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AC Atlassian Coimbatore Session Slides( 22/06/2024)
- 1. Actionable Strategy for implementation of AI Help Desk Gen AI and Future of Help Desks
- 2. Agenda 1. Top Workplace Applications 2. Common Help Desks 3. AI Help Desk 4. Traditional vs AI Help Desk 5. Benefits of AI Help Desk 6. Implementation Strategy 7. Getting Started
- 3. Top Workplace Applications Initiative ROI Considerations Content Creation High Reduced content creation costs, Increased content output, Improved content personalization Data Analysis & Insights High Improved decision-making, Identification of new opportunities, Reduced time spent on manual data analysis Help Desk Automation High Reduced support operations costs, Improved customer satisfaction, Frees up human agents for complex tasks Enterprise Search Moderate Improved information retrieval, Increased knowledge sharing, Improved employee satisfaction Code generation Moderate Increased developer productivity, Reduced development time and costs, Potentially fewer coding errors
- 4. Common Help Desks IT Help Desk Provide technical support and resolve technology-related issues Common queries: - I've forgotten my password. How do I reset? - I need access to Jira - VPN isn't working for me?
- 5. Common Help Desks HR Help Desk Address employee-related inquiries and support HR processes Common queries: - How do I request time off? - How do I change my 401(k) contribution? - What's our work-from-home policy?
- 6. Common Help Desks Customer Service Help Desk Provide support and assistance to external customers Common queries: - How do I change our account administrator? - How do I cancel my subscription? - We're experiencing [specific error]. How can we resolve this?
- 7. AI Help Desk
- 8. AI Help Desk
- 9. Traditional vs AI Help Desk Complex Form Filling Interface Natural Chat Interface vs
- 10. Traditional vs AI Help Desk 9-5 Availability 24*7 Availability vs
- 11. Traditional vs AI Help Desk Slow Manual Resolutions Unlimited Instant Resolutions vs
- 12. Benefits Easily and cost-effectively scale your support operations with Gen AI powered copilots 95% 90% 60% Guaranteed end-user satisfaction High acceleration rate High automatic resolution rate
- 13. Implementation Strategy 1. AI Assessment 2. AI Knowledge Engineering 3. Chatbot Prototype 4. Continuous Adapative Learning 5. Advanced AI Automations 6. Human Agent Augmentation 7. AI Insights 8. AI Security and Governance
- 14. Implementation Strategy 1. AI Assessment Asses your existing help desk processes and identify areas for automation
- 15. Implementation Strategy 2. AI Knowledge Engineering Organize existing company documentation, policies, and product information
- 16. Implementation Strategy 3. AI Chatbot Prototype Build a simple FAQ knowledge base
- 17. Implementation Strategy 4. Continuous Adaptive Learning ● Set up Continuous learning by connecting to knowledge sources like Confluence, SharePoint ● Train the chatbot on historical help desk tickets and past conversations in Slack/Teams
- 18. Implementation Strategy 5. Advanced AI Automations ● Understand complex questions ● Mimic human actions in business apps ● Provide personalized responses ● Understand images and videos
- 19. Implementation Strategy 6. AI Agent Augmentation ● Rephrase answers for tone adjustment ● Summarize customer conversations ● Identify situations that need human handover ● Understand emotional tones and trigger escalations ● Turn conversations into knowledge assets
- 20. Implementation Strategy 7. AI Insights Monitor and track automation rates, identify gaps and opportunities
- 21. Implementation Strategy 8. AI Security and Governance - Anonymize training data, user queries - Ensure company data is not used train LLMs - Ensure ISO, SOC2 and GDPR compliance
- 22. Getting Started Build a custom Gen AI chatbot using - Data layer for RAG (e.g., LlamaIndex, LangChain) - Foundation models (e.g., Open AI, Claude, Gemini) - Vector databases (e.g., Pinecone) Leverage purpose-built vendor products like Enjo AI
- 23. Get Started with Enjo AI 1. Personalized Enjo demo 2. Help desk automation potential assessment 3. 14 days no obligation free trial 4. 3 months guided Pilot program
- 26. theproductguy.xyz Who am I? ➔ Product Consultant | Strategy and Design ➔ Information Technology and Psychology ➔ Convenor - The Product Space ➔ Organizer - Google Developer Groups and Friends of Figma, Coimbatore
- 27. How Generative AI works? Table of contents The Rise of Generative AI What is Generative AI capable of? Assessing Your Business Needs Future Trends and Opportunities Conclusion 01 02 03 04 05 06
- 28. Artificial Intelligence (AI) Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to mimic human actions and cognitive processes. The Rise of Generative AI
- 29. Logical Reasoning & Problem-Solving Abstract Thinking Learning & Adaptation Memory Language & Communication Perception & Sensory Processing Emotional Intelligence
- 30. Social Intelligence Creativity & Imagination Decision-Making Metacognition Spatial Reasoning Numerical & Quantitative Skills Practical Intelligence Moral & Ethical Reasoning
- 31. Expert systems, rule-based systems, automated reasoning, theorem proving, constraint satisfaction algorithms. Deep learning, neural networks, generative models (e.g., GANs, VAEs), reinforcement learning. Natural language processing (NLP), natural language understanding (NLU), natural language generation (NLG), machine translation, chatbots, language models (e.g., GPT-4). Machine learning (supervised, unsupervised, semi-supervised, and reinforcement learning), adaptive systems, transfer learning, lifelong learning systems. Knowledge graphs, semantic networks, databases, memory-augmented neural networks, long short-term memory (LSTM) networks. Computer vision, speech recognition, audio processing, sensor fusion, image and video recognition systems.
