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What AI can do for your business
This document provides an overview of what artificial intelligence (AI) can do for businesses and some key considerations for implementing AI. It discusses how AI can perform tasks like acting, drawing, playing games, and building things. The document also covers different types of machine learning like supervised learning, unsupervised learning, and reinforcement learning. It emphasizes the importance of having the right use cases, specialists, and technology infrastructure to make AI work for a business. Finally, it addresses some controversies and risks to consider, such as data privacy, bias and ethics, explainability, and unpredictability.
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What AI can do for your business
- 1. What AI CanDo for Your Business Thomas Lee PhD MBA thomaslee@yam.ai / linkedin.com/in/tomlee / twitter.com/lee_tom YAM AI Machinery www.yam.ai 8-May-2019 1
- 2. Thomas Lee PhDMBA Co-Founder and CEO of Throput and YAM AI Machinery ● Throput: cloud architecting and digital transformation consultancy ● YAM: machine learning software development and consultancy Assistant IT Director, Dept of Computer Science, HKU Worked in major enterprises as R&D / IT architecture manager: ● Senior Program Manager (Azure Cloud), Microsoft Asia Pacific R&D Group ● IT Architecture Manager of Hong Kong Jockey Club, Airport Authority HK ● CTO of Center for E-Commerce Infrastructure Development, HKU Served various international and local technology committees ● United Nation Network of Experts for Paperless Trade and Transport for in Asia and the Pacific ● Chief Assessor, HK ICT Awards (Smart Business Awards) ● Certification Assessor, CPIT – System Architect, HK Institute for IT Professional Certification Outstanding IT Achiever Awards 傑出資訊科技人員奬 by HK Computer Society 2
- 3. What AI CanDo 3
- 4. AI Can Actand Fake 4 TED: https://youtu.be/o2DDU4g0PRo BuzzFeedVideo: https://youtu.be/cQ54GDm1eL0
- 5. AI Can Drawand Paint 5 NVIDIA: https://youtu.be/p5U4NgVGAwg NVIDIA: https://youtu.be/gg0F5JjKmhA
- 6. AI Can Play 6 OpenAI:https://youtu.be/-FoZAM9xqS4 SCMP: https://youtu.be/X8CmiYbpp2E and Build
- 7. What AI CanDo 7 ≠ What AI Can Do for Your Business
- 8. AI, Machine Learning &Neutral Networks 8
- 9. Unsupervised Learning Supervised Learning Reinforcement Learning Machine Learning Transfer Learning AI 9 Rulesengines, expert systems, search, knowledge-base, fuzzy logic, .. Symbolic AI (Logic-based) A Simplified AI Landscape “Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford University
- 10. Logic-Based 10 Logic- Based Programming (“Rules”) X Y Training (“Labeled Data”) Learning- Based XY Labeling f(X’) = Y’ ● Logic programmed ● Humans as programmers ● Logic maintained by humans ● Data driven ● Humans as data scientists ● Self-improving via labelled data feedback Learning-Basedvs
- 11. Deep Neural Network 11 #Health #Transport #Business News HiddenLayers Input Layers Output Layers w1 1,1 w1 3,5 w2 1,1 w3 5,5 w4 1,1 w4 5,3 Wi,j Data as program k X1 X2 X3 Y1 Y2 Y3 Good AI programs require good data
- 12. How to MakeAI Work 12
- 13. Key Elements Making AIWork 13 Use Case Specialists Data Tech
- 14. Use Cases 14 Discriminative Generative Risk Assessment Natural Language Understanding Data ClassificationText / Image Generation Recommendation Engine Object Recognition Data Augmentation Pricing Optimization Conversational AI Robotics Sentiment Analysis Graphics Upscaling Discriminative AI is mature
- 15. Specialists 15 Software / Data Engineers Data Scientists Business Specialists Aggregate data Labeland curate data Build, train & improve ML models (Develop data pipelines) Organization restructuring & process re-engineering are often needed AI suggests decisions to business specialists and they tell AI whether its decisions are accurate
- 16. 16 Data Data Aggregation Data Cleansing Feature Extraction Data Labeling & Curation Training & Testing Evaluating& Relabeling Predictions Feedback for Retraining Structured Data Unstructured Data Transactions Records System logs Images Text Videos Reports
- 17. YamFlow Reference ML Workflow proposed byYAM https://flow.yam.ai 17
- 18. Tech (Infrastructure) Compute (GPUs)& Storage System Platforms Machine Learning Toolkits Open-source software Vendor neutral platforms Cloud or on-premises Lack of on-premises off-the-shelf AI platforms
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- 20. Paradigm Shift fromApp Dev to AI Dev 20 Process Driven Specification → Implementation → Testing → Maintenance Existing Organization Business as app user IT as app developer Re-organization IT as infra provider Business as ML trainer Intelligence Management Data Driven Aggregation, Cleansing, Curation, Modeling, Training, Evaluation, Security
- 21. ● AI laboratoryto kickstart ML experimentation ● Machine learning infrastructure (on cloud / on-premises) ● Proof of concept applications ● Redefinition of roles: ○ Business: AI assists business specialists to make decisions and business specialists train AI ○ Chief Technology Officer / IT to develop and provide ML infrastructure ○ Chief Intelligence Officer / Intelligence Management as custodian of business data, ML models and predictions, and analytics insights ● AI from PoC to production Digital Transformation for AI Enablement 21 Corporate AI strategy and roadmap
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- 24. Bias & Ethics 24 Reuters: https://www.reuters.com/article/us-microsoft-ai/microsoft-turned-down-f acial-recognition-sales-on-human-rights-concerns-idUSKCN1RS2FV The Verge: https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfill ment-centers-productivity-firing-terminations EUGDPR: http://www.privacy-regulation.eu/en/a rticle-22-automated-individual-decisi on-making-including-profiling-GDPR. htm
- 25. Explainability & Understandability 25Artificial Intelligenceand National Security, Congressional Research Service, 30-Jan-2019, https://fas.org/sgp/crs/natsec/R45178.pdf Dawn Meyerriecks, Deputy Director for Science and Technology at the CIA, expressed this concern, arguing, “Until AI can show me its homework, it’s not a decision quality product.” MIT Technology Review: https://www.technologyreview.com/s/604087/the-dark-secret- at-the-heart-of-ai/ ...unless we find ways of making techniques like deep learning more understandable to their creators and accountability to their users. Otherwise, it will be hard to predict when failures might occur - and it’s inevitable they will
- 26. 26 “The future isalready here — it’s just not very evenly distributed.” William Gibson
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