AI 歷史

Rule-based decision making 是在2000s

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廣告

Stanford CoreNLP – a suite of core NLP tools

Joe

這是一個 Sentiment Analysis 的工具.

  • a set of natural language analysis tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get quotes people said, etc.

語言,句子是可以分解,分析,分類 找出情緒傾向(人 時,地, 事件)

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用途

  • An integrated toolkit with a good range of grammatical analysis tools //語法分析工具
  • Fast, reliable analysis of arbitrary texts //文本分析
  • The overall highest quality text analytics
  • Support for a number of major (human) languages
  • Available interfaces for most major modern programming languages
  • Ability to run as a simple web service

這對人文,社會學科會有些影響吧.  文本分析都有工具可以處理了, 分析文本的情緒意向, 是愛是恨? 是贊成是反對? 是同意是不同意? 是支持或不支持?  高盛已有在用這工具了.

這工具是開源, 但注意若要轉用商用或用在自己軟件裡要申請授權(note1),

這工具,廣告公司,民調中心, 經濟政策研究單位會用的到.

  • Download at http://stanfordnlp.github.io/CoreNLP/download.html
  • 或 http://search.maven.org/#search%7Cga%7C1%7Ca%3A%22stanford-corenlp%22

(note1: Stanford CoreNLP is licensed under…

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Google A.I. Platform For Free

FULL Nvidia GPU Cloud Presentation | Computex 2017

人工智能和深度学习

BAT 馬雲马化腾李彦宏谈人工智能

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.

(note1)

  • 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

  • “OPENNESS" 

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.

(note1: https://techcrunch.com/2017/03/09/google-in-the-cloud/)

(note2: http://www.networkworld.com/article/3179127/cloud-computing/4-ways-google-cloud-will-bring-ai-machine-learning-to-the-enterprise.html)

(note3:https://techcrunch.com/2015/07/21/as-kubernetes-hits-1-0-google-donates-technology-to-newly-formed-cloud-native-computing-foundation-with-ibm-intel-twitter-and-others/)

(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,” )

(note5: https://cloud.google.com/)

(Reference : Google Cloud Platform 入門)

(Reference: https://technews.tw/tag/google-cloud-platform/)

 

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)

TensorFlowonSpark system architecure

teosorflow

不知哪些台企在用?

(Yahoo supercharges TensorFlow with Apache Spark)

 

AI will reduce management

AI用的好,會減少例行性的行政工作,  省下的時間可以專注在抉擇判斷, 反思, 創意企劃發想,實驗驗證(試一試), 在科技界,學界一直在往人工智慧方面前進,但我是沒看到台灣社會有這麼把AI 當回事,  從概念,應用,教育,沒特別感覺有在轉變, 社會上還有其他重要的議題要關注, 這也是事實,但產業,個人如果跟科技業太脫軌, 這樣的落後是個警訊.  產業大概落後15 年.  我不希望見到台灣落後矽谷太多.   社會上新技術的普及速度相見還是緩慢滿多.  學這駕馭工具, 機器, 專注在設計, 創意,企劃,  邁進AI化, 社會化的技能跟人的網絡與連結 也會更重要, 但前提是AI推進社會進化.

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AI 領域包含的東西很多, 儘量學, 學的起來分享給親朋好友, 用在好的地方, 讓社會往對的方向前進

How Artificial Intelligence Will Redefine Management

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