What Is Artificial Intelligence in Product Management by Apple PM

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Juston Johnson
Making Products Better with
AI
Juston Johnson
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
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.
Spectrum of AI
AI or Narrow AI
AGI or General AI
ASI or Superhuman AI or maybe a Collective AI
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)
Common modules for consumer AI systems
ASR
TTS
NLP / NLU
Action Processing System
Knowledge Bases
Memory
Context
Natural language understanding
Teach / Train
Classify
Extract
Understand
Act
Correct
Learn / Re-train Pipeline
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
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
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)
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
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?
APPENDIX
Team member skill sets
Linguists
Writers
Scientists (Data, Machine Learning, Sociology)
Programmers
Infrastructure
Product
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?
Part-time Product Management Courses in
San Francisco, Silicon Valley, Los Angeles,
New York, Austin, Boston, Seattle, Chicago,
Denver, London, Toronto
www.productschool.com
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What Is Artificial Intelligence in Product Management by Apple PM

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  • 6. Include @productschool and #prodmgmt at the end of your tweet Tweet to get a free ticket for our next Event!
  • 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?
  • 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?
  • 23. Part-time Product Management Courses in San Francisco, Silicon Valley, Los Angeles, New York, Austin, Boston, Seattle, Chicago, Denver, London, Toronto www.productschool.com

Hinweis der Redaktion

  1. When you checked in tonight, you got an email inviting you to join our slack community In that community, we have 15k product people who have come through different companies like google, facebook, uber Sharing information about events, job offers from our partner companies, and valuable online content Please check your email and join - it’s free
  2. In our PM Course, we teach how to build products and how to get a job as a software product manager All our classes are 2 months, part time, and compatible with full time jobs. We have two options, Tues/Thurs in the evening and Saturdays in the morning Instructors- are senior level product managers from companies like Google, FB, Uber, etc
  3. In addition to our PM class, we offer our Coding for Managers class Also two months and part time tailored for professionals who don’t come from a traditional engineering background The goal of this course is not to make you a software engineer, but to give you enough technical background to build a fully functional website and pass the technical interview
  4. Similar to our coding course, we also offer our Data Analytics for Managers Tailored for people who don’t have a technical background but to give them enough knowledge of analytics to become product managers Also two months, compatible with full time jobs The goal of the course is not to make you a data scientist, but to make you technical enough to understand web analytics, learn SQL, and machine learning concepts
  5. We are also live streaming our event to our online audience If you want to share, please tweet @productschool and #prodmgmt for a free ticket to our next event
  6. Slides will be quick and boring so focus on our conversation and not factoids that can quickly be made out of date.
  7. Rules model the world and deep learning models the brain.
  8. AI represents the fastest growing segment of any size in the IT sector. By 2020 the market will surpass $40 billion and by 2025 $100 billion – Constellation research. In 2017, there will be a total footprint of 33 million voice-first devices in circulation. - VoiceLabs
  9. No slides on specific products - it would be outdated in 3 months
  10. Create a few compelling conversations people will want to have over and over again with aneeda. Don't place all the onus on the user to request content.
  11. Don’t think if you need to be mobile first or bot first or AI first, figure what is the most successful way for users to interact with your system