Artificial Intelligence,
History of Artificial Intelligence,
Artificial Intelligence Use Cases,
Artificial Intelligence Applications,
Ways of Achieving AI,
Machine Learning,
Deep Learning,
Supervised and Unsupervised Learning,
Classification Vs Prediction,
TensorFlow,
TensorFlow Graphs,
History of TensorFlow,
Companies using TensorFlow,
Using Deep Q Networks to Learn Video Game Strategies,
TensorFlow Use Cases,
AI & Deep Learning with TensorFlow,
How TensorFlow used today
For more updates on Big Data, Cloud Computing, Data Analytics, Artificial Intelligence, IoT subscribe to http://www.mybigdataanalytics.in
Currently hundreds of tools are promising to make artificial intelligence accessible to the masses. Tools like DataRobot, H20 Driverless AI, Amazon SageMaker or Microsoft Azure Machine Learning Studio.
These tools promise to accelerate the time-to-value of data science projects by simplifying model building.
In the workshop we will approach the AI Topic head on!
What is AI? What can AI do today? What do I need to start my own project?
We do all this using Microsoft's Machine Learning Studio.
Trainer: Philipp von Loringhoven - Chef, Designer, Developer, Markeeter - Data Nerd!
He has acquired a lot of expertise in marketing, business intelligence and product development during his time at the Rocket Internet startups (Wimdu, Lamudi) and Projekt-A (Tirendo).
Today he supports customers of the Austrian digitisation agency TOWA as Director Data Consulting to generate an added value from their data.
Artificial Intelligence,
History of Artificial Intelligence,
Artificial Intelligence Use Cases,
Artificial Intelligence Applications,
Ways of Achieving AI,
Machine Learning,
Deep Learning,
Supervised and Unsupervised Learning,
Classification Vs Prediction,
TensorFlow,
TensorFlow Graphs,
History of TensorFlow,
Companies using TensorFlow,
Using Deep Q Networks to Learn Video Game Strategies,
TensorFlow Use Cases,
AI & Deep Learning with TensorFlow,
How TensorFlow used today
For more updates on Big Data, Cloud Computing, Data Analytics, Artificial Intelligence, IoT subscribe to http://www.mybigdataanalytics.in
Currently hundreds of tools are promising to make artificial intelligence accessible to the masses. Tools like DataRobot, H20 Driverless AI, Amazon SageMaker or Microsoft Azure Machine Learning Studio.
These tools promise to accelerate the time-to-value of data science projects by simplifying model building.
In the workshop we will approach the AI Topic head on!
What is AI? What can AI do today? What do I need to start my own project?
We do all this using Microsoft's Machine Learning Studio.
Trainer: Philipp von Loringhoven - Chef, Designer, Developer, Markeeter - Data Nerd!
He has acquired a lot of expertise in marketing, business intelligence and product development during his time at the Rocket Internet startups (Wimdu, Lamudi) and Projekt-A (Tirendo).
Today he supports customers of the Austrian digitisation agency TOWA as Director Data Consulting to generate an added value from their data.
This slide is more of basic introduction to Artificial intelligence and a course designed for the students who would like to start there career in Artificial Intelligence.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
Gradient Boosting Machines (GBM): from Zero to Hero (with R and Python code)Data Con LA
Data Con LA 2020
Description
This talk will get you started with gradient boosting machines (GBM), a
very popular machine learning technique providing state-of-the-art
accuracy on numerous business prediction problems. After a quick intro
to machine learning and the GBM algorithm, I will show how easy it is to
train and then use GBMs in real-life business applications using some
the most popular open source implementations (xgboost, lightgbm and
h2o). We'll do all this in both R and Python with only a few lines of
code and this talk will be accessible for a wide audience (with limited
prior knowledge of machine learning). Finally, in the last part of the
talk I will provide plenty of references that can get you to the next
level. GBMs are a powerful technique to have in your machine learning
toolbox, because despite all the latest hype about deep learning (neural
nets) and AI, in fact GBMs usually outperform neural networks on
structured/tabular data most often encountered in business applications.
Speaker
Szilard Pafka, Epoch, Chief Scientist
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Cybernetic Ebooks: A Panel on Machine Learning and AI in Book Production - We...BookNet Canada
Tools are at the core of the daily work of book production, whether print or digital, because they directly impact our efficiency and the quality of the final product. Thanks to growing amounts of available data and the increased processing power of modern computers, Machine Learning (ML) and Artificial Intelligence (AI) have become popular tools to solve certain classes of problems.
