URL: https://professionalschool.eitdigital.eu/generative-ai-essentials Course on Generative Al Description: Generative AI is a world-changing power tool that is getting better by the day. So now is the time to get truly inspired, climb up the learning curve, and unleash more of your creative potential. Learning Topics: * Inspiration: What is Generative AI in the context of AI's history, present, and future * Climbing Up: Ways to accelerate your learning trajectory * Unleashing Creativity: Ways to stay future-ready in the AI era What You'll Take Away: By the end of this session, you'll understand the importance of upskilling with today's generative AI tools to get more work done, both faster and at higher quality, as well as some pitfalls to avoid, all within the broader context of the past, present, and future of Artificial Intelligence (AI) and Intelligence Augmentation (IA). Learning Topics Inspiration: What is Generative AI in the context of AI's history, present, and future. Climbing Up: Ways to accelerate your learning trajectory. Unleashing Creativity: Ways to stay future-ready in the AI era. Deep dive into ChatGPT's features. Techniques for basic and advanced prompting and real-world applications.

1. EIT DIGITAL

2. Session 1
Nov. 28: Jim Spohrer & Marco Podien

3. Session 2
Dec. 5: Antonio Nieto-Rodriguez

4. Session 3
Dec 12: Edwin M. Colella

5. Session 4
Dec 19: Neema Balolebwami Nelly & Gry Hasselbalch

6. • Jim
• Inspiration: Generative AI in the context of AI's history, present, and future.
• Climbing Up: Ways to accelerate your learning trajectory.
• Unleashing Creativity: Ways to stay future-ready in the AI era.
• Marco
• Deep dive into ChatGPT's features.
• Techniques for basic and advanced prompting and real-world applications.
Today’s Learning Topics

7. 1956
2023
2060 2080
1956
First AI
Workshop
2023
ChatGPT 100M
users in just 2
months
(1.5B visits in Sept)
2060 (Predicted)
Exascale for $1000
(~ one human
brain)
2080 (Predicted)
Ronnascale for $1B
(~ billion human brains)
Progress in IA (Intelligence Augmentation) for nations can be estimated as
GPD/worker.
Progress in AI (Artificial Intelligence) is directly connected to the cost of
computing.
REMEMBER THESE DATES

8. • First impressions
• Insanely great productivity
• Insanely great quality
• Awesome progress, but…
• Impressive
• Imperfect
• What is really going on?
• Decreasing cost of computation
• Increasing GDP/worker
• Advantage of empowered people
Inspiration

10. Icons of AI Progress
• 1955-1956: Dartmouth Workshop organized by:
• Two early career faculty
• John McCarthy (Dartmouth, later Stanford)
• Marvin Minsky (MIT)
• Two senior industry scientists
• Claude Shannon (Bell Labs)
• Nathan Rochester (IBM)
• 1997: Deep Blue (IBM) - Chess
• 2011: Watson Jeopardy! (IBM)
• 2016: AlphaGo (Google DeepMinds)
• 2017: All you need is attention (Google) - Transformers
• Attention heads (working memory) to predict what comes next
• 2018: AlphaFold (Google DeepMinds)
• 2020: Language models are few-shot learners (OpenAI)
• 2022: DALL-E 2 & ChapGPT (OpenAI)
• 2022: Constitutional AI (Anthropic) – “Behave yourself!”
• 2023: New Bing+ (Microsoft) & GPT-4 (OpenAI)
• 2023: ?Q* (OpenAI) - to reduce hallucinations (generation stage errors)
11/27/2023
pohrer

11. http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html https://cdn.openai.com/papers/gpt-4.pdf
1955 2023

12. 1960 1980 2000 2020 2040 2060 2080
Time

13. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
Cost

14. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
Cost of Computation (Diagonals)

15. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
Cost of Computation (Diagonals)
Note: Adjust Kilo and Mega scales slightly to fit data better (early days – more cost – learning curve).

