Using artificial intelligence (AI) in consumer products is the new hot trend; except, it's not new at all. Juston Johnson, Product Manager at Apple, gave a product management view of what makes a good AI system and what the key success criterias are to using it in any consumer facing product. He clarified common misconceptions about what AI can do as well as provided examples of how to best use of AI in products. Juston discussed how machine learning is what makes AI systems intelligent, how voice assistants give AI systems a natural interface and how chatbots add humanity and intangible benefits to AI systems.

1. What Is Artificial Intelligence in Product
Management by Apple Product Manager
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7. Juston Johnson

8. Making Products Better with
AI
Juston Johnson

9. Juston’s background overview
Stanford (BS-Computer Science - AI, BA - Religious Studies - Ethics)
Deloitte Consulting
Dartmouth (MBA - GLobal Business)
Samsung
Beats by Dr. Dre
Apple
i.am+
AI Product Experiences - Fitness, Music, Assistants, Enterprise

10. Why is AI back in the spotlight?
AI - Artificial Intelligence - a catch-all term to describe any system or agent that makes a decision which
includes rule based systems that have existed since the 50s. Today’s excitement for AI has to do with
advancements in hardware capabilities and deep learning practices.
ML - Machine Learning - a category of algorithmic approaches the rely on the system learning and
improving automatically through training and interaction
Supervised learning is when a human annotator identifies all the correct responses in advance.
Unsupervised learning is when the machine clusters related data and those clusters are deemed correct.
Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks which are
capable of learning unsupervised from data that is unstructured or unlabeled.

11. Spectrum of AI
AI or Narrow AI
AGI or General AI
ASI or Superhuman AI or maybe a Collective AI

12. Examples of products using levels of AI
Chatbots
Recommendation Engines
Predictive services (i.e. Drive times, Spam filters)
Devices using sensors (i.e. Phones, Fitness Trackers, Self-Driving features)
Controlled Assistants
Uncontrolled Agents (Games, Uber)
Enterprise & Gov use cases (i.e security, fraud, finance, customer support)

13. Common modules for consumer AI systems
ASR
TTS
NLP / NLU
Action Processing System
Knowledge Bases
Memory
Context

14. Natural language understanding
Teach / Train
Classify
Extract
Understand
Act
Correct
Learn / Re-train Pipeline

15. What are the big companies struggling with?
The problems Google, Amazon, IBM, Apple and Microsoft face:
● the need for vast amounts of data to power deep learning systems (don’t
plateau as quickly);
● machine learning can be costly to train.
● their inability to create AI that is good at more than one task;
● the lack of agility in large companies;
● product manager biases are in every algorithm so some values mismatch with
society

16. How smaller companies can compete
Small companies can still compete in the AI space by:
- Using someone else’s platforms or solutions for solved problems like ASR / TTS
- Investing in figuring out unsolved problems like extraction, knowledge resolution,
etc
- Finding domains that are useful but not in focus and scale quickly
- Making superior UX

17. Next steps for AI -> Augmented decision making
Handle a mix of commands and unstructured dialog (conversational)
Needs to expand understanding of non-lingual cues (i.e facial)
Needs a more nuance understanding of context (important or not vs long or short
term)
Needs to be proactive (one-way conversations are BORING!)
Develop a relationship with the user (you don’t need access to all my emails on
day 1 in order to be useful)

18. High level AI product management cycle
1. Find a problem to solve
2. Develop use cases and user stories
3. Determine what decision points in your user story can be aided by AI
4. Establish a technical team to create happy path application logic
5. Determine if the input information is available
6. Determine what data is you will train your models
7. Test the applications usability and performance
8. Refine your use cases to handle error conditions and non-happy path flows
(utilize context and the 80/20 rule)
9. Update application logic
10.Retrain models
11.Repeat 7-10

19. Questions to ask yourself
1. Do I want to add natural language capabilities (voice and/or text) for your application?
2. What level of AI do I need? (Rules based, Machine learning predictions/recommendations, Deep
Learning AI)
3. How much of your App will be AI-driven versus user driven?
4. Is the only goal of your AI application to reduce the number of human steps? — It’s ok sometimes to
add steps when working with conversational use cases instead of command based use cases.
5. What will make users hesitate to use your AI? (privacy, not personal, cultural bias, etc)
6. What are the key metrics based on which you will evaluate the success of your AI ?
7. What context is useful to save for your users?
8. How will the AI fail? How will the AI handle failure?
9. Should we use external components or build in house?

20. APPENDIX

21. Team member skill sets
Linguists
Writers
Scientists (Data, Machine Learning, Sociology)
Programmers
Infrastructure
Product

22. AI 100+ years in the future
What instincts would self-aware AI develop?
What would make a machine want to cheat or lie?
What would make a machine want to cry?
If AI becomes be self-aware like animals, could be it domesticated?
If AI become human like or superhuman, would humans be there lab rats?
Would ASI even want to be human like?
AI making AI - ownership or reproduction?

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