However, alongside the growing opportunities and potential advantages of applying these technologies to book production, there is also a perceived “dark side” to ML/AI that has many in the book industry worried that it will automate and replace their jobs.
The panelists will share their different approaches to applying ML/AI at their companies and their outcomes, highlighting both strengths and limitations, as they consider a vision of a more automated publishing workflow.
March 19, 2019
ebookcraft.booknetcanada.ca
#EbookCraft
From embodied Artificial Intelligence to Artificial LifeKrzysztof Pomorski
The methodological stages presented in embodied Artificial Intelligence are given. Systematically we broaden the concept AI so finally we can approach systems related to Artificial Life.
Mengenal Machine/Deep Learning, Artificial Intelligence dan mengenal apa bedanya dengan Business Intelligence, apa hubungannya dengan Big Data dan Data Science/Analytics.
How to implement artificial intelligence solutionsCarlos Toxtli
In this presentation, we show how a novice can learn artificial intelligence and implement the basic principles in real-world solutions. There is an easy quick start guide.
Section 510(k) of the US Food, Drug and Cosmetic Act requires product manufacturers to comply with safety and quality requirements. AI introduces new opportunities and risks in the important area of our health. This presentation provides a brief history of AI, how it might be used in life sciences, and offers a path to further learning by validation professionals.
This slide is more of basic introduction to Artificial intelligence and a course designed for the students who would like to start there career in Artificial Intelligence.
This presentation attempts to explain some of the concepts used when describing data science, machine learning, and deep learning. IT also describes data science as a process, rather than as a set of specific tools and services.
Gradient Boosting Machines (GBM): from Zero to Hero (with R and Python code)Data Con LA
Data Con LA 2020
Description
This talk will get you started with gradient boosting machines (GBM), a
very popular machine learning technique providing state-of-the-art
accuracy on numerous business prediction problems. After a quick intro
to machine learning and the GBM algorithm, I will show how easy it is to
train and then use GBMs in real-life business applications using some
the most popular open source implementations (xgboost, lightgbm and
h2o). We'll do all this in both R and Python with only a few lines of
code and this talk will be accessible for a wide audience (with limited
prior knowledge of machine learning). Finally, in the last part of the
talk I will provide plenty of references that can get you to the next
level. GBMs are a powerful technique to have in your machine learning
toolbox, because despite all the latest hype about deep learning (neural
nets) and AI, in fact GBMs usually outperform neural networks on
structured/tabular data most often encountered in business applications.
Speaker
Szilard Pafka, Epoch, Chief Scientist
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Cybernetic Ebooks: A Panel on Machine Learning and AI in Book Production - We...BookNet Canada
Tools are at the core of the daily work of book production, whether print or digital, because they directly impact our efficiency and the quality of the final product. Thanks to growing amounts of available data and the increased processing power of modern computers, Machine Learning (ML) and Artificial Intelligence (AI) have become popular tools to solve certain classes of problems.
However, alongside the growing opportunities and potential advantages of applying these technologies to book production, there is also a perceived “dark side” to ML/AI that has many in the book industry worried that it will automate and replace their jobs.
The panelists will share their different approaches to applying ML/AI at their companies and their outcomes, highlighting both strengths and limitations, as they consider a vision of a more automated publishing workflow.
March 19, 2019
ebookcraft.booknetcanada.ca
#EbookCraft
From embodied Artificial Intelligence to Artificial LifeKrzysztof Pomorski
The methodological stages presented in embodied Artificial Intelligence are given. Systematically we broaden the concept AI so finally we can approach systems related to Artificial Life.
Mengenal Machine/Deep Learning, Artificial Intelligence dan mengenal apa bedanya dengan Business Intelligence, apa hubungannya dengan Big Data dan Data Science/Analytics.
How to implement artificial intelligence solutionsCarlos Toxtli
In this presentation, we show how a novice can learn artificial intelligence and implement the basic principles in real-world solutions. There is an easy quick start guide.
Section 510(k) of the US Food, Drug and Cosmetic Act requires product manufacturers to comply with safety and quality requirements. AI introduces new opportunities and risks in the important area of our health. This presentation provides a brief history of AI, how it might be used in life sciences, and offers a path to further learning by validation professionals.
This slide was used by Mr.Viju Chacko at FAYA:80 that gave a basic introduction to Ai. It act as an introduction to different terminologies related to AI that could enable its audience to understand the technology better.
