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AI in the Enterprise - Looking Forward
- 1. AI in the Enterprise – Looking Forward David Vandegrift
- 2. Introduction Me Focus of the presentation Chicago AI Meetup Coursera ML course Self-exploration Technical background TodayAncient history (10-50 yrs ago) ~3-5 years out Here be dragons 2
- 3. A framework to discuss AI Technologies Applications Use Cases Machine Learning Non-ML Robotics Autonomous vehicles Smart image search/analyticsAudio transcription Predictive maintenance Writing Product recommendation Chat bots Voice of the customer analytics Targeted advertising Search engines Natural Language Processing (NLP) Natural Language Understanding (NLU) Natural Language Generation (NLG) Natural Language Querying (NLQ) Computer vision Voice-to-text Recommendation engine Anomaly detection Categorization Audio generation Image generation Symbolic logic Expert systems Decision trees (Artificial) neural networks Deep neural networks Convolutional neural networks Recurrent neural networks Generative adversarial networks Linear regression Logistic regression Clustering Random forest Support Vector Machines Markov processes Note: far from exhaustive 3
- 4. Use Case #1 – Customer Service Assistants 4 Description AI-driven “assistants” work alongside contact center workers (both chat and phone) to recommend relevant knowledge base articles and answers as they listen in on customer conversations These capabilities get better over time, increasingly automating customer service agents’ jobs Best-in-class today is ~30% automation Enabling technologies Deep learning → disambiguation in NLP → improving nuanced interpretation Industry-specific ontologies → enable fuzzy matching Human-level voice-to-text → accurate NLP on phone calls Interesting startups DigitalGenius AgentIQ Kasisto
- 5. Use Case #2 – Employee Performance & Compliance 5 Description Central AI keeps an eye on employee communications to: 1. Identify compliance/risk behavior 2. Improve performance Out-of-the-ordinary is flagged for manual review; outcome fed back into the model Judgment improves over time, leading to more automation Enabling technologies Deep learning → disambiguation in NLP → improving nuanced interpretation Translation of text into structured data → empowers anomaly detection Availability of data in the enterprise and employees accepting being monitored Interesting startups Digital Reasoning NexLP Glint BetterWorks
- 6. Use Case #3 – Image Analytics 6 Description “Winning” the ImageNet competition in 2015 with CNNs was one of the major catalysts of the AI Spring Algorithms are now at human levels in many types of image processing; these capabilities have been opened up publicly in the last ~12 months Key constraint today is identifying business cases: • Insurance – property assessment • Construction – drone surveying • Google – automated mapping Enabling technologies As of 2015, CNNs have achieved human-level object detection in images Facebook and Google have proven out super-human face detection algorithms In 2015 researchers proved out super-human emotion detection Interesting startups Orbital Insight Clarifai OmniEarth (EagleView) Face++
- 7. Use Case #4 – Automated Creation 7 Description AI will be able to create realistic sounds (including speech in a given person’s voice) and images virtually for free Massive impact potential on creatives (voice actors, graphic designers, photographers, etc.) Business cases completely unexplored but include accessibility, advertising personalization, and data visualization Enabling technologies GANs → step-change improvement in audio and image creation Interesting technologies WaveNet Lyrebird Face2Face arXiv 1605.05396 arXiv 1512.00570 arXiv 1609.04802
- 8. Dragons (5+ years out) 8 Reinforcement learning agents and process automation Mass-automation of customer service Unified Information Access Text/speech summarization/synthesis