The Turing Test
Imagine you’re chatting with two mysterious entities—one is human, the other is a machine. You can ask anything: riddles, jokes, deep philosophical questions. If you can’t tell which one is human, does that mean the machine is thinking?
This is the essence of the Turing Test, a challenge proposed by Alan Turing, the father of modern computing.
Who Was Alan Turing?
Alan Turing wasn’t just another mathematician—he was a war hero, a codebreaker, and a visionary. During World War II, he cracked the Nazi Enigma code, saving millions of lives. But his mind didn’t stop at cryptography. He asked one of the most profound questions of all time:
"Can machines think?"
Since “thinking” is hard to define, Turing proposed a test in 1950. If a machine could convince a human that it was also human, then it should be considered intelligent.
How Does the Turing Test Work?
Picture this:
1. You’re the judge, sitting at a computer.
2. You’re chatting with two entities—one human, one AI.
3. Your job? Figure out which is which.
If the AI manages to fool you as often as a real human would, it passes the test. Simple, right?
Alan Turing’s idea sparked the AI revolution, but we’re still far from machines that truly think (maybe Not😁). Maybe one day, an AI won’t just pass the Turing Test—it will ask us its own questions.
Imagine you’re chatting with two mysterious entities—one is human, the other is a machine. You can ask anything: riddles, jokes, deep philosophical questions. If you can’t tell which one is human, does that mean the machine is thinking?
This is the essence of the Turing Test, a challenge proposed by Alan Turing, the father of modern computing.
Who Was Alan Turing?
Alan Turing wasn’t just another mathematician—he was a war hero, a codebreaker, and a visionary. During World War II, he cracked the Nazi Enigma code, saving millions of lives. But his mind didn’t stop at cryptography. He asked one of the most profound questions of all time:
"Can machines think?"
Since “thinking” is hard to define, Turing proposed a test in 1950. If a machine could convince a human that it was also human, then it should be considered intelligent.
How Does the Turing Test Work?
Picture this:
1. You’re the judge, sitting at a computer.
2. You’re chatting with two entities—one human, one AI.
3. Your job? Figure out which is which.
If the AI manages to fool you as often as a real human would, it passes the test. Simple, right?
Alan Turing’s idea sparked the AI revolution, but we’re still far from machines that truly think (maybe Not😁). Maybe one day, an AI won’t just pass the Turing Test—it will ask us its own questions.
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Forwarded from Artificial Intelligence (Artificial Intelligence)
CEOs in 2025: "AI will write 90% of the code...!"
CEOs in 2026: "AI will write 100% of the code..!!"
CEOs in 2027: 'Wait… why is nothing working?"
Devs in 2027: "AI is now writing only 10% of the code… and we're getting paid 2x more to clean up the mess it made. The circle of tech life continues!" 😆💸
AI writing code is cool… until you realize debugging AI’s “creativity” is way harder than writing code yourself. AI won’t replace Devs - it’ll just make them more necessary😆
CEOs in 2026: "AI will write 100% of the code..!!"
CEOs in 2027: 'Wait… why is nothing working?"
Devs in 2027: "AI is now writing only 10% of the code… and we're getting paid 2x more to clean up the mess it made. The circle of tech life continues!" 😆💸
AI writing code is cool… until you realize debugging AI’s “creativity” is way harder than writing code yourself. AI won’t replace Devs - it’ll just make them more necessary😆
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How a 17th-century math troll kept the world busy for 358 years?
I’ve always been fascinated by number theory — and one theorem that stole my heart (and everyone's patience) was "Fermat’s Last Theorem".
Quick refresher:
👉 Fermat claimed that there are no positive integers a, b, and c that satisfy:
a^n + b^n = c^n
for any n > 2 .
He even wrote, "I have a truly marvelous proof of this proposition, but this margin is too small to contain it."
(The ultimate flex.)
Fast forward 358 years— it took the legendary Andrew Wiles, hiding in his attic for years, to finally prove it in 1994. A proof so intricate that it combined deep modern mathematics (like elliptic curves and modular forms).
💻 Recently, I challenged myself to implement Fermat's Last Theorem computationally:
- I checked small cases manually (no luck — Fermat was right!).
- Then visualized how cn grows way faster than a^n + b^n or a^n - b^n — intuitively showing why no integer solution sneaks in.
Here's the implementation: https://github.com/benasphy/Mathematics/blob/main/Fermat's_Last_Theorem.ipynb
Moral of the story?
