There are 6 things I identified in the last 2 Years I have been working in AI. The Problem is - Hysteria The lack of context is leading to Noise The Noise is distracting from the attention and urgency where AI should really be Executives want a Solution and Directions. THE GOOD NEWS IS: You don’t need to know the HOW to do, leave this to the tech dudes. You need to know the WHY? You need to create a culture of enablement. A culture of Data

1. AI Leadership AI
The basics of the truth and noise
Lucio Ribeiro
linkedin.com/in/lucioribeiro/

2. Today
NOT technical
NOT coding
NOT how-to
YES basic terminology
YES where to start
YES what’s Noise what’s not

3. Agenda
1) Definitions of AI for Business Leaders.
2) Noise and Truth.
3) Where to start?

4. histeria

6. histeria
reality
https://hbr.org/2019/02/research-automation-affects-high-skill-workers-more-often-but-low-skill-workers-more-deeply?

7. The USA will go from 1 million grounds and maintenance workers
in to only 50,000 in 10 to 20 years, because robots will take over those jobshisteria
reality
https://rodneybrooks.com/the-seven-deadly-sins-of-predicting-the-future-of-ai/
MarketWatch
Up to 2017
0 Jobs were lost because of robotization in ground and maintenance

8. https://rodneybrooks.com/the-seven-deadly-sins-of-predicting-the-future-of-ai/

9. The rise of AI presents an opportunity for executives in every
industry to differentiate and defend their businesses. But
implementing a company-wide AI strategy is challenging, especially
for legacy enterprises.
Don’t go for utopia or dystopia

10. We tend to overestimate the effect of a
technology in the short run and underestimate the
effect in the long run.

11. But who is Lucio anyway?

12. ● Former Ogilvy, McCann-Erickson, founder of OCDigital
● Lecturer at RMIT and Deakin University
● Award Winner utilizing AI applications in Ad Campaigns
● Melbourne/Canada/Brazil.
Academic Background
1. Law Degree
2. MBA
3. Certification in
• Psychology (Univ. Toronto)
• Buddhism Scriptures (Harvard)
• Marketing (MIT)
• Game Theory (Stanford)
• AI Implications for Business (MIT Sloan)
www.linkedin.com/in/lucioribeiro/ or lucio.ai
Lucio Ribeiro

13. Part 1
1) Definitions of AI for Business Leaders.
2) Noise and Truth.
3) Where to start?

14. What is AI?
Machines acting in ways that seems intelligent.
What are the Branches of AI?
1. ML
2. NLP
3. Robotics
4. Neural Networks
5. Deep Learning (similar to ML)

16. Part 2
1) Definitions of AI for Business Leaders.
2) Noise and Truth.
3) Where to start?

17. "I think there is a world market for maybe five
computers.”
Thomas Watson, president of IBM, 1943

19. NPL - What’s working or not?
Working Very Well
● Search
● Information Extraction
(remember...in corporates the majority of
information is stored in documents)
● Text Generation
● Spam Detection
● Identification of Name
entity
● Translating NEWS
Good Progress
● Sentiment Analysis
● Coreference Resolution
● Parsing
● Sense Disambiguation
● Machine Translation of
anything besides NEWS
Still Really Hard
● Question Answering
● Paraphrase
● Dialog
● Anything that depends
highly in compliance.
● Cognitive tasks

20. ML - Syntactic and Semantic Mistakes
Example. The phrase “At last, a computer that understands you like your mother.”

21. Things are still a long way to go
Mistake on
automatic traders
utilising AI sentiment
Analysis.

22. Machine Learning

23. Machine Learning
Here are some of the verbs that have been applied to machines, and for
which machines are totally unlike humans in their capabilities:
anticipate, beat, classify, describe, estimate, explain, hallucinate, hear,
imagine, intend, learn, model, plan, play, recognize, read, reason, reflect,
see, understand, walk, write

24. Part 3
1) Definitions of AI for Business Leaders.
2) Noise and Truth.
3) Where to start?

25. 1. Formulate an use case. (HBR)
2. in any industry, start small (E.g. a bot)
3. Use the data you already have
4. Build an Ecosystem, not just the talent
5. You need to create a culture of enablement. A culture of Data
6. Start collecting data
My Advice

26. Strategic Advantages - utilising Porter’s framework
Cost Leadership
Being the low cost producer
To reduce costs by improving operations
E.g. Robotics, thief observation
Differentiation
Being unique on dimensions that customers
value, such as quality
To create better products, such as by incorporating new
features that were never possible before
Google
Focus
Tailoring products to a narrow segment
To help companies understand and address the unique
needs of niche customers
Netflix Suggestion Engine
Porter’s Strategies AI Function

27. 1) Factories - to shape or assemble componentes
2) Warehouses - to pick, sort, move goods
3) To perform miscellaneous physical services - deliver, manipulate or locate goods.
Robotics AI 3 Main ways is being used today

29. AI Vision Recognition
Machines have made real strides in distinguishing among similar-looking
categories of images.

30. 1) Massive, free computing
2) Excited People
3) Emerging round table (people working
with Psychologists, Ethics)
4) Accumulated progress
5) Better questions
The third wave gave us Machine Learning. The
4th Wave has started.
Professor Patrick Winston -
phw@csail.mit.edu

31. AI moving forward
Think about History Evolution
● Tractors were designed to replace humans muscle power with mechanical.
● Assembly lines were built to substitute machine precise for artisanal labor.
● Computer were developed to eliminate cumbersome tasks for humans and replace with digital
perfection.
For most parts these technologies has worked. Despite 200 years of labor-saving automation, the fraction of
adult population that participates in the labour market has increased.
1. O-Ring Principle - a collection of tasks that need to be done together to successfully accomplish a main
task. If some of the tasks involved can be automated, the economic value of the human inputs for the
other tasks that can’t be done by machines will increase.. It comes from the Challenger spaceship that
exploded because of a piece of rubber band (O-Ring). Broadly speaking as we automated tasks, we
complement our expertise.
2. Never Enough Principle - As we get wealthier, we deploy new things to “worry” and engage our attention
about it. “Invention is the mother of necessity”

33. It’s easy to predict the future, but it’s hard to predict the details of the future.
Machines are good in finding patterns but not good in figuring things out.
Do we really know the future?

34. THANK YOU - GRACIAS
BE in touch.
(Send me your photos/ask me questions)
Lucio.ai (work in progress)
linkedin.com/in/lucioribeiro/