23. 陳昇瑋 / 人工智慧民主化在台灣
State of AI In The Enterprise, 2018
23
Deloitte interviewed 1,100 IT and line-of-business executives
from US-based companies in the 3rd quarter of 2018.
82% of enterprise AI early adopters are seeing a positive ROI from
their production-level projects this year.
69% of enterprises are facing a “moderate, major or extreme”
skills gap in finding skilled associates to staff their new AI-driven
business models and projects.
63% of enterprises have adopted machine learning, making this
category the most popular of all AI technologies in 2018.
38. 陳昇瑋 / 從大數據走向人工智慧
持續的團隊支援
41
A common data platform and workflow is
crucial for enterprise success.
Data Engineer ML Engineer Biz Analyst DevOps DevOps +
ML Engineer
App
Developer
(Credit: IBM Systems Lab Services)
(all under the supervision of Data Scientist)
51. What we can and cannot today
What we can have
Safer car, autonomous car
Better medical image
analysis
Personalized medicine
Adequate language
translation
Useful but stupid chatbots
Information search,
retrieval, filtering
Numerous applications in
energy, finance,
manufacturing,
commerce, law, …
What we cannot have
(yet)
Machine with common
sense
Intelligent personal
assistants
“Smart” chatbots
Household robots
Agile and dexterous robots
Artificial General
Intelligence (AGI)
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52. Change is the only constant.
- Heraclitus (535 BC - 475 BC)
(Slide Credit: Albert Chen)
53. 陳昇瑋 / 人工智慧民主化在台灣 81
Mobile computing, inexpensive sensors collecting terabytes of data, and the
rise of machine learning that can use that data will fundamentally change the
way the global economy is organized.
- Fortune, “CEOs:The Revolution is Coming,” March 2016
59. 陳昇瑋 / 人工智慧民主化在台灣
人機協作
Camelyon Grand Challenge in 2016
根據切片檢查偵測轉移性乳癌
The Winning Team
深度學習演算法: 92.5％
病理科醫師: 96.6％
兩者合作: 99.5％
人類與機器擅長不同的預測層面
人類與機器犯不同類型的錯。
確認這兩種不同的能力，結合人類與機器的預測來克服這
些弱點，這樣的組合可以大幅減少錯誤率。
88
60. 陳昇瑋 / 人工智慧民主化在台灣
machine-learning model from 30,000+ deals from the last decade that draws from
many sources, including Crunchbase, Mattermark, and PitchBook Data. For each deal,
we looked at whether a team made it to a series-A round by exploring 400 features and
identified 20 features as most predictive of future success.
One of the insights we uncovered is that start-ups that failed to advance to series A had
an average seed investment of $0.5 million, and the average investment for start-ups
that advanced to series A was $1.5 million.
Another example insight came from analyzing the background of founders, which
suggests that a deal with two founders from different universities is twice as likely to
succeed as those with founders from the same university.
from the 2015 cohort of seed-stage companies, 16 percent of all seed-stage companies
backed by VCs went on to raise series-A funding within 15 months. By comparison, 40
percent of recommended by ML (2.5 times improvement)
Human + AI would yield the best performance: 3.5 times the industry average
89
https://www.mckinsey.com/industries/high-tech/our-insights/a-machine-learning-
approach-to-venture-capital
61. 陳昇瑋 / 人工智慧民主化在台灣
AI 發展才正開始
今天的 AI 如同 1994 年的 World Wide Web
今天學 ML 可能如同 1994 年學寫 HTML and CGI
技術快速進展及堆疊，這是最值得投資技術的時代
AI 在許多領域有殺手級應用，是技術人投入重點領域的黃
金時代
AI 不會在這裡停住，AI 技術才剛開始發展
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