Post 3 讀物：生成式人工智慧簡介

以下是有關生成式人工智慧的彙編讀物：

Readings: Introduction to Generative AI

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:

https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html#Gener

ativeModels

● Building the most open and innovative AI ecosystem:

https://cloud.google.com/blog/products/ai-machine-learning/building-an-open-generativ

e-ai-partner-ecosystem

● Generative AI is here. Who Should Control It?

https://www.nytimes.com/2022/10/21/podcasts/hard-fork-generative-artificial-intelligen

ce.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-b

elievable-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-intellig

ence.html

● Proactive Risk Management in Generative AI:

https://www2.deloitte.com/us/en/pages/consulting/articles/responsible-use-of-generati

ve-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:

https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html/Langu

ageModels

● 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)#:~:text=Transfor

mers%20were%20introduced%20in%202017,allowing%20training%20on%20larger%20da

tasets.

● 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

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