The comparative advantage of human and computer


(Source from MIT SMR)


Google, AI, Machine Learning

Google cloud platform (note5)

Target : the enterprise cloud market

  • Position- To be a developer-friendly platform
  • Weakness

1) Not strong and no impression  on cloud service and the enterprise segment

2) No contribution to the open source community before.


  • Stength : ASIC, GPU and TPU hardware in its cloud
  • Opportunity

1) begin to work with open source projects (note3)

  • Cloud Native Computing Foundation- the open-source container management tool

run by the Linux Foundation (note4)

Other partners in the new foundation include AT&T, Box, Cisco, Cloud Foundry Foundation, CoreOS, Cycle Computing, Docker, eBay, Goldman Sachs, Huawei, IBM, Intel, Joyent, Kismatic, Mesosphere, Red Hat, Switch SUPERNAP, Twitter, Univa, VMware and Weaveworks.

Absent : Microsoft, Amazon, Pivotal, and  Taiwanese tech companies)

the popular open source container orchestration system

  • TensorFlow for machine learning

-Spanner for launching massive distributed databases

-Draco for 3D graphics compression

2) To be a  developer-friendly platform


1)letting customers run whatever open source stack they choose on Google’s infrastructure,

2)releasing and supporting open source projects and making the ecosystem

3)the partners who build tools and technologies on top of GCP, a first class citizen on the platform.

4) treating them as part of the whole and the net is bringing the tech you want and using Google technology or using any of the [partner] services

The KSF :

1)Being open to win the mind shares of developers

2) much more supportive of the open source community makes people feel better about Google and makes developers feel better about working with their tools because they can avoid lock-in

  • Threat : peers: AWS (2006. 1st public cloud, market leader, 1st mover), Microsoft, IBM
  • Strategy

UsingKubernetes, the popular open source container orchestration system offer robust open source tools, something that surprised some people in this market.

  • 4 ways Google will enable enterprises to adopt machine learning and AI (note2)

1). Machine learning computing in Google Cloud

a deep learning algorithm can have tens of millions of parameters, training these machine learning models requires enormous computational resource

the Cloud Machine Learning Engine. This capability is designed for companies with data scientists and machine learning experts who are able to build their own unique machine learning models with libraries such as Tensorflow.

Google’s infrastructure as the solution to speed training times and improve the return on investment. Google has specialized ASIC, GPU and TPUhardware in its cloud to accelerate training and improve the ROI with on-demand cloud resource utilization. After the model is trained, it is deployed in range of platforms—from on-premise to mobile devices.

2. Algorithms and pretrained machine learning models

建ML model 需用 the machine learning engine, 用 Google’s pre-trained models (full list) using APIs to add machine learning capability to their applications, such as understanding natural language, images and natural language.

An API beta for understanding videos

demo: This 3-minute video of the demonstration of the Cloud Video Intelligence beta

3. Google acquires Kaggle for data

Google acquired Kaggle for data sets and talent. Kaggle, founded in 2010, is a community of 850,000 data scientists from around the world that hosts competitions to create the most accurate predictive models and market models, as well as to acquire new public data sets in a variety of fields.

4. Expertise

the Advanced Solutions Lab for customers with ambitious goals to develop machine learning to solve complex problems.




(note4: The mission of Linux foundation :The mission of this new foundation is to “help facilitate collaboration among developers and operators on common technologies for deploying cloud native applications and services,” )


(Reference : Google Cloud Platform 入門)



Machine Learning for Marketing

The marketing big data ecosystem being impacted by machine learning in four major areas:

  1. Automated data visualization (including ML results) will become more rich, and user-friendly.
  2. Content analysis (textual, lexical, multimedia/rich) will be used to drive better marketing conversations.
  3. Incremental ML techniques will become more prevalent, leading to real-time, not just on-going and automated, changes in marketing execution.
  4. Learning from ML results will accelerate the growth and skills of marketing professionals.
  • Automated Data Visualization tools: Tableau and Qlikview

Predictive model : The objective of ML is to build predictive model for forecast.

the ability to modify a solution that is already in place by introducing new data rather than having to stop using the current solution before building a new model from scratch.

