đź’§ The puddle was in de-nile about not being a spooky lake
In this issue, we have personal podcast feeds, random forest exploration and another way to hack chatGPT to steal your info. We’ll look into AI for real-time natural disaster response. And definitely check out my Pythondeadlines updates; closing in on a huge milestone. Oh, and I’m officially officially Dr. Dramsch, even in Germany!
Late to the Party 🎉 is about insights into real-world AI without the hype.
Hello internet,
hear ye, hear ye, it’s a surprise late edition!
In this issue, we have personal podcast feeds, random forest exploration and another way to hack chatGPT to steal your info. We’ll look into AI for real-time natural disaster response. And definitely check out my Pythondeadlines updates; closing in on a huge milestone. Oh, and I’m officially officially Dr. Dramsch, even in Germany!
But let’s dive right in and enjoy more machine learning!
The Latest Fashion
- Apparently you can now create personal podcasts from articles with NotebookLM!
- This data visualisation lets you explore complicated random forests intuitively.
- Hackers have planted “false memories” in chatGPT that allow them to perpetually steal your personal info.
Worried these links might be sponsored? Fret no more. They’re all organic, as per my ethics.
My Current Obsession
This is completely vain; I apologise. However, in Germany, a PhD title is still considered pretty significant. To the point that when you complete your “Dr.”, you can have the state put it in your passport and ID cards. To the point that calling yourself Dr. when you don’t have the title is actually illegal and can be prosecuted. Long-time followers know that I did my PhD in Denmark, but I managed to get my title accredited, and my new Passport now officially says “Dr. Dramsch”. I know it’s a small thing, but it feels really nice and like my achievement is now officially valid.
You know that every couple of months, I am now struggling with feelings of burnout and overwhelm. It’s that time again. But I booked a vacation! I am escaping some of the November Gray and going on something called a “Liveaboard”. This isn’t really known outside the scuba diving community, but it’s essentially a vacation on a boat that gets you to far-out reefs you couldn’t easily access normally. It will be my first time, and I’m very excited (and a little terrified) to go!
Thing I Like
I wanted to treat myself, so I got one of these fancy room scents from Rituals, and it’s so lovely.
Hot off the Press
I made two videos about “haunted hallucinations” where I talk about creepy AI.
The first video talks about why we find AI images so incredibly creepy.
And the second video is about your right to not do TikTok dances forever.
In Case You Missed It
My 10-minute course on creativity with AI now has over 70 students and a nice positive review. How lovely!
On Socials
I posted about my colleagues improving ai-models to now run completely on open data from the ECMWF!
I shared about my data science class and that one made the rounds as well.
Threads really liked the prompt engineering guide and Mastodon liked graph-RAG.
Python Deadlines
I found new deadlines for PyCon Tanzania, PyCon Namibia, PyCon Italia, and PyCon Estonia.
The CfP for PyData Global was extended is ending soon-ish.
As you know, I’ve been working over the last few years to archive all the Python conferences I can find. This has now led to a pretty significant archive, and I think I found almost all conferences going back to 2002. I have added a little counter at the bottom of each page, as I am approaching 1,000 conferences soon. I also found a funny map bug that I fixed.
Machine Learning Insights
Last week I asked, What are the latest advancements in AI for real-time natural disaster response and management?, and here’s the gist of it:
We can see it happening right now; real-time disaster management is becoming more relevant as climate change progresses. But how can AI help in a real-time response to natural disasters? These innovations enhance early warning systems, improve response coordination, and revolutionise infrastructure monitoring. Let's explore the cutting-edge applications of AI in disaster management and their potential to save lives and mitigate damage.
Enhanced Early Warning Systems
AI-driven early warning systems could represent a pretty significant breakthrough in disaster preparedness. At the forefront of this innovation are:
- Weather Forecasting: AI models developed by companies like DeepMind and NVIDIA have dramatically improved weather prediction accuracy. With unprecedented precision, these systems can forecast severe events such as thunderstorms, hurricanes, and tornadoes. Although there is room to improve, which I am currently involved in.
