Raphaellll

I did a project that attempts to generate these types of instructions: https://map2seq.schumann.pub/nllni/demo/

You can simply pre-render them and store the png tiles in a folder. The folder structure and naming has to be such that e.g. leaflet understands it. Did this for a research demo here: https://map2seq.schumann.pub/nllni/demo/

"hallucination" was coined in the context of text generating RNNs. Specifically in this blog post by Karpathy in 2015: https://karpathy.github.io/2015/05/21/rnn-effectiveness/

1-2 seconds in Overpass which also uses OSM:

https://overpass-turbo.eu/s/1NTy

Yes, it's far from perfect. We currently work on augmenting the prompt with e.g. relevant key-value pairs extracted from https://wiki.openstreetmap.org/wiki/Map_features

we systematically tested this: https://arxiv.org/pdf/2308.16060.pdf

Tell me more about that coal burning record in Germany.

You can always argue that the model has seen some variation of a given problem. The question is if there are problems that are not a variation of something that already exists. How often do you encounter truly novel problems in your life?

There is a paper?

I get motion sick hearing brown noise. I associate it with being on a plane or train but I am sitting still, so it feels off and I get dizzy.

It's really a hit and miss. This [1] book also came as print-on-demand but looks perfectly fine. Good layout and clean colors.

[1] https://mml-book.github.io/book/mml-book.pdf

I actually bought this as a physical book on Amazon. Naturally it came as a print-on-demand book. Unfortunately it has many problems in this format. E.g. the lack of margins makes it hard to read the end of sentences towards the gutter. Also some text is pushed into each other. Not sure what source file format you have to provide to Amazon, but it's certainly not the pdf provided in the repo.

Edit:

It seems the overlapping text also occurs on some pdf readers: https://github.com/BoltzmannEntropy/interviews.ai/issues/2

Why is this interesting?

hunter2

It’s basically how large language models like GTP-3 work. They simply predict the next most probable word and suddenly you can prompt them to give you answers to rather intelligent tasks.

Yes, I got this. Just wanted to point out that a particular consequence of extrapolating would be that players can see enemys around corners. You already mention that the game would be unplayable if players get teleported every few ms, and above just describes why, aside from aesthetics, makes it unplayable.

Another problem of predicting the movement of other players is that if they stop before you can see them, the trajectory would be interpolated further such that you can see them around the corner for a split second, knowing they are waiting there.

By the time of the Turing Award, LSTMs were still state of the art in NLP. Arguably with the use of attention which came from Bengios lab.

I always wondered why it is possible to simply switch off a stolen phone. If it would require the PIN to switch it off you have the remaining battery time to find your phone.

If someone is looking for a YikYak replacement, consider Jodel

You can highly customize the Google Maps javascript service to load and connect you custom panoramas hosted on your own server.

This is again deep linked to a specific test page.

The footage from inside Italien hospitals a month ago shows most patients lying on their stomach.

Starting at 1min https://youtu.be/rfkbv_WQtn0

This was (is?) also possible with Lyft. When I was interning in the US, my visa sponsor sent me a SIM card that they clearly reused several times a year. Opening the Lyft app with this SIM automatically logged me in to the attached account. I didn’t noticed this and took a 70$ trip from SF to SV. Next morning I realized it wasn’t my account and credit card details. Wrote to Lyft support but never heard back. It wasn’t even possible to log out of this account and create a new one.

Did you use any library?

My experience is that this is only shared by people on LinkedIn who have no clue what they are talking about.

Edit distance allows for insertion, deletion and substitution. Some allow custom cost for substitution (e.g. characters closer together on a keyboard have lower substitution cost than other; for typo normalization). What I describe in my comment is to use the cosine similarity between learned character embeddings as substitution costs.

I once trained unsupervised character embeddings and used levenshtein distance with embedding cosine as character replacement weights. And it worked better as a similarity metric than soundex

LSTM 1997

https://dl.acm.org/citation.cfm?id=1246450