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AI Unleashed - Shaping the Future -Starting Today - AIOUG Yatra 2025 - For College Students.pdf
This session inspires students to explore the world of AI by blending technical insights, the latest trends (like Generative AI), real-world use cases, career roadmaps, and personal motivation. It's designed to both educate and energize students to pursue AI confidently.
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AI Unleashed - Shaping the Future -Starting Today - AIOUG Yatra 2025 - For College Students.pdf
- 1. AI Unleashed: Shapingthe Future, Starting Today Sa n d esh Ra o Vi ce P re sid en t , A p p li ed AI AIOUG Yatra 2025
- 2. Safe Harbor The followingis intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.
- 3. Introduction to AI •Definition of AI • Simulation of human intelligence in machines. • Historical Background • AI roots in the 1950s with Alan Turing. • Importance of AI Today • AI transforms industries and daily life. • AI in Education, Business, and Healthcare • Personalized learning, predictive analytics, diagnostics. • Role of AI in Oracle's Technology Stack • Vector Databases , RAG • Agentic AI • Oracle Cloud Infrastructure (OCI) services.
- 4. Types of AI •Narrow AI • Performs a specific task (e.g., recommendation engines). • General AI • Theoretical AI with human-like capabilities. • Superintelligent AI • Hypothetical AI surpassing human intelligence. • Reactive Machines vs. Limited Memory • From basic to adaptive models. • Oracle’s Practical Use Cases in Narrow AI • Fraud detection, chatbot assistance.
- 5. Key Concepts inAI • Machine Learning (ML) • Algorithms that learn from data. • Deep Learning (DL) • Neural networks with multiple layers. • Natural Language Processing (NLP) • Enables machines to understand human language. • Computer Vision • Understanding images and videos. • Oracle AI Services • OCI Vision, OCI Language, Data Science tools.
- 6. Foundation Models • Definition •Large-scale models trained on vast data sets. • Examples • ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Mistral, LLaMA (Meta) , Grok (xAI) • Characteristics • High generalization, zero/few-shot learning • Use in Oracle AI Ecosystem • Integration through OCI Generative AI. • Benefits • Scalability, versatility, task adaptability.
- 7. Comparison of FoundationModels • GPT-4 • Strong reasoning, language generation. • Claude 3 • Context length edge, safe by design and excellent for coding and MCP • Gemini • Multimodal from the start • LLaMA • Open-source and customizable. • Oracle Integration • Model flexibility with API and secure tenancy.
- 8. Pros and Consof Foundation Models • Pros • Versatility, context-awareness, transfer learning. • Cons • Cost, bias, hallucinations. • Efficiency Tradeoffs • Bigger models vs. speed. • Security Concerns • Data leakage risks • Oracle’s Approach to Safe AI • Role-based access, private tenancy models, instance and resource principals
- 9. AI Embeddings &Vectors • What are Embeddings? • Numerical representations of data (text/images). • Vector Space Representations • Enables similarity comparison using distance functions – cosine , Euclidean.. • Applications • Search, recommendations, clustering • Embeddings in Oracle’s AI Services • Used in search optimization, predictive models. • Example Tools • OpenAI Embeddings, HuggingFace, FAISS, Oracle Vector DB 23ai
- 10. Retrieval Augmented Generation(RAG) • What is RAG? • Combines retrieval + generative models. • Process • Retrieve relevant content → generate informed output. • Benefits • Up-to-date info, lower hallucination risk. • Use Cases • Enterprise Q&A, documentation agents. • Oracle Applications • OCI AI Agents use RAG for enterprise data. • Autonomous Database with Select AI
- 11. Agentic AI Systems •What are Agentic Systems? • AI entities that plan, act, and adapt. • Components • Planning, memory, tool use. • Benefits • Autonomy, task delegation. • Agents in the DB ecosystem • Automate IT tasks, customer engagement, fraud detection , anomaly detection • Challenges • Control, alignment, debugging
- 12. Chain-of-Thought & Tree-of-ThoughtReasoning • Chain-of-Thought • Step-by-step reasoning approach. • Tree-of-Thought • Exploratory and recursive reasoning. • Use Cases • Problem solving, code generation. • Comparison • Tree = better for complex, branching logic. • Tools • LangChain, AutoGen support these paradigms.
