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How to Conquer Artificial Intelligence

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Scott Porter, VP, Methods, and Aliza Pollack, VP Qualitative, led a session at the Planningness 2015 unconference. Scott works in marketing analytics, and regularly uses Artificial Intelligence algorithms to sort through large quantities of data to find plausible causal models for the interrelation of drivers, outcomes, and intermediate or mediating variables.

In facilitating discussions between marketers and modelers, Scott has realized that the process of gearing up to work with AI can help us think better. Computer algorithms have the advantage when sorting through large amounts of data, but they have their limitations. With current technology, artificial intelligence has to sort through the data it is given--it generally can’t intuit missing data that would be important. However, as humans, we excel at this sort of intuition. Or, at least we can... we have to overcome our human tendency to stop when we've uncovered the first plausible answer.

We shared a structured approach to brainstorming that forces us to push wider to additional context that might be important. These exercises (looking for multiple causes, looking for side effects, looking for missing causes) are the steps we would need to go through in order to select the right data for a computer to have sufficient information to build a quantified model. However, the steps are useful regardless of whether or not we later quantify the model, because the techniques help us push beyond where we would normally stop because we found a single reasonable explanation.

After going through an overview of the theory and the process, we put it into practice. We took turns leading small groups in structured hypotheses sessions to systematically unpack potential complexities of real client challenges shared by members of the session, and brainstorm what information we (or algorithms) will need to better understand potential opportunities.

Speaker note annotations are available within the deck if you download the pdf (orange boxes at the top left of each slide).

Published in: Business, Data & Analytics

How to Conquer Artificial Intelligence

  1. an insight agency focused on Strategic Marketingthat works.
  2. Aliza Pollack talks to humans about humans Scott Porter talks to computers about humans
  3. Dealing with Flux Creating change Responding to change
  4. Success stories Data Macro trends Data mining Outliers Human Had an insight Had an experience Intuition
  5. Predictive Analytics Predictivemodel
  6. Past evidence Did not hurry hurried Arrived on time Arrived late
  7. Prediction Predictive model Willbelate Won’tbelate
  8. Predictive modelingis not enough New policy: Expectation: Late 1 in10 meetings baseline: Late less often “Stop hurrying… hurrying is linked with being late”.
  9. Predictive modelingis not enough New policy: Expectation: Late 1 in10 meetings Late 5 in 10 meetings baseline: Late less often “Stop hurrying… hurrying is linked with being late”. result:
  10. What went wrong?
  11. Identifyingwhat might matter Tireless Difficultyprocessing complexity Tend to single solution No context Don’t recognize sillyanswers
  12. Knowledge representation vs. prediction. Usable by both computers and humans
  13. Easing human communications, too X5 X4 X3 X2 X1 ? answer Blackbox approachrequires a game of telephone. Graphicalmodels facilitatediscussion andcollaboration.
  14. What are our roles? Human: Brainstorm potential inputs Computer: search possible models trust Attribute 1 favorability Attribute 2 Other outcome trust Attribute 1 favorability Attribute 2 Other outcome
  15. What’s in the diagrams? behavior perception outcome event intention Variables (thingswe already measure or things we think we should start measuring) Relationships between variables… arrows from causes to effects (guesses fine… don’t have to besure about) 1 2
  16. Don’t forget our goal Identify what is under your control. Identify what could change. We can easilychoose to change this. You can’t control this, but you might suspect it could change.
  17. Which is more actionable? Why?
  18. Hypothesis Generation Step 1. Start with the outcome you hope to change.
  19. Hypothesis Generation Step 1. Start with the outcome you hope to change. Step 2. For every hypothesized variable, add at least 2 causes.
  20. Hypothesis Generation Step 1. Start with the outcome you hope to change. Step 2. For every hypothesized variable, add at least 2 causes.
  21. Hypothesis Generation Step 1. Start with the outcome you hope to change. Step 2. For every hypothesized variable, add at least 2 causes. Step 3. For every hypothesized variable, add at least 1 side effect. $
  22. Hypothesis Generation Step 1. Start with the outcome you hope to change. Step 2. For every hypothesized variable, add at least 2 causes. Step 3. For every hypothesized variable, add at least 1 side effect. Step 4. Find at least 2 items that have a common cause. Add that cause. $ repeat
  23. Can we draw what we think is going on…

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