Understanding AI Basics

What is Artificial Intelligence? Just the basics, please.

One of the most helpful things for me, in learning about Artificial Intelligence (AI) was to look at a diagram, like the one I drew on this page, that shows how AI is an umbrella term that encompasses many disciplines, including Machine Learning.

If you don’t have that basic context, or you’re not approaching AI from the perspective of a researcher or developer, AI can definitely feel more like a topic for Sci-Fi plots, and less relatable to our everyday lives.

But, it’s both… and so much more!

At its core, Artificial Intelligence represents a branch of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence. These tasks include, among many other use cases, things like speech recognition, decision making, language to language translations, and identifying patterns or objects in images.

The Genesis of AI

The field of Artificial Intelligence began in the mid-20th century, when Alan Turing published a paper, “Computing Machinery and Intelligence,” that posed the question: “Can machines think?” This question gradually evolved into efforts to make machines not just think but learn and adapt. Over the decades, AI has transitioned from simple rule-based algorithms to complex machine learning and deep learning models, capable of performing tasks that range from playing strategy games, like Chess and Go, at championship-levels, to driving cars.

Machine Learning & Neural Networks: The Brains Behind AI

Two terms often emerge when you start reading more about AI: “Machine Learning” and “Neural Networks”.

• Machine Learning is often described as working in a similar was as if you were teaching a child through examples. By exposing an AI model to huge datasets, we enable it to learn and make predictions or decisions based on patterns it identifies, without being explicitly programmed for each task — much like a child!
• Neural Networks, inspired by the human brain’s architecture, are a series of algorithms that mimic the operations of a human brain to recognize relationships in a set of data through a process that mirrors human learning to a certain extent. Elon Musk often refers to his own brain as his “neural net;” I only know that because I read Walter Isaacson’s biography of Elon Musk (Audiobooks count as reading, right? :) It was really worthwhile, I highly recommend it.)

Interestingly, the operation of AI, particularly in generating or understanding natural language (which is what the new “Generative AI” models, such as ChatGPT, do), involves predicting the next most likely sequence of words (or, more technically, tokens). This method used by Large Language Models (LLMs), while simplified here, forms the basis of how AI models understand and produce text, code, and even multimedia content — yes, including OpenAI’s Sora model which generates video from a text prompt. More information on how Sora works can be found on OpenAI’s website, here.

AI: Beyond Science Fiction

When I casually bring up the topic of AI while I’m out and about — as I did the other day with my dental hygienist while I was in for a cleaning, one of the most common things for people to bring up, in turn, is Science Fiction portrayals of AI — so basically movies with AI characters, or the other thing people bring up a lot is the show, Black Mirror — which, at least as of this article’s writing, I have not yet watched.

I suppose that it’s unavoidable to bring up popular culture references, since Movies and TV Shows have long dramatized musings of what it might be like to have artificially intelligent humanoid robots living among us. But that’s not where the conversation should end.

AI is already here with us in our day-to-day lives, and I think that we can and should do both (a) understand it in it’s current and evolving forms and (b) have fun thinking about the future. What I am hoping to avoid is a wholesale redirect into an apocalyptic, anxiety-producing future dramatization that forgets or ignores our reality.

While AI’s portrayal in science fiction often veers towards the dramatic, the reality is both profound and practical. AI is already interwoven into our daily lives, from the recommendations on streaming platforms (powered by AI algorithms) to virtual assistants (hi, Google, Siri & Alexa) in our smartphones and homes. The future of AI promises even more integration, with potential advancements that could revolutionize healthcare, environmental conservation, and various other facets of human life.

I think it is important to keep in mind that this future comes with its set of challenges and considerations, including privacy concerns, ethical use, and the mitigation of biases in AI systems. Understanding AI’s capabilities, limitations, and impact on society is crucial as we navigate this evolving landscape. Even so, those risks and challenges are not a reason to deter innovation, and indeed, the cat’s out of the bag on this one folks, there’s no going back and that’s why I feel that it’s so important that EVERYONE has a fair chance at learning about AI and using AI tools.

Embark on the AI Journey with TechExplorer!

Embarking on a journey to understand Artificial Intelligence is not just about comprehending the technology but appreciating its potential to augment human capabilities and address complex global challenges. As we dive deeper into AI’s applications and ethical dimensions in subsequent articles, remember that the essence of AI, from machine learning models to neural networks, revolves around the quest to extend and enhance human intelligence.

For those intrigued by the deeper mechanics of AI or seeking to explore further, we have a whole host of content for you at TechExplorer.AI.