The field of Artificial Intelligence (AI) has been revitalized in this decade, primarily due to the large-scale application of Deep Learning (DL) and other Machine Learning (ML) algorithms. This has been most evident in applications like computer vision, natural language processing, and game bots. However, extraordinary successes within a short period of time have also had the unintended consequence of causing a sharp difference of opinion in research and industrial communities regarding the capabilities and limitations of deep learning. A few questions you might have heard being asked (or asked yourself) include:
a. We don’t know how Deep Neural Networks make decisions, so can we trust them?
b. Can Deep Learning deal with highly non-linear continuous systems with millions of variables?
c. Can Deep Learning solve the Artificial General Intelligence problem?
The goal of this seminar is to provide a 1000-feet view of Deep Learning and hopefully answer the questions above. The seminar will touch upon the evolution, current state of the art, and peculiarities of Deep Learning, and share thoughts on using Deep Learning as a tool for developing power system solutions.
Slides to a talk from @Chris_Betz, (https://data42.de) on AI, artificial intelligence, machine learning. What's driving AI hype and what's behind it. Understand general concepts and dig deep into explainability, debugging, verification, testing of machine learning solutions.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2017-alliance-vitf-samek
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Wojciech Samek of the Fraunhofer Heinrich Hertz Institute delivers the presentation "Methods for Understanding How Deep Neural Networks Work" at the Embedded Vision Alliance's September 2017 Vision Industry and Technology Forum. In his presentation, Dr. Samek covers the following topics:
▪ Unbeatable AI systems
▪ Deep neural network overview
▪ Opening the "black box"
▪ Summary
Camp IT: Making the World More Efficient Using AI & Machine LearningKrzysztof Kowalczyk
Slides from the introductory lecture I gave for students at Camp IT 2019. I tried to cover artificial inteligence, machine learning, most popular algorithms and their applications to business as broadly as possible - for in-depth materials on the given topics, see links and references in the presentation.
[Srijan Wednesday Webinars] Artificial Intelligence & the Future of BusinessSrijan Technologies
“AI is the new electricity” – Andrew Ng, former Chief Data Scientist, Baidu
Artificial Intelligence is the new frontier for human evolution. It will upend industries, cause fundamental shifts in processes and jobs, and create unprecedented innovation.The question one wishes to answer is: how and why it impacts industry, and how can it be leveraged by businesses.
This session will introduce AI and machine learning: the process of creating AI, and go on to discuss the key applications of these emerging technologies. We will also dive into a preliminary review of ML algorithms and how they work.
Key Takeaways:
- Define AI and ML, and the philosophy behind these new technologies
- The impact of AI on jobs, communities, business, and industry
- The use cases of AI in different industries like hi-tech, manufacturing, healthcare, publishing and media, education, transportation etc.
-Introduction to machine learning algorithms like classification, regression, neural networks etc.
Check our webinars series and sign up for future webinar notifications at: www.srijan.net/webinar/past-webinars
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
3. 3
Approaches to Intelligence
Neuroscience
Brain Science
Psychology
Economics
Mechanical
Engineering
Computer Science
(a.k.a. AI)
Observe
brain activities
Observe
human behavior
Mathematical model
- Game theory
- Optimization
Differential equation
Control theory
Mimic human
intelligence
4. 4
Technology Focus of AI Research has Changed Over Time
Hiroshi Maruyama
1st Wave of A. I. (1956-1974)
• Symbol Processing (LISP)
• Means-End Analysis
• Language Parsing
2nd Wave of A. I. (1980-1987)
• Knowledge Representation
• Expert System
• Ontology
3rd Wave of A. I. (2008- )
• Statistical Machine
Learning
• Deep Learning
• Blackbox optimization
- Garbage Collection
- Search Algorithms
- Formal Language Theory
- :
- Object-Oriented Language
- Modeling
- Semantic Web
- :
Inductive Programming
Blackbox computing
5. “AI” is the name of a research field, but …
Research Field Derived
Technologies
Applications
Physics
AI
Internal Combustion
Engine
Semiconductor
Automobile
Computer
Search algorithm
Speech recognition
Image recognition
Car navigation
AI speaker
Autonomous
driving
We do not call
them “Physics”
Some call
them “AI”
6. “Artificial Intelligence” is an Overloaded Term
1. For researchers, AI is a research activity (or field)
to study intelligence by simulating it by machine
— Search, Inference, Optimization, Recognition, NLP, …
2. For AI vendors, AI is ANY information system that
utilizes ANY of above research results
3. For general public, AI is a human-like machine
intelligence
6
8. 8
What is Deep Learning? – A (Stateless) Function
Y = f(X)X Y
Very high-
dimensional, any
combination of
continuous and
categorial variables
Low-dimensional for
classification, very
high-dimensional
for generation
9. 9
Example: Converting Celsius to Fahrenheit
Hiroshi Maruyama
double c2f(double c) {
return 1.8*c + 32.0;
}
Input: C
Output: F
Where F is Fahrenheit
equivalent of C in Celsius
Requirements
Algorithm
F = 1.8 * C + 32Model
A Priori
Knowledge
Model must be know in advance, and
Algorithm must be constructible
10. Training Data Set
Observation
Training(search for parameter θ)
No knowledge on model or algorithm is required!
