So, picture this: Three months of yawning boredom, countless Netflix binges, and a growing suspicion that my brain cells were staging a protest. What better way to beat the blues and upskill than by diving headfirst into the weird world of Artificial Intelligence (AI)?

So, here I am, armed with determination, a laptop, and a questionable amount of caffeine. My journey into the digital wilderness begins. Time to buckle up, because we’re about to explore neural networks, deep learning, and maybe—just maybe—unlock the secrets of the universe. 🚀

Where to Start?

The main thing holding me back from getting into these tools was a general confusion about the various terms and buzzwords surrounding artificial intelligence. Our opinions are shaped by the “magical” things we see in the products and movies we consume, but the reality about AI is not some weird alien intelligence, it’s 1s and 0s planted firmly in the computers we already use and understand.

I thought I needed some obscure math knowledge or deep theory, and that just wasn’t the case. I assumed ML engineers had a lock on some important knowledge unavailable to me… nope. So let’s peel back some of the layers to understand what we’re working with.

Free is the Way

In 2024, you don’t have to pay for access to decent AI. You can benefit from tons of free resources that give you access to the latest commercially available models without paying. If you do want to go the paid route (e.g. ChatGPT Pro or Midjourney) your cost layout is relatively minor.

I don’t feel like I know enough about this subject yet to commit to paying for a particular implementation so I’ve been leaning toward open source projects and freely available commercial offerings. Most of the tech big players want in on this space and they make a ton of resources available to use for free.

Buzzwords vs Ideas

It’s never good when executives start talking about tech trends before you do, but that was the case for me when it came to machine learning (ML). It was some kind of miracle technology that was going to understand our customers’ sentiments and shortcut our internal processes for us. What is actually was, was a fancy way of saying big expletive Excel spreadsheet.

Essentially, this form of AI is a prediction engine that gets better with training data. Based on millions of examples, it can take input and guess what the output is. That’s it.

Combine ML with natural language processing (NLP) and you get a chatbot that appears to understand written human speech. This is what gives chatbots the uncanny valley where you might think (at first) you are talking to a real human. As we’ve all seen, these apps are easy to “fool” into giving you out of bounds responses, since they tend to have very little training outside their specialty area.

The terms neural networks and deep learning refer to flavours of the same thing, and are just ways of describing the complexity of the data beneath the ML. Deep learning in particular differentiates itself by having hundreds of millions, even trillions, of connecting nodes from which to draw inferences. This is the technology behind popular tools like ChatGPT; large language models (LLMs) are a particular type of neural network built on a transformer model that produces generative text based on queries.

Doing Things

What does it all mean? Knowing what the words are is one thing, actually doing something useful seems tricky at first.

The trick to using AI effectively is understanding what it does and doesn’t excel at.

Searching

If you ask AI to search for something you’ll get a Google-like result with a bunch of inaccuracies, so a lot of people give up at this step. Either the bot gives you a bunch of information that seems useful but you have to verify, or you are a subject matter expert and can immediately see all the problems in the results.

The reason for this is AI doesn’t actually know anything. All it can do is synthesize the data it was trained on. Any missing gaps get filled in to sound intelligent but really aren’t (this process is called hallucination).

Hallucinating sounds bad, but it’s actually an important part of how we can leverage these tools to do unique and creative things.

Writing

AI can absolutely write articles and papers for you. It’s even capable of writing in different voices and styles, and producing surprisingly unique-sounding prose. But as a finished product these works are garbage.

Although it’s good at mimicking aspects of human writing, there is a deep uncanny valley and “off” factor in articles written by machiens that don’t have actual personalities and don’t truly understand the concepts they’re writing about.

In particular, text generation is done word-for-word with no real knowledge of what came before and what will come later. Longer works especially will not flow, and the coherent text we do get fails to live up to the expectations of a native reader.

All that isn’t to be down on AI as a tool for writing. Some of this article, in fact, was written by ChatGPT. Can you tell which parts? AI is a fantastic tool to help inspire and speed up the creative process; it won’t replace a human entirely. You still have to edit the work and be prepared to describe, in great detail, what you want.

Which brings us to…

Idea Generation

If you’re stuck, describe your problem to an AI and it will spit back a list of ideas that will kick-start your next adventure.

Not sure what to cook for dinner? Take a picture of your refridgerator’s contents and ask for 5 meal ideas based on what it sees.

Cooking for picky houseguests? Ask for a mealplan tailored to their dietary preferences and tastes.

Working on the latest spacecraft for NASA? AI can shortcut the design of totally alien structures that perform better than traditional manual processes.

Specific Software

So what does it all mean? How do you actually jump in and do something useful with all this?

I kept getting stuck on tutorials that wanted me to install a bunch of Python libraries and code my problems, but that isn’t necessary at all. Here are my three favourite pieces of software you can use right now to dive into this.

Bing Copilot

Finally, a use for Bing! Remember Bing? That’s the search engine you get when you accidentally open Edge instead of Chrome. As it turns out, it’s being used as a clever gateway into AI - accessible and totally free.

Go to https://www.bing.com/chat and start talking to the AI. Ask it to make you a meal plan. A picture of a dog balancing on a circus ball. You need a (free) Microsoft Live account to sign into this but you can begin right away and get some impressive results.

Today Copilot uses a mix of GPT 3.5 and GPT 4 for language and Dall-E 3 for image generation. It advances fast, of course, and by the time you read this they’ve no doubt moved on to something even newer and better. Microsoft is able to flex their relationship with OpenAI (they are a partner and their largest investor) and has chosen to double down on their research in its products including Bing.

LM Studio

Stable Diffusion

Just Dive In

Learning AI has been transformative so far. The software in this article are not just gateways to understanding and getting practical use out of complex algorithms; they are the keys to unlocking a future of possibilities. By downloading and engaging with these tools, you’re taking the first step towards shaping that future. Whether creating intelligent solutions that tackle real-world problems or understanding the technology that’s changing the face of every industry, don’t just witness the AI revolution—be a part of it.

AI is not coming for your job. People who effectively use AI to make their work more valuable are coming for your job.

Seize this opportunity to learn, innovate, and lead the charge into a smarter, more connected world.

P.S. If you’re wondering whether I’ve accidentally created a sentient AI during my late-night coding sessions, fear not. My chatbot still thinks “banana” is the answer to everything. 🍌