Replace DL in research examples with non-paywalled examples/variants.

episodes/1-introduction.Rmd

@@ -294,9 +294,9 @@ This is a good time for switching instructor and/or a break.
 Here are just a few examples of how deep learning has been applied to some research problems. Note: some of these articles might be behind paywalls.
 
 * [Detecting COVID-19 in chest X-ray images](https://arxiv.org/abs/2003.09871)
-* [Forecasting building energy load](https://ieeexplore.ieee.org/document/7793413)
+* [Forecasting building energy load](https://arxiv.org/abs/1610.09460)
 * [Protein function prediction](https://pubmed.ncbi.nlm.nih.gov/29039790/)
-* [Simulating Chemical Processes](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.98.146401)
+* [Simulating Chemical Processes](https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc04934j)
 * [Help to restore ancient murals](https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-020-0355-x)
 
 

episodes/6-outlook.Rmd

@@ -92,7 +92,7 @@ Large Language Models (LLMs) are deep learning models that are able to perform g
 They are trained on large amounts of texts, such all pages of Wikipedia. 
 In recent years the quality of LLMs language understanding and generation has increased tremendously, and since the launch of generative chatbot ChatGPT in 2022 the power of LLMs is now appreciated by the general public.
 
-It is becoming more and more feasible to unleash this power in scientific research. For example, the authors of [Zheng et al. (2023)](https://doi.org/10.1021/jacs.3c05819) guided ChatGPT in the automation of extracting chemical information from a large amount of research articles. The authors did not implement a deep learning model themselves, but instead they designed the right input for ChatGPT (called a 'prompt') that would produce optimal outputs. This is called prompt engineering. A highly simplified example of such a prompt would be: "Given compounds X and Y and context Z, what are the chemical details of the reaction?"
+It is becoming more and more feasible to unleash this power in scientific research. For example, the authors of [Zheng et al. (2023)](https://doi.org/10.1021/acscentsci.3c01087) guided ChatGPT in the automation of extracting chemical information from a large amount of research articles. The authors did not implement a deep learning model themselves, but instead they designed the right input for ChatGPT (called a 'prompt') that would produce optimal outputs. This is called prompt engineering. A highly simplified example of such a prompt would be: "Given compounds X and Y and context Z, what are the chemical details of the reaction?"
 
 Developments in LLM research are moving fast, at the end of 2023 the newest ChatGPT version [could take images and sound as input](https://openai.com/blog/chatgpt-can-now-see-hear-and-speak). 
 In theory, this means that you can solve the Dollar Street image classification problem from the previous episode by prompt engineering, with prompts similar to "Which out of these categories: [LIST OF CATEGORIES] is depicted in the image".