AI & LLMs — For the Circular Economy
This week there is not one but two videos supporting my argument that understanding the differences in technology will ensure we apply the right tech for the correct application. Although I am not a techie knowing the difference between LLMs and other forms of AI will go a long way towards how we use emerging technologies.
What are Large Language Models?
In simple terms, a Large Language Model (LLM) is a computer program that has been fed enough examples to be able to recognise and interpret human language or other types of complex data. Many LLMs are trained on data that has been gathered from the Internet — thousands or millions of gigabytes’ worth of text. However, the quality of the samples impacts how well LLMs will learn natural language, so LLM’s programmers may use a more curated data set.
LLMs use a type of machine learning called deep learning to understand how characters, words, and sentences function together. Deep learning involves the probabilistic analysis of unstructured data, which eventually enables the deep learning model to recognise distinctions between pieces of content without human intervention.
LLMs are then further trained via tuning: they are fine-tuned or prompt-tuned to the particular task that the programmer wants them to do, such as interpreting questions and generating responses, or translating text from one language to another.
LLMs can be trained to do a number of tasks. One of the most well-known uses is their application as generative artificial intelligence or GenAI : when given a prompt or asked a question, they can produce text in reply. The publicly available LLM ChatGPT, Gemini (previously Bard) and Copilot, for instance, can generate essays, poems, and other textual forms in response to user inputs.
GenAI describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Recent breakthroughs in the field have the potential to drastically change the way we approach content creation.
What is AI?
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.
This definition alone separates it from LLMs. We all agree that AI is in its infancy and its usage and applications will explode over the coming years, moving beyond self-driving cars, creating pictures or recognising faces. What is far more interesting is how AI can be applied to the circular economy.
AI and the Circular Economy
Now, here is where it begins to get really interesting and where AI demonstrates its true potential. Let’s start with supply chains with this video created by Nvidia one year ago. If this particular application was optimised for the circular economy, we would be able to track data on packaging, types of materials, destinations of products and the availability of collection services or reverse logistics to bring materials back for reprocessing — material inflows and outflows.
There are limitations and risks to using AI in supply chains — especially when implementation is rushed or poorly integrated across organisations and supply chain networks. AI tools are only as powerful as their input data, so they are limited by the quality and availability of data from supply chain partners. Broadly, the risks that come with fewer human touchpoints — like lack of transparency or ethical and legal considerations — are best managed with strong governance and working with experienced partners.
Above all else, AI is an idea — an ideal — shaped by worldviews and sci-fi tropes as much as by math and computer science. Figuring out what we are talking about when we talk about AI will clarify many things. We won’t agree on them, but common ground on what AI is would be a great place to start talking about what AI should be
1 — Design circular products — using iterative machine learning and AI suggestions that will prolong the product life cycle and tackle resource scarcity. With AI, you can predict product and carbon costs right from the initial design phase to ensure optimised scenarios. For example, you can source local products and reduce the carbon footprint associated with transport or product substitution during the manufacturing phase.
2 — Use data-driven AI algorithms — to develop innovative circular business strategies and frameworks for sustainable growth by combining previously recorded and real-time data from other stakeholders, including producers, manufacturers, suppliers, and consumers for process optimisation and automated decision-making.
3 — Circular production and reprocessing — ensures infrastructure is fully optimised and, by mathematical modelling, material flow is created for acquiring used products, assessing waste, and reprocessing.
AI is already helping to create value for circular material flows and enhancing the selection of materials and products by sorting post-consumer mixed material streams through visual recognition techniques.
Conclusion
There is a lot going on with AI and it is easy to get confused as to what it means and how to apply it. It can also be expensive, staff need to be trained and senior management must be laser-focused on its applications and intended outcomes. Since Chat GPT, Bard, Copilot etc burst on the market the world has become enamoured with AI without fully comprehending the diversity of AI applications. LLMs are such a very small player in the big picture of AI and its future. The real value of AI will emerge when its usage becomes much more ubiquitous and integrated into the everyday operations of all-size companies around the world to manage resources and the Circular Rs.
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Ms Adrienna Zsakay is the Founder and CEO of Circular Economy Asia Inc, and this article represents her opinions on the circular economy. Circular World Video of the Week is brought to you by Circular World™ Media — a brand owned by Circular Economy Asia Inc.
For all the best content, join one of the fastest-growing circular economy groups on LinkedIn — Circular Economy Asia.
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References
‘What is a large language model (LLM)?’ published by Cloudflare.
‘What is generative AI?’ by Aamer Baig, Lareina Yee, Alex Singla and Alexander Sukharevsky, published by McKinsey, 02 April 2024.
Artificial Intelligence — Wikipedia
‘What is AI? Everyone thinks they know but no one can agree. And that’s a problem’ by Will Douglas Heaven, published in MIT Technology Review, 10 July 2024.
‘How supply chains benefit from using generative AI’ by Glenn Steinberg, Matthew Burton and Ayoub Abielmona, published by EY, 09 January 2024.
‘Creating a circular economy through AI’ by Pratyasha Shishodia, published by Capgemini, 14 February 2023
Originally published at https://www.linkedin.com.
