Part 1: “Homology” without Pre-requisites, except “Function” (he un-rigourously interchanges with “Mapping”, although Function is stricter with 1-and-only-1 Image) .

Part 2: Simplex (单纯形)

Topology History : Euler Characteristic eg. (V – E + R = 2) ,
Poincaré invention

This video uses Algebra of point, line, triangle… to explain a Simplex (plural: Simplices) in R^{n} Space, that is organizing the n-Dimensional “Big Data” data points into Simplices, then (future Part 3, 4…) compute the “holes” (or pattern called Persistent Homology).

Part 3: Boundary

Part 3 justifies why triangles (formed by any 3 data points) called “Simplex” 单纯形(plural: Simplices) are best to fill any Big Data Space.

李彦宏Baidu CEO : Internet 3 Episodes : PC->Mobile ->AI



李彦宏 Baidu CEO Cambridge Speech 剑桥大学演讲
《3 waves of Internet》:
1) PC- based (1997-)

  • Search Webpages
  • 6-month software update cycle

2) Mobile-based (2010 -)

  • “APP” is born
  • Eco-System : eg. Apple Appstore, Google PlayStore
  • O2O (Online to Offline) : Same day Hotel booking/Restaurant /…
  • SW Update everyday few times

3) AI-based (2017 – now)

  • Voice recognition sans keyboard input
  • Image recognition (eg. Customer ePayment :McDonald’s )
  • Natural language Pattern NLP (Salesman Virtual Assistant)

MIT New Course (Prof Gilbert Strang) : Linear Algebra and Learning From Data


[MIT OCW Online Course Videos]


[Full Video]


丘城桐:基础数学和AI, Big Data

AI and Big Data are Twins, their Mother is Math.

“AI 3.0“ today, although impressive in “DeepLearning“, is still using “primitive” high school Math, namely:

AI has not taken advantage of the power of post-Modern Math invented since WW II, esp. IT related, ie :

That is the argument of the Harvard Math Dean Prof ST Yau 丘城桐 (First Chinese Fields Medalist), who predicts the future “AI 4.0“ can be smarter and more powerful.


… Current AI deals with Big Data:

  1. Purely Statistical approach and experience-oriented, not from Big Data’s inherent Mathematical structures (eg. Homology or Homotopy).
  2. The Data analytical result is environment specific, lacks portability to other environments.

3. Lack effective Algorithms, esp. Algebraic Topology computes Homology or Co-homology using Linear Algebra (Matrices).

4. Limited by Hardware Speed (eg. GPU), reduced to layered-structure problem solving approach. It is a simple math analysis, not the REAL Boltzmann Machine which finds the most Optimum solution.


AI 1.0 : 1950s by Alan Turing, MIT John McCarthy (coined the term “AI”, Lisp Language inventor).

AI 2.0 : 1970s/80s. “Rule-Based Expert Systems” using Fuzzy Logic.

[AI Winter : 1990s / 2000s. Failed ambitious Japanese “5th Generation Computer” based on Prolog-based “Predicate” Logic]

AI 3.0 : 2010s – now. “DeepLearning” by Prof Geoffry Hinton using primitive Math (Statistics, Probability, Calculus Gradient Descent)

AI 4.0 : Future. Using “Propositional Type” Logic, Topology (Homology, Homotopy) , Linear Algebra, Category.

A Programmer’s Regret: Neglecting Math at University – Adenoid Adventures

Advanced Programming needs Advanced Math: eg.

Video Game Animation: Verlet Integration

AI: Stats, Probability, Calculus, Linear Algebra

Search Engine : PageRank: Linear Algebra

Abstraction in Program “Polymorphism” : Monoid, Category, Functor, Monad

Program “Proof” : Propositions as Types, HoTT


Abstraction: Monoid, Category


AI with Advanced Math helps in discovering new drugs


Advanced Mathematical Methods with AI is a powerful tool:

  • Algebraic Topology (Persistent Homology)
  • Differential Geometry
  • Graph Theory