Alan Morrison retweeted
Two math olympiad champions wrote a training manual in 1993 on two old Macintosh computers, and every American kid who has won a major math competition in the last decade learned to think from it.
Their names are Sandor Lehoczky and Richard Rusczyk. The book is called The Art of Problem Solving. Most people in math know it as AoPS.
Since 2015, every single member of the US International Math Olympiad team has been an AoPS student. Not most of them. Every one.
That statistic sounds impossible until you understand what the book actually does.
Lehoczky and Rusczyk were not professors. They were competitors. Lehoczky earned the sole perfect AIME score in 1990 and led the national first place team. Rusczyk was a USA Mathematical Olympiad winner and a perfect AIME scorer in 1989. They had both survived the same brutal selection process the book was designed to train students for.
And the first thing they decided was that almost every existing math textbook was teaching the wrong thing.
School math gives you formulas. You memorize them. You apply them. You pass the test. Then you sit down in front of a real competition problem and the formula does not apply, and you have nothing underneath it.
That is the gap. The gap is not knowledge. It is thinking.
The entire premise of AoPS is that problem-solving is a transferable skill, not a bag of memorized tricks. A student who genuinely understands why a technique works can adapt it, combine it with something else, and deploy it in a context they have never seen before. A student who only memorized the technique freezes the moment the problem looks different.
The book teaches the difference between a formula and a method.
A formula tells you what to compute. A method tells you how to see. The students who win olympiads are not the ones who know more formulas. They are the ones who have trained themselves to look at an unfamiliar problem and recognize its structure. To see that this problem is secretly asking the same question as a problem they solved three weeks ago, just dressed differently.
Rusczyk calls this "learning to read the problem." Not reading the words. Reading what the problem is actually asking underneath the words.
The second thing they built into the book is tolerance for being stuck.
Most students treat confusion as a signal to stop. The book treats confusion as the starting point. Every chapter pushes students past the point where the obvious approach runs out. That moment of running out is not failure. That is where the actual thinking begins.
Lehoczky once described it this way. If you can solve a problem quickly, you are not learning. You are performing. Learning only happens when you are past the edge of what you already know.
The book was written on old Macintosh computers in 1993. Rusczyk launched the AoPS website in 2003. Today the community has over one million users. Thousands of students enroll in AoPS online courses every year. Most winners of every major American math competition are AoPS alumni.
A platform built by two kids who were good at math competitions has become the infrastructure that produces the next generation of mathematicians, engineers, and scientists who are good at thinking.
The formulas you memorized in school will eventually be obsolete.
The thinking you trained will not.
What is one problem in your life right now that you have been avoiding because you do not yet know the right formula to solve it?
Alan Morrison retweeted
“Holistic AI is about creating each organization’s starting point for what will become a dynamic mirrorworld. Ideally, that mirrorworld will accurately reflect shifts in business realities.”
“Explaining GraphRAG to an Executive Audience” - @AlanMorrison
graphrag.info/2025/10/25/exp…
Alan Morrison retweeted
"Linear Algebra"
The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & ML.
Absolutely beginner friendly. Available FREE.
Alan Morrison retweeted

Andrew Ng@AndrewYNg25 Sep 2025
United States
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Last week, China barred its major tech companies from buying Nvidia chips. This move received only modest attention in the media, but has implications beyond what’s widely appreciated. Specifically, it signals that China has progressed sufficiently in semiconductors to break away from dependence on advanced chips designed in the U.S., the vast majority of which are manufactured in Taiwan. It also highlights the U.S. vulnerability to possible disruptions in Taiwan at a moment when China is becoming less vulnerable.
