With this release Catfood Earth is 20 years old! This update includes version 2023c of the Time Zone Database and the following bug fixes.
The National Weather Service changed one letter in the URL of their one hour precipitation weather radar product. It needs to be BOHA instead of BOHP. Presumably just checking that data consumers are paying attention? Weather radar is working again.
Not to be left out the Smithsonian Institution Global Vulcanism Program has decided to drop the www from their web site. The convention here is to redirect but they're content with just being unavailable at the former address. Recent volcanoes are working again as well.
The final fix is to the locations layer. Editing a location was crashing. This was due to a new format in the zoneinfo database that was not contemplated by the library that I use. As far as I can tell this isn't maintained any more since the death of CodePlex. While working on this update I started using GitHub Copilot, their AI assistant based on GPT 3.5. I was amazed at how helpful it was figuring out and then fixing this rather fiddly bug. The locations layer is back to normal, and I have regenerated all the time zone mapping as well.
By Robert Ellison. Updated on Wednesday, November 6, 2024.
If I'm going to be replaced with AI then I may as well be the person to do it. I need an AI Rob that I can be proud of and that's going to take some work.
My approach so far is to generate some training data. I've answered lots of questions in a spreadsheet. This is an ongoing project and there will be dot releases as I work towards a usable product (one that I can just plug into email or Teams). Probably this is going to require a mix of fine tuning and retrieval augmented generation (RAG). To start with I'm just fine tuning GPT 3.5 Turbo from OpenAI.
Fine tuning was painless. As usual the difficult part was randomly trying different versions of Python to find one that would coexist with some stubborn dependency (tiktoken in this case, which will live with Python 3.11 but is very unhappy with Python 3.12).
You can try this below - just leave a comment and Rob 2.0 will reply. Anything you post goes through the regular moderation system, this is just to stop spam. any legitimate questions are fair game (and likely to make it into the training corpus if the answer is no good!).
Due to safety systems it doesn't swear like the real thing. That might require a different model / corporate host at some point in the future. I'll update this post as I make progress.
Updated 2023-12-20 00:46:
I had most of a day spare today and so decided to get a little closer to my own personal singularity. Rob 2.1 is live and answering your questions in the comments below.
The first thing I did was add a few hundred more questions and answers to my training data set. I then fine tuned GPT 3.5 on the new data.
I wanted to get the LLM trinity - prompt, retrieval augmented generation (RAG) and fine turing. Initially I thought that I could just use the OpenAI assistant API to get there, and I got as far as coding the whole thing up before stubbing my toe on a harsh reality. It only supports retrieval for gpt-3.5-turbo-1106 and gpt-4-1106-preview. Hopefully this changes at some point but no way to get everything I need from assistants yet.
Not a big deal - I rolled up my sleeves (and also GitHub Copilot's sleeves) and added my own RAG based on the Q&A training data and refined my prompt to include the most relevant answer as well as some more specific instructions. It's pretty basic - whatever you ask is compared to the existing question library using cosine distance of OpenAI embeddings. Maybe I'll add a vector database if I have the patience to answer enough questions about myself, but a brute force in memory search works fine for now.
By Robert Ellison. Updated on Saturday, September 23, 2023.
Catfood Earth for Android now supports random locations. The slice of Earth displayed will change periodically throughout the day. You can still set a manual location or have Catfood Earth use your current location. Install from Google Play, existing users will get this update over the next few days.
Summer Solstice 2023 is at 14:58 UTC on June 21. The image above shows the exact moment of the Solstice as rendered in Catfood Earth. It's the official if not sartorial start of Summer in the Northern Hemisphere and Winter if you find yourself on the other side of the Equator.
By Robert Ellison. Updated on Saturday, November 18, 2023.
It usually takes me a few weeks to get a new website up and running. Last weekend I tried an experiment with Cloudflare Pages and generative AI.
I have wanted to find an excuse to test Pages for a while. It's a pretty awesome product. I'm not doing anything too fancy with it - I have a local generator app that creates the pages for my site. Committing to the right branch in git automatically deploys to Cloudflare's edge network. It seems to do the right thing with all the file types I've thrown at it so far. My only complaint at this point is that it doesn't handle subdirectories. Everything needs to hang off the root unless you want to write some code. I think this is possible with Cloudflare Workers but that's for another day.
The generative piece is automatically writing content for review and publication. For each generated page I'm creating a prompt to write the post, and then another prompt to summarize it for meta descriptions and referencing it from other pages. I also create an embedding to use for interlinking related posts. Finally I create a third prompt to gin up an appropriate image. The site generator stitches these together into HTML and as soon as I commit, the updates are live.
The site is not yet a work of art, and there is plenty to optimize and add, but the basic thing was working in a few hours. It's all ridiculously cheap as well. I'm more than a little frightened for Google given how much of this must be going on right now. And then the next generation of LLMs will be trained on the garbage produced by the current crop.
