Effective ESG reporting is crucial for transparency and for meeting regulatory requirements, such as the new Corporate Sustainability Reporting Directive (CSRD) in the EU, and attracting investors. In this context, artificial intelligence can be a powerful tool to transform and enhance this reporting, providing accurate, comprehensive, and real-time insights. By automating complex processes and delivering deeper insights, AI can support organizations in improving their ESG performance and transparency, paving the way for more sustainable and responsible business practices.

Mats Andersson, a PhD student at Sahlgrenska Academy's neuroscience department, is researching how synapses in the brain work. This research is important for understanding conditions where synaptic turnover is affected, such as autism, schizophrenia, and depression, as well as neurodegenerative diseases like Alzheimer's and Parkinson's. Using cutting-edge tools and collaborating with other scientists, this research aims to make a real difference in understanding and eventually treating or managing these conditions.

Unlike traditional computers that provide deterministic outputs, Large Language Models (LLMs) introduce a new paradigm with their probabilistic nature. This shift allows for variability and adaptability, closely mimicking human-like behavior and expanding the scope of what technology can achieve. This means we need to take a new approach to computers, and a structured approach to architecture and implementations.

AI offers significant opportunities for innovation and efficiency. However, alongside these advancements it is important to ensure AI is developed and deployed responsibly. We have all heard about “by design”-approaches, and now is the time for "Responsible AI by design". This approach mitigates risks, reduces long-term AI model maintenance costs, and builds trust with stakeholders. It is also key to reducing the environmental impact of AI.

More and more companies are relying on the analytical and generative capabilities of LLMs and other generative models in their day to day activities. Simultaneously there are growing concerns about how factual errors and underlying biases produced by these models may have negative consequences.

Most of us have experienced serverless architecture as a way to build and run applications and services without having to manage infrastructure. One of the key advantages of serverless technology is its ability to handle dynamic workloads. AI applications often require processing large volumes of data, and serverless platforms can automatically scale to meet these demands.

With a growing world population and escalating consumption levels, production and consumption patterns need to shift towards sustainability. To combat climate change effectively, business models must separate resource use from economic gain. This could be the circular business model, which revolves around reusing and recycling resources within an infinite system. Through this approach, known as decoupling, the link between resource consumption and environmental degradation can be broken. Transitioning to a circular economy is essential to meet climate targets and pave the way for a more sustainable future.