After the U.S. started restricting AI chip sales to China, China dramatically ramped up its semiconductor research and investment to move toward self-sufficiency. These efforts are starting to bear fruit, and China’s willingness to cut off Nvidia is a strong sign of its faith in its domestic capabilities. For example, the new DeepSeek-R1-Safe model was trained on 1000 Huawei Ascend chips. While individual Ascend chips are significantly less powerful than individual Nvidia or AMD chips, Huawei’s system-level design approach to orchestrating how a much larger number of chips work together seems to be paying off. For example, Huawei’s CloudMatrix 384 system of 384 chips aims to compete with Nvidia’s GB200, which uses 72 higher-capability chips.
Today, U.S. access to advanced semiconductors is heavily dependent on Taiwan’s TSMC, which manufactures the vast majority of the most advanced chips. Unfortunately, U.S. efforts to ramp up domestic semiconductor manufacturing have been slow. I am encouraged that one fab at the TSMC Arizona facility is now operating, but issues of workforce training, culture, licensing and permitting, and the supply chain are still being addressed, and there is still a long road ahead for the U.S. facility to be a viable substitute for manufacturing in Taiwan.
If China gains independence from Taiwan manufacturing significantly faster than the U.S., this would leave the U.S. much more vulnerable to possible disruptions in Taiwan, whether through natural disasters or man-made events. If manufacturing in Taiwan is disrupted for any reason and Chinese companies end up accounting for a large fraction of global semiconductor manufacturing capabilities, that would also help China gain tremendous geopolitical influence.
Despite occasional moments of heightened tensions and large-scale military exercises, Taiwan has been mostly peaceful since the 1960s. This peace has helped the people of Taiwan to prosper and allowed AI to make tremendous advances, built on top of chips made by TSMC. I hope we will find a path to maintaining peace for many decades more.
But hope is not a plan. In addition to working to ensure peace, practical work lies ahead to multi-source, build more chip fabs in more nations, and enhance the resilience of the semiconductor supply chain. Dependence on any single manufacturer invites shortages, price spikes, and stalled innovation the moment something goes sideways.
[Original text: deeplearning.ai/the-batch/is… ]
Alan Morrison retweeted

Nassim Nicholas Taleb@nntaleb1 Mar 2025
United States
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2/ Remember that LLMs are just blind probabilistic lookups from higher dimensional tables, which sets you up for the mother of all problem of induction when you hit a (consequential) missing entry.
Alan Morrison retweeted
Artificial general intelligence (AGI): What “General” really means
buff.ly/3WMhjrs v/ @DataScienceCtrl by @AlanMorrison
#DataScience #AI #MachineLearning
Cc @jblefevre60 @ahier @TamaraMcCleary @Nicochan33 @data_nerd @Fabriziobustama
Alan Morrison retweeted
A few enterprise takeaways from the #AI hardware and edge AI summit 2024
buff.ly/4dxRb9x v/ @DataScienceCtrl by @AlanMorrison
#MachineLearning #AIEthics
Cc @DeepLearn007 @KirkDBorne @SpirosMargaris @dinisguarda @FrRonconi @DigitalNStream
Alan Morrison retweeted
#BestOf2024
Scaling understanding with the help of feedback loops, knowledge graphs and #NLP
buff.ly/4cGOdz4 v/ @DataScienceCtrl by @AlanMorrison
#DataScience #AI #MachineLearning
Cc @jblefevre60 @ahier @TamaraMcCleary @Nicochan33 @data_nerd @Fabriziobustama
Alan Morrison retweeted
Scaling understanding with the help of feedback loops, knowledge graphs and #NLP
buff.ly/3IJJC2q v/ @DataScienceCtrl by @AlanMorrison
#DataScience #AI #MachineLearning
Cc @jblefevre60 @ahier @TamaraMcCleary @Nicochan33 @data_nerd @Fabriziobustama
Alan Morrison retweeted
Americans waste $427+ Billion every year on things they never use.
This Nobel Prize winner spent 40 years studying how companies manipulate our buying behavior.
