That is also not correct anymore as Northern countries were reallocated to include the Baltics in UN as an example.

Ireland is not part of Schengen as well for example, it is because that is much harder to practically achieve for folks living on islands.

Ireland is not part of Schengen because the UK never was (and they wanted to keep the Common Travel Area).

Just for the context UBI kind of already exists in some countries but in a different form. If you are in Ireland and do not work you get the benefits provided you have somewhere to live and can pick them up in the post office (~200eur/week). The problem is that it solves only part of the issue because people have to live somewhere, and sure the gov will even contribute towards it (they do already) but somehow not enough is being built. The benefit is basically not enough to get the mortgage, nor is there housing available.

Not vr/ar but 3d visualisations and walkthroughs were a thing like 20 years ago. I did try to leverage it with the clients but what happened was that once people could see everything they had more opinions about how to change it. It helped me to get clients though. Sometimes it would go on for quite a ”few” iterations. I think it is more scalable not to do it, i.e. not to be on the cutting edge, unless the customer pays premium for it. I am pretty sure vr/ar has the same challenges where people are like “oh I thought the ceiling was higher, can we increase it by 2 inches and move the stair case a bit?” then you do it and something is wrong again.

I'd see that as evidence that the visualization is doing its job. It's much cheaper to discover "I wish the ceiling were 2" higher" before anyone starts building than after. The challenge is reducing the cost of each iteration. That's where newer tooling starts to matter.

It looks like the message here is “make sure to use $100k worth of Claude when doing any analysis or evaluation” and the given examples show that prior effort could have been improved or made faster. But to me 100k is an opportunity cost, and there is a possibility that these results are not reproducible, so spending it on some researcher or a grad student would buy you more in a long term. If it was 1k then sure it is worth throwing at a large problem space to find things, like using fuzzing.

Went through the comments here and there and one thing to note is that there was a question about who do you think should have won instead. This is a good question because it is possible that all submissions were like this or there were ones that looked just worse. It would be quite useful to know who came close as well in this case. If you knew which submissions were good you could have a process to revoke the prize and give it to someone else in case of fraud or negligence or similar.

Having said that it is also possible that the mistakes and claims were a human error, sure a lot gets ai generated these days but there is a chance in which case the accusation does not look so severe anymore.

Assuming this description is accurate, if they were all like this then none of them should have won. They should have all been disqualified and the organizers should have looked at themselves in a mirror for a long time.

I think the underlying problem here is that no single human brain has enough glycogen in reserve to thoughtfully process all the AI slop. It simply cannot be done by mortals.

I've noticed this over and over again with "professionals actually prefer LLM responses" studies. Typically the human generated responses seem better to me on a quick sample, but if I had to review 50 of them I'd probably start taking lazy shortcuts; using superficial language aptitude or factual comprehensiveness instead of critically reading.

It does seem like the human judges here might have given credit for e.g. a 20pg arXiv paper without actually reading it. I can blame them professionally but emotionally I have nothing but sympathy. I truly hate LLMs.

It was like a bunch of mythos scans through and through which then generated the reports for everyone to implement, not sure if in all orgs though. Mythos was great as it came from the top, i.e. a clear incentive. I think bug reporting otherwise does not reach the engineers unless an incident is raised by the customer care against a responsible team.

“think and grow rich” by n.hill is an interesting mention to say the least.

It might make even more sense once we get to the point of a wider use of encoding the data into dna. For now we have these few commercial players in the field that cad do it (eg look up dna microfactory for storage archiving), IIRC genomika was saying they can do an MB for a 100-200eur.

I did masters in a similar way, just to get some credentials and fill in the gaps and learn something new. There is an idealistic part to it which is quite romantic as you spend the nights learning and doing the assignments. The structure of such online based learning systems is great for a determined person. However the “other” part of such courses are cheating and ai use. It is depressing to know that the specific credentials prove little because of it. So the only valid signal is: this person did not quit and they know how to write a report, use references. You’d need to test them to fully validate the credential.