Alternative Approach – Data-Driven, Inductive Programming
(aka Statistical Modeling)
11. 11
Deep Neural Net as a Universal Computing Mechanism
⚫ Very large number of parameters
⚫ Can approximate ANY high-
dimensional function*
➔ Pseudo Turing Complete!
Output
Input
* G. Cybenko. Approximations by superpositions
of sigmoidal functions. Mathematics of Control,
Signals, and Systems, 2(4):303–314, 1989.
14. Fundamental Limitation of ML (1)
Training data
set
Model
Statistical Machine Learning works only if the
future is similar to the past
Timeline
Data is sampled
at some point in
the past
Training
Inference (i.e., prediction)
based on the trained
model
15. Fundamental Limitation of ML (2)
⚫ Powerless on data in unseen regions
Training Data Set
Interpolation
Extrapolation
??
Statistical Machine Learning does not improvise
16. 16
Fundamental Limitation of ML (3)
⚫ Always works statistically
Original Distribution
i. i. d.
Training Data Set
Trained Model
Random
Sampling !!
No guarantee of “100% correctness”
17. What is Deep Learning – Recap
⚫ A new way of programming (inductive programming)
— No prior knowledge on model or algorithm
⚫ Preparing training dataset is the key
— Creative “teacher signal” allows innovative applications
⚫ It’s statistical modeling
— Assume i. i. d. (independent and identically-distributed)
— Approximation only (no exact answers)
17
20. 20
X: Sensor Input
Y: Actuator Output
Y = f(X, θ)
u(S, Y): Reward function
S: Current State
21. 21
Blackbox optimizers
Optuna: “define-by-run” Bayesian optimizer
https://optuna.org/
Whitebox Optimization
- Simplex algorithm
- Internal point method
The utility function is
known in advance
Blackbox Optimization
- Reinforcement learning
- Bayesian optimization
- Utility function is not known in advance
- Use an external oracle for individual
utility values
x
u(x)
出典:Wikipedia
22. 22
“Programming by Optimization” – How to optimize your program
for particular subset of input
Parametric
Source code
Weaving
Blackbox
Optimizer
Hoos, Holger H. "Programming by optimization." Communications of the ACM 55.2 (2012): 70-80.
Optimized
Code
23. cf. Evolution of Science
Law of Gravitation
1/15, 201623
Hiroshi Maruyama
Model with the smaller number of parameters is the correct one
24. 24
High dimensional science:
Cancer diagnosis based on ExRNA expressions
Cancer diagnosis
Scientists tend to look for a
small set of dominant
parameters (simpler models)
Deep neural network (w/ a
large # of parameters) gives
much higher accuracy
https://www.preferred-networks.jp/en/news
26. Evolution of Computing
Whitebox Computing Blackbox Computing
Theoretical
foundation
Discrete mathematics, esp.
Boolean logic
Probability Theory
Computational
mechanism
Turing Machine Deep Learning, Bayesian Optimization,
…
Problems to solve Well-defined, low-dimensional Ill-defined, very high-dimensional
Programming Hand-crafted (constrained by
human cognitive capacity)
Inductive and/or search-based
Accuracy No error Approximation only
Design principles Modularization, separation of
concerns
Integration
26
28. Maruyama’s Conjecture:
In 2020, more than half of newly developed software have
inductively-trained / blackbox-optmized components
This is the largest paradigm shift since the inventin of digital computer!
30. 30
Myth 1: Deep Learning is unsafe
Wall Street Journal, 7/7, 2016
http://jp.wsj.com/articles/SB11860788629023424577004582173882125060236
Tesla accident, 2016
However, …
Can you guarantee 100% safety if you do
conventional V-shaped development?
出典:Wikipedia
31. 31
Typical bug density (per 1,000 loc in equiv. assembly code)
http://www.softrel.com/Current%20defect%20density%20statistics.pdf
Do not pretend that there are “100% safe” programs!
