I don't think I understand what you mean by search algorithm. The part where I generate the map is already done. I want to do the part where the character chooses if he heals, flee or attack depending on various situation so I thought RL was good for this.

Why not use standard search algorithms for this? Why do you need to use reinforcement learning?

The arms race has been going for over a decade now...

questionnaire to Reddit https://docs.google.com/forms/d/e/1FAIpQLSekbvorz3RUhlDd1JqC2P8n6E9dTU8CWdugEJMcyd-pqUpPDw/viewform?usp=sf_link

Possible solution:

• train minirocket/hydra, which were designed for time series classification, on the labelled dataset (probably as four one-vs-many problems, eg s1 vs the rest, s2 vs the rest etc)
• you'll get sets of 1D convolutional kernels; these can be convolved with time series of any length
• only one of these should 'fire' strongly for each different heartbeat phase, so you should get univariate signals for each phase
• convolve these kernel sets with your unsegmented data
• segment the data based on the strongest signal corresponding to the relevant phase of the heartbeat.

You may need to apply some transformations to the signals to get this to work well though (eg softmax &/ smoothing, or some kind of changepoint detection, which I don't know much about).

All data used in this paper are stored as high-resolution FILTERBANK and HDF5 format collected and generated from observations by the Robert C. Byrd Green Bank Telescope, which are available through the Breakthrough Listen Open Data Archive at http://seti.berkeley.edu/opendata.

Do we have large data sets for alien signals to train model?

Well done, little AI!

we got front-row seats to this race and a chance to participate, +1 great time to be alive

nice find! would be helpful, as well, to compare with similar papers from 2022 that this paper cites, but did not compare to in results section

("We note that our work is concurrent with Chen et al. (2022) and Gao et al. (2022), both generating the reasoning chain in Python code and calling a Python interpreter to derive the answer. While we do not compare with them empirically since they are not yet published...")

Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks (Chen)
https://arxiv.org/abs/2211.12588

PAL: Program-aided Language Models (Gao)
https://arxiv.org/abs/2211.10435

I'm wondering too. We'll see if these repeat. It would be great if they did. I thought this is a cool application of AI/machine learning though.

I wonder what’s the probability that these signals were naturally occurring

Remember we look at where it will be a few papers down the road.

Hold on to your papers!

Man, i feel like we're living the beginning an arms race of AI.

What a time to be alive! ( Like one of my favorite YouTubers would say)

Possibly not the ideal solution but I would suggest taking sections of the spectrogram as images (perhaps with overlap) and feeding that into a multi-label classifier. If you’re after a bounding box then the upper and lower bounds should be apparent based on the location of your classes within the spectrogram i.e. sound intensity occurs at similar frequency. If transfer learning from a general image model I would advise against using false colour to generate the three channels and instead would generate different types of spectrograms (Reassignment method/Multi-tapered/etc.) Due to the nature of spectrograms you don’t really want scale invariance so segmentation models that use feature pyramids can be problematic. I found decent success using Compact Convolutional Transformers but that may not be what you need for your task.

Amazing, thanks for posting.

which approach in particular? I am also interested, can you share the reference?

AFAIK neural networks are best for modelling a function of some parameters. In contrast regime detection in financial systems prefer Gradient-Boosted Trees, Random Forests and markov chains. Autoregressive models such as ARMA, ARIMA and GARCH utilise regression, while game regression tests favour reinforcement learning techniques.

It depends on the application basically.

I just worked on a quartet search tree project to create optimal evolutionary trees. So, not dead yet!

Thank you for the update

thank you so much! I will drop you a message once I'm at my desk.

No problem usually when I point out this people call me names and block me. Thanks random internet person.

You're right, that was out of place, I apologize.

What crawled up your butt?

OP asked if ML was going away in place of AI.

My response is that many people still use linear regressions for problems.

Idiot responds hope they stop. That’s like saying we shouldn’t use cash because credit cards or phone wallets are better.

Underlying many ML and AI models are regression models. That’s all I said. Nothing about reducing everything to its basic parts. But something bothered you enough to basically insult me and make assumptions. Did someone like that make you feel inadequate enough to harass strangers?

Dare I say you must be fun at parties and before you respond back. Yeah I know Im not.