AI From Zero - Lesson 6: Smart vs. Just Fast: Understanding AI's Capabilities in Depth
AI vs Traditional computer programming techniques
"Intelligence is the ability to adapt to change." - Stephen Hawking
In our journey through AI so far, we've learned that AI is incredibly powerful and fast. But "fast" doesn't always mean "smart" in the human sense. Understanding the distinction between how traditional computer software works and how AI "thinks" will help you use these tools more effectively and responsibly.
Rules vs. Learning: The Core Difference
Think about a traditional computer program, like a calculator or a basic word processor. These programs operate based on a set of very specific, pre-programmed rules. Every action, every calculation, every function is explicitly told to the computer by a programmer. If a rule isn't written, the program can't do it.
AI, especially through Machine Learning, is different. Instead of being told every rule, it's given a large amount of data and then it learns the rules or patterns from that data.
Traditional Software: A calculator knows 2+2=4 because a programmer wrote that specific rule.
Artificial Intelligence: An AI recognizes a cat in a photo not because someone wrote a rule saying "if it has pointy ears, a tail, and whiskers, it's a cat," but because it learned those patterns by seeing millions of cat photos.
This ability to learn from data is what makes AI so adaptable and powerful.
How AI Makes "Guesses" Based on Patterns
When an AI responds to your prompt, generates an image, or makes a prediction, it's essentially making a highly educated "guess" based on the patterns it identified during its training on vast amounts of data. It's trying to predict the most probable next word in a sentence, the most likely outcome, or the most fitting image component, given its training.
For example, when a text AI generates a paragraph, it's not "understanding" the words in the way a human does. It's using statistical patterns from billions of text examples to figure out which word is most likely to follow another, given the context you've provided. This makes it incredibly efficient at generating fluent and often creative text, but it also leads to its limitations.
Why Faster Isn't Always Smarter: AI's Reasoning Process
While AI is incredibly fast at processing information and generating responses, its "smartness" is still quite different from human intelligence.
Lack of True Understanding: AI doesn't have common sense, empathy, or personal experience. It doesn't genuinely "understand" the world or the meaning behind the words it uses. It processes symbols and patterns.
"Garbage In, Garbage Out": The quality of AI's output is heavily dependent on the quality and nature of its training data. If the data is flawed, biased, or incomplete, the AI's "guesses" can be inaccurate or unfair.
Hallucinations: AI can confidently present false information (hallucinations) because it's prioritizing sounding coherent and probable based on patterns, not necessarily factual accuracy.
So, while AI can perform complex tasks with incredible speed, it lacks the critical thinking, nuanced judgment, and human understanding that are still essential for many applications. This is why human oversight remains crucial.
Important Considerations: Privacy and Copyright
When using AI, especially Generative AI, it's vital to think about the data involved:
Privacy Information: Be extremely cautious about entering any sensitive or private information into general-purpose AI tools. While providers often state they try to protect data, there's always a risk that your input could be stored, processed, or even inadvertently used for future training data. This means that details like your personal financial information, medical history, or confidential business plans should not be shared with public AI chatbots. Always review the tool's privacy policy.
Copyrighted Material: Generative AI models are trained on massive datasets that include text, images, and other content found across the internet. Much of this training data may be copyrighted material. This raises complex questions about whether content generated by AI, which learned from copyrighted works, might infringe on existing copyrights. While laws are still evolving, it's wise to be mindful of this. If you use AI to create content, especially for commercial purposes, consider how original it is and if it might inadvertently mimic existing copyrighted work.
Iterating on Hallucinations: What to Do When AI "Freespaces"
A crucial concept to grasp is hallucination. This is when an AI generates information that sounds completely plausible and factual, but is, in reality, incorrect, made up, or nonsensical. AI doesn't "know" it's wrong; it's simply generating the most probable sequence of words based on its training data.
Example of Hallucination: An AI might create a fake quote from a famous person, invent a non-existent company, or cite a study that was never published.
How to Deal with Hallucinations
Be Skeptical: Always, always, always verify important information generated by AI, especially facts, dates, names, or statistics. Treat AI as a highly creative assistant, not a definitive source of truth.
Provide Facts: If you need specific facts, provide them to the AI in your prompt rather than expecting it to know them accurately.
Ask for Sources: If the AI mentions information, ask "What are your sources for this claim?" If it can't provide verifiable sources, be cautious.
Refine the Prompt: If an AI hallucinates, clarify your prompt. For example, if it invents a product, you might say, "Do not invent any products; focus only on existing ones."
Understanding hallucinations is vital for responsible and effective AI use.
Practice Exercise
Ask an AI: "Explain how AI is different from a calculator, using a pizza analogy." Analyze the AI's explanation. Did it successfully use the analogy? Was it clear? Reflect on whether you'd feel comfortable entering your personal financial details into this general AI tool based on what you've learned about data training and privacy. Why or why not?
Fun Fact
AI models are so good at identifying patterns that they can even find connections in data that humans might miss. This is why AI is increasingly used in scientific research to discover new materials, understand complex biological systems, or analyze astronomical data!
Learning Reinforcement Questions
What is the main difference between a traditional computer program and AI, in terms of how they solve problems?
Speed of calculation.
Ability to learn from new information.
Size of the computer.
Cost of the software.
When AI "makes guesses," what are those guesses primarily based on?
Why might an AI's "fast" answer not always be the "smarter" or correct one?
True or False: Information you enter into a general AI tool is always kept completely private and is never used for training.
True or False: AI "hallucinations" are intentional lies from the AI.
Why is "copyright" an important consideration when using Generative AI for creative content?
Once you've given it a shot, you can find the <guidelines to answering these questions here> to check your understanding.
Next up
In our next lesson, Lesson 7: AI Ethics Made Simple: Fairness, Privacy, and Human Oversight, we will investigate the balance between the capabilities of AI and the need for human oversight, alongside ethical concerns such as bias, privacy and fairness.
Licensing, Attribution and Commercial use
© 2025 Nacha – AI Activation Hub, a division of Asset Thinking Ltd. All rights reserved.
For commercial licensing, partnerships, adaptations, integrations, usage within an organization or consulting inquiries, please contact the author via email: zack@nacha.life
Today I learnt that AI could hallucinate and that is very very serious... like seriously. Thank you Mwalimu