32. Myth 2:BBC is unexplainable, uncontrollable
⚫ Is Deep Learning unexplainable?
— DL today runs on a digital computer
◆ The same input / training data set / hyper parameters / random number
seeds yields exactly the same output
— You can trace the computation bit-by-bit
— However, it is completely another story if mere human can understand the
trace
⚫ What is “explainability”?
32
33. Could we explain how Fukushima disaster had occurred?
33
東京電力福島原子力発電所における事故調査・検証委員会 最終報告書「概要」27ページ
http://www.cas.go.jp/jp/seisaku/icanps/SaishyuGaiyou.pdf
⚫ The Independent Investigation Commission spent 14 months to
produce total 1,700 pages of the report
“Many points are still unclear”
34. Can you control a complex system?
⚫ Flipping “Kill switch” does not mean “control”
— You cannot shut down the system of a flying airplane, a surgical robot while operation, …
⚫ W. Ashby’s Law of Requisite Variety (1958)
— “If a system is to be stable, the number of states of its control mechanism must be greater
than or equal to the number of states in the system being controlled”
34
It’s the problem’s complexity that makes system
unexplainable / uncontrollable
It’s not because of Deep Learning or Blackbox
Optimization!
35. Can you reduce the complexity of your system?
35
C.S. Holling, Resilience Cycle
Holling, C.S. and Lance H. Gunderson. 2002
Reduction of complexity comes with collapse
→ We may need keep complexity
→ Anticipate big disturbance in your design
J. Casti, X-Events: The Collapse of Everything
ISBN-13: 978-4023311558
https://www.researchgate.net/publication/261338523_ICHIGA
N_Security_-
_A_Security_Architecture_That_Enables_Situation-
Based_Policy_Switching
36. Myth 3:Optimization gives what you want
What happens if we increase the collision penalty to the infinity?
36
Cars that do not move!
You have to be explicit in stating the balance between the utility and the safety
37. 37
A case of Smart Robot
You: “Get me coffee”
The smart robot goes to Starbucks downstairs, sees many people in
the line, kills everybody, and gets coffee to you
Precisely specifying the objective function is very hard
This is “Frame Problem,” still an open problem in AI research
IJCAI 2017 Keynote by Stuart Russell, “Provably Beneficial AI”
38. BBC makes us think
3 Myths
1. BBC is not safe
2. BBC is unexplainable, uncontrollable
3. BBC gives what you want
We have to be explicit about
1. No such thing as “100% safe”
2. Complexity is the enemy, not BBC
3. You have to be careful when you say you want something
Think what we really want!
39. The role of engineering
Theories(e.g.,
structure)
* Safety Factor
New technology is accepted by the society only after it becomes engineering descipline
Civil Engineering Handbook, p999
Why do we trust bridges? Because of the accumulated knowledge
called Civil Engineering
40. 40
We started a SIG in JSSST(MLSE)
https://sites.google.com/view/sig-mlse
Kick-off Symposium (5/17, ~500 participants) MLSE workshop (7/1-2)
JSAI MLSE Session (6/8)
JSSST Annual Convension (8/29-31)
42. The risk that our society relies too much on information systems
42
⚫ Enlightenment (啓蒙思想): Our society’s fundamental assumption
— Every person can reason and choose with his / her free will
— Basis for democracy, capitalism, science, …
⚫ Because of study on AI and cognitive science, the very existence of free
will is in question
“Just as scientific study of the Bible
inadvertently undermined faith in the
Christian God, scientific study of the
mind is inadvertently undermining faith
in the liberal humanist God: the freely-
choosing individual. “
http://quillette.com/2018/03/18/wizard-prophet-steven-pinker-yuval-noah-harari/
ISBN-13: 978-1784703936
43. Two Sides of “Digital Sovereignty” (デジタル主権)
⚫ Originally, Internet is borderless
— Open, bottom-up(IETF, W3C, …)
⚫ Controlling Internet means controlling people
— Have people to buy: A/B Test, Recommendation, …
— Have people to vote: Fake News, Echo-Chamber Effect
⚫ Threat of GAFA
— Fear for giants controlling everything
— GDPR: EU’s “Digital Sovereignty”
⚫ China and Russia to follow suit
— Internet to control citizen
— As a viable alternative to democracy!
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This is one example of the implications of IT – Please think
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As IBM Technical Leaders, You should …
Be true to the technologies
Don’t oversell or undersell
Think, and discuss their implications