OPINION PIECE (PodCast) - AI Explained, 1950s to 2026
- archiedonovan
- 1 day ago
- 6 min read
Artificial Intelligence (AI) is when computers are designed to think and learn in ways that are similar to humans.
Instead of just following fixed instructions, AI systems can:
Learn from data (like photos, text, or numbers)
Recognise patterns
Make decisions or predictions
Improve over time
A Simple Example
Think about how Netflix recommends movies. It looks at what you’ve watched before and learns what you might like next. That learning and predicting is AI at work.
Another example is voice assistants like Siri or Google Assistant. They understand what you say and respond in a helpful way.
In One Sentence
AI is technology that helps machines learn, think, and solve problems a bit like humans do.
AI has the potential to become smarter than humans in some ways, but it depends on what we mean by “smarter.”
Already Smarter in Some Areas
AI is already better than humans at certain tasks, such as:
Doing extremely fast calculations
Analysing huge amounts of data
Playing complex games like Chess (for example, AlphaZero became stronger than any human chess player)
In these specific areas, AI is already “smarter.”
Not Smarter in Other Ways
Humans are still far better at:
Common sense reasoning
Understanding emotions
Creativity with real-world understanding
Making moral and ethical judgments
AI doesn’t truly “understand” things the way humans do — it processes patterns.
Could AI Become Smarter Overall?
Some scientists believe we could eventually build Artificial General Intelligence (AGI) — AI that can think and learn across many areas like a human.Others think that’s still far away or may never fully happen.
If AI ever becomes better than humans at almost everything, that’s sometimes called superintelligence.
Simple Answer:
AI can already be smarter than humans at specific tasks.Whether it becomes smarter than humans at everything is still unknown.
Singularity (or technological singularity) is a theoretical future moment when AI becomes so advanced that it can improve itself faster than humans can understand or control.
In Simple Terms
It’s the point where:
AI becomes smarter than humans
It starts designing even smarter AI
This improvement happens very quickly — like a snowball rolling downhill
After that, the world could change in ways that are hard for us to predict.
Where the Idea Comes From
The idea was popularised by futurist Ray Kurzweil, who predicts it could happen sometime this century.
The concept is also discussed by thinkers like Nick Bostrom, who wrote the book Superintelligence about the risks and possibilities of advanced AI.
Why It Matters
If the singularity happens, AI might:
Solve major problems (disease, climate change, poverty)
or
Create serious risks if not properly controlled
No one knows for sure if it will happen — it’s still a theory.
Simple One-Sentence Definition:
The singularity is the hypothetical moment when AI becomes smarter than humans and starts rapidly improving itself beyond our control.
How close are humanity to reaching AI singularity ?
Short answer: we’re not close — and no one really knows how close we are.
The AI singularity is still a theoretical idea, not something we can measure with a countdown clock.
Where We Are Today (2026)
Current AI systems (like those built by OpenAI or DeepMind) are very powerful at:
Language
Image recognition
Strategy games
Data analysis
But they:
Don’t truly understand the world
Don’t have independent goals
Can’t improve themselves without human engineers
Lack human-level general reasoning
This means we do not yet have Artificial General Intelligence (AGI) — which many believe would be required before a singularity could happen.
Predictions Vary a Lot
Some futurists like Ray Kurzweil predict singularity could happen around mid-century (often 2045).
Other researchers say:
It could take much longer
It may never happen
We don’t yet understand intelligence well enough to predict it
There is no scientific consensus.
What’s Missing?
For a singularity to happen, AI would likely need:
Human-level general intelligence
The ability to improve its own design
Rapid self-improvement without human limits
Right now, AI systems still depend heavily on human guidance and computing infrastructure.
So How Close Are We?
Most experts would say:
We are seeing rapid progress
But we are not on the brink of a singularity
It could be decades away — or may never occur
What is Simulation ?
A simulation is a model or imitation of something real, created so we can study it, test it, or experience it safely.
In Simple Terms
A simulation is like a practice version of reality.
Instead of doing something in the real world, we recreate it in a controlled environment — often on a computer.
