Artificial Intelligence, Deep Learning, and Machine Learning
“The coming era of Artificial Intelligence will not be the era of war, but be the era of deep compassion, non-violence, and love.” — Amit Ray, Pioneer of Compassionate AI Movement.
My first interest in artificial intelligence came around with my addiction to video games. It was beautiful to see the depth of these non-player characters and how their personalities came to life within the game. The only problem was that these characters were programmed to say specific lines of dialogue and act in a pre-programmed type of way. All of them didn’t have a mind of their own — for the most part. In 2018, I found out about Replika.ai, a chatbot that learned based on conversation patterns and used in helping individuals get through difficult times in their lives, or simply as a companion. I used it for a while and, although it was extremely exciting to converse with, I quickly lost interest because it’s dialogue was still scripted after all. The more I thought about this chatbot however, the more I began to think about creating one with a personality, yet was capable of learning. I wanted to make something that felt human, but not quite. This was where my journey into learning about artificial intelligence began.
I wanted to talk about some basic terminology with artificial intelligence, machine learning, and deep learning at a very surface level so that anyone who might want to learn about it can do so. I wanted to make an entry point into this complex world of technology that is starting to grow exponentially. Hopefully, this blog can be a relatively easy read and pique the interest of those who might not have been interested before.
As someone who is interested in a career working with AI, I’ll also briefly go over possible career choices for those who are interested as well as some requirements. Lastly, I wanted to give my thoughts on the future of AI.
According to Wikipedia, Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals.
The basic definition of artificial intelligence can be obtained by separating the two words and then defining them. From Oxford Languages, artificial is defined as “made or produced by human beings rather than occurring naturally, especially as a copy of something natural.” Intelligence is defined as “the ability to acquire and apply knowledge and skills.” Putting these two together would create the definition of “something that is produced by human beings and is able to demonstrate and apply knowledge and skills.”
Artificial intelligence is a general umbrella term for anything that has the capacity to perform a human-like skill on its own. There are hundreds of things that you interact with already that have some form of AI within them. To list just a few examples, Google Maps usage of anonymized location data to help suggest faster routes during rush hour; mobile check depositing apps being able to decipher and convert handwriting into text; and Facebook’s ability to recognize faces and suggest which friends to tag. This is just the tip of the iceberg, as once we start to dive deeper into how some of this AI works, we will need to know what machine learning is.
Machine learning is a subset of AI. According to MIT Technology Review, machine learning algorithms use statistics to find patterns in massive amounts of data. Common uses of machine learning involve image and speech recognition, extraction of data from web pages, and learning associations. The last one might sound a little unfamiliar at the moment, but have you ever purchased a product online and other related items get recommended to you? That’s learning association. Some of the most popular applications we use, such as Netflix, YouTube, Spotify, and even Amazon, utilize machine learning for making recommendations towards things we might like.
When it comes to Amazon, it’s hard not to talk about the virtual assistant Alexa. Alexa is a great example of a subset of machine learning known as deep learning. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. This technology is the main driving force behind virtual assistants, facial recognition, and self-driving cars!
Here’s an image I found to help visualize the relationship of the three:
If any of this stuff interests you, there are plenty of online introductory courses for learning about AI, machine learning, and deep learning. The cofounder of Coursera, Andrew Ng, has a course called AI for Everyone that is available for free. For those who are interested in more formal education, a bachelors in computer science is typically a good starting point. Many mathematics focused degrees are applicable as well. Some job roles that involve working with AI are data analytics, natural language processing, user experience, computer science and artificial intelligence researcher, and software engineering.
The future might not have flying cars like in the Jetsons, or robots walking among us pretending to be human beings like in Terminator, but AI is most certainly here and deeply intertwined with our lives already. On a daily basis, we interact with AI more than we think we do with the likes of Alexa, Siri, and Google. The future of AI will only continue to expand, and there will always be a need for people who can interact and engineer them better.