16. 1960 1980 2000 2020 2040 2060 2080
$1,000,000,000,000
(Trillion)
$1,000,000
(Million)
$1,000,000,000
(Billion)
$1,000
(Thousand)
$1
GDP/Employee
Trend
Estimating Knowledge Worker Productivity
Based on USA
Historical Data
Year Value
1960 $10K
1980 $33K
2000 $78K
2020. $151K
2023 $169K
Cost of computation goes down by 1000x every 20 years (left to right diagonals), driving knowledge worker productivity up.

17. 7September 2018 / © 2018 IBM Corporation
Petaflops = 1,000,000,000,000,000 or a million
billion = 10 ** 15
Megaflops = 1,000,000 = million = 10 ** 6
Gigaflops = 1,000,000,000 = billion = 10 ** 9
One of the AI Super Computers in the World,
= 13 MegaWatts of Power (HOT!)

18. 8September 2018 / © 2018 IBM Corporation
Exascale = 1,000,000,000,000,000,000 or a
billion billion = 10 ** 18
Megaflops = 1,000,000 = million = 10 ** 6
Gigaflops = 1,000,000,000 = billion = 10 ** 9
Human Brain
= 20 Watts (COOL!)

21. “NorthPole Chip”
Far less energy.

22. Impressive, but imperfect
Physical realm: Energy
Social realms: Digital property theft

23. • How to keep up with accelerating change?
• Social learning
• Who do you follow?
• What kind of person are you?
• Let’s level set…
• *** Survey ***
• Diving in!
• We get the future we invest in
• … so invest wisely
• Awesome stuff that lies ahead
Climbing Up

24. Optimistic Realistic
Knowing
Doing
How to keep up with accelerating change? Follow a diverse collection of people… make up dimensions meaningful to you!
Sadly for me… my brain is biased into thinking I can understand older, white, males the best… maybe AI can help overcome!

25. Let’s level set – how many of you know about…
Ethan Mollick (UPenn Wharton) Don Norman (UC San Diego)
Scott Pelley (CBS, 60 Minutes)
Tristan Harris and Aza Raskin
(Center for Humane Technology) Generative AI Tools
To Output:
Text/Writing
Images
Code/Programming
Videos
Audio
Music
Game Worlds
Digital Twin
Other?
11/27/2023
r (ISSIP.org)

26. Survey
Q: How often are you using a generative AI tool?
a. Never
b. Occasionally
c. At least once a week
d. Multiple times a day
Q: How many generative AI tools have you used?
a. None
b. One
c. A few
d. Over a dozen

27. We get the future we invest in:
AI tools to experiment with today
• #1 Magic Eraser
• #2 Craiyon
• #3 Rytr And GPT-3, ChatGPT, GPT-4, Bing
• #4 Thing Translator
• #5 Autodraw
• #6 Fontjoy
• #7 Talk to Book
• #8 This Person Does Not Exist
• #9 Namelix
• #10 Let's Enhance
Thanks to @TessaRDavis
for compiling this list:
“Service providers
will not be replaced by AI,
but trusted service providers
who use AI (well and responsibly)
will replace those who don’t.”
National Academy - Service Systems and AI
27
Try at least two
from the list
as soon as possible
What do you think?
, DALL-E and Stable Diffusion
Every person in a role in an organization is a service provider.
11/27/2023

28. • How to stay future ready?
• Learn the building blocks
• Marco Podien will help you with ChatGPT building block shortly…
• Oh, by the way, you are going to need bigger goals too
• When you have 100 digital workers working for you, what is your main goal?
• From serial entrepreneur to parallel entrepreneur
• Use Q&A feature – almost time for Q&A
Unleashing Creativity

29. 11/27/2023
1955 1975 1995 2015 2035 2055
Learn: Explore and Exploit Better Building Blocks

30. Read Wakefield
(2020)
enough to
understand what a
”digital twin” of
you might be like in
the future decades
with very advanced
AI capabilities.
Also see Rouse
(2018; 2022) ”Life
with a Cognitive
Assistant.”
ervice Systems and AI
AI Tools
in coming
decades…
11/27/2023

31. 11/27/2023
Innovation Professionals (ISSIP.org)
Advice…
1. AI upskill
2. Build your
“digital twin”
3. Set bigger
goals(*)
(*) This directly implies learning better strategies for coping with failures, and resiliently rebounding.