What he discovered will make you question everything you know about your favorite brands: 🧵
Alan Morrison retweeted
What to consider when selecting a learning model
By Alan Morrison
on Data Science Central #dsc #datasciencecentral #LLM #AI
datasciencecentral.com/what-…
Alan Morrison retweeted
Introducing KGARevion, a KG+LLM agent 🤖designed for knowledge-intensive medical QA. Led by @xiaorui_su @GaoShanghua @valegiunca Here's why it is unique:
1️⃣ Open-ended reasoning: Generates nuanced answers without relying on predefined options
2️⃣ Robustness: LLMs can be surprisingly sensitive to how candidate answers are ordered and indexed in multi-choice setups
Unlike LLMs, which show order sensitivity in multi-choice setups (favoring answers in the first position!), KGARevion is immune to this bias, ensuring more reliable selections
3️⃣ Flexibility: Combines LLM-generated triplets with KGs for verified answers. The multi-step process makes KGARevion adaptable to different reasoning styles, outperforming RAG models
Medicine relies on various reasoning strategies, including rule-based, prototype-based, case-based, and analogy-based vertical reasoning (Patel et al., 2005). This variety calls for approaches that accommodate different styles and integrate specialized, in-domain knowledge. For instance, an organism such as Drosophila is used as an exemplar to model a disease mechanism, which is then applied by analogy to other organisms, including humans. In clinical practice, the patient serves as an exemplar, with generalizations drawn from many overlapping disease models and similar patient populations.
4️⃣ Versatility: Works with different LLMs and medical KGs.
Preprint: arxiv.org/abs/2410.04660
Thanks Discover AI for highlighting KGARevion: youtu.be/Fm68I-phaiY?si=DM4J…
@HarvardDBMI @harvard_data @KempnerInst
Alan Morrison retweeted
The real headline is that 25% of Utah voters don't think their vote is accurately counted. See my latest post for why they can rest assured it is. windley.com/archives/2024/10…
75% of Utah voters trust mail-in votes are accurately counted, a poll finds. | By @BrighamTomco
deseret.com/politics/2024/10…
Alan Morrison retweeted
After 15 years of work with @rosemaryleith to make the web more accessible, affordable and a trusted space for all we wind down @webfoundation to focus on building pro-human systems that respect and empower individuals.
Huge thank you to all involved.
webfoundation.org
Alan Morrison retweeted
Which dev teams are best at preventing, spotting and fixing software bugs and breaches?
Linux (open source) outperforms private closed companies. OS allows for more transparency & more collaboration
What most people think about open source & safety is wrong
Andrew Ng@AndrewYNg26 Sep 2024United States
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
A decision on SB-1047 is due soon. Governor @GavinNewsom has said he's concerned about its "chilling effect, particularly in the open source community". He's right, and I hope he will veto this.
If you agree, please like/retweet this to show your support for VETOing SB-1047!
Alan Morrison retweeted
Defining transformation in the era of AI + FAIR data
By Alan Morrison
on Data Science Central bit.ly/3WAqSsB #dsc #datascience #AI
Alan Morrison retweeted
When semantically connected data matters most
by Alan Morrison
on Data Science Central bit.ly/4fBkg6f #dsc #datascience #data #AI #GenAI
Alan Morrison retweeted
In the last year or so the #KnowledgeGraph community has seen a rise of enthusiasm over making #LLMs & KGs collaborate. This technical text bridges the gap b/n theoretical talk & practical application by demonstrating how to build a simple RAG retriever. hubs.la/Q02yRt3S0
Alan Morrison retweeted
Here are 12 charts showing the state of the United States immigration system and the southern border under Biden vs. Trump.
Alan Morrison retweeted
Scaling understanding with the help of feedback loops, knowledge graphs and #NLP
buff.ly/3IJJC2q v/ @DataScienceCtrl by @AlanMorrison
#DataScience #AI #MachineLearning
Cc @jblefevre60 @ahier @TamaraMcCleary @Nicochan33 @data_nerd @Fabriziobustama

























