Let’s be honest, Artificial Intelligence is a term straight out of an old-fashioned science fiction film. The conversation about it is very easy to imagine talking robots, sentient supercomputers, or dystopian Sci-Fi settings in which machines rule the world. However, when you remove the jargon and corporate speak, what is artificial intelligence in real-world terms?
Simply put, AI is a computer program that has been taught to perform tasks traditionally associated with the human brain. It’s about figuring out what you see, what you translate, what you decide, what you learn from, what you do next when you goof up, what you like next on Spotify, etc.
When a computer is programmed to follow step-by-step instructions, each of those instructions must be written by a person. In a typical computer program, the person has to write down every single instruction that the computer must follow step by step. It’s not magic, it’s not alive. It’s merely super smart math on insanely quick hardware.
Key Takeaways
- Artificial intelligence is basically teaching computers to think and spot patterns like humans.
- AI isn’t magic or a living robot, it’s just super smart math running on massive amounts of data.
- Machine learning and generative AI are specialized subfields inside the bigger AI umbrella.
- Today’s AI is “Narrow AI,” meaning it’s only good at specific tasks, not general thinking.
- Understanding AI helps you stay ahead of tech trends without falling for all the hyper-exaggerated hype.
AI Overview
Artificial intelligence (AI) is the study of creating computer systems that can simulate human thought processes, such as learning, reasoning and problem-solving. Unlike traditional rule-based systems that are hand-coded, AI systems analyze vast amounts of data using algorithms to identify patterns and generate predictions. From spam emails to facial recognition for unlocking phones, or creating new text, AI functions by learning from existing data and making proactive decisions. If you don’t know much about AI, this is the book for you.
Artificial Intelligence Explained: The Non-Techie Breakdown
The best way to understand what an artificial intelligence definition entails in the real world is to consider the difference between normal software and AI software.
Consider standard software as a strict recipe as in cooking. The recipe says: If you wish to make a cake, you need to:
- Mix 2 cups of flour.
- Add 2 eggs.
- Bake at 350°F for 30 minutes.
If you do the same thing, you end up with a cake. What if you put a shoe in the mixer? The recipe fails or a terrible mess is created. The same thing happens with the normal software, it runs with a set of rules created by a programmer, which are of the form “if this, then that. When a user does something that the programmer didn’t expect, the program crashes or generates an error code.
| Software Type | Process / Logic | Outcome |
| Standard Software Logic | Input Data → Pre-programmed Explicit Rules | Fixed Output |
| AI / Machine Learning Logic | Input Data 1 → Advanced Learning Algorithm (ALA) 1 → Pattern Discovery 1 | Prediction 1 |
Now think of an AI system, instead. Instead of writing out a list of steps that are followed in making a cake, pie, and shoe, you present the computer with 10,000 photos of each completed cake, pie, and shoe. You say to it, “Find out what makes a cake a cake.
All the pixels, colors, shapes and textures are analyzed by the AI. After a time, it discovers that cakes are typically round, have frosting, and are soft to the touch, whereas shoes have laces and rubber soles. Then, if you present it with a picture of a random dessert it has never seen before, it is able to look at the patterns and say, “Yep, that’s 98% likely to be a cake.
The ability to learn new and unexpected information without breaking is what makes AI technology so powerful. No one wrote the code that says “If a golden retriever dressed in a hot dog costume runs across 5th street, stop. Rather, the computer vision system in the car uses millions of miles of driving data to identify an obstacle in real-time and apply the brakes automatically.
How Does Artificial Intelligence Work? (Under the Hood)
Having a computer science degree is not essential to understand how artificial intelligence works. Upon closer inspection, all the various AI systems you use are built on just four key components: Data, Algorithms, Training, and Prediction.
| Component | Definition / Function |
| Massive Data (Inputs) | Raw data that are entered into a system. |
| Data Base (Math Engine) | The data stored in the computer. |
| Inputs (Math Engine) | Data fed into the computer. |
| Training (Trial & Error) | Modifying internal parameters until they are more accurate. |
| Predictions & Answers | Output generated for new data. |
1. Feeding the System (Massive Data):
AI is the horsepower that comes from data. Without the information, an AI system is unusable. To get an AI to create blog posts, you’re going to have to feed it millions of content pieces from the web. For it to be able to detect skin cancer, you’d need to provide it with thousands of medical scans classified by real doctors.
