Remember: Gen AI is at the Peak of Expectations*

This course introduces the basics of prompt engineering and progresses to advanced prompt techniques - https://lnkd.in/dyEt4DGt [28 NOV] - Links below are the extra resources to go deeper:

• getting-started-with-generative-ai-and-foundation-models.pdf (awsstatic.com) 
• What is NLP? - Natural Language Processing Explained - AWS (amazon.com) 
• [1808.03314] Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network (arxiv.org) 
• [1706.03762] Attention Is All You Need (arxiv.org) 
• [2112.10752] High-Resolution Image Synthesis with Latent Diffusion Models (arxiv.org) 
• [2203.11171] Self-Consistency Improves Chain of Thought Reasoning in Language Models (arxiv.org)
• [2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large Language Models (arxiv.org)
• [2005.11401] Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (arxiv.org) 
• [2303.09014] ART: Automatic multi-step reasoning and tool-use for large language models (arxiv.org) 
• [2210.03629] ReAct: Synergizing Reasoning and Acting in Language Models (arxiv.org) 
• Introduction to prompt design (anthropic.com) 
• Prompt Engineering (ai21.com) 
• Generative AI – Innovate Faster with Foundation Models - AWS (amazon.com) 
• Foundational Models - Amazon Bedrock - AWS 
• AI Accelerator - AWS Trainium - AWS (amazon.com) 
• AI Chip - AWS Inferentia - AWS (amazon.com) 
• AI Code Generator - Amazon CodeWhisperer - AWS 
• Machine Learning Service, Machine Learning Ide - Amazon SageMaker - AWS 

A free course - https://lnkd.in/eiRtk-6q delivered by Isa Fulford (Open AI) and Andrew Ng (Deep Learning.AI) outlining strategies and techniques for obtaining desired outputs. The training is especially cool because there is a side-by-side window to experiment with the code discussed and view results. Spark your imagination for application development. [29 NOV 2023]

• Guidelines for opening a session and getting started
• Iterative - start small and iterate until desired results
• Summarizing - like cliff notes for any content
• Inferring - for example, sentiment about a set of feedback data
• Transforming - language translation, even output a response as a pirate
• Expanding on given information
• ChatBot - make a simple pizza ordering solution

Aimed at explaining what Gen is, how it is used, and how it differs from traditional machine learning methods - https://lnkd.in/eBQXfBe9 [29 NOV 2023]

Dr. Gwendolyn Stripling (Google) shares Introduction to Generative AI (G-GENAI-I). Here are the assembled readings on generative AI:

• Ask a Techspert: What is generative AI? https://blog.google/inside-google/googlers/ask-a-techspert/what-is-generative-ai/ 
• Build new generative AI powered search & conversational experiences with Gen App Builder: https://cloud.google.com/blog/products/ai-machine-learning/create-generative-apps-in-minutes-with-gen-app-builder 
• What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai 
• Google Research, 2022 & beyond: Generative models:  Google Research, 2022 & beyond: Language, vision and generative models – Google Research Blog 
• Building the most open and innovative AI ecosystem:  Building the most open and innovative AI ecosystem | Google Cloud Blog 
• Generative AI is here. Who Should Control It? https://www.nytimes.com/2022/10/21/podcasts/hard-fork-generative-artificial-intelligence.html
• Stanford U & Google’s Generative Agents Produce Believable Proxies of Human Behaviors: https://syncedreview.com/2023/04/12/stanford-u-googles-generative-agents-produce-believable-proxies-of-human-behaviours/
• Generative AI: Perspectives from Stanford HAI: https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives 
• Generative AI at Work: https://www.nber.org/system/files/working_papers/w31161/w31161.pdf 
• The future of generative AI is niche, not generalized: https://www.technologyreview.com/2023/04/27/1072102/the-future-of-generative-ai-is-niche-not-generalized/ 
• The implications of Generative AI for businesses: https://www2.deloitte.com/us/en/pages/consulting/articles/generative-artificial-intelligence.html
• Proactive Risk Management in Generative AI: https://www2.deloitte.com/us/en/pages/consulting/articles/responsible-use-of-generative-ai.html
• How Generative AI Is Changing Creative Work: https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work 

Here are the assembled readings on large language models: 

• NLP's ImageNet moment has arrived: https://thegradient.pub/nlp-imagenet/ 
• LaMDA: our breakthrough conversation technology: https://blog.google/technology/ai/lamda/ 
• Language Models are Few-Shot Learners: https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf 
• PaLM-E: An embodied multimodal language model: https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html 
• PaLM API & MakerSuite: an approachable way to start prototyping and building generative AI applications: https://developers.googleblog.com/2023/03/announcing-palm-api-and-makersuite.html 
• The Power of Scale for Parameter-Efficient Prompt Tuning: https://arxiv.org/pdf/2104.08691.pdf 
• Google Research, 2022 & beyond: Language models: Google Research, 2022 & beyond: Language, vision and generative models – Google Research Blog 
• Solving a machine-learning mystery: https://news.mit.edu/2023/large-language-models-in-context-learning-0207 

Additional Resources: 

• Attention is All You Need: https://research.google/pubs/pub46201/ 
• Transformer: A Novel Neural Network Architecture for Language Understanding: https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html 
• Transformer on Wikipedia: https://en.wikipedia.org/wiki/Transformer_(machine-learning_model)
• What is Temperature in NLP? https://lukesalamone.github.io/posts/what-is-temperature/ 
• Model Garden: https://cloud.google.com/model-garden 
• Auto-generated Summaries in Google Docs: https://ai.googleblog.com/2022/03/auto-generated-summaries-in-google-docs.html 

Learn to use machine learning in Python in this introductory 7-week course on artificial intelligence by David J. Malan and Brian Yu -  https://lnkd.in/eu4mZaAG   

It will take a few days/weeks to complete.

Presented by the visionary Pinar Seyhan Demirdag, the basics of Gen AI covers topics including what it is, how it works, guidance on creating your own content, different types of models, future predictions, and ethics - https://lnkd.in/eYNWzXUX 

Haven't watched the whole training, but I haven't seen references to Microsoft yet.

A comprehensive course in prompt engineering with over 60 content modules; Takes you from beginner to advanced in the art of prompt engineering - https://lnkd.in/eNi_YNSe  

Learn what Responsible AI is, why it’s essential, and how Google implements it in its products - https://lnkd.in/eTrwSU89  

Learn about training data and how to use a set of data to discover potentially predictive relationships; You'll also learn how to build a movie recommendation system and data science techniques - https://lnkd.in/eX28syMJ 

Gain essential skills in expanding the use cases and capabilities of language models in application development using LangChain - https://lnkd.in/evZVJbNy  

Gain essential skills in expanding the use cases and capabilities of language models in application development using LangChain - https://lnkd.in/evZVJbNy