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AI - How Artificial Intelligence Will Impact Your Business


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AI - How Artificial Intelligence Will Impact Your Business

AI (Artificial Intelligence) has the potential to radically transform employment, productivity and society. Business decision makers need to mitigate underlying risks and invest appropriately to drive future competitive advantage.

Published in: Business
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AI - How Artificial Intelligence Will Impact Your Business

  1. 1. Artificial Intelligence For Business Learning Decision Makers
  2. 2. My area of focus is how technology will enable business and societal change 3 years in the future.
  3. 3. 50’s & 60’s 60’s & 70’s 70’s & 80’s 90’s & 2000’s 2000’s 2010’s Beginning now The Innovation sweet spot for the next generation will be artificial intelligence and machine learning Technology Waves
  4. 4. General Purpose Technologies ? ?
  5. 5. What Exactly Is Artificial Intelligence? ‘A good way to think about artificial intelligence is as computer software that’s indistinguishable from human intelligence’. • Brendon Frey, Co-founder of the Vector Institute Or said another way; Artificial intelligence is the study of how to make computers do things that people are better at … Or would be better at if: They had access to a World Wide Web-sized amount of data and ….. Not make any mistakes !
  6. 6. Algorithm •Arguably the single most important concept in our world! •A methodical set of steps that can be used to make calculations •Not a particular calculation but the method followed when making the calculation!
  7. 7. Simple Mathematical Algorithms • Calculating the average of two numbers • Add the two numbers together • Divide the numbers by two • Works for any two numbers
  8. 8. Artificial General Intelligence?
  9. 9. What Is Machine Learning? ( Narrow AI) Building systems that enable a computer system to learn some intelligent behavior by training it on massive amounts of data. Said another way; Replacing systems where algorithm are programed in advance with systems where algorithms learn from data.
  10. 10. 90% of ML Algorithms Do One Thing Takes an input A and delivers an output B • Picture Is it you? • Loan application Should you get it? • Advertisement Will you click? • Audio Transcript • English French • Image What it is • Manufactured product Is it acceptable
  11. 11. The Five Major Techniques of Machine Learning 2018 Adapted from Pedro Domingos; The Master Algorithm
  12. 12. Until recently Connectionists (neural networks or Deep Learning) were all but shunned by the AI research community. They had been around since the earliest days of AI, and had produced very little in the way of “intelligence.” The problem was even the most basic neural networks were very computationally intensive, it just wasn’t a practical approach. Still, a small research group led by Geoffrey Hinton at the University of Toronto kept at it, finally parallelizing the algorithms for supercomputers to run and proving the concept. With the deployment of GPUs (and now TPU’s) for AI the promise was realized
  13. 13. Sample complex machine learning task 2016 $240,000.00 Similar task 2018 $12.75
  14. 14. Three Major Types of Machine Learning 2019 • Supervised • Data labelled • Goal – “Classify” new data – most of what we’ll discuss here • Unsupervised Learning • Data without labels • Goal – Find patterns • Reinforcement Learning • No data no labels • Goal – classify a reward
  15. 15. What Can Machine Learning Do Now? Image recognition Loan approvals Resume’ screening Targeted online ads Speech recognition Language translation Self driving cars Preventative maintenance Manufacturing quality control Medical diagnosis Combinations of the above …………. More things literally every day
  16. 16. AI will boost global GDP by nearly $16 trillion by 2030
  17. 17. And / Or
  18. 18. “If [self-driving cars] make the world safer, it’s going to be a very good thing, but it won’t be a good thing for auto insurers.” – Warren Buffet
  19. 19. One More (Really Important) Thing
  20. 20. David Hume’s Problem of Induction • “How can we generalize from what we’ve seen to what we haven’t?” • One of the most important questions in philosophy • By extension one of the most important questions in machine learning. • The problem of overfitting or hallucinating patterns that aren’t really there. • Also what if ‘what we’ve seen’ is not accurate in the first place?
  21. 21. When humans (data) trains the machine …
  22. 22. Innovation Opportunities!
  23. 23. Today Every Company is a Tech Company Anything a typical person can do in less than 1 second of thinking can probably now or soon be automated Every company has AI opportunities Brainstorm the above Be Data Savvy – It all starts with Data Train. Experiment. Measure success. Commercialize. Build a centralized data warehouse – 50 different databases makes AI difficult (impossible?) Create a centralized AI team matrixed to BU’s to help execute? How Should Organizations (people) Prepare?
  24. 24. Artificial Intelligence For Business Learning Decision Makers