Everyday Examples
A flight simulator lets pilots practice flying without a real plane.
Weather simulations predict storms before they happen.
Driving simulators help learners practice safely.
Video games simulate worlds, physics, and people.
Why We Use Simulations
We use simulations to:
Train safely (pilots, doctors, military)
Predict outcomes (weather, climate, traffic)
Test ideas without real-world risk
Save money and time
In Technology & AI
In AI, simulations are often used to:
Train robots in virtual environments
Test self-driving cars safely
Model complex systems like economies or ecosystems
Simulation Theory
There’s also a philosophical idea called “simulation theory,” which suggests our entire universe might itself be a simulation. This idea has been discussed by thinkers like Nick Bostrom — but it’s speculative and not proven.
Simple One-Sentence Definition:
A simulation is a controlled imitation of real life used for learning, testing, or predicting outcomes.
Was it a Logic based approach or a Neural Network approach by scientists that lead to AI as we know it today ?
Both approaches shaped modern AI — but neural networks are the main reason AI looks the way it does today.
The Logic-Based Approach (Early AI)
In the 1950s–1980s, many scientists believed intelligence was mainly about rules and logic.
Researchers like John McCarthy (who coined the term “Artificial Intelligence”) worked on systems that:
Used clear rules (“if X, then Y”)
Tried to represent knowledge with symbols
Solved problems step-by-step logically
This is called symbolic AI or rule-based AI.
It worked well for:
Math problems
Structured reasoning
Early expert systems
But it struggled with:
Vision
Speech
Real-world messy data
The Neural Network Approach (Modern AI)
Neural networks are inspired by how the brain works.
Early pioneers like Geoffrey Hinton, Yann LeCun, and Yoshua Bengio pushed this approach forward.
Youtube LINK to an interesting interview with Geoffrey Hinton:-
Neural networks:
Learn from data instead of being given rules
Detect patterns automatically
Improve with more data and computing power
When computing power and data became large enough (around the 2010s), neural networks — especially deep learning — suddenly became extremely powerful.
This led to breakthroughs in:
Image recognition
Speech recognition
Language models
Self-driving technology
So What Led to AI as We Know It Today?
Logic-based AI built the foundation of the field.
Neural networks powered the modern explosion in AI capability.
Today’s AI systems (like ChatGPT-style models) are primarily large neural networks, not rule-based logic systems.
Simple Answer:
Logic started AI.Neural networks made it powerful.
Do adults over 60 years of age need to worry about AI advancements ?
Adults over 60 generally don’t need to worry about AI, but understanding it can help them benefit from it and avoid common risks.
Why There’s No Need to Panic
AI is mostly being used to:
Improve healthcare diagnostics
Make banking and fraud detection safer
Power helpful tools like voice assistants
Support customer service
For many older adults, AI can actually make life easier, not harder.
For example:
Voice assistants can help with reminders and calls
AI-powered health tools can monitor conditions
Translation and accessibility tools improve communication
What Older Adults Should Be Aware Of
The biggest practical concerns are:
Scams and Deepfakes
AI makes scam calls and fake messages more convincing. Fraud targeting seniors is already common, and AI can amplify it.
Misinformation
AI-generated images, audio, and news can look real.
Job Impact (If Still Working)
Some industries are being automated — but this varies widely.
The Bigger Fears (Singularity, Superintelligence, Simulation etc.)
The dramatic “AI takes over” scenarios are speculative and not something individuals need to lose sleep over right now. Governments, researchers, and companies are actively working on safety and regulation.
Youtube LINK to an interesting interview with Dr. Roman Yampolskiy:-
The Healthy Approach
For adults over 60:
Stay curious, not fearful
Learn basic AI literacy
Be extra cautious with unexpected calls/messages
Use AI tools where helpful
Simple Answer:
AI is more likely to be a helpful tool than a personal threat — but being informed is smart at any age. more likely to be a helpful tool than a personal threat — but being informed is smart at any age.
If you want to interact directly, via command line querying, then use this Link as a good example of AI possibilities:-
ChatGPT AI interface
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