- 32. Affective computing, sentiment analysis, emotion recognition systems, empathy bots. Social robots, conversational agents, virtual assistants, social network analysis. Meta-learning, self-improving AI, automated machine learning (AutoML), reflective agents. Generative adversarial networks (GANs), creative AI, music composition AI, art generation AI, creative writing AI. Decision support systems, recommendation engines, optimization algorithms, predictive analytics. Robotic perception, pathfinding algorithms, spatial analytics, autonomous navigation systems, 3D modeling.
- 33. Data analytics, statistical analysis software, financial modeling AI, algorithmic trading systems. Robotics, autonomous systems, smart appliances, context-aware computing. Generative adversarial networks (GANs), creative AI, music composition AI, art generation AI, creative writing AI. AI ethics frameworks, fairness-aware AI, explainable AI (XAI), bias detection and mitigation tools.
- 34. Artificial Intelligence[AI] Machine Learning [ML] Natural Language Processing [NLP] Deep Learning Vision Speech Robotics Planning Expert Systems Neural Networks Generative AI
- 35. The Rise of Generative AI Machine Learning (ML) Machine Learning (ML) is a subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed. Deep Learning Deep Learning is a subset of machine learning that uses neural networks with multiple layers to learn hierarchical representations of data.
- 36. Generative AI Generative AI falls under the umbrella of Machine Learning, particularly within the realm of deep learning. It's a specialized type of model that leverages neural networks (often very large and complex ones) to generate new data that resembles the data it was trained on. The Rise of Generative AI
- 37. ✦ Abstract Thinking ✦ Language & Communication ✦ Creativity & Imagination
- 39. 1966 2017 2023 OpenAl GPT-3 May: OpenAl releases GPT-3, the largest language model to date with 175 billion parameters. Microsoft Introduces GPT-4 March: Microsoft debut OpenAl's GPT-4 likely a multimodal trillion parameter version of GPT-3 Introduction of Transformer Models Transformer Models are introduced through papers like Google's Transformer: A Novel Neural Network O Architecture for Language Understanding and Attention Is All You Need, Vaswani et al., 2017. 2020 2024 Meta introduces LLaMA 3 June: AI model that surpasses previous versions in terms of versatility and language generation, with better contextual understanding and reduced biases. Statistical Language Model (N-gram model) 2022
- 40. Statistical Language Model (N-gram model) An n-gram model breaks text down into chunks of n consecutive words (or "grams") to predict the next word in a sequence. Let's use a 3-gram (trigram) model for simplicity. Our model has been trained on a large corpus of text, and it has learned that after the sequence "The cat is on the", the most probable next words are "roof", "floor", "bed", or "mat", let's say. It knows nothing more than the statistical probability of each of these words appearing after the input sequence based on its training data. So, if "roof" appeared most frequently in its training data after the phrase "The cat is on the", it would predict "roof" as the next word.
- 41. Neural Network Language Model (like GPT-4) These models take a more sophisticated approach. They don't just look at the immediate previous words, but they understand the entire context of the input and have a notion of word meaning derived from their training data. Now, if we had a more nuanced sentence like: "The cat spotted a mouse. Quietly, it started to climb. The cat is on the..." Despite the commonality of phrases like "the cat is on the floor/bed/mat", a neural network model like GPT-4 might predict "chase" or "prowl", as it can understand from the earlier part of the sentence that the cat is likely pursuing the mouse, and "climb" implies an upward movement, possibly indicating something like a table or a counter.
