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Case Study: Smart Speaker

  • “Hey Device, tell me a joke”
    • Steps (AI Pipeline):
      1. Trigger work/wakeword detection A) Hey device”? -> B) 0/1
      2. Speech recognition A) Audio -> B) “tell me a joke”
      3. Intent recognition A) Joke? vs, B) time?, music?, call?, weather?
      4. Execute joke
    • These could be 4 different teams
  • “Hey device, set timer for 10 minutes”
    • Steps (AI Pipeline):
      1. Trigger work/wakeword detection A) Hey device”? -> B) 0/1
      2. Speech recognition A) Audio -> B) “Set timer for 10 minutes”
      3. Intent recognition A) “set timer for 10 minutes -> B) Timer
      4. Execute
        1. Extract duration 
          1. “Set timer for 10 minutes”
          2. “Let me know when 10 minutes is up”
        2. Start Timer with set duration
  • Challenge:
    • Each function is a specialized piece of software.
    • This requires companies to train users on what the speaker can, and can not do.

Case study: Self driving car

  •     Steps for deciding how to drive
    1. Image/Radar/Lidar
      • Car detection
      • Pedestrian detection
    2. Motion planning 
      • Steer/acceleration/Brake
  • Key Steps
    1. Car detection (supervised learning)
    2. Pedestrian detection (supervised learning)
    3. Motion Planning (SLAM – Simultaneous localization and mapping)
  • Challenge:
    • Each function is a specialized piece of software.

Roles in AI teams

  • Software Engineers (30% +)
  • Machine Learning Engineer. focused on A -> B mapping
  • Applied ML Scientist: Using State of the art to today’s problems
  • Machine Learning Researcher. Extend the state-of-the-art in ML
  • Data Scientist. Examine data and provide insights. Make presentation to team/executives. Some may be Machine Learning Engineer.
  • Data Engineers. Organize data. Make sure data is saved in an easily accessible, secure, and in a cost effective way
  • AI Product Manager. Help define what to build. What feasible and valuable

AI Transformation Playbook

  1. Execute pilot projects to gain momentum
    • Success is more important than value
      • Need to get the flywheel moving
    • Show traction within 6 to 12 months (quiz said 6 to 10)
    • Can be in-house or outsourced
  2. Build an in-house AI team
    • Can be under: CTO, CIO, CDO, or CAIO
    • Have a central AI center of excellence. Matrix them in to start, untill understanding of AI is throughout the org
    • CEO should provide funding to start, not from BU.
  3. Provide broad AI training
  4. Develop an AI strategy
    • You do this at step 4 to gain concrete experience, vs starting with an academic strategic approach to something so new.
    • Leverage AI to create and advantage specific to your industry sector
    • Design strategy aligned with the “Virtuous Cycle of AI”
      • Better product -> More users -> More data -> [Repeat]
    • Create a data strategy
      • Strategic data acquisition
      • Unified data warehouse/lake
    • Create network effects and platform advantages
      • In industries with “winner take all/most” dynamics, AI can be an accelerator
    • Leverage classic frameworks as well. Low cost/ focus
    • Consider humanity.
  5. Develop internal and external communication
    • AI can change a company and its products
    • Investor relations. to properly value your company
    • Government relations. to align on regulations.
    • Consumer/use education
    • Talent/recruitment
    • Internal communications. to address questions and concerns.

AI pitfalls to avoid

  • Don’t
    • Expect it to do everything
    • Hire 2-3 ML engineers and expect then to come up with use cases.
    • expect it to work the first time\
    • Don’t expect Traditional planning process to apply yo AI
    • Don’t wait for a superstar, get going with what you have today.
  • Do
    • Be realistic
    • Beginners should be linked with business
    • Work with  the AI team to develop new timelines, KPIs, etc

Taking your first steps

  • Get friends to learn about AI
    • Courses
    • Reading group
  • Start brainstorming projects (no project too small)
  • Hire a few ML/DS people to help
  • Hire or appoint an AI leader
  • Discuss with CEO/Board possibilities of AI transformation
    • Will the company be more valuable, or more effective if we are good at AI.

Supervised learning

Unsupervised learning

Transfer learning


Knowledge graphs

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