In BrandClickX we like to say that the AI model is only as smart as the data you feed into it. You put in shoddy, biased, or substandard data and you will get shoddy, biased, or substandard results from it.
How do you solve this problem? What is the solution for this problem?
An Algorithm is actually just a mathematical equation or set of rules. In AI, algorithms make calculations on raw data to make a prediction. The algorithm is not conscious of what a dog is like; all it does know is these are the combinations of pixels that are typically associated with a dog.
3. Practice Makes Perfect (Training Phase)
At the training time, the AI makes guesses repeatedly. Initially it is very poor. It considers a picture of a toaster and guesses “banana.
As it makes mistakes, it corrects its internal settings (known as weights and biases) to reduce the likelihood of repeating the mistake. It does this many times, millions or billions, until the accuracy level becomes ridiculously high.
4. Making the Call (Prediction Output)
When the training period is complete, the AI is ready to go to the real world. You feed it something new it’s never encountered before, and it returns a prediction, a suggestion, or fresh text, based on everything it has learned in its training.
Why Narrow AI is Fooling Us
Large language models, which write entire essays in seconds, are still Narrow AI.
Why? Because a generative model doesn’t actually “know” what it’s saying. It never experiences emotions or has opinions or general life experiences. It’s just an insanely advanced text predictor that is able to predict what word should logically come next, based on billions of pages of training material. Ask a language model to drive a tractor or cook a meal, and it can’t do so it can only process and generate text patterns.
The Core Building Blocks: AI Subfields Made Simple
Artificial intelligence is too big to be just one single program. It’s actually a group of particular subfields that do the job of a team to perform complicated tasks.
| Key AI Subfield | Focus / Function |
| NLP: Natural Language Processing | Comprehension of human speech, grammar and text. |
| Computer Vision | Examining, interpreting and understanding digital images and video feeds. |
| Predictive Analytics | Using past trends in data to predict future outcomes. |
| Robotics & Automation | The incorporation of artificial intelligence software and physical machines in an integrated manner to interact with the physical world. |
Natural Language Processing (NLP)
How computers understand human speech and text is defined by NLP.NLP defines how computers understand human speech and text. Human language is a jumbled language, it’s slangy and sarcastic, it’s double entendres, it has all these weird grammar quirks.
By breaking down sentences, understanding context, and responding like a real person, NLP enables an AI system to understand complex language and maintain natural interaction. NLP enables an AI system to process and comprehend complex language and interact naturally with the user, breaking down sentences and understanding context. It’s what powers translation apps, automated voice lines, and conversational search tools.
Computer Vision
Computer vision is the ability to see what a computer is looking at, and where it’s looking at. Computer vision is providing computers with eyes, where they look and what they see.
It is used to allow software to search digital images and/or video streams and respond to the results. It’s applied in all sorts of ways: in medical software, for detecting tumors in X-rays; in manufacturing, to inspect products for minute scratches as they roll down an assembly line.
Predictive Analytics
Predictive Analytics involves using historical data and statistical modeling to make predictive statements about the future. Retail outlets rely on it for their hot cocoa stocks in advance of cold fronts, banks use it for foretelling economic trends and streaming services use it to predict what show you might want to binge next Friday night.
Robotics and Automation
The classic factory robot is actually very stupid: it performs the same motion with the mechanical arm over and over again. When combined with AI, you have smart robotics! An AI-powered robot can navigate a chaotic warehouse, lift boxes of various shapes and sizes without dropping them and avoid humans who are walking around.