32. Two disciplines: Two approaches to the future
Artificial Intelligence is almost seventy-years-old discipline in computer
science that studies automation and builds more capable technological
systems. AI tries to understand the intelligent things that people can do
and then does those things with technology. (https://deepmind.com/about “...
we aim to build advanced AI - sometimes known as Artificial General Intelligence (AGI) - to
expand our knowledge and find new answers. By solving this, we believe we could help
people solve thousands of problems.”)
Service science is an emerging transdiscipline not yet twenty-years- old
that studies transformation and builds smarter and wiser socoi-
technical systems – families, businesses, nations, platforms and other
special types of responsible entities and their win-win interactions that
transform value co-creation and capability co-elevation mechanisms
that build more resilient future versions of themselves – what we call
service systems entities. Service science tries to understand the
evolving ecology of service system entities, their capabilities,
constraints, rights, and responsibilities, and then then seeks to improve
the quality of life of people (present/smarter and future/wiser) in those
service systems.
Artificial Intelligence
Automation
Generations of machines
Service Science
Transformation
Generations of people
(responsible entities)
Service systems are dynamic configurations of people,
technology, organizations, and information, connected
internally and externally by value propositions, to other
service system entities. (Maglio et al 2009)

33. Jim Spohrer is a Silicon Valley-based Advisor to industry, academia, governments,
startups and non-profits on topics of AI upskilling, innovation strategy, and win-
win service in the AI era. Most recently with a consulting team working for a top
10 market cap global company, he contributed to a strategic plan for a globally
connected AI Academy for achieving rapid, nation-scale upskilling with AI. With
the US National Academy of Engineering, he co-led a 2022 workshop on “Service
Systems Engineering in the Era of Human-Centered AI” to improve well-being.
Jim is a retired IBM Executive since July 2021, and previously directed IBM’s open-
source Artificial Intelligence developer ecosystem effort, was CTO IBM Venture
Capital Group, co-founded IBM Almaden Service Research, and led IBM Global
University Programs. In the 1990’s at Apple Computer, as a Distinguished Engineer
Scientist and Technologist, he was executive lead on next generation learning
platforms. In the 1970’s, after his MIT BS in Physics, he developed speech
recognition systems at Verbex (Exxon) before receiving his Yale PhD in Computer
Science/AI. In 1989, prior to joining Apple, he was a visiting scholar at the
University of Rome, La Sapienza advising doctoral students working on AI and
Education dissertations. With over ninety publications and nine patents, he
received the Christopher Lovelock Career Contributions to the Service Discipline
award, Gummesson Service Research award, Vargo and Lusch Service-Dominant
Logic award, Daniel Berg Service Systems award, and a PICMET Fellow for
advancing service science. Jim was elected and previously served as Linux
Foundation AI & Data Technical Advisory Board Chairperson and ONNX Steering
Committee Member (2020-2021). Today, he is a UIDP Senior Fellow for
contributions to industry-university collaborations, and a member of the Board of
Directors of the International Society of Service Innovation (ISSIP) and ServCollab.
Jim Spohrer, Advisor
Retired Industry Executive (Apple, IBM)
UIDP Senior Fellow
Board of Directors, ServCollab
Board of Directors, ISSIP.org
Changemaker Priorities
1. Service Innovation
2. Upskilling with AI
3. Future Universities
4. Geothermal Energy
5. Poverty Reduction
6. Regional Development
Competitive Parity
Technologies
1. AI & Robotics
2. Digital Twins
3. Open Source
4. AR/VR/XR
5. Geothermal
6. Learning
Platforms

