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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Post 2 睇吓有關於AI嘅資訊

 

每一個人都在講AI，而AI股票蒸蒸日上。 喺呢度，我哋一齊睇吓有關於AI嘅資訊啦。

What is Generative AI and how does it work? What are common applications for Generative AI? Watch this video to learn all about Generative AI, including common applications, model types, and the fundamentals for how to use it.

乜嘢係生成式人工智能，佢係如何工作嘅？ 生成式AI有哪些常見應用？ 觀看此視頻，瞭解有關生成式AI嘅所有信息，包括常見應用程序、模型類型以及如何使用它的基礎知識。这里有一些片段供参考。

Post 1 哪里有免费AI学。

人一世 物一世。只有几个十年。每一项都要学一下。在Bing Chat看到有AI课程。 There are many online resources available to learn Artificial Intelligence (AI). Here are some of the best free and paid online courses and programs to learn AI:

1. Coursera: Coursera offers a wide range of AI courses and programs, including “Introduction to Artificial Intelligence with Python” from Harvard University, “ChatGPT Prompt Engineering For Developers” from DeepLearning.AI, “Intro to TensorFlow for Machine Learning” from Udacity, “Reinforcement Learning” from Georgia Tech, and more 1.

2. Stanford Online: Stanford Online offers an AI Intelligence program that provides a rigorous introduction to machine learning, as well as opportunities to explore theoretical and project-based learning in natural language processing and understanding 2.

3. edX: edX offers a variety of AI courses and programs, including “Artificial Intelligence” from Columbia University, “AI for Everyone” from DeepLearning.AI, and more 3.

4. Simplilearn: Simplilearn offers a free introduction to AI program that teaches the basics of AI, including supervised, unsupervised, and reinforcement learning 4.

5. Tech.co: Tech.co has compiled a list of the best free AI training courses that focus on generative AI and foundational concepts in artificial intelligence. Some of the courses include Google’s Generative AI Learning Path, “Introduction to Artificial Intelligence” from IBM, and more 5.