Artificial Intelligence in Everyday Life
While the term “high-tech” is associated with artificial intelligence, it doesn’t have to be that way. If you have ever used any text-to-speech or synthesis services, you’ve been using AI technology without realizing it. This is where it becomes commonplace:
Have you ever wondered how your smartphone photos are so crisp even in bad light? As soon as you tap the shutter button, AI image processing adjusts exposure, cuts down noise, and enhances color in the blink of an eye.
Email Spam Filters: Pattern Recognition Algorithms that check the incoming emails in your Inbox. The AI filters out emails with shady language, dubious links or odd sender email addresses.
Predictive AI as seen on Netflix’s “New on Netflix” suggestions or Amazon’s “You might also like” feature is an analysis of what you are watching and buying compared to millions of others.
Navigation & Rideshare Apps: Google Maps and Waze rely on real-time location data, speed limit history data, and user reports to determine the fastest route to drive. Rideshare apps leverage AI to connect you with drivers in your area and adjust prices in real-time.
In the case of a fraud alert, if you live in New York and someone tries to purchase a $1,000 laptop in another country using your card, the AI security monitoring system of your bank detects the anomaly and freezes the transaction right away.
Humans vs. Artificial Intelligence
| Feature | Human Intelligence | Artificial Intelligence |
| Learning Style | Humans acquire knowledge through extensive experiences, sensory feedback, emotions and social interaction. | An AI only learns using numbers, pixels, and text files. It takes a toddler one time to see a single dog in a park and know for the rest of their lives what a dog is, while an AI needs to see thousands of images before it gets it right every time. |
| Adaptability | Humans are very good at learning from one thing and using it in other. If you know how to ride a bike you can learn how to ride a motor scooter in no time. | If you try to have an AI trained to play chess to play checkers, it’s completely without idea—you need to clear its slate and start training it again. |
| Energy Efficiency | The human brain is one of the greatest wonders of nature. It does complex reasoning, pumps blood around your body, understands what you see and hear, and can create art masterpieces, while using just 20 watts of electricity, which is roughly equivalent to the power usage of a single weak lightbulb. | Building a large AI model, by contrast, necessitates very large data centers using megawatts of electricity. |
How Did We Get Here? A History of AI
How Did We Get Here? is a brief, humorous, and informative history of the world.
Although AI may seem like a new invention that just appeared a few years ago, researchers have been busy with smart machines for more than 70 years.
| Period | Landmark Event / Era |
| 1950s | The term “AI” is officially coined; the Turing Test is proposed by Alan Turing at the Dartmouth Conference in 1950. |
| 1970s-1980s | The ‘AI winters’ of the 1970s-1980s: when the availability of computing power makes funding and research interest wane. |
| 1990s-2000s | 1990s-2000s: IBM Deep Blue defeats Garry Kasparov at chess, hardware and algorithms continue to get better. |
| 2010s–Present | 2010s–Present: The era of generative AI tools, powerful GPUs, big data, and deep learning explosion. |
The 1950s: The Birth of the Idea
The 1950s represent the birth of the idea. The 1950s is the birth of the idea.
Legendary British mathematician Alan Turing posed a simple question in his 1950 paper: Can machines think? He invented the Turing Test, a test that was designed to determine whether a computer could have a conversation with a human being so convincingly that the human couldn’t tell if it was with a human or computer.
In 1956 a group of scientists met at the Dartmouth Conference and the term Artificial Intelligence was formally established. They were way too optimistic and they thought that the intelligent machines that are equal to humans are just 10 years away. (They were so off-base, in fact, they were wrong SPOILER!).
The 1970s–1980s: The “AI Winters”
As those early pledges were not fulfilled, governments and investors grew impatient. Computers were not fast enough and had insufficient memory for more complex jobs. Research stalled in what was known as the “AI Winters,” and funding fell off.
The 1990s–2000s: Brick by Brick
After a slowdown, progress resumed with the development of faster, cheaper microchips. IBM’s supercomputer, Deep Blue, won the world chess championship in 1997 when it defeated world chess champion Garry Kasparov. Fans for the first time were allowed to watch regular people do battle with machines at elaborate strategic games and discover they could not outwit the machines.