- 43. Model The result of the machine's learning process. The model holds the patterns and insights the computer discovered from the training data, allowing it to make predictions or take informed actions on new information. Foundation Model Adapted Models Domain-Specific Models Task-Specific Models Hybrid Models Multimodal Models Explainable & Interpretable Models Personalized Models
- 44. Foundation Model BERT, GPT-n, DALL-E,.. Adapted Models BioGPT Domain-Specific Models BloombergGPT Task-Specific Models Whisper Hybrid Models Multimodal Models Gemini Explainable & Interpretable Models Personalized Models Apple Intelligence
- 47. Generative pre-training Fine-tuning Retrieval-augmented generation (RAG) Prompt engineering Complexity Accuracy Cost Time to Implement Domain Specificity Flexibility
- 51. Generative pre-training Complexity Accuracy Cost Time to Implement Domain Specificity Flexibility
- 53. LLM OS Agents RAG Chat Bot Question & Answers Levels of LLM Apps Predicts answers based on patterns learned from a vast corpus of text. Engages in interactive dialogues by generating contextually relevant responses. Retrieves and incorporates information from external knowledge sources to enhance responses. Executes actions in external systems based on user requests and retrieved information. Orchestrates multiple agents and processes, managing complex tasks and workflows through a unified interface.
- 54. ✦ MAKER Train and build custom models ✦ SHAPER Tune foundational Industry Models ✦ TAKER Use pre-trained ML API models and point to your apps
- 56. The fallacies of Generative AI Opportunities & Challenges in productionizing
- 57. Introduction Who am I? Overview of Session Goals • Fallacies of Generative AI • Opportunities & Challenges • Strategies for product • Impacts and Future of AI Agenda
- 58. The AI Pyramid Don't care about AI Use AI at work Integrate AI in Enterprise solutions Build AI from scratch Big tech landscape Opportunity Opportunity
- 60. Hype Cycle Trends that would reach productivity < 2 years • Retrieval Augmented Generation (RAG) • GenAI Enabled applications • GenAI Workload accelerators • GenAI enabled virtual assistants
- 61. Common Fallacies š Fallacy 1: Generative AI is a Magic Bullet š Myth: One magic pill for all our problems š Reality: Needs specific training and fine-tuning, richness of AI comes from your data š Fallacy 2: Generative AI Understands Context š Myth: Perfect comprehension of context and nuance š Reality: Limitations in understanding complex, nuanced queries š Fallacy 3: Generative AI is Completely Autonomous š Myth: No human intervention needed š Reality: Requires human oversight and validation
- 62. Data Powers AI - Challenges The data needed to power AI are scattered Data are not NLP friendly Solution: Transform your data platform as single source of truth with NLP friendly JSON schemas Data platforms are not built for AI AI may not be able to chew our complex production schemas Solution: Keep the data for AI mostly in denormalized form like a datawarehouse Complex production data Duplication of data results in Bias Solution: Qualitative assessment of data Data duplication
- 63. Challenges in Productionizing Bias and Hallucination Explanation of bias in AI models Examples of hallucination and its impact Inference and Infrastructure Technical challenges in deploying Generative AI Infrastructure requirements and associated costs Cost Considerations Budgeting for AI development and maintenance Cost vs. benefit analysis
- 64. Product Strategy š Not all products and solution need Generative AI š A successful Generative AI based product exhibits o Generative AI is not a force fit into the product o Product delivers value even without Generative AI o Solves a real user pain point or a problem o Use of generative AI amplifies the value delivered o Automates a complex set of operations within the product
- 65. Idea to Product PoC / MVP with commercial APIs (ChatGPT, Gemini) Rich business domain dataset Self hosted LLM models A/B & RC with feedback Fine tuning 01 02 03 04
- 66. Model Landscape
- 67. Team Composition AI architect Data Scientist Data Engineer Developer Business Analyst / Scrum Master Quality Analyst Product Manager NLP Engineer (Optional) MLOps Engineer (Optional)
- 68. Other Considerations š AI assisted software delivery š AI assisted content creation š AGI? š AI and workforce š Success Stories š Adobe – Generative Fill š Microsoft 360 – GenAI Designer
- 69. Recap š Opportunities and challenges š Your AI Strategy
- 70. Questions?