The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and AI Gamification
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Popular media is full of stories about self-driving cars, video deepfakes, and robot citizens. But this kind of popular artificial intelligence is having very little business impact. The actual impact of AI on business is in automating business processes and in creating the "AI Platform Business Revolution". Platform companies create value by facilitating exchanges between two or more groups. AI is central to these businesses for matchmaking between producers and consumers, organizing massive data flows, eliminating malicious content, providing empathetic personalization, and generating engagement through gamification. The platform structure creates moats which generate outsized sustainable profits. This is why platform businesses are now dominating the world economy. The top five companies by market cap, half of the unicorn startups, and most of the biggest IPOs and acquisitions are platforms. For example, the platform startup ByteDance is now worth $75 billion based on three simple AI technologies. In this talk we survey the current state of AI and show how it will generate massive business value in coming years. A recent McKinsey study estimates that AI will likely create over 70 trillion dollars of value by 2030. Every business must carefully choose its AI strategy now in order to thrive over coming decades. We discuss the limitations of today's deep learning based systems and the "Software 2.0" infrastructure which has arisen to support it. We discuss the likely next steps in natural language, machine vision, machine learning, and robotic systems. We argue that the biggest impact will be created by systems which serve to engage, connect, and help individuals. There is an enormous opportunity to use this technology to create both social and business value.
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The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and AI Gamification
- 1. The AI Platform Business Revolution Matchmaking, Empathetic Technology, and AI Gamification Steve Omohundro, Ph.D. Possibility Research
- 2. Popular AI Dominates News Headlines https://www.nytimes.com/2019/04/22/business/elon-musk-tesla-autopilot.html https://www.arabnews.com/node/1555921/world https://www.thedailybeast.com/inside-the-deepfake-arms-race
- 3. AI that is creating business value A/B testing Recommender Systems Matchmaking Personalization Gamification Malicious Content Removal
- 4. Platforms create value by facilitating exchanges between 2 or more groups Platform Producer Producer Consumer Consumer Producer Consumer
- 5. AI Enables the Platform Business Model • AI does matchmaking between producers and consumers • AI manages data flows • AI blocks malicious actors • AI personalizes interfaces for consumers • AI sends demand information back to producers • Platforms create moats for sustainable outsized profits
- 6. $78 billion TikTok: 3 simple AI drivers • AI Lip-synching to Music • AI Video Effects • AI Recommender https://knowledge.insead.edu/entrepreneurship/the-tiktok-strategy-using-ai-platforms-to-take-over-the-world-11776
- 7. Platforms are Transforming the World Economy 7/10 of the world’s most valuable companies are platforms Amazon, Apple, Google, Microsoft, Facebook, Alibaba, Tencent 60% of the billion dollar unicorn startups are platforms Over $3 trillion in platform firm market cap Most IPOs and acquisitions are platforms https://www.cnbc.com/2019/06/11/amazon-beats-apple-and-google-to-become-the-worlds-most-valuable-brand.html
- 8. Economics of Platforms 2003: Jean Tirole
- 9. Platforms are rapidly taking over the economy https://www.slideshare.net/InfoEcon/platform-revolution-chap-01-intro-how-platforms-are-changing-commerce
- 10. Platforms dominate in China • Alibaba controls 80% of the Chinese ecommerce market • Baidu has 70% of Chinese search • Tencent has 850 million users on WeChat • Didi Kuadi dominates the Chinese taxi market • Bytedance (TikTok) is the most valuable startup in the world, worth $78 billion • $41.5 Trillion mobile transactions in China in 2018
- 12. Coase Theorem: Driving the Future of Business 1960 Coase Theorem: Information + Cheap Contracting -> Economic Efficiency • Societal resources better used (e.g. Airbnb) • Consumer needs better met (e.g. long tail) • More producers enabled (e.g. Uber drivers) • Sustainable outsized profits for the platform • Winner-take-all dynamics (e.g. Alibaba) • Creates both business value and social value AI data + Smart Contracts ->