- 13. Claude + Code+ SQL + MCP • Claude’s Code Abilities • Can write and understand code. • SQL Understanding • Generate, debug complex SQL queries • Anthropic’s Model Context Protocol (MCP) • Claude used to accelerate agentic and code development • Benefits • Developer productivity, automation. • Demo • Claude SQL code generation example with SQLcl https://blogs.oracle.com/database/post/introducing-mcp-server-for-oracle-database
- 14. Multimodal AI Shift •Definition • Models processing text, image, audio together. • Use Cases • AI tutors, enterprise insights, diagnostics. • Key Models • Gemini, GPT-4V, Claude Opus. • Oracle’s Vision APIs • Support image classification, OCR. • Future Trends • 3D, video input capabilities.
- 15. Privacy & Securityin Agentic AI • Data Privacy • Concerns with training on sensitive data. • Secure Model Usage • Isolated tenancies, encryption. • Explainability • Knowing *why* an agent acted. • Oracle’s Strategy • Integrated IAM, audit logging, vault-based key management. • Regulatory Compliance • GDPR, HIPAA-ready services.
- 16. n8n for AIIntegration • What is n8n? • Workflow automation platform. • AI + API Workflows • Use OpenAI, Claude, HuggingFace. • Oracle Integration • Webhooks, REST API connections to OCI. • Use Cases • Email bots, data enrichment. • Code Sample • Simple JSON node connection example.
- 17. Oracle AI CaseStudies • Retail • Personalized recommendations using AI models. • Finance • Fraud detection and risk modeling. • Healthcare • Predictive diagnostics, NLP from patient data. • Manufacturing • AI-powered predictive maintenance. • Public Sector • Smart city planning and citizen services using AI.
- 18. Oracle Cloud GenAIServices • OCI Language • Text analysis, NER, sentiment detection. • OCI Vision • Image analysis and OCR. • Generative AI Service • Foundation model API for text generation. • Integration Options • SDKs, REST APIs, pre-built agents. • Real-time Use Cases • Customer support, marketing, coding.
- 19. Vector DB Comparisons •Oracle Vector DB • Fully integrated, enterprise-secure. • Pinecone • Managed, scalable but external. • Weaviate • Open-source, flexible, plugin support. • Use Cases • Semantic search, chatbot memory. • Comparison • Oracle: best for OCI integration and compliance.
- 20. Embedding Creation andTuning • Embedding Tools • OpenAI, Oracle, HuggingFace. • Data Preprocessing • Clean, chunk, deduplicate text. • Fine-tuning • Use supervised learning to guide relevance. • Vector Store Optimization • Adjust indexing methods (e.g., HNSW). • Oracle Methods • Automated via OCI GenAI Agent workflows. • Coding using Oracle Vector Database 23ai
- 21. AI Governance &Bias Mitigation • Governance Tools • Oracle AI governance dashboard. • Fairness Checks • Analyze demographic parity. • Transparency • Model interpretability options. • Audit Trails • Immutable logging in OCI. • Real-world Challenges • Trade-offs between accuracy and equity.
- 22. Open Source vsProprietary Models • Open Source (Mistral, Gemma, LLaMA) • Transparent, modifiable, lower cost. • Proprietary (GPT-4, Claude, Gemini) • High performance, limited access. • Oracle’s Flexibility • Supports both via APIs. • Customization • Fine-tune open models on OCI. • Cost & IP Tradeoffs • Open: cheaper | Closed: faster, refined.
- 23. Deploy LangChain Agentson Oracle • Deployment Targets • Oracle Functions or Kubernetes. • Secrets Management • Use OCI Vault to store API keys. • CI/CD Pipelines • GitHub Actions + OCI DevOps. • Monitoring • Logs and metrics via OCI Logging. • Benefits • Full-stack AI agent deployment on OCI.
- 24. RAG + LangGraph+ Oracle DB Architecture • Architecture Layers • Data, Retrieval, Generation. • LangGraph • Modular graph logic. • Oracle Vector DB • Semantic similarity search. • OCI GenAI • Text output using contextual prompt. • Use Case • Context-aware enterprise chatbot.
- 25. Foundation Model AdvancementsOverview • Larger Context Windows • Handle longer documents, context-rich queries. • Better Tokenization • Enhanced for non-English and code. • Fine-tuned on Diverse Tasks • Improves zero-shot and few-shot accuracy. • Integration in Enterprise Stacks • Models as backend APIs for products. • Example: Cohere 4’s 128k token context.
