🦘 A lazy kangaroo is just a pouch potato
This issue covers LLM security measures, AI research overviews, web-based dashboards, a geography game, a milestone celebration, machine learning for soil erosion prediction, a data map visualization tool, and fun things found around the web!
Late to the Party 🎉 is about insights into real-world AI without the hype.
Hello internet,
where’d January go?! Anyways!
In this issue, we’ll see some LLM security measures, AI research overviews, and web-based dashboards! I’ll talk about a little game I have been loving. I celebrate a big milestone, and we’ll dive deep into machine learning for soil erosion prediction and mitigation! Finally, we’ll round it off with a beautiful data map visualisation tool and some fun things I found around the web!
Let's dive right in!
The Latest Fashion
- Want to secure your LLM? Try out Guardrails
- 10 noteworthy AI research papers from 2023 (non-LLM ones are at the bottom)
- Web-based dashboards in Python with NiceGUI avoid “magic” while still looking good
Worried these links might be sponsored? Fret no more. They’re all organic, as per my ethics.
My Current Obsession
I have been playing Travle a lot lately. It’s a daily game where you try to move from country A to country B via land connections. Through this game, I’ve been getting much better at geography. For the first time, my knowledge of Africa, the Middle East, and Central America isn’t abhorrent anymore. Today, I noticed a new, harder weekly challenge: travelling from Oman to Gabon along the coast! That was a lot of fun to figure out!
I’m also super happy that my chatGPT class on Skillshare has reached 1,000 students already!
Thing I Like
I went tobogganing in -19°C weather and my new thermos saved me (and survived all crashes).
Hot off the Press
My course, “Unlock your Creative Potential with AI: ChatGPT for Creative and Content Creators,” just reached 1,000 students!
On Socials
People loved Brandon Rohrer’s Build Transformers from Scratch on Linkedin and Mastodon.
Data2Vec also found a reasonable amount of interest. And Mastodon liked these Computer Vision notebooks.
Python Deadlines
After the January onslaught, Python conferences seem to be fairly quiet. However, I did find SciPy US 2024, which closes its CFP in three weeks.
Machine Learning Insights
Last week, I asked, Can AI be effectively used to predict and mitigate the impacts of soil erosion?, and here’s the gist of it:
AI can indeed be effectively used to predict and mitigate the impacts of soil erosion, offering a promising avenue for both understanding and combating this environmental challenge.
Soil erosion is a natural process that can be accelerated by human activities. It leads to the loss of fertile topsoil, reduced agricultural productivity, and increased sedimentation in waterways. The complexity of factors influencing soil erosion, such as rainfall intensity, soil type, topography, vegetation cover, and land management practices, makes it an ideal candidate for AI-based solutions.
AI in Predicting Soil Erosion
- Machine Learning Models: Machine learning models can analyse these vast datasets to identify patterns and predict erosion risk areas. These models can integrate various factors, including weather patterns, soil characteristics, and land use, to forecast erosion susceptibility accurately.
- Remote Sensing Data: AI algorithms can process and analyse data from satellite imagery, aerial photos, and novel drone footage to assess land cover, vegetation health, and changes over time. This information is crucial for identifying areas at risk of soil erosion and monitoring the effectiveness of erosion control measures.
- Simulation of Erosion Processes: AI can enhance the simulation models that predict how different factors contribute to soil erosion under various scenarios by speeding up costly simulations through surrogate models. These simulations can help understand the potential impact of extreme weather events, like heavy rainfall or droughts, on soil erosion.
AI in Mitigating Soil Erosion
- Precision Agriculture: AI-driven precision agriculture techniques can optimise land use, reducing the risk of erosion. By analysing data on soil conditions, crop types, and weather predictions, AI can help experts make informed decisions on crop rotation, planting density, and irrigation practices that minimise soil disturbance and erosion.
- Erosion Control Measures: AI can assist in the design and implementation of effective soil conservation practices, such as contour ploughing, terracing, and the establishment of vegetation cover. By predicting the areas most at risk, AI can guide the placement of these measures to maximise their effectiveness in addition to the established expertise.
- Policy and Planning: AI models can provide policymakers with insights into the long-term impacts of land management practices and climate change scenarios on soil erosion through explainable AI. Here, AI can inform more sustainable land use policies and conservation strategies.
Challenges and Considerations
- Data Quality and Availability: The effectiveness of AI in predicting and mitigating soil erosion depends on the availability of high-quality, comprehensive datasets. Data availability can be problematic when areas most affected by soil erosion are often also those least covered by sensors that enable data collection.
- Model Complexity: Soil erosion is influenced by numerous interlinked factors, making model development and interpretation challenging. Explainable AI can help inform modelling decisions, such as features and data sources.
- Local Adaptation: AI models must be adapted to local conditions to accurately predict and mitigate soil erosion, requiring local data and expertise. It might be interesting to explore foundation models and fine-tuning to reduce the modelling and computational burden on local communities.
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Data Stories
Clustered data isn’t hard to visualise.
But what if we could do more with less effort?
DataMapPlots makes labelled 2D-data maps visually appealing with reasonable defaults but enough customisation to be useful.
Take, for example, the Arxiv ML landscape.
We know this data is excellent to cluster, but has it ever looked this good?
I also love the automatic labels and the neat organisation in a static plot.
Ready for presentation and publication!
Source: Arxiv and Tutte Institute
Question of the Week
- Can you elaborate on the concept of attention mechanisms in neural networks and their impact on model interpretability?
Post them on Mastodon and Tag me. I’d love to see what you come up with. Then, I can include them in the next issue!
Tidbits from the Web
- This one has to be Travle, an addictive game that teaches you geography
- How good is chatGPT at mixing drinks?
Jesper Dramsch is the creator of PythonDeadlin.es, ML.recipes, data-science-gui.de and the Latent Space Community.
I laid out my ethics including my stance on sponsorships, in case you're interested!