🪱 Saw two silk worms racing, but it ended in a tie
Late to the Party 🎉 is about insights into real-world AI without the hype.
Hello internet,
this week just flew by like nothing! Let’s relax with some lovely machine learning!
The Latest Fashion
- Marimo is a new twist on Python notebooks for reproducible and reactive execution that works with git!
- Stephan Rasp talked about ML in data assimilation in his talk
- Distilling the whisper model gives a 6x speed-up at 50% of the size within a 1% error of the big model.
Worried these links might be sponsored? Fret no more. They’re all organic, as per my ethics.
My Current Obsession
I learned about recursive DNS and tried implementing one with Unbound. That was a fun little experiment and seems to be working quite well! It’s a privacy-oriented way to surf the web instead of asking Google or Cloudflare where a website is actually located when we type in something like a late.email. I love learning about these things!
I’ve added Gmail-specific settings to my Guide to ensure you get these emails every week!
In Germany, scary things are happening behind closed doors. An investigative journalist collective has infiltrated a clandestine group of neo-nazis, far-right organisers, including AfD politicians, doctors, lawyers and rich investors. They were meeting to (and it sounds unbelievable to write this in 2024) discuss a “masterplan” to deport anyone in Germany who is “not of German heritage”, including those with German passports. I have certainly heard that story before. The bombshell report comes with pictures, names and a detailed timeline of what happened over 100 protests across Germany just this weekend. The protest in the city where I spent my formative years, Hamburg, was so large, with over 150,000 people, that it had to be stopped at some point to maintain the safety of the protestors. I will be in attendance at a protest tomorrow, of course, despite the freezing temperatures, ice and snow. Moreover, I signed the petition to ban the petition to have the Supreme Court evaluate the constitutionality of the AfD; please feel free to sign it as well.
Thing I Like
I bought the overshoe snow spikes, which have been a lifesaver with the current weather!
Hot off the Press
I wrote about my predictions for generative AI in 2024. I based it on an interesting question by Ian Oszvald, so it’s worth a look!
Lately, I have been thinking about the security of operational systems and LLMs. So how would you get involved with hacking LLMs?
In Case You Missed It
As the ECMWF is hiring (again, yes… again!), my blog post about finding my job and the hiring process at ECMWF is picking up some wind!
On Socials
My post about the book all of you have been getting for free was quite popular!
And I posted my interview with Daliana Liu again on the Data Scientist Show and people seemed to be enjoying it.
I also posted the announcement of the new AIFS, and that took off quite significantly!
Python Deadlines
I found Geopython and the deadline is right tomorrow!
I also found the conferences Europython, Swiss Python Summit, and Kiwi Pycon, but their CfP isn’t posted yet.
As for deadlines coming up, we have PyCon Namibia ending today(!) and Geopython tomorrow!
Machine Learning Insights
Last week, I asked, Can you elaborate on the concept of attention mechanisms in neural networks and their impact on model interpretability? here’s the gist of it:
Attention mechanisms in neural networks have become a cornerstone of modern machine learning.
To understand their impact, let’s first delve into what they are. Then, we’ll explore their influence on model interpretability.
What are Attention Mechanisms?
At its core, an attention mechanism allows a neural network to focus on different parts of its input for a given task. Imagine a spotlight that shines brighter on the most relevant parts of the data while dimming on less relevant areas. The exciting part is that we often use self-attention, so the network learns to focus on the important parts of the input data based on the input data itself!
In the context of NLP, when a model processes a sentence, it doesn’t give equal weight to each word. Instead, it learns to pay more attention to specific words crucial for understanding the sentence’s meaning in a given context.
There are various types of attention mechanisms, like additive attention, multiplicative or dot-product attention, and multi-head attention (popularised by the Transformer model).
Impact on Model Interpretability
- Enhanced Understanding: Attention mechanisms can make neural networks more interpretable. By examining the attention weights, we can get a sense of which parts of the input data the model is focusing on. This can be interpreted as getting a glimpse into the priorities of the model. It is important to remember that we often use multi-head attention, so the attention mechanism works on multiple levels, and this complexity limits the interpretability of the model.
- Application Example: In text translation, attention weights can reveal which words in the source sentence had the most impact on generating a word in the translated sentence. Investigating this connection can be instrumental in debugging and improving models.
- Limitations: However, interpretability via attention should be approached with caution. High attention weights don’t always equate to high importance in decision-making. There’s ongoing research to understand better and interpret these weights.
- Earth Science Application: In meteorology, attention mechanisms can be used in models processing satellite images, weather patterns, or climate data. For instance, a model might learn to pay more attention to cloud formations or sea surface temperatures when predicting a weather event, providing insights into what features are most predictive of certain weather conditions. We successfully used a transformer U-net to post-process weather ensemble forecasts to give a concrete example.
- Broader Impacts: Beyond interpretability, attention mechanisms also generally improve the performance of neural networks by allowing them to dynamically focus on the most relevant information, leading to more accurate and efficient models. Not to spoil the AIFS blog post, but we are now using graph transformers!
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Question of the Week
- How do you approach the challenge of explainability in complex machine learning models when presenting to domain experts?
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
- A massive scam happened in the gaming world: A Day to Remember
- How would it look like to play Clue in real life?
- Michelle Khare goes to Santa School
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!