From an age perspective (but the crowd here will not like that): before I trusted myself I could always find it back so I don't need to save it. Now I can't anymore, but I don't care so much.
I am not so sure, but indeed it is perhaps also a sad realization.
You compare this to "a human" but also admit there is a high variation.
And, I would say there are a lot humans being paid ~=$3400 per month. Not for a single task, true, but for honestly for no value creating task at all. Just for their time.
So what about we think in terms of output rather than time?
Which also showed that you can make NNs weight agnostic and just let the architecture evolve using a GA.
Even though these approaches are cool and NEAT even is somewhat easier to implement than getting started with RL (at least that is what based on so many AI Youtubers starting with NEAT first) they didn't ever seem to fully take off. Although knowing about metaheuristics is still a good tool to know IMO.
A few weeks ago I was planning to design a model I could send to a local 3d printer to replace a broken piece in the house for which I knew it would be impossible to find something that would fit exactly.
I looked around through a couple of open source/free offerings and all found them frustrating. Either the focus on easy of use was too limiting, the focus was too much on blob, clay-like modeling rather than strong parametric models (many online tools), or they were too pushy to make you pay, or the UI was not intuitive (FreeCAD).
OpenSCAD was the one which allowed me to get the model done, and I loved the code-first, parametric-first approach and way of thinking. But that said I also found POV-Ray enjoyable to play around with around the 2000s. Build123D looks interesting as well, thanks for recommending that.
The major advantage of Build123D for your use case -- sending it to someone else to fabricate it -- is STEP output support.
This really expands your options for what you can make and who you can ask to make it. There are now some online fabrication places that will do CNC from mesh formats, but really the only way to have proper control is sending them a STEP file.
I follow RL from the sides (I have dabbled with it myself), and have seen some of the cool videos the article also lists. I think one of the key points (and a bit of a personal nitpick) the article makes is this:
> Thus far, every attempt at training a Trackmania-playing program has trained the program on one map at a time. As a result, no matter how well the network did on one track, it would have to be retrained - probably significantly retrained
This is a crucial aspect when talking about RL. Most of the Trackmania AI attempts focuses on a track at a time, which is not really a problem since they want to, given an individual track, outperform the best human racers.
However, it is this nuance that a lot of more business oriented users don't get when being sold on some fancy new RL project. In the real world (think self-driving cars), we typically want agents to be way more able to generalize.
Most of the RL techniques we have do rather well in these kinds of constrained environments (in a sense they eventually start overfitting on the given environment), but making them behave well in more varied environments is way harder. A lot of beginner RL tutorials also fail to make this very explicit, and will e.g. show how to train an agent to find the exit in a maze without ever trying it on a newly generated maze :).
Wow, this brought back memories. I could swear I wrote a blog post about this years ago but couldn't find it.
A quick search on the local file system revealed `vnccrawl/crawler.py` from 2016 [1] using what looks like a Shodan data dump and calling out to `vncviewer.exe`. I remember randomly logging into some instances and also seeing a lot of cool random systems, including a lot of them controlling industrial systems. Guess I never ended up writing that post.
One would think that on today's Internet it would take only a couple of seconds for those to get compromised, but obfuscation as security, perhaps?
[1]: A random tip from that file: Using a password of 12345678 gives access to way more 'weakly secure' instances.
This reminds me of a short story by Ken Liu, The Message, which details a xeno-archaeologist digging into a place full of radiation. The main character doesn't get the warning message until it is too late and almost loses his daughter.
Googling it now it seems at one point is was going to get adapted to film [1], but seems like that went nowhere.
v1 used a very limited (albeit very easy and already quite impressive) form of transfer learning, e.g. take a pretrained network's 1000dim vector outputs given a bunch of images belonging to three sets (since the original was trained on Imagenet), and then just use K-NN to predict what a set "new" image falls into.
v2 does actually finetune weights of a pretrained network. At the time, it was a nice showcase how fast fast JS ML libraries were evolving.
