Thomas Paula

Machine Learning, Computer Science, and Software Engineering. I'm passionate about technology.

[Ihtesham Ali @ihteshamali]

An MIT professor taught the same math course for 62 years, and the day he retired, students from every country on earth showed up online to watch him give his final lecture.

I opened the playlist at 2am and ended up watching three of them back to back.

His name is Gilbert Strang. The course is MIT 18.06 Linear Algebra.

Every machine learning engineer, every data scientist, every quant, every self-taught programmer who actually understands how AI works learned the math from this one man. Most of them never set foot on MIT's campus. They just opened a free playlist on YouTube and let him teach.

Here's the story almost nobody tells you.

Strang joined the MIT math faculty in 1962. He retired in 2023. That is 61 years of standing at the same chalkboard teaching the same subject to 18-year-olds.

The interesting part is what he did when MIT launched OpenCourseWare in 2002. Most professors were skeptical. They worried that putting their lectures online would make their classrooms irrelevant. Strang did not hesitate. He said his life's mission was to open mathematics to students everywhere. He filmed every lecture and gave it away.

The decision quietly changed how the world learns math.

For decades linear algebra was taught the wrong way. Professors started with abstract vector spaces and proofs about field axioms. Students drowned in the abstraction. Most never recovered. They walked out believing they were bad at math when they had simply been taught in an order that nobody's brain is built to absorb.

Strang inverted the entire curriculum.

He started with matrix multiplication. Something you can write down on paper. Something you can compute by hand. Something you can see. Then he showed his students that everything else in linear algebra eigenvectors, singular value decomposition, orthogonality, the four fundamental subspaces was just a different lens for understanding what the matrix was actually doing under the hood.

His rule was strict. If a student could not explain a concept using a concrete 3 by 3 example, that student did not actually understand the concept yet. The abstraction was supposed to come last, not first. The intuition was the foundation. The proofs were just confirmation that the intuition was correct.

The second thing Strang changed was the classroom itself. He said please and thank you to his students. Every single lecture. He paused mid-derivation to ask "am I OK?" to check if anyone was lost. He never used the word "obviously" or "trivially" because he knew exactly what those words do to a student who is one step behind. He treated 19-year-olds learning math for the first time the way he treated his own colleagues. With patience. With respect. With the assumption that they belonged in the room.

For 62 years.

The result is something that has never happened in the history of education. A single math professor became the default teacher of his subject for the entire planet.

Universities in India, China, Brazil, Nigeria, every country with a computer science department, started telling their own students to just watch Strang's lectures. The University of Illinois revised its linear algebra course to do almost no in-person lecturing. The reason was honest. The professor said they could not compete with the videos.

His final lecture was in May 2023.

The auditorium was packed with students who had never met him before. He walked to the chalkboard, taught for an hour, and at the end the entire room stood and applauded. He looked confused for a moment, like he genuinely did not understand why they were cheering. Then he smiled and waved them off and walked out.

His written comment under the YouTube video of that final lecture was four sentences long. He said teaching had been a wonderful life. He said he was grateful to everyone who saw the importance of linear algebra. He said the movement of teaching it well would continue because it was right.

That was it. No book promotion. No farewell speech. No legacy management.

The man whose teaching is the foundation of modern AI just thanked the audience and went home.

20 million views. Zero ego. The entire engine of the AI revolution sits on top of math that millions of people learned for free from one quiet professor in Cambridge.

The course is still on MIT OpenCourseWare. Every lecture, every problem set, every exam, every solution. Free.

The most important math course of the 21st century is sitting one click away from you. Most people will never open it.

[Google DeepMind @GoogleDeepMind]

We’ve developed Gemini Diffusion: our state-of-the-art text diffusion model.

Instead of predicting text directly, it learns to generate outputs by refining noise, step-by-step. This helps it excel at coding and math, where it can iterate over solutions quickly. #GoogleIO

[Joshua Achiam @jachiam0]

> benchmarking on video games
> everyone is talking about RL
> OpenAI has a robotics team

I am no longer entirely sure what year it is

[Graham Neubig @gneubig]

Google’s Gemini recently made waves as a major competitor to OpenAI’s GPT. Exciting! But we wondered:

How good is Gemini really?

At CMU, we performed an impartial, in-depth, and reproducible study comparing Gemini, GPT, and Mixtral.

Paper: https://t.co/S3T7ediQLa
🧵

[Christian S. Perone @tarantulae]

Just sharing ~100 slides about PyTorch 2 internals focusing on recent innovations (Dynamo, Inductor, and ExecuTorch).
I had a lot of fun preparing this and hope you'll enjoy it. I'm planning to record it soon.

PDF: https://t.co/dlln8veNUA

Slideshare: https://t.co/c1OgpH0ZgC

[Sundar Pichai @sundarpichai]

Introducing Gemini 1.0, our most capable and general AI model yet. Built natively to be multimodal, it’s the first step in our Gemini-era of models. Gemini is optimized in three sizes - Ultra, Pro, and Nano

Gemini Ultra’s performance exceeds current state-of-the-art results on 30 of the 32 widely-used academic benchmarks. With a score of 90.0%, Gemini Ultra is the first model to outperform human experts on MMLU.
https://t.co/yCsjfQKO9F

[Thomas Paula @tsp_thomas]

@tarantulae Sorry to hear about that 😢

[Thomas Paula @tsp_thomas]

Really interesting paper that explores how LLMs use context. A lot of good insights on context size vs. performance. https://t.co/UIonSygtGY

[Thomas Paula @tsp_thomas]

Large language models are everywhere these days 😂

[Yann LeCun @ylecun]

The AI Apocalypse Scorecard, with quotes from tonight's debaters:
@MelMitchell1, @tegmark, Yoshua Bengio, and me, among several others.

https://t.co/1oiDsc7Zbh

[Chip Huyen @chipro]

New post: bringing LLM applications to production!

1. Challenges of LLM engineering & the solutions that I’ve seen

2. How to compose multiple tasks and incorporate tools (e.g. SQL executor, bash, web browsers, third-party APIs)

3. Promising use cases

https://t.co/XWmZfIc5mr

[Thomas Paula @tsp_thomas]

@hempael @Corinthians Genial hahaha

[Pelé @Pele]

A inspiração e o amor marcaram a jornada de Rei Pelé, que faleceu no dia de hoje.

Amor, amor e amor, para sempre.
.
Inspiration and love marked the journey of King Pelé, who peacefully passed away today.

Love, love and love, forever.

[Thomas Paula @tsp_thomas]

@henryzord Thank you for your kind words, @henryzord !

[Thomas Paula @tsp_thomas]

Really happy to receive my third patent plaque today! I'm humbled to have worked with many great people in the past. Looking forward to the next ones!

[Thomas Paula @tsp_thomas]

Amazing work from DeepMind! https://t.co/hJo9jpd9cP

[Jeff Dean @JeffDean]

ML assisted coding is helping thousands of Google software engineers.

"We see a 6% reduction in coding iteration time (time between builds and tests) [vs. control]" &

"Currently, 3% of new code (measured in characters) is now generated from accepting ML completion suggestions."

[badger @badger69420]

How deep is the ocean?👀

[Thomas Paula @tsp_thomas]

A video that could be from today https://t.co/P8JFnof1mV

[Thomas Paula @tsp_thomas]

I'm officially an ICLR paper now, it looks like 😂

"THOMAS: TRAJECTORY HEATMAP OUTPUT WITH
LEARNED MULTI-AGENT SAMPLING"

https://t.co/8ZwyRaZAaB