【鄂维南:机器学习是应用数学几十年未有之机遇 – 今日头条】
https://m.toutiao.com/is/RJetHU4/
Supervised Machine Learning (ML), Unsupervised ML, Reinforced ML, use Function Approximation, Probability Distribution, Bellman Equation, resp.
【鄂维南:机器学习是应用数学几十年未有之机遇 – 今日头条】
https://m.toutiao.com/is/RJetHU4/
Supervised Machine Learning (ML), Unsupervised ML, Reinforced ML, use Function Approximation, Probability Distribution, Bellman Equation, resp.
Data Science =
Math (Fourier FFT) +
AI (Convoluted Neural Network) +
Big Data
https://www.kdnuggets.com/2020/02/fourier-transformation-data-scientist.html
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:
…
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.
Notes:
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.
Microsoft 40+ series of FREE Python Tutorials on Youtube : AI / Machine Learning, Data Analytics, Automation Scripting.
Github:
“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 :
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.
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
(中文) :
https://zhuanlan.zhihu.com/p/24339995
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 :
Note:
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).
https://www.technotification.com/2018/04/programming-languages-for-ai.html
The above author recommends 5 best AI languages:
Note 1: I have reservation for # 3) & #4) which are the 1970s / 1980s obsolete languages due to the issues in performance and no “practical” platforms (in mobile phone age), besides lack of major SW/HW vendor support (Google, Oracle, Microsoft, etc), and the small user community unlike the other three languages.
Note 2: Functional Programming (FP) is the modern AI language MUST-HAVE feature – only Kotlin & Clojure are “FP”.
The first 4 books (by Strang, Lang, etc) are the Masterpieces.
Strang’s MIT OpenCourse:
https://tomcircle.wordpress.com/2016/12/21/animation-linear-algebra/
https://tomcircle.wordpress.com/2016/02/28/best-linear-algebra-by-mit/
3 Waves of AI Evolution:
1st Wave (1950s) : Alan Turing “The Father of AI” and his Princeton Prof Alonzo Church (Lambda Calculus). MIT Prof Malvin Minksy’s “Lisp” Functional Programming (a.k.a. Symbolic or Declarative) Language.
2nd Wave (1980s – 1990s) : Knowledge-Based Rule Engine Expert Systems.
Failed because knowledge acquisition process is too difficult with limited rigid rules.
3rd Wave (2010s -): DeepLearning is the latest AI tool for Machine Learning, famous after 2016 “AlphaGo” game by a former Funan-center UK Kid Demis Hassabis (UK/Greek father & Singapore Chinese mom teacher) beat 2 “Go” World Champions (Korean Lee Sedol 李世乭 and China 柯洁).
Great Books Recommended –
1. Learn Everything in 《Deep Learning》:
Note: Available at Singapore National Library (LKC Reference #006.31).
Order at Amazon: https://www.amazon.com/gp/aw/d/0262035618/ref=mp_s_a_1_3?ie=UTF8&qid=1516412628&sr=8-3&pi=AC_SX236_SY340_FMwebp_QL65&keywords=deep+learning
2. 《The Master Algorithm》 (Book or Audio)
http://catalogue.nlb.gov.sg/cgi-bin/spydus.exe/FULL/EXPNOS/BIBENQ/7400075/198909068,1
5 “Tribes” of Machine Learning, all with 3 layers (Representation, Evaluation/Scoring, Optimisation) :
Notes:
1. Bill Gates recommends this excellent book for 2018 reading, also found it on Chinese President’s Xi JingPing’s Office Bookshelf in 1 Jan 2018 New Year Speech.
http://www.chinadaily.com.cn/a/201801/15/WS5a5bf75aa3102c394518f26b.html
2. All available copies of this book in the Singapore National Library Board have been loaned out !! (Unusual in low-readership Singapore). Please reserve it via online queue.
3. Audio version (10 CDs) is excellent for in-car listening while driving, or travelling on plane/train/bus for busy persons.
In 15 years, AI driven driverless car will change the transport/work/environment landscape… it is true not futuristic… behind AI is advanced math which teaches computer to learn without a fixed algorithm but by analysing BIG DATA patterns using Algebraic Topology !