(Source from How Machine Learning Will Be Used For Marketing In 2017)


Photo credit: fulvio di marco via / CC BY-NC-ND

一片導演太多, 供需失衡. Over Supply. 若一部電影週營收只有幾百萬, 比7/11 週營業額還小, 基本上這導演已經結束了.


迎合觀眾卻得到這樣的結果, 還有多少內涵可供消費. Content 也不會是King 了

電影重複操作是走不通的,  精明人很快就看出你玩完了. 沒有深思創作, 只想重複老梗, 妥協求全, 被唾棄也是剛好.

好的作品 對觀眾而言載體規格不是重點, 重點是時間有限,只能取捨,只取好作品. 多裝置的對應策略是什麼?  土法煉鋼有用嗎? 有多少做法是重複?

現在國片是兩頭空, 票房爛, 沒藝術,

不如拍短片, 找小眾, 找到知音,而非取寵, 重要的是培養出自己的電影語言,能存在於影史的脈落, 成為作者,  拍很多片不見得等於是作者,  是否能醞釀出自己的電影語言?   作者論雖很多問題,但以台灣環境還算適合的.

很多基本的東西, 是否在媚俗的過程中失去了?

還有多少時間可以找回來 或再發現新的價值?

以為電影能是經濟火車頭, 現今是全軍覆沒

當票房很差的時候, 應該回到學生的初衷, 像創業失敗一樣, 重新學習,

電影是什麼? 本質是什麼?  從經典去觀摩, 什麼是永恆的價值,什麼是浮光掠影,  小沒有關係, 如果小的有價值,有內涵.  把所有要素一一檢討清楚. 如果是環境已經改變, 就要重新認清現實, 


導演, Stay Hungry, Stay Foolish.


之前寫過一次  (note1)

CDN 內容傳遞網路(Content delivery network或Content distribution network)

1 定義

指一種透過網際網路互相連接的電腦網路系統,利用最靠近每位使用者的伺服器,更快、更可靠地將音樂、圖片、影片、應用程式及其他檔案傳送給使用者,來提供高效能、可擴展性及低成本的網路內容傳遞給使用者 (note2)

2 技術

內容傳遞網路節點會在多個地點,多個不同的網路上擺放。這些節點之間會動態的互相傳輸內容,對使用者的下載行為最佳化,並藉此減少內容供應者所需要的頻寬成本,改善使用者的下載速度,提高系統的穩定性 (note2)

3 CDN 推薦 (note2)

AWS – Amazon Cloudfront
Microsoft CDN
Google CDN
CloudFlare (note4)
ChinaCache CDN
Akamai CDN

4 網頁開發者適用的免費開放式 CDNs (note3)

1) jsDeliver //
2) Microsoft Ajax Content Delivery Network //
3) Open Source Software CDN (OSSCDN) //
4) The Google Hosted Libraries //
5) Bootstrap //

5 其他的CDN //

  1. CDN 的選擇標準 (note5) : 重點是改善的效果,效益,和效能提昇的程度







Docker Compose


  1. Compose is a tool for defining and running multi-container Docker applications.
  2. 3 個步驟 :

Using Compose is basically a three-step process.
1) Define your app’s environment
2) Define the services that make up your app in docker-compose.yml
3) Lastly, run docker-compose up and Compose will start and run your entire app.

  1. compose documentation

安裝 :
1) Install Docker Engine: 依OS 選一種
2) The Docker Toolbox installation includes both Engine and Compose, so Mac and Windows users are done installing. Others should continue to the next step.

4 啟動
Get started with Docker Compose //

1) 先把Docker engine和Docker compose 裝好
2) 然後有6個步驟 : Step // 參;

3) 快速啟動指引: Next, try the quick start guide for Django, Rails, or WordPress.
5 指令檔: command-line reference, 這要留起來,不然要用時會找不到

6 Docker-compose 怎麼用?

要看這個: Overview of docker-compose CLI
參 :

7.Compose CLI environment variables
參 :






這領域就是需要人多的地方 ,要交流 要互動,要掌握流行; 但是真正的高技術能這樣交流嗎?

