AI in Telecom: How artificial intelligence is reshaping the vision of telco industry. Gabriele Randelli, HPE
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1) Artificial intelligence is reshaping the telecommunications industry by enabling new customer-centric services and helping telcos compete with over-the-top players. 2) AI can help telcos optimize network infrastructure, improve customer service through predictive analytics, enable personalized marketing, and create self-healing networks. 3) Deep learning techniques are well-suited for the telco domain due to large amounts of network and customer data, distributed computing resources, and the ability to perform online training at network edges.
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AI in Telecom: How artificial intelligence is reshaping the vision of telco industry. Gabriele Randelli, HPE
- 1. AI in Telecom How artificial intelligence is reshaping the vision of telco industry Gabriele Randelli May 18th, 2018
- 2. Once upon a time in a telco operator… 2 - Traffic Volume - Infrastructure as asset - Customer-agnostic - Data-agnostic
- 3. … there was an intruder 3 - Service oriented - No infrastructure - Customer-centric - Data eager
- 4. Over-the-top (OTT) Players 4 OTTs scrape out 44% of revenues No network infrastructure costs Customer-centric Leverage on data insights Low return from investment Customer-agnostic Content-agnostic OTTs market value seven times bigger than CSPs
- 5. Network costs vs revenues 5 US$354 billion into maintaining, building and upgrading networks in 2014
- 6. Towards a subscriber-centric paradigm 6 AI AI-assisted customer service Next Best Action Predictive Maintenance Churn/Traffic PredictionNetwork Anomaly Detection Content Usage Trends Fraud Detection Customer Profiling Self-healing Networks Intelligent Edge IoT & Edge Converge data sources (voice, data, fixed, mobile, sensors, billing, CRM, network) Automate network processes (zero-touch) Customer-centric services Generate new revenue streams Enable AI-based services
- 7. AI models adopted in Telco 7 Prediction Supervised: Artificial Neural Networks, Decision Trees learning, Regression Analysis, Support Vector Machines, Naive Bayes Customer Churn Predictive Maintenance Fraud Propensity Traffic Peak Prediction Problem Use Case Model Anomaly Detection (Semi-)Supervised: one-class Support Vector Machine Unsupervised: Auto-encoders, cluster analysis, self-organizing maps, k-means Traffic Analysis Behavior Analysis Network Monitoring Predictive Maintenance Profiling Supervised: Recurrent Neural Networks (RNN) Unsupervised: Principal Component Analysis Customer Profiling Network Usage Next Best Offer Classification Supervised: Artificial Neural Networks, Decision Trees, Logistic Regression, Support Vector Machines Customer Care Failure Analysis Content Classification
- 8. Spotlight on Deep Learning Data Availability - 2.5 exa-bytes (1018) per day!!! - Deep learning performance scales almost linear wrt. data amount Text here Computation Power - Deep learning is eager for training resources - Telco data centers embed thousands of machines with very fast connection Text here Edge Computing - 5G moves intelligence at the edge - Deep learning scales very well on dedicated hardware (e.g. GPU, TPU) - Collaborative learning techniques - Mobile AI frameworks (TensorFlow Lite) Text here Online Model Training - From batch analysis to real- time streaming - AI models need to re-adapt to evolving patterns - What yesterday was anomalous… today is not! - Still a lot to do in this area… Text here Unsupervised Learning - Labeling datasets is not feasible - No training effort - Anomaly detection algorithms largely adopted for traffic analysis (e.g. Auto- encoders) 8
- 9. AI-based Customer Care • By 2020, 85% of all customer interactions will be handled without a human agent • Resolving customer service issues before they arise could significantly lower customer churning rate • Combination of AI, NLP, chat-bots 9 HOW • Anticipate customer needs by continuously profiling user behaviors (anomaly detection) • Extract potential complaints published on social networks (sentiment analysis) • Correlate user complaints with detected network failures (cluster analysis) • Compare incoming problems to support cases already evaluated (root cause analysis) • Predict potential problems (time-series analysis)
- 10. Customized Marketing 10 • 79% of CMOs are planning to boost customer experience with AI • AI algorithms combine historic patterns and behaviors to provide personalized offers for subscribers HOW • Extract enriched customer insights from multiple data sources • Predict the potential interest of each customer for available offers (prediction and ranking) • Automatic offering proposition (when, what, how) • Feedback collection and model update (reinforcement learning, incremental learning)
- 11. Self-healing Networks 11 • A UK mobile operator was recently fined £1.9 million for a network design configuration that would have compromised access to the 911 emergency service • AI predicts future traffic peaks and load distribution for automatic network scaling • AI predicts potential network failure for automatic alarm triggering and network re-configuration • Combination of AI and network virtualization (NFV) HOW • Predict what will happen in the network (predictive networks) • Evaluate and guide in assessing the impact of the prediction (prescriptive networks) • Automatic action selection to mitigate the impact of a predicted outcome (self-healing networks)