“No, Machine Learning is not just glorified Statistics” by Joe Davison https://link.medium.com/fv3z50FDYY
Simplest explanation by Cheh Wu:
(4 Parts Video : auto-play after each part)
The Math Theory behind Gradient Descent: “Multi-Variable Calculus” invented by Augustin-Louis Cauchy (19 CE, France)
1. Revision: Dot Product of Vectors
2. Directional Derivative
3. Gradient Descent (opposite = Ascent)
Deeplearning with Gradient Descent:
In memory of Prof Zhang Shoucheng 张首晟教授 who passed away on 1 Dec 2018.
Key Points :
- Quantum Computing with “Angel Particle” (no anti-particle) : [Analogy] Complex Number (a + i.b) , ‘Anti’ = Conjugate = a – i.b, ‘No anti’ = Real number = a
- A. I. Natural Language Algorithm : “Word To Vector” eg. King / Queen (frequently appear together) , etc.
- Data Privacy and Big Data Analytics with A. I. : Homomorphic Encryption, ie reveal data but not privacy. (eg. Millionaire Problem)
From current jobs:
To future jobs:
Lee Kai-Fu 李开复: Former Apple VP of AI, Microsoft VP, Google (China) Chairman. Currently the founder of a Technology Venture Company in Beijing.
Unsupervised learning is the future ML (Machine Learning) – of which AI is a branch – with the latest algorithm Deeplearning showing only 5% of its potential (more yet to be invented).
Singapore has recently launched an AI program to educate 10,000 students & workers. (Partnership with Microsoft and IBM, a 3-hour free lesson).
The world’s 4 AI gurus :
- (UK/Canada) Prof Geoffrey Hinton (*) , the inventor of DeepLearning, and
- his post-doctorate associate (France) Prof Yann Lecun ,
- The ex-Google & ex-Baidu AI Chief Prof Andrew NG 吴恩达,
- The AlphaGo creator Demis Hassabis
Andrew and Demis both studied in Singapore secondary schools (NG in Raffles Institution) before pursuing university in Stanford and Cambridge, respectively.
Note (*) : Prof Geoffrey Hinton was involved in the 80s Expert Systems where rule-based knowledge engine was the AI (2.0) . This AI failed because of fixed rules knowledge base under “supervised learning” from human domain experts, who each differed from another in opinions, to give an un-biased “weights” (rule probabilities from 0 to 1). Prof Hinton continued the AI research by moving from UK to Canada, where he developed the Deeplearning algorithm with unsupervised learning from Big Data Training feed to calculate the “Costs” (ie deviations of AI result versus actual result, using Cauchy’s Calculus eg. “Gradient Descent”, etc).