🐲 The knight with the best boundaries is Sir Veyor
In this issue, we have an investigation of the flaws of “factual search AI”, a YouTube summariser to combat clickbait and an answer to why Python keeps growing as much. Then, I go into breadth on the challenges of data scarcity in real-world machine learning.
Late to the Party 🎉 is about insights into real-world AI without the hype.
Hello internet,
I got carried away with writing the ML Insight today, but still sending this out on the back end of the weekend!
In this issue, we have an investigation of the flaws of “factual search AI”, a YouTube summariser to combat clickbait and an answer to why Python keeps growing as much. Then, I go into breadth on the challenges of data scarcity in real-world machine learning.
Let’s dive right in!
The Latest Fashion
- This article investigates how wrong “factual search AI” gets it, beyond recommending eating glue
- This project summarises Youtube videos for you to combat clickbait
- Just a nice overview article on why Python keeps growing by Github
Worried these links might be sponsored? Fret no more. They’re all organic, as per my ethics.
My Current Obsession
I went to a medieval rock/metal concert in an original German castle this weekend, and it was SO much fun. I posted a picture on the Latent Space for the curious. It was really pretty, and I’m so happy I went!
In trying to improve pythondeadlin.es more, I have now implemented a first version of data validation using Pydantic. It felt like a good first project to try Pydantic on, and it already caught some data inconsistencies. Can’t wait to play around more with it!
Thing I Like
Every time I travel, I’m happy I brought my power cube. Native USB, multiple outlets, and I just have to bring one converter plug. Best travel hack there is.
Hot off the Press
Python Deadlines
There are no new deadlines, but PyCon South Korea and PyHEP.dev deadlines are approaching.
Machine Learning Insights
Last week I asked, How do you address the challenge of data scarcity when applying ML to rare environmental phenomena?, and here’s the gist of it:
Addressing data scarcity when applying machine learning (ML) to rare environmental phenomena involves several strategies to ensure models can learn effectively despite limited data. Here are some key approaches:
Data Augmentation
Data augmentation techniques artificially increase the size of the training dataset by creating modified versions of existing data. In environmental studies, this might involve applying transformations such as noise addition, scaling, or temporal shifts to existing data.
- Example: Using physical models to derive perturbations for the existing data.
Transfer Learning
Transfer learning involves pre-training a model on a large, related dataset and then fine-tuning it on the small dataset of interest. Here, we can leverage the encoded knowledge the model has gained from the broader dataset to improve performance on the specific task.
- Example: Using a model pre-trained on global weather patterns to predict rare tornado occurrences.
Synthetic Data Generation
Creating synthetic datasets through simulation models can help overcome data scarcity. These models use known physical laws to generate realistic data for rare phenomena. Additionally, synthetic data generation could be used. Take this with a grain of salt, though, as methods like SMOTE tend to underperform in anything but toy datasets.
- Example: Generating synthetic hurricane paths using coupled atmospheric and oceanic simulation models.
Anomaly Detection Techniques
Since rare environmental phenomena are often anomalies, using anomaly detection methods can be effective. These techniques are designed to identify rare events by learning patterns in the most common data and recognizing deviations.
- Example: Detecting rare heatwaves by identifying temperature anomalies from historical data.
Ensemble Methods
Combining multiple models through ensemble methods can enhance prediction accuracy and robustness, particularly when each model is trained on different subsets of the data or uses different approaches. This is also why gradient boosted trees are such a wildly popular method.
- Example: Using an ensemble of decision trees, neural networks, and statistical models to predict rare floods.
Active Learning
Active learning involves iteratively selecting the most informative data points for labelling and training. This approach maximizes the model’s performance with a limited amount of labelled data by focusing on the most challenging or uncertain samples.
- Example: Prioritizing the labelling of borderline weather patterns that are difficult to classify.
Collaborative Data Sharing
By fostering collaboration with other researchers, institutions, and organizations to share data, we can collectively gain access to more extensive and diverse datasets. This collaborative approach is particularly important for addressing data scarcity in rare events, fostering a sense of community and shared purpose in our research.
- Example: Sharing climate event data across international meteorological agencies.
Hybrid Modeling Approaches
Combining physical models with ML can improve predictions for rare phenomena. Physical models based on scientific principles can provide a solid foundation, while ML can enhance predictions by learning complex patterns.
- Example: Using a physical climate model to simulate potential hurricane paths and an ML model to refine the predictions based on historical data.
Class Balancing Techniques
In cases where the dataset is imbalanced, with significantly more common events than rare ones, class balancing techniques can help ensure the model pays sufficient attention to the rare events.
I usually recommend resampling techniques such as oversampling the minority class (duplicating rare event data) or undersampling the majority class (reducing common event data) to create a more balanced training dataset.
- Example: Balancing datasets for rare lightning strikes by oversampling instances of lightning and undersampling clear weather data.
Bayesian Methods
Bayesian methods provide a probabilistic modelling approach, particularly useful for making predictions under uncertainty, a common scenario with rare environmental phenomena.
More generally, we can use Bayesian Inference. By incorporating prior knowledge and continuously updating with new data, Bayesian models can improve predictions for rare events due to “expert knowledge.”
Specifically, we can use Bayesian Networks as an implementation. These graphical models represent the probabilistic relationships between variables, helping understand dependencies and improving predictions.
- Example: Using Bayesian networks to model the probability of rare seismic events based on geological and historical data.
Model Calibration
Model calibration involves adjusting the model’s probability estimates to better reflect the true likelihood of events, which is crucial when dealing with rare phenomena where accurate probability predictions are needed. One way is Platt Scaling, which is a method for transforming the outputs of a classification model into calibrated probabilities by fitting a logistic regression model. Alternatively, we can use Isotonic Regression, a non-parametric calibration method that fits a piecewise constant function to the predicted probabilities, ensuring they are monotonically increasing.
- Example: Calibrating the probability estimates of a model predicting extreme weather events to ensure that the predicted probabilities closely match the actual observed frequencies.
By leveraging these strategies, machine learning can be effectively applied to rare environmental phenomena, enhancing our ability to predict and mitigate the impacts of these events. Data augmentation, Bayesian methods, and model calibration ensure that models remain accurate and reliable, even with limited data. These approaches provide a robust framework for tackling the challenges of data scarcity and improving our understanding and response to rare occurrences in the natural world.
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Question of the Week
- What's the role of AI in achieving sustainable energy solutions?
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
- Please enjoy this distinguished gentleman.
- I genuinely wouldn’t mind a full playlist of this.
- This is way too funny. Happy Monday.
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!