I've been experimenting using ChatGPT to generate candidate questions given document text input. The example is on Ground Source Heat Pumps (GSHP) from a British Geological Survey Report in the NORA collection. It might be useful for organisations to store a 'question bank' of such Generative AI outputs (questions) for a corpus, sliced in numerous... Continue Reading →

I’ve been experimenting using text embeddings to identify relative topic emphasis in text corpora, as an example of similarity based unsupervised machine learning. The examples below show the relative similarity of the word vectors for ‘aquifer’ (top) and ‘groundwater’ (bottom) to word vectors of various forms of contamination, comparing the US Geological Survey public collection... Continue Reading →

Using text embeddings for lookbacks. I’m making this app freely available to the Norwegian Petroleum Directorate and UK North Sea Transition Authority along with various NLP outputs for the benefit of the geoscience community. This is as input to the hackathon organised by FORCE led by Peter Bormann This particular example uses 800 license relinquishment... Continue Reading →

Discovering topics in text. This is an interactive noun-noun-phrase network of body text within 1,500 UK NERC Open Research Archive (NORA) groundwater hydrology reports related to aquifers. These inductive statistical type techniques can be a useful first pass to assess key topics and trends in a large amount of documents. Reference van Eck, N.J. and... Continue Reading →

Text embeddings can capture some interesting semantic relationships. Given an analogy “Quartz is to Sandstone” what “…….. is to Limestone” - using vector additions and subtractions, latent trajectories in embedding space produce “calcite” as the answer. Given enough text, this technique may be capable of producing results that spark new lines of thought in science... Continue Reading →

Our current obsession over artificial intelligence chat technology may tell us more about ourselves than the technology. I highly recommend this open access article by Berghel (2023). I’ve written about search engines and epistemology “how we come to know things” back in 2017. Here Berghel discusses ChatGPT and AIChat epistemology. It is a deep, thought... Continue Reading →

Applying Generative AI is easier than perhaps many people may think. The hard work has been done by the engineers and data scientists that have created Large Language Models (LLM). Some smaller models in Huggingface can be downloaded and run locally on your laptop. Others like OpenAI GPT can be used via an API key... Continue Reading →

Sentiment of the past 24hr global tweets containing ‘global warming’. Despite its biases, sentiment analysis of social media can be one source of data for ‘subjective well being’ towards a topic - supporting UN Sustainable Development Goals (SDG). Cities and governments are increasingly incorporating these indicators with traditional economic metrics. This display is using the... Continue Reading →

Social media activity on flooding (red=high) during a rainstorm in urban environments termed Public Concern Index (PCI). From Wang et al (2020) who proposed these data could be used to inform policies toward flood remediation. Natural Language Processing (NLP) has been used to map emotions in time (to events) and space (geographical location) for many... Continue Reading →

Delighted to win the 2023 Best Digital Leadership Award. It’s a pleasure to work and connect with so many wonderful people. Congratulations to the other winners and finalists. Thank you to the judges and staff at Business Awards UK. There is so much exciting collaboration and innovation happening across the subsurface and geoscience community. To... Continue Reading →