While speaking with a friend recently I was jolted into reality by a question about my job as a writer, “Dude, why are you even bothering with that anymore? In 5 years robots will be writing better stories than you ever could.”
I gave him my customary, “Yeah whatever,” and brushed off his negativity. Then I took a few days to think on the subject and what the future might hold for creators like myself.
This time thinking led me down a digital rabbit hole, and I’m here to tell you about how I went from knowing nothing about these new technologies to being hired to train Large Language Models in under six months.
What follows is a guide on how to get your foot in the door to the world of A.I.
To skip ahead for a moment, I did far more than ‘think’ on the topic. I also went and took all of the classes I could take on AI and now have certifications in Machine Learning, Large Language Models, Generative AI, Prompt Engineering, Natural Language Processing, and a slew of other branches of Artificial Intelligence. And to add to your amazement, I’ll tell you that these certifications came from a combination of DeepLearning.AI, IBM, Coursera, and MIT.
Okay, so you must be a computer nerd, right?
Nope. Not even close. I still can’t code Python (though I admit the idea of reading it is far easier now than before these courses). I was a Political Science major in college, and I’m pretty good with languages (restaurant Spanish counts right?), but for most of my post-college life I’ve been a bartender, writer, and amateur stand-up comedian. The only computer programs I used before this were Microsoft Word, a series of inefficient restaurant ordering systems, and my iPhone. That’s it.
Like a lot of us, I saw IBM’s Watson win at Jeopardy a few years back and became amazed at its capabilities. Aside from that, fears of Skynet and an eventual Terminator were all I really understood about Artificial Intelligence. Don’t let the robots take over, and unplug it all before it’s too late, that was my previous attitude on the subject. Joining the wave of curious minds diving into the world of podcasts I would occasionally hear some executive or tech entrepreneur discussing the rise of AI and how it was going to change my life, not yet realizing that in some ways it already had. You’ve heard of autocorrect, right? That thing that makes you type ‘duck’ every time you mean to type ‘fuck’. Seriously, there’s no way that more people mean to type ‘duck’ than ‘fuck’. This is a great example of how AI isn’t perfect. It doesn’t know shit. Only shit we help it know.
So how did you know what to do?
Well, I got in touch with a friend of mine from the world of tech and asked him what he would suggest as a means of getting up to date on some of these newer technologies. I was then told to google “NLP” which stands for Natural Language Processing, and to see how much of it I understood as a concept. Though it might be difficult to understand without giving it a look yourself, I’ll tell you that my exposure to a second language did a great deal in helping me understand some of the concepts of NLP. His next suggestion was that I dive right into learning Python coding. However, as I said before, I’m good with languages, but I’m not gonna pretend that I can speed up the process of learning to code over a few months and get myself into a competitive position. So I asked, since I couldn’t code, what classes should I take to make up for not having that skill?
For those of you still curious, there is one resource that came from this conversation which I would call irreplaceable, and that is the learning platform of Coursera. Which was started by members of Stanford University, and mainly Andrew Ng, who is the head of DeepLearningAI, so there’s a lot of reason to believe they know what they’re talking about on the subject.
Next, I got an account with Coursera at only $59 a month. This is where the majority of these courses can be found.
With this new account I then took a series of classes as a means of categorizing a verifiable list of all of the information I was absorbing, as well as a set of credentials about my knowledge as a whole. This especially mattered to me because I speak damn near fluent Spanish and I have no proof of it. I mean, if you speak Spanish, we can get to the bottom of it real quick, but if not, I can’t tell you the name of any certification I’ve obtained in the language to verify my knowledge of it. So for once, I wanted to remember to catalogue this new skill and the information I was absorbing. Plus I hear it makes getting a job easier if you can show proof of work. Having no particular guidance beyond what I just told you, I ended up signing up for every AI class I could and taking them down one at a time.
