WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013

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Gigaom

Presentation from Sean Gourley, Quid #dataconf More at http://event.gigaom.com/structuredata/

Technologie

David Brooks
Op-Ed, February 13th 2013

“What Data Can’t Do”

“Big Data has trouble
with Big Problems”
- David Brooks

Liking Curly Fries
on Facebook is
top predictor of
intelligence

Iraq
Frequency

α = 2.31

xmin

Attack Size

Iraq

PREDICT?
Frequency

α = 2.31

xmin

Attack Size

Impact of increasing troop numbers

Zhao et al, Phys. Rev. Lett. 2009

Data:SCIENCE Data:Intelligence
Improvement 10% 10x

Data:SCIENCE Data:Intelligence
Improvement 10% 10x
Model goal Predict/Optimize Create/Change

Data:SCIENCE Data:Intelligence
Improvement 10% 10x
Model goal Predict/Optimize Create/Change
Decision Algorithm Human

Data:SCIENCE Data:Intelligence
Improvement 10% 10x
Model goal Predict/Optimize Create/Change
Decision Algorithm Human
Data Big/Clean Small/Messy

STRATEGIC TOOLKIT

Google + Excel + Powerpoint

“What is the most
effective way to allocate
capital to spur growth in
K-12 education
technology”!

**…and how will I know if we’re successful!

“What are the dominant
narratives about
climate change in
India”!

**…and how does it vary between age groups!

“What are my competitors
doing with advanced
ﬂexible display
technology”!

**…should I compete or partner with them?!

“What is the
structure of the
insurgent groups
in Syria”!
**…what is the likely impact of peace keepers!

Five Heuristics
For Using Data to Solve
Big Problems

1. Data needs to be designed
for human interaction

2. Understand limits of
human processing.

3. Data is messy,
incomplete and biased.

5. Data needs stories…
stories need data

Equa%on
governing
Insurgent
Dynamics

Mathematical

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Driven by the rapid progress in Artificial Intelligence (AI) research, intelligent machines are gaining the ability to learn, improve and make calculated decisions in ways that will enable them to perform tasks previously thought to rely solely on human experience, creativity, and ingenuity. As a result, we will in the near future see large parts of our lives influenced by AI. AI innovation will also be central to the achievement of the United Nations' Sustainable Development Goals (SDGs) and will help solving humanity's grand challenges by capitalizing on the unprecedented quantities of data now being generated on sentiment behavior, human health, commerce, communications, migration and more. With large parts of our lives being influenced by AI, it is critical that government, industry, academia and civil society work together to evaluate the opportunities presented by AI, ensuring that AI benefits all of humanity. Responding to this critical issue, ITU and the XPRIZE Foundation organized AI for Good Global Summit in Geneva, 7-9 June, 2017 in partnership with a number of UN sister agencies. The Summit aimed to accelerate and advance the development and democratization of AI solutions that can address specific global challenges related to poverty, hunger, health, education, the environment, and others. The Summit provided a neutral platform for government officials, UN agencies, NGO's, industry leaders, and AI experts to discuss the ethical, technical, societal and policy issues related to AI, offer reccommendations and guidance, and promote international dialogue and cooperation in support of AI innovation. Please visit the AI for Good Global Summit page for more resources: https://www.itu.int/en/ITU-T/AI/Pages/201706-default.aspx If you would like to speak, partner or sponsor the 2018 edition of the summit, please contact: ai@itu.int