34. Thank-you for
participating today!
You are all awesome!!

35. Backup Slides
Template slides and others

36. Topics for Discussion
• Beyond Language for Communications
• Here is how my AI, using my digital twin of you, predicted that you would respond to my
request – could you please ask your digital twin of yourself to check this response and
suggest improvements?
• How to keep up with accelerating change?
• Who do you follow? What two main dimensions do you try to balance? Hype-buster <->
Super-Optimist, Doing (Try this) <-> Knowing (News)
• How to verify results of generative AI?
• How to deal with verification challenge? Run Open AI ChatGPT 3.5, Google Bard (waiting
for Gemini), Anthopic’s Claude, Microsoft Bing power by Open AI ChatGPT 4 – in parallel
and have them critique each others’ responses (where they agree, where and how they
differ) – if possible, also run Meta and open source to compare (Generate-Test-and-
Debug)
• How to deeply understand how generative AI works?
• Monkey’s at the typewrite in high dimensional spaces that map to low dimensional spaces
• One dimensional space is time – what comes next? (Predictors, unsupervised learning)
• Two dimensional space is what people gravitate towards in business schools (Sorters,
supervised learning)

37. Today’s talk
• Intro: AI (by 1955 definition) has arrived
• Just took 68 years, but…
• What’s really going on?
• Your data is becoming your AI… IA transformation
• AI Digital Twin = IA (Intelligence Augmentation)
• Adjustment period underway…
• Part 1: Solving AI: Leaderboards/Profession Exams
• Roadmap and implications
• Open technologies, innovation
• Part 2: Solving IA: Better Building Blocks
• Solving problems faster, creates new problems
• Identity, social contracts, trust, resilience
• Part 3: ”Solving All Problems”
• What could go wrong? Be prepared.
• 37-year long adjustment period is now underway…
11/27/2023
pohrer

38. Predict the Timeline: GDP/Employee
National Academy - Service Systems and AI 38
(Source)
Lower compute costs translate into increasing productivity and GDP/employees for nations
Increasing productivity and GDP/employees should translate into wealthier citizens
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA
Alistair Nolan (OECD AI for Science Productivity): “It has been stated that the number of engineers proclaiming the end of Moore's Law doubles every two years.”
Rouse WB, Spohrer JC. (2018) Automating versus augmenting intelligence. Journal of Enterprise Transformation. 2018 Apr 3;8(1-2):1-21.
Read Rouse & Spohrer (2018)
enough to understand this slide
including what ”exascale” means
11/22/22
Part 1: Solving AI

39. Types: Progression of Models : Verified, Trusted, Wise
Models = instruction_set of future: Better building blocks
11/27/2023
Understanding Cognitive Systems
39
Task & World Model/
Planning & Decisions
Self Model/
Capacity & Limits
User Model/
Episodic Memory
Institutions Model/
Trust & Social Acts
Tool + - - -
Assistant ++ + - -
Collaborator +++ ++ + -
Coach ++++ +++ ++ +
Mediator +++++ ++++ +++ ++
Cognitive
Tool
Cognitive
Assistant
Cognitive
Collaborator
Cognitive
Coach
Cognitive
Mediator
Part 2: Solving IA
Solving IA also requires
All of this and done well
As a “bicycle for the mind”
To make us stronger,
Not weaker
When tech is all removed

40. Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012) The Knowledge: How to
Rebuild Civilization in the Aftermath of a
Cataclysm. Westminster London: Penguin
Books.
11/27/2023
Jim Spohrer (ISSIP)
40
Part 3: “Solving All Problems”

41. Long slide title
Awesome subtitle

42. Slide title
Slide subtitle

43. Slide content
Slide title
Box Box content Box Box content

44. • Slide bullet points
• Elaborating
• On
• Awesome
• Tech and
• Business
Slide Title

45. BOX 1
Subtitle for box
BOX 2
Subtitle for box
Transaction finalization
Block 2
text
Block 3 text
Block 1
text
Slide title
Box 3

46. Slide title
Slide subtitle

47. Slide title

48. Meet the team

49. Thank you

50. ANNEX