Image: AI logo medium-blue in color electric current surges across imagery | Source: Bing Image Creator
You can't go anywhere without being confronted with it.
It's in your PC, your smartphone or tablet, and that's not to mention the numerous other devices where AI dwells.
However, there's a slight problem...
There are people out in the world who still don't know how to make use of AI technology!
Well... Today is their luckiest day!
LOL, thought I was going to say lucky eh?
Let's face it, artificial intelligence is everywhere; from our phones to cars that drive themselves.
It's not just for tech experts anymore...
Anyone can learn it!
All it takes is having access to key information to unlock the possibilities of AI power.
Today, newcomers to the world of AI are welcome to read this informative guide that will cover the basics of AI.
And by the time they're done, they will have a better understanding of what it is; and how they can use it!
Key Takeaways
- Understanding Artificial Intelligence Fundamentals
- AI Needs to Follow The Rules of Learning
- Exploring Machine Learning & Artificial Neural Networks
- New AI Uses Need to Understand how the Digital Brain Processes Memory & Information
- AI Prompts: Learn The Basics ASAP!
- Start Your AI Journey Today!
Understanding Artificial Intelligence Fundamentals
That's where we all start at, right?
Which is always with the basics...
And when it comes to understanding the fundamentals of AI, it's wise to start with learning how machines think, learn, and make decisions.
Now, when you hear how a machine can think, just remember machines can't actually think like people.
For them, it's comparative to "pseudo thinking," and the term "pseudo" means false.
So, at its core, AI is about teaching computers how to spot patterns, and how to use data that helps it improve over time.
And for Sci-Fi movie lovers who saw the movie "Chappie," they saw how an android starts off learning like a child.
Chappie had to learn by recognizing his surroundings "via" pattern recognition.
Over time, he learned to identify being within various environments, how to walk, talk, and even learn the differences of right and wrong.
It was a perfect way to demonstrate how an AI system can learn and evolve in this case, it did so while fitted within the body of a droid!
However, from a human perspective, people will be tasked to interact with AI; and each time they do the machine is in the process of learning something new.
Every artificial intelligence system needs to be exposed to large amounts of data; that enables it to take the next step as it progresses in advancement.
AI Needs to Follow The Rules of Learning
Oh yeah, that's a true blue fact, a computer's AI must be given a set of rules to follow and uphold.
And it comes in an abundance of information, because it can better learn from tons of data; which later helps them spot trends and make key predictions.
There are two main types of learning here:
1. Supervised Learning
2. Unsupervised Learning
With Supervised Learning, you give the machine labeled examples (like pictures of cats and dogs).
This is what AI does best, which is learning how to classify content of various types.
And with Unsupervised Learning, it's left up to the machine to figure out patterns without the aid of pre-labeled examples.
Then there’s neural networks, which mimic the human brain’s structure.
These networks are made of layers of nodes that work together to process information, just like how our brain processes thoughts and reactions.
The more layers, the deeper the learning AI can explore!
AI is also about improving and adapting over time, which makes it incredibly powerful.
Machines don’t just stick to the same rules; they evolve as they process more data.
In a way, AI is always improving, just like we do when we learn something new.
AI isn’t just tech, it’s philosophy.
It’s almost like giving machines the ability to *think* for themselves—what does that even mean for humanity?
It’s all pretty simple when you break it down, all of it centers around gathering data, learning new skills, and increasing levels of AI efficiency!
Exploring Machine Learning & Artificial Neural Networks
Machine learning and neural networks might sound futuristic, but they’re already surrounding us; especially in this 2020 decade.
Just take two based examples such as Netflix Picks and AI art.
Both are high-tech products managed by artificial intelligence, and it's done without the slightest clue anyone would bother to notice.
And that's the ideal behind a silently robust workflow, and what was just mentioned describes it quite well...
In retrospect, machine learning is necessary because products such as AI Chatbots gather data in order for them to connect dots toward solving problems.
Instead of giving AI rules, we just show it patterns and say “figure it out.”
Traditional coding says, ‘IF THIS HAPPENS, DO THAT.’
Hm...
"If this is that, then that is this."
That’s classic rule-based computer programming language.
It’s commonly known as conditional logic, and it’s the backbone of traditional coding.
This kind of logic is rigid: “If X happens, do Y.” Nothing more, nothing less!
It’s also the foundation that early computer programs were built on—and yes, it's technically tied to machine learning too.
But here’s where machine learning shakes things up...
Instead of hardcoding rules, it flips the script, and compares new info to thousands of past examples.
From there, the system learns patterns, makes predictions, and responds in real time.
So, when the human user asks something, or makes a request, AI just isn’t running a single command.
Behind the scenes it’s analyzing, adjusting, and figuring out the most likely answer to kick back out fast as it can.