- 13. Platform Paradoxes • One side may be charged zero or negative prices because they add value to the other side (e.g. Visa cashback, Google search, etc.) • Create a linear business and open it up to “competitors” (e.g. Amazon books -> third party sellers, Alexa) • Winner-take-all dynamics: huge investments, no profits • Inversion of the firm: Move HR, marketing, innovation, finance, logistics, etc. outside the firm • Instagram bought for $1 billion after 2 years with 13 employees, “most brilliant tech acquisition ever made”
- 14. AI That Enables Platforms Platform Producer Producer Malicious Producer Malicious Consumer Consumer Consumer AI Matchmaker AI BlockingAI Blocking AI ReputationAI Reputation AI Recommender Systems AI Creation Tools AI Analytics AI Pricing AI Consumer Models AI Demand Model AI GamificationAI A/B Testing AI UXAI UX AI UX AI Personalized Marketing AI Smart Contracts AI Consumption Tools
- 16. 1957: Rosenblatt’s “Perceptron” http://www.rutherfordjournal.org/article040101.html “The embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.” https://en.wikipedia.org/wiki/Perceptron
- 17. 2007: Deep Learning Neural Nets http://www.nicta.com.au/content/uploads/2015/02/deep.jpg
- 18. 2009: Deep Learning Successes • 2009 Speech Recognition TIMIT: Cortana, Skype, Google Now, Siri, Baidu, Nuance, etc. • 2012 Image Recognition ImageNet • 2012 Drug Discovery Merck Challenge • 2013 Natural Language Sentiment • 2014 Image Captioning • 2014 Natural Language Translation • 2015 Atari Video Games DeepMind • … https://en.wikipedia.org/wiki/Deep_learning From: “Attention is All You Need”
- 19. 2,497 AI Startups, $60B in Funding
- 20. http://cdn-media-1.lifehack.org/wp-content/files/2014/07/Cash.jpghttps://www.itu.int/pub/S-GEN-ISSUEPAPER-2018-1 AI Will Create $70 Trillion by 2030 US GDP is $19.4 Trillion
- 22. Deep Learning Revolution • 1986 Recurrent Nets: Reinforcement Learning, LSTM, DQN, … • 1989 Convolutional Nets: LeNET, GoogLeNet, DeepDream, … • 2013 Pretraining: word2vec, GloVe, ELMo, ULMFIT, GPT, BERT,… • 2014 Generative Adversarial Nets: GAN, BiGAN, StyleGAN, CycleGAN, … • 2017 Transformers: BERT, GPT-2, MT-DNN, XLNet, RoBERTa, … • 2019 Better Performance Measures: GLUE, RACE, SuperGLUE, … • ….
- 23. Software 2.0 Infrastructure • Deep Learning Platforms: TensorFlow, PyTorch, Caffe, Keras, … • NVIDIA: CUDA, GPU, DGX-1 Deep Learning Server, DGX-2, Titan V, … • Google TPU, FPGAs, Compilers, Cloud, … • Karpathy: https://medium.com/@karpathy/software-2-0-a64152b37c35 • Differentiable Programming: Zygote, TensorFlow, AutoGrad, Myia, … • Probabilistic Programming: Stan, PyMC, Pyro, Gen, …
- 24. Problems with Deep Learning • Huge data requirements • Slow and expensive to train and use • Inscrutable encodings • Adversarial examples • Not explainable • Not governable • No proof of domain coverage • No precise semantics • No modularity • Poor reuse of learned knowledge
- 25. Model Merging, Data Structures, Semantics • Model merging: one-shot fast learning algorithm • Widely applicable: grammar learning, robotics, vision, geometry,… • Model complexity adapts to the domain • Can learn symbolic, continuous, and stochastic domains • Explainable, modular, reusable • Fast data structures: 50x on robotics task, bumptrees, balltrees,… • Unified semantics: Topos theory, Set theory, Type theory, Montague grammar, Distributional semantics, Denotational semantics, … https://steveomohundro.com/scientific-contributions/
- 26. Opportunities • AI models of platform dynamics, network effects, virality • AI resource allocation throughout the networks • Smart contracts: semantic tools lowering transaction costs • AI risk management for all parties: automated insurance and options • AI producer model: feedback from consumption to creators • AI semantic creation tools: words, music, speech, image, video, … • AI consumer model: recommenders based on user emotional journey • AI semantic consumption tools: smart books, smart audio, smart video, smart learning, smart games, …
- 27. Huge Opportunity for Business and Social Value! • Platforms are taking over the world economy • Coase’s theorem is driving platforms • New AI technology is enabling Coase • AI is improving rapidly, but new ideas are needed • Huge business value in new AI to meet human needs • Must understand both platform dynamics and AI technology • Enormous opportunities for creating business and social value • Must act now!