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Last year, for our Managing Socio-Economic Dilemmas elective, we had to create a video which explained a current socio-economic issue in a creative way. My group was tasked with a topic which we found very intriguing: the rise of AI and how it could have an impact on jobs. However, we did not just want to talk about it – we wanted the AI to speak for itself. We decided to create a video which was created mostly by genAI, with only the script being contributed by us.

Up to that point, the only AI tool that I had ever used was ChatGPT, and I had no idea what could be used to create realistic voiceovers or visuals. After looking at different tools, we landed on RunwayML for the visuals and Murf.AI to generate the voiceover. Especially working with RunwayML was an eye-opening experience. It was incredible how it was able to create impressive videos with simple inputs. Of course, there were still some issues – it could not really imitate human movement very well and some things still looked very “AI-generated”. However, with a bit of tinkering, we ended up being very happy with the results. In the end, we created a video showcasing the potential issue of AI, while not even appearing in the video ourselves.

Fast-forward a year, and these technologies have improved exponentially. Recently, I saw the results of the second annual AI Film Festival, hosted by RunwayML. The festival challenges participants to create short films using only their generative AI model. The difference really is staggering compared to what we worked with only a year ago. Issues we faced such as unrealistic human movement have almost completely been solved. The AI created short films with much more realistic and dynamic characters and even managed to fix the infamous “hand problem” which was a problem in earlier versions. Sure, there can still be some issues if you look closely, but the overall progress is incredible.

Overall, I am blown away by how much GenAI has been improving in helping in the creative space. I can’t help but wonder: will we see entire movies or TV shows created by AI soon?

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Businesses are facing increasing pressure from both consumers and governments to not only be innovative but also sustainable. The recent introduction of more sustainable reporting guidelines introduced by the EU forces companies to report on their sustainability and to start looking at what their environmental impact is and how they can mitigate it. Figuring out the complex product lifecycle, from the mining of the raw materials to how they get disposed of, can sometimes feel like too big of a mountain to climb, especially for smaller companies. Could this be where AI lends a helping hand?  

Lifecycle assessments are one of the most in-depth ways for companies to see the environmental impact of their product. It maps the impact of the entire value chain of a product, a massive amount of work for companies that produce many different products. Many components and raw materials of products come from countries that place less importance on tracking and reporting their environmental footprint. When performing an LCA, this can make it difficult to accurately predict the footprint of a product. AI can use its high predictive accuracy to help fill in these missing values (Romeiko et al., 2024).  

Many startups are working on LCA AI technology, one example is Makersite. They have created an AI which easily shows a company’s entire value chain, simply by uploading their bill of materials (Makersite GmbH, 2024a). Big MNOs such as Microsoft are already using the technology to envision their carbon footprint (Makersite GmbH, 2024b). 

In my opinion, this technology will be extremely helpful to companies. Across all sectors there is more and more pressure from consumers, governments, NGOs and other stakeholders to become more transparent about their impact. The usage of AI will make it easier for companies to get an understanding of their value chain and where they can make a change. It is one of the AI developments which could truly have a positive impact on the world and has the potential to grow in the future.   

References

Makersite GmbH. (2024a, April 2). Automated LCAs with Makersite – Makersite GmbH. https://makersite.io/get-to-net-zero/automated-lcas/ 

Makersite GmbH. (2024b, June 2). Microsoft’s LCA methodology with Makersite – Makersite GmbH. https://makersite.io/customer-story/microsofts-lca-methodology/ 

Romeiko, X. X., Zhang, X., Pang, Y., Gao, F., Xu, M., Lin, S., & Babbitt, C. (2024). A review of machine learning applications in life cycle assessment studies. The Science of the Total Environment, 912, 168969. https://doi.org/10.1016/j.scitotenv.2023.168969