- 26. Multi-Head Attention –How it Works • Core Mechanism in Transformers • Enables focus on multiple parts of input simultaneously. • Query, Key, Value (QKV) • Calculate relevance scores between tokens. • Multi-head vs Single-head • More heads → diverse perspectives on relationships. • Visual Analogy • Like reading text with multiple highlighters. • Model Examples • BERT, GPT-3, DeepSeek use multi-head attention.
- 27. Advancements in ReasoningAbilities • Structured Training Data • Logic puzzles, code, math problems. • Step-by-Step Prompting • Enables internal reasoning chains. • Tool Use + Memory • Agents that look up and recall facts. • Evaluation Techniques • CoT Benchmarks, GSM8K, MATH dataset. • Real-World Result • Better logical consistency and accuracy.
- 28. Chain-of-Thought Reasoning Display •Example Prompt • “If Sarah has 2 apples and buys 3 more...” • Output Steps • Lists individual reasoning steps. • Benefit • Easier to debug and verify output. • Used In • GPT, Claude, PaLM models. • Oracle Use • Improves chatbot explainability in GenAI.
- 29. Model Explainability Tools •SHAP & LIME • Feature attribution for model outputs. • Attention Heatmaps • Shows which words influenced results. • LangSmith + LangChain • Track reasoning and intermediate tools. • Oracle Logging & Auditing • Records decisions for transparency. • Human-in-the-Loop Interfaces • Review AI decisions interactively.
- 30. What is ModelDistillation? • Definition • Transferring knowledge from large model to smaller one. • Why It Matters • Faster, cheaper inference. • Common Methods • Teacher-student training, logits matching. • Tools • HuggingFace Transformers, DistilBERT. • Oracle Application • Fast inference in resource-limited scenarios.
- 31. Model Distillation Techniques •Logit-Based • Match output probabilities. • Feature-Based • Match hidden layer representations. • Response-Based • Match final outputs only. • Fine-Tuning After Distillation • Refine smaller model’s accuracy. • Use Case • Run on-device AI, real-time scoring.
- 32. Best Practices forSpecialized Models • Use Case Clarity • Clearly define what the model must do. • Narrow Domain Data • Use domain-specific corpora (e.g. legal, finance). • Prompt + Fine-Tune Combo • Combine instruction tuning with small-data fine-tuning. • Distillation from Larger Models • Compress while keeping accuracy. • Oracle Workflow • Data Science + GenAI API + Vector DB.
- 33. Introduction to MultimodalModels • What is Multimodal? • Inputs/outputs span text, image, audio, video. • Key Models • Gemini, GPT-4V, DeepSeek-VL, Claude Opus. • Use Cases • Document Q&A, video captioning, audio analysis. • Fusion Techniques • Early, late, and hybrid fusion. • Oracle Vision + Language APIs • Combine for multimodal workflows.
- 34. Generating Audio withAI • Text-to-Speech (TTS) • Converts text to human-like speech. • Popular Models • ElevenLabs, Azure TTS, Google WaveNet. • Features • Emotional tone, multilingual support. • Use Cases • Voice assistants, audiobooks, accessibility. • Oracle Integration • Combine TTS with GenAI for agents.
- 35. Generating Video withAI • Text-to-Video Generation • Prompt-based video synthesis. • Leading Models • Veo (Google), Runway Gen-3, Sora (OpenAI). • Pipeline • Text → storyboard → motion frames → render. • Constraints • Expensive, context limits, ethics concerns. • Emerging Tools • Pika Labs, Luma Labs.
- 36. ElevenLabs – AdvancedTTS • Overview • AI voices that sound realistic and expressive. • Voice Cloning • Generate speech in your own voice. • Pros • Speed, realism, customization. • Use Cases • Games, news, podcasts, agents. • Privacy Considerations • Prevent misuse with watermarking and approval.
- 37. Google Veo –Text-to-Video • About Veo • Google’s most advanced video model. • Capabilities • Natural motion, cinematic quality, consistent characters. • Example Prompts • “A golden retriever in a snowstorm...” • Limitations • Limited public access currently. • Enterprise Use • Educational videos, marketing, simulation.
- 38. Audio + Video+ Text Agents • Agents With Senses • Multimodal input processing (text, sound, image). • Prompt Routing • Send each modality to the right model. • Example • Oracle chatbot with voice + document ingestion. • Toolchains • LangChain, LangGraph, RAG + Vision APIs. • Potential • Fully autonomous, sensor-rich AI assistants.