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Keynote at IEEE Services 2021: Abstract: https://conferences.computer.org/services/2021/keynotes/sheth.html Video: https://lnkd.in/d-r3YXaC Video of the same keynote given at DEXA2021: https://www.youtube.com/watch?v=u-06kK9TysA September 9, 2021 15:00 - 16:20 UTC ABSTRACT Knowledge representation as expert system rules or using frames and a variety of logics played a key role in capturing explicit knowledge during the hay days of AI in the past century. Such knowledge, aligned with planning and reasoning is part of what we refer to as Symbolic AI. The resurgent AI of this century in the form of Statistical AI has benefitted from massive data and computing. On some tasks, deep learning methods have even exceeded human performance levels. This gave the false sense that data alone is enough, and explicit knowledge is not needed. But as we start chasing machine intelligence that is comparable with human intelligence, there is an increasing realization that we cannot do without explicit knowledge. Neuroscience (role of long-term memory, strong interactions between different specialized regions of data on tasks such as multimodal sensing), cognitive science (bottom brain versus top brain, perception versus cognition), brain-inspired computing, behavioral economics (system 1 versus system 2), and other disciplines point to need for furthering AI to neuro-symbolic AI (i.e., hybrid of Statistical AI and Symbolic AI, also referred to as the third wave of AI). As we make this progress, the role of explicit knowledge becomes more evident. I will specifically look at our endeavor to support human-like intelligence, our desire for AI systems to interact with humans naturally, and our need to explain the path and reasons for AI systems’ workings. Nevertheless, the variety of knowledge needed to support understanding and intelligence is varied and complex. Using the example of progressing from NLP to NLU, I will demonstrate the dimensions of explicit knowledge, which may include, linguistic, language syntax, common sense, general (world model), specialized (e.g., geographic), and domain-specific (e.g., mental health) knowledge. I will also argue that despite this complexity, such knowledge can be scalability created and maintained (even dynamically or continually). Finally, I will describe our work on knowledge-infused learning as an example strategy for fusing statistical and symbolic AI in a variety of ways.

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Everyone has heard at least once of the magical powers of machine learning - who wouldn't want to be able to program a driverless car on its own? How many times, however, have you heard about the "dark sides" of data-driven technology? In this talk, we will show live demos of five aspects of deep learning that any developer should be aware of: (i) algorithmic bias; (ii) adversarial attacks on trained systems; (iii) breaches of privacy; (iv) safety threats; and (v) hidden technical debts. The lesson is: beware of blind reliance on deep learning - unless you are looking for "deep" troubles!

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WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013

1. DATA

2. David Brooks Op-Ed, February 13th 2013 “What Data Can’t Do”

3. “Big Data has trouble with Big Problems” - David Brooks

4. Advertising

5. Advertising Prediction

7. DATA:SCIENCE

8. Jeff Hammerbacher! DJ Patil!

10. Data Science Kosinski et al PNAS 2013

11. Liking Curly Fries on Facebook is top predictor of intelligence

12. DATA:SCIENCE

13. DATA:intelligence

14. Iraq:2007

15. Open Source Data Collection War IED

16. Iraq Frequency α = 2.31 xmin Attack Size

17. Iraq PREDICT? Frequency α = 2.31 xmin Attack Size

18. Insurgent model: Nature 2009

19. Impact of increasing troop numbers Zhao et al, Phys. Rev. Lett. 2009

20. DATA:SCIENCE vs. DATA:intelligence

21. Data:SCIENCE Data:Intelligence Improvement 10% 10x

22. Data:SCIENCE Data:Intelligence Improvement 10% 10x Model goal Predict/Optimize Create/Change

23. Data:SCIENCE Data:Intelligence Improvement 10% 10x Model goal Predict/Optimize Create/Change Decision Algorithm Human

24. Data:SCIENCE Data:Intelligence Improvement 10% 10x Model goal Predict/Optimize Create/Change Decision Algorithm Human Data Big/Clean Small/Messy

25. Data:SCIENCE Data:Intelligence Improvement 10% 10x Model goal Predict/Optimize Create/Change Decision Algorithm Human Data Big/Clean Small/Messy Communication Equations Stories

26. Data:SCIENCE Data:Intelligence Improvement 10% 10x Model goal Predict/Optimize Create/Change Decision Algorithm Human Data Big/Clean Small/Messy Communication Equations Stories Problem Tactical Strategic

27. STRATEGIC TOOLKIT Google + Excel + Powerpoint

28.

29. “What is the most effective way to allocate capital to spur growth in K-12 education technology”! **…and how will I know if we’re successful!

30. “What are the dominant narratives about climate change in India”! **…and how does it vary between age groups!

31. “What are my competitors doing with advanced ﬂexible display technology”! **…should I compete or partner with them?!

32. “What is the structure of the insurgent groups in Syria”! **…what is the likely impact of peace keepers!

33. Five Heuristics For Using Data to Solve Big Problems

34. 1. Data needs to be designed for human interaction

35. Human Machine

36. Human Centered UI

37. 2. Understand limits of human processing.

38.