ChatGPT and clean technology

If you've ever played with ChatGPT through a free account, you’ve probably been trying to see what answers you can get to common questions. Or maybe you’ve been trying to see if it can work as a virtual assistant if you’re planning travel. Or maybe you’re a software developer who’s been using Copilot and other tools that use LLMs to help you answer questions about your code when you’re stuck.


We’ve been hearing a lot about ChatGPT and large-language models (LLMs) and their impacts on the jobs that are going to be available and the skills that people will need to acquire. These models together form the field of GenAI or Generative AI - often called that because they appear to generate or synthesize knowledge. Compared to regular search engines, it often seems that ChatGPT and its equivalents are producing results that are closer to human conversation. However, these models are still only a type of machine learning method; the difference is that they have been trained on extremely large data sets which allows them to collate data from multiple sources and hence appear more intelligent.


So, what kind of impact could ChatGPT have in the clean technology sector? As we’ve discussed before, the challenge in clean tech is that data are limited and highly complex. Compared to images about cats or text from books, the data available in water, agriculture, energy, and climate are both limited in size and highly complex with spatial and temporal variability and interconnected, non-linear processes. This is one reason why machine learning and data science algorithms need to be adapted and modified to be of use in the sector! In the case of ChatGPT as it stands today, there are fewer direct applications in terms of model development  - however, there are certain problems where it can be of great use.


As these are large language models, the biggest impact is when ChatGPT can be used to analyze reports and collate data from several published sources. Anyone working in sustainability is familiar with how difficult and labor intensive it is to obtain information from the different reports that are published by organizations and companies about their water, energy and material impacts. For example, if you’re trying to track Scope 3 emissions for a certain sector, it can be quite difficult to collect the information about supply chains, where the material flows, the amount of emissions associated with all the materials used in the manufacture process and the locations where water use is an issue. 


Being able to use ChatGPT in this case is extremely helpful and can save a sustainability professional a lot of time and effort. It wouldn’t completely replace the knowledge and judgement that an expert needs - but it would contribute to making the expert more efficient.

Similarly, being able to use ChatGPT to search through research papers in a sector and identify useful trends or extract information that is needed is another use case that would help the human expert. Or in the case of the professional in clean-tech who is writing code, using a tool like Copilot which helps gather information about how to answer a question could be more efficient than searching through Stackoverflow for example.


While ChatGPT can be useful, something to remember is that it is still a tool - and in the very early stages of development. While it can be useful, the answers it provides should still be verified - as one scientist in the field said “AI sometimes does hallucinate answers”!


We’ll be having some fun sessions discussing ChatGPT and it’s uses in the new year, so stay tuned…

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