Sometimes, even the tiniest scribble in the margin can spark centuries of obsession, genius, and breakthroughs.
#NumberTheory #Mathematics #CodingFun #FermatsLastTheorem #CuriosityNeverDies
I’ve always been fascinated by number theory — and one theorem that stole my heart (and everyone's patience) was "Fermat’s Last Theorem".
Quick refresher:
👉 Fermat claimed that there are no positive integers a, b, and c that satisfy:
a^n + b^n = c^n
for any n > 2 .
He even wrote, "I have a truly marvelous proof of this proposition, but this margin is too small to contain it."
(The ultimate flex.)
Fast forward 358 years— it took the legendary Andrew Wiles, hiding in his attic for years, to finally prove it in 1994. A proof so intricate that it combined deep modern mathematics (like elliptic curves and modular forms).
💻 Recently, I challenged myself to implement Fermat's Last Theorem computationally:
- I checked small cases manually (no luck — Fermat was right!).
- Then visualized how cn grows way faster than a^n + b^n or a^n - b^n — intuitively showing why no integer solution sneaks in.
Here's the implementation: https://github.com/benasphy/Mathematics/blob/main/Fermat's_Last_Theorem.ipynb
Moral of the story?
Sometimes, even the tiniest scribble in the margin can spark centuries of obsession, genius, and breakthroughs.
#NumberTheory #Mathematics #CodingFun #FermatsLastTheorem #CuriosityNeverDies
GitHub
Mathematics/Fermat's_Last_Theorem.ipynb at main · benasphy/Mathematics
Contribute to benasphy/Mathematics development by creating an account on GitHub.
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⚙️📊🤖Master ML with 30 Projects💪
After months of refining, I’m finally sharing 30+ hands-on Machine Learning projects — clean, practical, and designed to help you understand and master ML, not just learn it.
Tired of tutorials that just show you how to predict house prices for the 100th time? 🏠📉 Same here. That’s why I built these projects to actually make sense — helping you learn the real stuff, from classic algorithms to applied ideas you can use, tweak, and build on.
GitHub Repo: https://github.com/benasphy/ML_projects 💻
Live Demo: https://mlprojects-algorithmslist.streamlit.app 🚀
✨Don't forget to Star and fork it!!
Explore, Star, fork, learn — and if you want to connect, collab, or just nerd out over ML:
TG: @benasphy 💬
Let’s build smarter, together!
#MachineLearning #MLProjects #GitHub #AI #LearningByBuilding
After months of refining, I’m finally sharing 30+ hands-on Machine Learning projects — clean, practical, and designed to help you understand and master ML, not just learn it.
Tired of tutorials that just show you how to predict house prices for the 100th time? 🏠📉 Same here. That’s why I built these projects to actually make sense — helping you learn the real stuff, from classic algorithms to applied ideas you can use, tweak, and build on.
GitHub Repo: https://github.com/benasphy/ML_projects 💻
Live Demo: https://mlprojects-algorithmslist.streamlit.app 🚀
✨Don't forget to Star and fork it!!
Explore, Star, fork, learn — and if you want to connect, collab, or just nerd out over ML:
TG: @benasphy 💬
Let’s build smarter, together!
#MachineLearning #MLProjects #GitHub #AI #LearningByBuilding
GitHub
GitHub - benasphy/ML_projects
Contribute to benasphy/ML_projects development by creating an account on GitHub.
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BeNN
⚙️📊🤖Master ML with 30 Projects💪 After months of refining, I’m finally sharing 30+ hands-on Machine Learning projects — clean, practical, and designed to help you understand and master ML, not just learn it. Tired of tutorials that just show you how…
I'll post a details into those ML algorithms and stay tuned!
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For anyone interested in ML and wanna do a lot in it, but got confused. Here's my advice
The advice for ML is (because once u master ML, the rest of the concepts in AI will get simpler to understand):
🔰 Honestly, from the mistakes I made and learnt: at first, understand that it will take you time and also needs consistency. Don't rush to finish it in 6 months or less. Just give at least 3 hours a day and do the iteration for a year — then you can master it because it has mathematics and deep concepts. Otherwise, you will suffer from imposter syndrome.
🔰 Then I honestly don't recommend watching those YouTube full tutorial videos after the first time you saw them. Use I.am.ai website to get every step of the roadmap, and then use websites like Analytics Vidhya and Medium to understand the whole thing. After that, you can use ChatGPT and videos like Andrew Ng’s to understand it better.