世界趨勢,可作參考
矽谷预测AI後的10年大未來
現在因為人工智能(AI)的發展,配合更高速度的積體電路,科技正在加快速度的進展。據悉,在很短的5 -10年後,医療健保、自駕汽車、教育、服務業都將面臨被淘汰的危機。
1. Uber 是一家軟體公司,它沒有擁用汽車,卻能夠讓你「隨叫隨到」有汽車坐,現在,它已是全球最大的Taxi公司了。
2. Airbnb 也是一家軟體公司,它沒有擁有任何旅館,但它的軟體讓你能夠住進世界各地願出租的房間,現在,它已是全球最大的旅館業了。
3. 今年5月,Google的電腦打敗全球最厲害的南韓圍棋高手,因為它開發出有人工智能(AI)的電腦,使用能夠「自己學習」的軟體,所以它的AI能夠加速度的進步,達到比專家原先預期的、提前10年的成就。
4. 在美國,使用IBM 的Watson電腦軟體,你能夠在幾秒內,就有90%的準確性的法律顧問,比較起只有70% 準確性的人為律師,既便捷又便宜。
所以,你如果還有家人親友在讀大學的法律系,建議他們停學省錢,因為市場已大幅的縮減了,未來的世界,只需要現在10%的專業律師就夠了。
5. Watson 也已經能夠幫病人檢驗癌症,而且比醫生正確4 倍。
6. 臉書也有一套AI的軟體可以比人類更準確的鑒察(辨識)人臉,而且無所不在。
7. 到了2030年,AI的電腦會比世界上任何的專家學者還要聰明。
8. 2017年起,會自動駕駛的汽車就可以在公眾場所使用。
約在2020年,整個汽車工業就會遭遇到全面性的改變,你再不需要擁用汽車。
你可以用手機叫自動駕駛的車,來帶你到你想去的目的地。
9. 未來的世界,你再也不必擁有車,或花時間加油、停車、排隊去考駕照、交保險費,尤其是城市,將會很安靜,走路很安全,因為90%的汽車都不見了,以前的停車場,將會變成公園。
10. 現在,平均每10萬公里就有一次車禍,造成每年全球有約120萬人的死亡。
以後有AI電腦控制的自動駕駛汽車,平均每1000萬公里才有一次車禍,約減少一百萬人死亡。
因為保險費和需要保險的人極少,保險公司會面臨更多的倒閉風潮。
11. 大部份的傳統汽車公司會面臨倒閉。Tesla、 Apple、及 Google 的革命性軟體,將會用在每一部汽車上。
據悉,Volkswagen 和 Audi 的工程師非常擔心Tesla革命性的電池和人工智能軟體技術。
12. 房地產公司會遭遇極大的變化。
因為你可以在車程中工作,距離將不是選住房屋的主要條件之一。市民會選擇住在較遠、但是較空曠且環境優美的鄉村。
13. 電動汽車很安靜,會在2020變成主流。所以城市會很變成安靜,而且空氣乾淨。
14. 太陽能在過去30年也有快速的進展。 去年,全球太陽能的增產超過石油的增產。
預計,到2025年時,太陽能的價格(低廉)會使煤礦業大量的破產。
因為電費非常的便宜,淨化水及海水淡化的費用大減,人類將能解決人口增加的需水問題。
15. 健保:今年醫療設備商會供應如同「星球大戰」電影中的 Tricorder,讓你的手機做眼睛的掃瞄,呼吸氣體及血液的化學檢驗:用54個「生物指標」,就可檢驗出你是否有任何疾病的徵兆。
因為費用低,幾年後,全球人類都可以有世界級的疾病預防服務。
16. 立體列印(3D printing):預計10 年內,3D列印設備會由近20000美元減到400美元,而速度增加100倍快。
所有的「個人化」設計鞋子,將開始用這種設備生產,其他如大型的機場,其零件也能使用這種設備供應,至於人類太空船,也會使用這種設備。
17. 今年底,你的手機就會有3D掃瞄的功能,你可以測量你的腳送去做「個人化」鞋子。據悉,在中國,他們已經用這種設備製造了一棟6 層樓辦公室,預計到2027年時, 10% 的產品會用3D的列印設備製造。
18. 產業機會:
a. 工作:20年內,70-80% 的工作會消失,即使有很多新的工作機會,但是不足以彌補被智能機械所取代的原有工作。
b. 農業:將有 $100 機械人耕作,不必吃飯、不用住宅、及支付薪水,只要便宜的電池即可。在開發國家的農夫,將變成機械人的經理。溫室建築物可以有少量的水。
到2018年,肉可以從實驗室生產,不必養豬、雞或牛。30%用在畜牧的土地,會變成其他用途的土地。很多初創公司會供給高蛋白質的昆蟲當成食品。
c. 到2020年時 ,你的手機會從你的表情看出,與你說話的人是不是說「假話」? 是否騙人的? 政治人物(如總統候選人)若說假話,馬上會被當場揭發。
d. 數位時代的錢,將是Bitcoin ,是在智能電腦中的「數據」。
e. 教育:最便宜的智能手機在非州是$10美元一隻。
f. 到2020年時,全球70%的人類會有自己的手機,所以能夠上網接受世界級的教育,但大部份的老師會被智能電腦取代。所有的「小學生」都要會寫 Code,你如果不會,你就是像住在Amazon森林中的原住民,無法在社會上做什麼。你的國家,你的孩子準備好了嗎?
參考一下;這也是矽谷 VC, Innovators,Entrepreneurs … 談的資料。
Interesting Math education evolves since 19th century.
“Elementary Math from An Advanced Standpoint” (3 volumes) was proposed by German Göttingen School Felix Klein (19th century) :
1) Math teaching based on Function (graph) which is visible to students. This has influenced all Secondary school Math worldwide.
2) Geometry = Group
After WW1, French felt being behind the German school, the “Bourbaki” Ecole Normale Supérieure students rewrote all Math teachings – aka “Abstract Math” – based on the structure “Set” as the foundation to build further algebraic structures (group, ring, field, vector space…) and all Math.
After WW2, the American prof MacLane & Eilenburg summarised all these Bourbaki structures into one super-structure: “Category” (范畴) with a “morphism” (aka ‘relation’) between them.
Grothendieck proposed rewriting the Bourbaki Abstract Math from ‘Set’ to ‘Category’, but was rejected by the jealous Bourbaki founder Andre Weil.
Category is still a graduate syllabus Math, also called “Abstract Nonsense”! It is very useful in IT Functional Programming for “Artificial Intelligence” – the next revolution in “Our Human Brain” !