- Wildfire Detection: Companies such as Pano AI and OroraTech are pioneering real-time wildfire detection using AI to analyse satellite and drone data. These systems can identify potential fire outbreaks faster than ever before, allowing for rapid response and containment.
- Earthquake Prediction: While still in the early stages, AI shows promise in earthquake prediction. Researchers use machine learning algorithms to analyse seismic data and identify patterns that may precede major quakes, potentially providing crucial early warnings. It is still in very localised tests, but this problem that was considered impossible may show its first cracks!
Advanced Flood Monitoring and Prediction
AI is revolutionising flood monitoring and prediction through innovative applications of satellite imagery and data analysis:
- Cloud-Penetrating Analysis: AI systems can now predict floods even in regions obscured by cloud cover. This is a significant advancement for areas prone to heavy rainfall and cloud formation during flood events. Who would've thought these coincide?
- Integrated Data Systems: NASA's HydroSAR system, augmented with AI, has significantly improved flood detection in remote regions like the Hindu Kush Himalayas. This integration of satellite and radar data provides emergency responders with a clearer picture of affected areas, enabling more effective aid deployment.
- Urban Flood Modeling: AI is used to create detailed urban flood models, considering factors like drainage systems, land use, and topography to predict flood patterns in cities more accurately.
Improved Disaster Communication and Infrastructure Monitoring
AI is enhancing communication during disasters and helping to maintain critical infrastructure:
- Multilingual AI Chatbots: Systems like UNESCO's AI-powered chatbot can disseminate accurate, multilingual alerts during emergencies. This technology ensures that vital information reaches populations in high-risk areas quickly and in their native languages.
- Infrastructure Assessment: Using drone footage, Companies like Ericsson use AI to assess critical infrastructure, such as radio towers. This helps ensure the resilience of communication networks during disasters.
- Social Media Analysis: AI algorithms are being developed to analyse social media posts during disasters, helping authorities identify areas in need of immediate assistance and track the spread of misinformation.
Multi-Hazard Management and AI Integration
The field is moving towards more comprehensive disaster management solutions:
- Multi-Hazard Monitoring: Projects under the Medewsa are show-casing AI systems capable of simultaneously monitoring multiple types of disasters. These systems use geodetic data to improve early detection capabilities for various hazards, from floods to earthquakes and landslides.
- Integrated Response Platforms: AI is being used to create integrated platforms that combine data from various sources (satellites, ground sensors, social media) to provide a comprehensive view of disaster situations, enabling more coordinated response efforts.
Challenges and Future Directions
While AI is making significant strides in disaster management, several challenges remain:
- Data Standardisation: There's a pressing need for standardised AI protocols across regions to facilitate seamless international disaster response.
- Ethical Considerations: As AI systems become more integral to disaster response, ensuring fairness and addressing potential biases in data and algorithms is crucial.
- Infrastructure in Developing Countries: Implementing advanced AI systems in developing countries with limited technological infrastructure presents ongoing challenges.
- Human-AI Collaboration: Developing effective ways for human responders to work alongside AI systems remains an important area of focus.
Conclusion
The integration of AI into natural disaster response and management is revolutionising our ability to predict, prepare for, and respond to catastrophic events. From more accurate early warning systems to enhanced communication and multi-hazard management, AI can be an invaluable tool in saving lives and mitigating the impacts of natural disasters. But it's essential to remember that AI systems should not be the final decision layer but be used to inform the decisions of humans. Moreover, we need to make sure that trusted and publicly accountable entities operate disaster warning systems. As technology advances, AI's potential to further improve disaster management is immense, promising a future where we can respond to nature's challenges with unprecedented speed and efficiency.
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Question of the Week
- What are the limitations of current AI models in predicting long-term climate change impacts?
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
- I couldn’t believe this isn’t cardboard…
- It’s spooky lake month, to those who celebrate!
- John Oliver is still working through the rage
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!