?? Bread so lazy, it's always loafin' around

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In this issue, we have an OpenAIs video generation model, helpful code snippets, and CVs rendered from a YAML file. I talk about fairness in AI, circular plots, and a bunch of fun things I’m up to.

This is "Light to the Party". All links and extra content can be found in the full issue. Want the latest in your inbox? Join 1111+ other curious minds.

Let’s dive right into some fascinating machine learning!

The Latest Fashion

• OpenAI hypes up their Video Generation model Sora as a “world model.”
• Some of Sebastian Raschka’s helpful code snippets for ML
• I like this idea of rendering your CV from a YAML file!

Worried these links might be sponsored? Fret no more. They’re all organic, as per my ethics.

My Current Obsession

I accidentally hyperfocused and started collecting Python conference archives from all the way to the beginning, like PyCon US. It’s probably entirely useless for everyone, but it’s kinda of fun having those collected. Trying for others, but many Python conferences were announced on Listserves back in the day. Yes, PyCon is that old…

This week, it was announced that I will be a co-chair of the Working Group on Modeling for the ITU/WMO/UNEP Focus Group on AI for Natural Disaster Management, which is moving towards becoming a global initiative.

Next week, we’re holding the big machine learning training at ECMWF, so that takes up all my bandwidth at the moment. I still have some lectures to finalize. (And with finalize, I mean start, of course…)

Hot off the Press

In Case You Missed It

Recently, my post on VSCode Extensions has been resurfacing. I should probably update it…

On Socials

People seem to be struggling with git!

My open PhD thesis is also quite popular!

Python Deadlines

I found Python fwdays, which closes in four days.

I’ve been doing a ton of work on the backend, updating Ruby and Jekyll and making the new Series feature robust. There are always those “cool things” that don’t see the spotlight but are necessary…

Machine Learning Insights

Last week, I asked, What methods do you recommend for ensuring fairness in AI algorithms, especially in high-stakes scenarios? and here’s the gist of it:

Ensuring fairness in AI algorithms, particularly in high-stakes scenarios such as healthcare, criminal justice, and finance, is not just a matter of preference but a critical necessity to prevent the potential harm of bias and discrimination. Here are several recommended methods to promote fairness:

• Diverse Data Collection: The first step towards fairness is to ensure that the data used to train AI models is diverse and representative of all groups affected by the algorithm. Taking this step involves actively seeking out and including underrepresented groups in the data. Interestingly, under-sampled data is a primary source of bias in meteorological data.
• Bias Detection and Mitigation Techniques: Implementing techniques to detect and mitigate bias in AI models is crucial. This can involve statistical methods to identify disparities in model performance across different groups and adjust the model or the data to reduce these disparities without pulling a Google Gemini and over-correcting.
• Fairness Metrics Evaluation: Utilizing various fairness metrics can help assess whether an AI model is treating all groups fairly. Some common fairness metrics include equality of opportunity, demographic parity, and predictive parity. The choice of metric should align with the specific notion of fairness that is most relevant to the scenario.
• Regular Auditing: Third-party audits of AI systems can help ensure that they continue to operate fairly over time. This involves technical evaluations of the models, their predictions, and broader impact assessments to understand how the AI system affects different groups. Possibly, this may even be mandatory, seeing as the EU AI Act just passed.
• Explainability and Transparency: Using explainable AI makes it easier to understand decisions and identify and correct biases. This involves developing models that explain their decisions or using techniques to interpret complex models.
• Ethical and Legal Frameworks: Developing and adhering to ethical guidelines and legal frameworks that mandate fairness in AI systems can provide a structured approach to addressing fairness. This includes both internal policies within organizations and external regulations like the EU AI Act.
• Stakeholder Engagement: Engaging with and involving stakeholders, including those directly affected by the AI system, is not just a suggestion but a crucial part of the solution. Their insights into potential biases and fairness concerns are invaluable. This can also include collaboration with experts in ethics, sociology, and domain-specific areas.
• Multi-disciplinary Approach: Tackling fairness in AI requires a multi-disciplinary approach, combining expertise from machine learning, social sciences, ethics, and domain-specific knowledge. This can help ensure that fairness measures are technically sound and socially relevant.

In high-stakes scenarios, where the consequences of unfair decisions can be particularly severe, these methods should be implemented with extra care and rigour. It’s crucial to understand that ensuring fairness is not a one-time task but an ongoing process, requiring continuous effort as models evolve and our understanding of fairness deepens. Your commitment to this process is vital.

This is "Light to the Party". All links and extra content can be found in the full issue. Want the latest in your inbox? Join 1111+ other curious minds.

Data Stories

Some visualizations thrive from being continuous.

pyCirclize makes this happen with different interfaces to create circular plots!

Of course, it’s not always the best choice.

But when it is…

It thrives!

Question of the Week

• How do you address the challenge of integrating diverse data types (like satellite imagery and ground sensor data) in ML models?

Post them and tag Dr. Jesper Dramsch . I’d love to see what you come up with. Then, I can include them in the next issue!

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