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![Artificial
Intelligence[AI]
Machine
Learning [ML]
Natural Language
Processing [NLP]
Deep Learning
Vision Speech
Robotics
Planning
Expert
Systems
Neural Networks
Generative AI](https://image.slidesharecdn.com/harnessingthepowerofgenerativeaiforyourbusinessbysiddharth-240628094031-06f2e2da/75/Harnessing-the-Power-of-Generative-AI-for-your-Business-By-Siddharth-pdf-11-2048.jpg)
Harnessing the Power of Generative AI for your Business By Siddharth.pdf
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- 3. 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
- 4. How Generative AIworks? 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
- 5. 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
- 6. Logical Reasoning & Problem-Solving AbstractThinking Learning & Adaptation Memory Language & Communication Perception & Sensory Processing Emotional Intelligence
- 7. Social Intelligence Creativity & Imagination Decision-Making Metacognition SpatialReasoning Numerical & Quantitative Skills Practical Intelligence Moral & Ethical Reasoning
- 8. Expert systems, rule-basedsystems, 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.
- 9. Affective computing, sentimentanalysis, 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.
- 10. Data analytics, statisticalanalysis 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.
- 11. Artificial Intelligence[AI] Machine Learning [ML] Natural Language Processing[NLP] Deep Learning Vision Speech Robotics Planning Expert Systems Neural Networks Generative AI
- 12. The Rise ofGenerative 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.
- 13. Generative AI Generative AIfalls 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
- 14. ✦ Abstract Thinking ✦Language & Communication ✦ Creativity & Imagination
- 16. 1966 2017 2023 OpenAl GPT-3 May: OpenAlreleases 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
- 17. 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.
- 18. Neural Network LanguageModel (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.
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- 20. Model The result ofthe 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
- 21. Foundation Model BERT, GPT-n, DALL-E,.. AdaptedModels BioGPT Domain-Specific Models BloombergGPT Task-Specific Models Whisper Hybrid Models Multimodal Models Gemini Explainable & Interpretable Models Personalized Models Apple Intelligence
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- 24. Generative pre-training Fine-tuning Retrieval-augmented generation (RAG) Promptengineering Complexity Accuracy Cost Time to Implement Domain Specificity Flexibility
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- 30. 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.
- 31. ✦ MAKER Train andbuild custom models ✦ SHAPER Tune foundational Industry Models ✦ TAKER Use pre-trained ML API models and point to your apps
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