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AI leadership. AI the basics of the truth and noise public


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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

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AI leadership. AI the basics of the truth and noise public

  1. 1. AI Leadership AI The basics of the truth and noise Lucio Ribeiro
  2. 2. Today NOT technical NOT coding NOT how-to YES basic terminology YES where to start YES what’s Noise what’s not
  3. 3. Agenda 1) Definitions of AI for Business Leaders. 2) Noise and Truth. 3) Where to start?
  4. 4. histeria
  5. 5. histeria reality
  6. 6. 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 MarketWatch Up to 2017 0 Jobs were lost because of robotization in ground and maintenance
  7. 7.
  8. 8. 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
  9. 9. We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.
  10. 10. But who is Lucio anyway?
  11. 11. ● 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) or Lucio Ribeiro
  12. 12. Part 1 1) Definitions of AI for Business Leaders. 2) Noise and Truth. 3) Where to start?
  13. 13. 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)
  14. 14. Part 2 1) Definitions of AI for Business Leaders. 2) Noise and Truth. 3) Where to start?
  15. 15. "I think there is a world market for maybe five computers.” Thomas Watson, president of IBM, 1943
  16. 16. NPL - What’s working or not? Working Very Well ● Search ● Information Extraction ( 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
  17. 17. ML - Syntactic and Semantic Mistakes Example. The phrase “At last, a computer that understands you like your mother.”
  18. 18. Things are still a long way to go Mistake on automatic traders utilising AI sentiment Analysis.
  19. 19. Machine Learning
  20. 20. 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
  21. 21. Part 3 1) Definitions of AI for Business Leaders. 2) Noise and Truth. 3) Where to start?
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. AI Vision Recognition Machines have made real strides in distinguishing among similar-looking categories of images.
  26. 26. 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 -
  27. 27. 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”
  28. 28. 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?
  29. 29. THANK YOU - GRACIAS BE in touch. (Send me your photos/ask me questions) (work in progress)