🔰 Concepts + maths + code should be the way you study ML algorithms. Regarding math, you better have some knowledge of Linear Algebra and Calculus — sometimes Calculus II concepts, like in partial derivatives during gradient descent.
🔰 For the coding part, it's better if you know Python and libraries like NumPy, Matplotlib, Pandas — most of all, Scikit-learn and Seaborn for ML. Then u can learn TensorFlow, PyTorch, Theano, and stuff in DL.
🔰 At the end, lemme leave you with Naval's quote:
"To learn new things, it's not 10,000 repetitions, it's 10,000 iterations."
You gotta be like neural nets — using backpropagation, learning from your mistakes, and keep going on.
🔰 Honestly, from the mistakes I made and learnt: at first, understand that it will take you time and also needs consistency. Don't rush to finish it in 6 months or less. Just give at least 3 hours a day and do the iteration for a year — then you can master it because it has mathematics and deep concepts. Otherwise, you will suffer from imposter syndrome.
🔰 Then I honestly don't recommend watching those YouTube full tutorial videos after the first time you saw them. Use I.am.ai website to get every step of the roadmap, and then use websites like Analytics Vidhya and Medium to understand the whole thing. After that, you can use ChatGPT and videos like Andrew Ng’s to understand it better.
🔰 Concepts + maths + code should be the way you study ML algorithms. Regarding math, you better have some knowledge of Linear Algebra and Calculus — sometimes Calculus II concepts, like in partial derivatives during gradient descent.
🔰 For the coding part, it's better if you know Python and libraries like NumPy, Matplotlib, Pandas — most of all, Scikit-learn and Seaborn for ML. Then u can learn TensorFlow, PyTorch, Theano, and stuff in DL.
🔰 At the end, lemme leave you with Naval's quote:
"To learn new things, it's not 10,000 repetitions, it's 10,000 iterations."
You gotta be like neural nets — using backpropagation, learning from your mistakes, and keep going on.
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BeNN
⚙️📊🤖Master ML with 30 Projects💪 After months of refining, I’m finally sharing 30+ hands-on Machine Learning projects — clean, practical, and designed to help you understand and master ML, not just learn it. Tired of tutorials that just show you how…
And if you think this repo could help you out, use it. Don't forget to Star it 😁
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Tomorrow, I will post about linear regression and I will try my best to explain as simple as possible.
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For Today lemme leave you with Naval's quote
"Desire is a contract with yourself to be happy until you get what you want"
"Desire is a contract with yourself to be happy until you get what you want"
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As you all know, it's quite difficult to write mathematical formulas on Telegram. So, I used Google Docs to explain everything in detail. I did my best to cover most of the essential concepts in linear regression.
For today’s post, I’ve focused mainly on the core concepts and mathematical foundation. In the next update, I’ll dive into visualizations, code implementations, and some real-world use cases to make it even more intuitive.
For today’s post, I’ve focused mainly on the core concepts and mathematical foundation. In the next update, I’ll dive into visualizations, code implementations, and some real-world use cases to make it even more intuitive.
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New Drop: 25+ Data Science Projects!
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Over the past few months, I’ve been diving deep into both Machine Learning and Data Science.
Alongside the 30+ ML projects I shared earlier, I’ve just released 25+ clean, practical Data Science projects — from EDA to real-world case studies.
GitHub: https://github.com/benasphy/Data_Science_Projects
Live Demo: https://datascienceprojects-lists.streamlit.app
✨Don't forget to Star and fork it!!
If you want to connect, collab, or just nerd out over ML, Data Science and stuff:
TG: @benasphy 💬
📊⚡
Over the past few months, I’ve been diving deep into both Machine Learning and Data Science.
Alongside the 30+ ML projects I shared earlier, I’ve just released 25+ clean, practical Data Science projects — from EDA to real-world case studies.
GitHub: https://github.com/benasphy/Data_Science_Projects
Live Demo: https://datascienceprojects-lists.streamlit.app
✨Don't forget to Star and fork it!!
If you want to connect, collab, or just nerd out over ML, Data Science and stuff:
TG: @benasphy 💬
GitHub
GitHub - benasphy/Data_Science_Projects
Contribute to benasphy/Data_Science_Projects development by creating an account on GitHub.
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