Like mentioned earlier about the robot Chappie, its AI eventually gets better and more accurate when making predictions.
Now, to the AI this is all practice, it's also info it can store within its database for later use.
New AI Users Need to Understand How the Digital Brain processes Memory & Information
How AI “Thinks” vs. How Humans Think:
It's commonly conceived by the average human user, where some people truly believe that AI is capable of thinking the same way they do.
Well... That's not quite the way it works like they think it does, in fact, there's a rundown list below to "weed out" some of those misconceptions.
1. Human Memory vs. AI Memory
Humans' memories are built from lived experience—neurons firing, forming connections, evolving over time. Emotions, context, and repetition all play a role in shaping what we remember.
AI: No neurons or nostalgia here. AI stores what it’s learned in the form of patterns and numerical weights inside neural networks. Unless it's designed to retain info across sessions (like using a database or a memory-enhanced model), it doesn’t actually “remember” anything you’ve said before.
2. How Info Gets Processed
Humans interpret the world based on emotion, intuition, memory, and experience.
Human beings tend to reflect, or assign meaning to things they acknowledge, they also have a tendency to overthink things at times.
AI: AI looks at huge amounts of data and finds patterns, because it's not capable of reflecting upon its actions—it just calculates what it's given.
It also operates upon layers of algorithms which makes it quite versatile.
What’s the most statistically likely response based on what I’ve seen before?”
3. The Two Types of AI Memory
Short-Term Memory: Most AI (like chatbots) can keep track of your conversation but only while the chat is still open.
However, once the session ends, the information is still there temporarily unless the user deletes it—then it's gone forever!
Long-Term Memory: Now, there are some AI platforms or systems may use databases that can store information for an extended period of time.
Just know that doesn't mean that information will continue to remain, it falls similarly in the same category as short-term (to degree).
So, again, information can remain but only if it’s not deleted from the user's database account.
Long-Term memory is capable of functioning more so as an digital library (to an extent) and users can search through it to retrieve a previous session.
But, all of the information acquired is incapable of maintaining a personal stash of individual past experiences.
4. AI Doesn’t “Think” the Way We Do
This is a big one, because AI doesn’t understand how to formulate thoughts, emotions, nor does it possess consciousness.
None of that means anything to an AI functioning system, so things like the the meaning of life or dealing with stress is trivial
Even when it sounds super insightful, it’s still just connecting the dots based on data, not any form of awareness or inner life.
5. Learning: Human vs. AI
This is the most important decider between humans and AI, and that imperative factor is—having the ability to learn on the fly!
This is what makes human being so valuable, even though AI is great, it still needs a human's creative touch to manage it's functioning systems.
The only way that changes, is if AI operating within an autonomous robot's body is capable doing for itself totally independently.
This has been hinted at in previous movies such as Terminator (particularly "Terminator 2") [1]
Humans also understand they will be made to be accountable for things they get wrong, or things mess up on.
Even so, it's a necessary step toward learning from the mistakes they make in life, and they learn from the experience in order to moving forward.
AI, on the other hand, will not be held accountable for any of its unfavorable actions.
There's also very few AI models capable of learning information on its independently
Once trained, they stay pretty much the same unless someone updates them with new data through a retraining process.
So, if you’re just getting into AI, remember: it’s a brilliant tool, not a digital mind.
It’s great at solving problems, analyzing patterns, and generating creative content—but it doesn’t “know” you or “feel” anything.
Use it for what it is, and you'll get the most out of what AI has to offer.
AI Prompts: Learn The Basics ASAP!
The most important thing you'll need to learn right away is learning how to prompt the AI effectively.
Once you get good at dishing out effective prompts, it will help save you a ton of time and effort.
Essentially, what you're looking to accomplish is creating something genuine, insightful, and most importantly helpful that inspire others.
Check out a few examples below to get a basic idea of how it works:
1. Type Up Specific Prompts: So, when you start prompting, don't start off with a generic prompt; or saying something to the effect of “Hey, whip up a kickass Sci-Fi action plot!?
1(a). Yeah, don't prompt that way, it's too simplistic. The aim is to provide a great pitch that resonates! So, looking back at a great Sci-Fi prompt would be something to the effect of; Pitch a thought-provoking sci-fi storyline centered on the ideal about an all AI society, with a group of human resistance protagonists plotting to rise up and take back the society that was stolen from them.”
1(b). Nevertheless, you get the idea of layering your sentences out in such a manner, crafting them clearly and intentionally, that's what gives you more clarity and powerful prompts leading to a better the output!