My super rapid site is called Shop Stories, collecting / dreaming takes of ecommerce heroics. I'll report back if anyone goes there.
Spring for the Northern Hemisphere, and Autumn south of the Equator, starts right now - 21:25 UTC on March 20, 2023. The image above shows the exact moment of the equinox in Catfood Earth.
(Published to the Fediverse as:
Vernal (Spring) Equinox 2023 #code#earth#equinox#spring#autumn#vernal Catfood Earth render of the exact moment of the Spring Equinox for 2023 (21:25 UTC on March 20, 2023).)
I'd like to make a time lapse of the moment when fog enters the Golden Gate and flows under the Golden Gate Bridge. It's surprisingly hard to know when conditions will be just right though. Often the weather is pleasant at my house while the fog is sneaking through and there is very little chance of me checking a webcam or satellite image. I decided to fix this about a year ago and started collecting data. The best bet seemed to be GOES-West CONUS - Band 2 which is a high resolution daylight satellite image that shows clouds and fog. I put together a Google Apps Script project to save an hourly snapshot and left if running. Here's a video of the data so far, zoomed in for a HD aspect ratio and scaled up a bit:
It's pretty obvious to me when conditions are just right. Could an ML model learn that this was about to happen from an image that was three hours older?
The first step was dividing thousands of images into two classes - frames where the fog would be perfect in three hours and frames where this was not going to happen. I built a little WPF tool to label the data (I don't use this often these days and every time I do I marvel at how the Image control has defaults that won't show the image FFS). This had the potential to be tedious so I built in some heuristics to flag likely candidates and then knocked out the false positives. Because the satellite images include clouds there is often white in the Golden Gate that is cloud cover rather than fog. At the end of the process I had two subfolders full of images to work with.
My goal this weekend was to get something working, and then refine every few months as I get more data. Right now I have 18 images that are in the Fog class and 7,539 that are NoFog. I also wanted this running on my blog, which is .NET 4.8 and will stay that way until I get a couple of weeks of forced bed rest. ML.NET says that it's based on .NET Standard and so should run anywhere.
Having local automl is very cool once you get it working. For large datasets this might not be a great option, but not having to wrangle with the cloud was also very appealing for this project.
Getting GPU training configured involved many gigabytes of installs. Get the latest Visual Studio 2022. Get the latest ML.NET model builder. Sign up for an NVIDIA developer account and install terrifyingly old and specific versions of CUDA and cuDNN. This last part was the worst because the CUDA installer wanted to downgrade my graphics driver, warned directly that this would cause problems and then claimed that it couldn't find a supported version of Visual Studio. I nervously unchecked everything that was already installed, and so far model builder has run fine and I don't seem to have caused any driver problems.
For image classification settings you can choose micro-accuracy (the default), macro-accuracy, logarithmic loss, or logarithmic loss reduction. Micro-accuracy is based on the contribution of all classes and unsurprisingly it's useless in this case as just predicting 'no' works very well overall. Maco-accuracy is the average of the accuracy of each class and this produced reasonable results for me. Possibly too good, I probably have some overfitting and will spend some time on that soon.
After training the model builder has an evaluate tab which is pretty worthless, at least for this model/case. You can spot check the prediction for specific images, and then there is one overall number for the performance of the model. I'm used to looking at precision and recall and it looks like I'll have to spend some time building separate tooling to do this. Hopefully this will improve in future versions.
At this point I have a .NET 6 console application that can make plausible looking predictions. Overall I'm very impressed with how easy it was to get this far.
Integrating with my blog though was very sad. After a lot of NuGet'ing and Googling I came to realize that ML.NET will not play nice with .NET 4.8, at least for image classification. Having dared to anger the NuGet gods I did a git reset --hard and called out to a new .NET 6 process to handle the classification. For my application I'm only running the prediction once per hour so I'm not bothered by performance. That .NET Standard claim proved to be unhelpful and I could have used just about anything.
The model is now running hourly. I have put up a dedicated page, Golden Gate Fog Prediction, with the latest forecast and plan to improve this over time. If this would be a useful tool for you please leave a comment below (right now it emails me when there is a positive prediction, it could potentially email a list of people).
Updated 2023-03-12 23:24:
After building some tooling to quantify this first model I have some hard metrics to add. Precision is 23%. This means there is a high rate of false positives. Recall is 78%. This means that when there really is fog the model does a pretty good job of predicting it. Overall the f1 score is 35% which is not great. In practice the model doesn't miss the condition I'm trying to detect often but it will send you out only to be disappointed most of the time. I'm not that surprised given how few positive cases I had to work with so far. My next steps are collecting more training data and looking more carefully at the labeling process to make sure I'm not missing some reasonable positive cases.
(Published to the Fediverse as:
Predicting when fog will flow through the Golden Gate using ML.NET #code#video#ml#fog Using Microsoft's AutoML in ML.NET to build an image classifier that predicts fog flowing under the Golden Gate Bridge.)