- 39. Ethics of GenerativeVideo/Audio • Deepfakes Risk • Fake content created with malicious intent. • Consent + Licensing • Who owns the voice/image? • Detection Tools • Audio/video watermarking, authenticity scoring. • Oracle’s Security Model • Logging, IAM, encryption across media pipelines. • Mitigation • Ethical guardrails + user alerts.
- 40. Trends in FoundationModel Research • Sparse Mixture of Experts • Only activate parts of the model. • Linear Attention • Speed up inference time. • Reinforcement Learning + LLMs • Add memory and goal-seeking to text agents. • Agentic Evolution • LLMs self-evaluate and improve over time. • Oracle’s Edge • Secure, modular deployment of new architectures.
- 41. Final Thoughts onMultimodal AI • Expanding Capabilities • Future models: text + audio + video + code + 3D. • Developer Opportunities • Need for tool integration, creativity. • Research Gaps • Evaluation metrics for multimodal performance. • Responsible Innovation • Balance capabilities with constraints. • Where Oracle Fits • Secure deployment of cutting-edge AI systems.
- 42. AI Career Overview •AI as a Growth Field • Huge demand across industries. • Interdisciplinary Opportunities • Tech, health, law, education, marketing. • Oracle's Role • Cloud AI, autonomous databases, GenAI APIs , Agentic AI. • Top Roles • Data Scientist, ML Engineer, AI Product Managers • Early Start Advantage • Projects, certifications, internships
- 43. Learning Pathways forAI • Online Courses • Coursera, Udemy, Oracle Learning. • University Courses • CS, Math, Cognitive Science, Ethics , Data Science • Specialized Tracks • NLP, Computer Vision, Data Engineering. • Oracle’s Learning Explorer • Free foundational AI and OCI courses. • Self Projects • Build a chatbot, sentiment analyzer, AI pipelines
- 44. Technical Skills inDemand • Programming • Python, SQL, JavaScript for AI apps. • Data Analysis • Pandas, NumPy, visualization tools. • Machine Learning • Scikit-learn, TensorFlow, PyTorch. • Cloud Platforms • Oracle OCI, Azure, AWS, GCP. • Deployment • REST APIs, Docker, serverless functions.
- 45. Soft Skills ThatMatter • Critical Thinking • Understand limitations and biases of AI. • Communication • Explain models to non-technical stakeholders. • Collaboration • Work across teams (dev, design, product). • Curiosity • Stay updated in this fast-evolving field. • Ethics Awareness • AI fairness, accountability, safety.
- 46. Building an AIPortfolio • GitHub Repositories • Share code and notebooks. • Blogs and Medium Articles • Write about projects and experiments. • Showcase Projects • Examples: AI tutor, summarizer, recommendation engine. • Oracle Cloud Free Tier • Host AI models or demos. • Kaggle Competitions • Gain visibility and experience.
- 47. Getting Internships inAI • Start Early • Apply in second or third year. • Resume Tips • Highlight relevant projects and certifications. • AI Hackathons • Participate to build and network. • Referrals • Connect with alumni and mentors.
- 48. Certifications to Pursue •Oracle Cloud Infrastructure AI • Recognized in enterprise roles. • Google Professional ML Engineer • Widely respected in tech industry. • Microsoft Azure AI Fundamentals • Entry-level for Microsoft ecosystem. • IBM Applied AI • Foundation in applied use cases. • HuggingFace or LangChain Courses • Specialized tool certifications.
- 49. AI Roles BeyondTech • AI in Healthcare • Diagnostics, treatment planning, med research. • AI in Marketing • Personalization, lead scoring, content generation. • AI in Law • Contract analysis, case prediction. • AI in Education • Tutoring systems, student analytics. • AI in Creative Industries • Generative art, music, writing.
- 50. Avoiding Pitfalls inYour AI Journey • Avoid Hype Traps • Focus on fundamentals over trends. • Overfitting Your Learning • Explore beyond just one tool/library. • Unrealistic Job Titles • “Prompt Engineer” ≠ Entry-level role. • Underestimating Soft Skills • Tech + communication = better prospects. • Impatience • AI mastery takes time and iteration.
- 51. Final Advice andResources • Stay Curious • AI is evolving—so should your learning. • Build Projects • Hands-on > theory for hiring. • Use Oracle Cloud • Leverage free resources for student devs. • Join Communities • Reddit, LinkedIn, Discord, Oracle forums. • Mentor Others • Teaching helps you learn and network.