2. Set the Scene with Context: A lot of people wonder if AI can tell whether their prompt is “good enough.” Truth is it can’t. AI doesn't judge your prompt like a human would; it analyzes the words you feed it and compares them to patterns it’s seen in effective, high-impact prompts. So, if you're working on something that demands over the top creativity; like nailing that cinematic presentation—then the key is to give the AI model of choice the right prompt signals to work with. It’s a bit like trial and error.
2(a). You have to play around with phrasing and keywords until you hit the sweet spot. Don't be afraid to tweak your tone or get specific about the vibe you're looking to use in your script. For example, instead of a generic “Write an action movie,” you could step it up a few notches and say:
2(b).“Write a wild, action-packed screenplay in the style of Quentin Tarantino. Set it in the rugged Old West. The story follows a vampire gang that robs small towns and drinks the blood of innocent locals; until they attract the attention of a relentless vigilante vampire hunter.”
2(c). Yeah, now that's the type of prompt to set the mood, tone, and pacing all in one! It's dark, gritty, and packed with cinematic potential. The more you guide the AI with that kind of rich context, the more it can deliver results that match your creative vision!
3. Break It Down Into Steps: Well, that's the obvious direction to go, especially if you're working on an enormous project such as building an cinematic world can feel overwhelming. Thing is to NOT try and unpack all of your ideas all in one total swoop.
3(a). Instead, try something like this, “First, describe a dystopian future where automation has replaced human labor. Then, introduce a rebel protagonist who challenges the system."
3(b). Breaking things down keeps the responses focused and coherent.
4. Iterate Like a Pro: Remember, that first draft you complete doesn't mean you're finished—in fact, that 's just the starting point... Many writers know to do the next thing, which would be to tweak their prompts, add or subtract an additional ideal set of words to see a follow-up prompt that might turn out to be what you're looking for:
4(a). “Can you make this more suspenseful?”
4(b). Let’s dive deeper into the antagonist’s motivations — “Add in the stumbling blocks" (or write in some internal conflict that challenges both the protagonist and the antagonist).
4(c). Write in some thoughtful "dialogue" to help strengthen the story's output taking it from good to fantastic!
5. Playing with Different Angles: Sometimes more depth may be required to help boost the significance of the prompt's effectiveness.
5(a). Just know it doesn't hurt to ask the AI (basically any of them) to analyze various perspectives.
5(b). A basic example: “Describe a battle scene from the hero’s POV. Now rewrite it from the villain’s." See that right there is a noble way to develop nuance, not to mention adding in some tension within the storytelling.
Keep in mind that these are key areas you want to get proficient in.
If you master these examples surrounding AI Prompts, then you're on your way to become a prolific content creator indeed!
Here's A Few Prompts to Try Out
Depending on you're level of creativity, you can come up with some effective prompt commands that can help you produce great content.
This comes in handy when you're tied up with other projects, and you're in need of some quick typed up material.
With that said, there's one thing you have to keep in mind at all times after scrapping content from ChatGPT, CoPilot, or Gemini— is just the beginning.
Never just copy and paste in material given to you from an AI, you have to be able to re-work that information thoroughly!
And if you're thinking about this next part of what will be said is surely what is pointed out in an article piece on breaking up AI blob sentences.
Keep in mind, this is done with the prompts that have been used to create the content you see at AI Shifting Gears.
Visit my Substack page to learn more where I give a few basic demonstrations on how to go about doing just that - FOR FREE!
Thank you for your readership! 😄
Primary Keyword:
#AI
Semantic Keywords:
#ArtificialIntelligence
#NeuralNetworks
#MachineLearning
#UnderstandingAi
#IntelligenceFundamentals
#FundamentalsAi
#AINeeds
References:
- 11/13/24/Horsey, Julian/www.geeky-gadgets.com/ai-pattern-recognition-vs-reasoning/ > Is AI Really Thinking? The Truth Behind Machine Reasoning
- 1/12/25/Pyke, Curtis/kingy.ai/blog/supervised-vs-unsupervised-learning-when-to-use-each-and-why/ > Supervised vs. Unsupervised Learning: When to Use Each and Why
- https://www.ox.ac.uk/news/2023-05-05-artificial-neurons-mimic-complex-brain-abilities-next-generation-ai-computing/ > Artificial neurons mimic complex brain abilities for next-generation AI computing
- 3/12/21/artificial-intelligence/www.ibm.com/think/topics/supervised-vs-unsupervised-learning/ > Supervised vs. Unsupervised Learning & Neural Networks?
- 2/14/25/www.digitalocean.com/resources/articles/supervised-vs-unsupervised-learning > Supervised vs. Unsupervised Learning: Which Approach is Best?
- 11/14/24/S. Lukesh/www.guvi.in/blog/tensorflow-project-ideas/ > 10 Outstanding TensorFlow Project Ideas [With Source Code]
