given that at the time of writing, there’s a global pandemic, and we’re all stuck at home, this is a great time to delve into this fascinating field.

If you’re also new to this and want to get into the field, come along with me for the journey and we can do this together. Shoot me an email at contact@kevinliuofficial.com, and we can set something up!

Do your best and don’t be too hard on yourself. Keep up the grind but also take care of yourself and give yourself breaks so you don’t burn out!

Until next time, stay safe!

Hi, my name is Kevin Liu, a 15-year old that’s super excited about learning about a wide variety of exponential technologies, and industries, and attempting to solve the world’s biggest problems (soon)! If you want to get connected with me and have us share knowledge with each other, feel free to contact me at contact@kevinliuofficial.com. I’d love to hop on a 10-minute call to get to know you as well.

16-year-old TKS Innovator, and AI Enthusiast, working on developing a legendary skillset to solve the world’s most important problems