文化產業該用什麼技術?   應該列個表 這些技術門檻有多高? 有高技術就有好創作嗎? 不是吧.

主要還是在新傳播方式,新媒體的技術及商業運用,  從那裡去學?


  • 搜尋分析技術, 就是數據分析, 就是大數據的掌握.
  • 新傳播技術, 就是行銷相關技術和新媒體.

這paper 表達的很籠統, 不過大致的框架是如此.

今天 Spotify 寄給我一份2016 我的播放記錄的資料分析和推薦. 這份報告就足以代表整個文化產業的技術應用與方向.




  • 以個人為中心的E2E (Everyone to Everyone)經濟觀點
  • 以因應來自社群媒體、行動裝置、雲端的即時數位資訊不斷改變消費者和企業互動模式的現況
  • 建立認知體驗的商業模式是成功的關鍵,面對認知時代,企業應重新思考數位再造的三個面向–策略、營運及科技



計概 note

“Content is King" 到底是什麼意思?

這句話是Bill Gates 1996 時說的.

“Content is where I expect much of the real money will be made on the Internet, just as it was in broadcasting.”

所以網路上,當內容被傳播, 是可以靠內容賺錢.  當時1996, 現在讀很正常.
現在應該進一步研究: 倒底“什麼內容"或是"內容裡有什麼" 是能賺錢 ?

好的內容幫助行銷, 行銷也幫助內容被更多人接受.  內容本身的內涵與讀者,觀眾之間的關聯必須要理解


  • Increasing visibility.
  • Encouraging backlinks for SEO, and social shares.
  • Optimising the website for long tail keywords that are harder to target through the
  • website’s static pages.
  • Generating new customers, or clients, or whatever your end goal is (hopefully).

好的內容能增加流量,對於搜尋和分享有助益, 內容中的關鍵字, 幫助搜尋, 有助尋找新客戶或粉絲.

“內容裡關鍵字的精準" 影響內容被搜尋到的程度.
因此,內容會變得更競爭, 很多內容關鍵字會是一樣的.
所以 內容要時時更新和相關. 好的內容行銷策略能提升網站流量, 增加客戶.

內容行銷需相當程度的客製化, 必須區隔清楚, 定位準確, 追求客戶忠誠度與粉絲經濟.
為了這一群死忠用戶, 產品加值並創造新的商機.

內容行銷原理是如此,  執行要練到很厲害,很精準,超熟練,很靈活,會調整. 多練習, 跟著數據調整, 很多行業都廣泛運用,但內容製作本身(創作者)也應該深度理解:

  • 內容的"含金量",是什麼 以吸引特定消費群
  • 內容裡的關鍵字是什麼?
  • 關鍵字精準的程度?

題材(主題), 故事, 情節, 對話, 文字, 對白…等等成為關鍵, 被搜尋到,被大數據認可的程度.

這方面的內容 各行各業差異就很大了.
有的行業很好做, 有的行業怎麼做都無效,  客戶根本就不吃這一套. 看行業,和內容屬性.要試一試,測一下效果.  這其中操作策略細節很多,不同國家又不一樣, 內容在地化和修辭的調整有很多的微妙之處.

內容製作者要深刻理解自己產出內容的品質, 能與內容行銷相輔相成.
在這些原理相通的前提下, 內容才是王.

但真正的現實是: (note1)


在網上進行內容行銷的時候,必須堅持下去,堅持到發佈的內容可以成為搜尋資料庫,而訂閱者的數量多到足夠將多年的投資變現. 內容行銷非常辛苦,需要持續不斷地更新發佈內容,知道有一天看到自己的文章瀏覽量終於有了起色,最後終於看到這個渠道帶來了可觀的潛在使用者數量,能這麼做的公司很少。 但是這樣的模式確實有用,能夠堅持下來的公司都能嘗到甜頭,他們不僅僅享受到激增的流量和潛在使用者的增長,他們能夠獲得一個可以預期,可實現規模化的渠道,能夠幫助公司以前所未有的速度成長.




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