As a part of combined efforts, I also began reading a series of books at this same time: (A list and links to which are found here…..but will be detailed at the bottom of this post)
These classes took me about 6 months in total to complete. Which compared to a four-year degree is a pretty efficient use of time. Most of them catered towards learning at your own pace and only one of them involved class discussion boards of any type. They weren’t particularly hard, but they weren’t particularly easy either. The only thing I can give you that might help speed up the process is the same thing I suggest for people when they’re listening to audiobooks, speed it up. Most of the videos and explanations are done so with a very monotone voice speaking at a slow rate which can be sped up to help you complete the videos in nearly half of the time. When you’re taking these classes make sure to take notes, lot of notes.
So where did all this get me?
Well. I finished my final class at the end of November and I can finally say I understand these technologies and what their implications can be for the future of our society and business. Since gaining that understanding I’ve applied to a slew of jobs that could serve as entrances into the world of A.I. In filling out applications I immediately went after a job in sales. Mainly because I’m close to 40 and I’m trying to make up for some lost time in the realm of making money. A few of the more notable companies that I applied to include OpenAI, Tesla, SpaceX, GitHub, Rockstar Video Games, and IBM. There are plenty more, but I figure for now I’ll just include the ones you’ve heard about.
Within a month of getting my certifications I had been through 3 rounds of interviews with IBM, 2 with a Swiss A.I. company, and another round of interviews with Tesla. While those were the early highlights, I ended up taking a job with a company called DataAnnotation to do human-supervised machine learning on Large Language Models.
The really important part???
Seven months ago none of these companies would have had any reason to take my resume seriously. Now I work for one of them.
So, What’s next?
Well, the more I learn about this, the more I’m going to share with you. And for starters, I’m working on my next post to help you understand the differences between some of the terms I brought up today. Mainly the difference between Deep Learning and Machine Learning, plus give you an idea of what Generative AI is. Until then, here are some resources that will help get you started.
Hope this was helpful.
2023 Relevant Reading List:
Natural Language Processing with Python: Natural Language Processing Using NLTK By: Frank Millstein
Algorithms: MIT Press Essential Knowledge Series By: Panos Louridas
Algorithms to Live By: The Computer Science of Human Decisions By: Brian Christian, Tom Griffiths
Accelerate: Building and Scaling High Performing Technology Organizations
Written by: Nicole Forsgren PhD, Jez Humble, Gene Kim
Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking Written by: Foster Provost, Tom Fawcett
The Age of AI: And Our Human Future By: Henry A. Kissinger, Eric Schmidt, Daniel Huttenlocher
The Etymologicon: A Circular Stroll Through the Hidden Connections of the English Language Written by: Mark Forsyth
The Human Advantage Written by: Jay W. Richards
Superintelligence: Paths, Dangers, Strategies Written by: Nick Bostrom
Storynomics Written by: Robert McKee, Thomas Gerace
Your Deceptive Mind: A Scientific Guide to Critical Thinking Skills
Written by: Steven Novella, The Great Courses
How Colors Affect You: What Science Reveals
Written by: William Lidwell, The Great Courses
Understanding Cognitive Biases
Written by: Alexander B. Swan, The Great Courses
Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones
By: James Clear
Written by: Programming Languages Academy
The Elements of Eloquence: Secrets of the Perfect Turn of Phrase
Written by: Mark Forsyth
So here are the classes in descending order of importance.
CLASSES:
Artificial Intelligence: Implications for Business Strategy M.I.T. Sloan & M.I.T CSAIL
Generative AI with Large Language Models DeepLearning.AI
Prompt Engineering for ChatGPT Vanderbilt University
Machine Learning: an overview Politecnico di Milano
Introduction to Large Language Models Google Cloud
Introduction to Generative AI Google Cloud
Introduction to Generative AI Studio Google CloudGetting started with AI using IBM Watson
IMB
Ethics in the Age of AI Specialization:
Artificial Intelligence Algorithms Models and Limitations
Artificial Intelligence Data Fairness and Bias
Artificial Intelligence Ethics in Action
Artificial Intelligence Privacy and Convenience Learn Quest
Full Disclosure:
I started certifications in Machine Learning and Deep Learning, but let it be known I never finished those classes. I was not proficient enough in Python coding to continue any further. I am also a little more than halfway through a certification in Applied Artificial Intelligence via IBM. There’s no good reason for not yet finishing this beyond lack of prioritization which came from not getting a job with IBM where these skills would be used.
the Stateside Wanderer 
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