The Big Bang: The 2010s to Today
Three events happened at the same time and triggered the modern AI explosion:
- The Internet: It produced huge amounts of digital information (text, photos, videos) for the systems to learn from.
- Supercharged Hardware: High-powered graphics cards (GPUs) allowed for super fast calculations of complicated math equations.
Multi-layered neural networks were discovered that could process unstructured data at scale.
It’s the combination that has helped create modern voice assistants, self-driving cars, computer vision and the generative AI models we use today.
What’s Next? The Future of Artificial Intelligence
What’s Next? This is the period where AI is shaping the future. This is the era of the Future of Artificial Intelligence.
So whither all this tech? People don’t have a crystal ball, but experts say that there are some obvious trends in artificial intelligence in the next few years:
Artificial Intelligence as a hidden utility.
Much like electricity or the internet, AI will no longer be thought of as an added on feature in many tools, apps, and software for much longer, but will be incorporated into every single one we use. “You’ll not say “I’m using an AI program ‘ just open your phone and smart features will be running smoothly in the background.
The AI engine in the device is smarter, faster, and on-device.
Early AI tools used to be cloud-based, meaning that you had to upload your data to massive power-consuming data centers to get your AI answer. Newer ones are getting increasingly efficient, and advanced AI will be able to operate right on your smartphone, laptop, or even the microchips in your car, without an internet connection required. This translates to quicker responses and much better privacy of your personal information.
The focus is on ethics and safety, with a strong emphasis on heavy emphasis.
Governments across the globe are taking action as AI takes on larger, real-world tasks, from processing loan applications and aiding in medical diagnosis, to operating cars. Be prepared for new regulations on data privacy, fair and transparent algorithms and more stringent safety guidelines.
Our vision at BrandClickX is that the ultimate purpose of the AI technology is not to take the place of humans, but to get them to do the hard work so they can focus more on being creative, building relationships and solving the big, complex problems.
Internal Linking Strategy
Here are some good articles to read to get a better understanding of the AI landscape:
Pillar Page Link: What is the end goal? For a detailed look at artificial intelligence, please read our Artificial Intelligence guide to learn more about the positive influence of these technologies on industries worldwide.
If you don’t know the technology behind Article Link 1, then cluster it. Check out our comprehensive article on AI vs Machine Learning: What is the Difference? to find out the difference in detail.
Group Article Link 2: Interested in the consequences of these algorithms on your everyday life? See our examples of how AI is being used in everyday life in How AI Is Used in Everyday Life.
If you are interested in tools that program code and/or create art, then you will find Article Link 3: Fascinated by tools that write code or create art interesting! For an in-depth look at What Is Generative AI? check out our deep dive.
Conclusion
If you remove all the buzzwords and jargon, it’s just a big leap forward in computer science. It’s not magic, it’s not an all-knowing digital brain, it’s high-level software that can deal with a lot of data, look for patterns, and then solve a problem for us.
Today, AI technology has transcended science fiction novels and entered the real world, powering algorithms that filter spam, predictive systems that warn physicians of disease onset, and much more. If you know the fundamentals of these systems, know what they do well, and know their weak spots, then you can confidently use these tools to simplify your personal and professional life.
Frequently Asked Questions
Is AI a threat?
No. Today’s AI doesn’t feel, it doesn’t have any consciousness, it doesn’t have any desires. The true threat lies in humans using very powerful tools in an unsupervised and inappropriate manner.
Will AI take away my job?
Routine and repetitive tasks are the ones that are going to be automated by AI and, as a result, change the way a lot of jobs are done. Those who can use AI to improve their productivity will be in demand in general.
Is coding required for utilizing AI tools?
Not at all! Most of the modern tools are based on the plain conversational language which means you can communicate to it in simple words.
What is the difference between an algorithm and an Artificial Intelligence (AI)?
An algorithm is a series of step-by-step procedures (like a recipe) to accomplish a task. The beauty of an AI system lies in its ability to learn, make predictions and adapt itself to the data without the need for a human to write new instructions every time.
