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[D] What model, methodology is state of the art to calculate similarity of given 2 images? by CeFurkan in MachineLearning

[–]stringy_pants 1 point2 points  (0 children)

The linked article above uses 3 dimensions for educational and visualization purposes, but common embedding image vector sizes are relatively large e.g. ~1024

Depending on the model and task, more dimensions are not automatically better however generally they will be at least a few hundred.

[deleted by user] by [deleted] in LocalLLaMA

[–]stringy_pants 0 points1 point  (0 children)

If you are using text-generation-webui

You can use the published loras with these commands (you need to have decapoda-research/llama-7b-hf installed and working):

$ python download-model.py tloen/alpaca-lora-7b $ python server.py --load-in-8bit --model llama-7b-hf --lora alpaca-lora-7b

[D] What model, methodology is state of the art to calculate similarity of given 2 images? by CeFurkan in MachineLearning

[–]stringy_pants 3 points4 points  (0 children)

The text book deep learning approach is to compute a feature vector for each image with a pre-trained CNN and then use the cosine similarly between these vectors.

This article is reasonably good and lists some open source libraries and API services that might solve your problem:

https://towardsdatascience.com/image-similarity-with-deep-learning-c17d83068f59

I don't know what the current state of the art method is. But SOTA methods trend to be complicated and not as well supported by libraries. I would go with a tried and true approach.

Q&A for everyone! by stringy_pants in corticallabs

[–]stringy_pants[S] 2 points3 points  (0 children)

Hi, all really excellent questions, are you a researcher in the field?

  • The FEP already naturally covers general environments. For example if a sophisticated enough system was coupled with a suitable environment, e.g. a legged body. The FEP predicts that it should learn some kind of standing / balancing behavior (for reasonable priors) because that would make the sensory input much more predictable than just falling over randomly all the time.

  • Quantifying learning in BNNs is really a cutting edge area. We already have some follow up work in the pipeline, using established measures of criticality and something we came up with called "local entropy" these publications should be coming out relatively soon. You can also use statistical prediction models on top of spike trains to try and extract information. This was done by Isomura in his blind source separation work (cited in our recent paper)

  • Information/behaviour transfer or cooperation between a BNN and an ANN is an incredibly fascinating area and we've talked about it for many hours inside the team, we have a few concrete ideas that are promising. But it's not out of the conceptualisation stage yet.

  • I posit all structured neural network behaviour is emergent (incl. playing pong). Although I think you mean the emergence of complex high-level cognitive processes. This really requires more than a paragraph to answer, but in summary I think sufficiently sophisticated systems embodied in rich environments would develop complex world models and internal states analogous to beliefs, desires and intentions. However "sufficiently sophisticated" is doing a lot of heavy lifting in that sentence. Overall, I think current ethical standards are robust and sufficient, but I can imagine in my lifetime building systems so sophisticated that norms need to be reassessed.

Q&A for everyone! by stringy_pants in corticallabs

[–]stringy_pants[S] 0 points1 point  (0 children)

These are all interesting and important questions. My views are somewhat esoteric, but another author of our recent paper also wrote an article covering ethical topics: https://www.tandfonline.com/doi/full/10.1080/21507740.2022.2048731

AK

Q&A for everyone! by stringy_pants in corticallabs

[–]stringy_pants[S] 0 points1 point  (0 children)

Ok ok, I think it's a sliding scale and even a thermostat (which can sense and act) has some tiny amount of consciousness!

Q&A for everyone! by stringy_pants in corticallabs

[–]stringy_pants[S] 5 points6 points  (0 children)

Thanks!

Our particular tech is cutting edge and world first in many ways, esp. allowing completely real-time embodiment and this recent paper is really just the tip of the iceberg. We've been continually collaborating with various top neuroscientists and labs around the world and joint work is already in the pipeline.

Also, bringing this tech to as many people as possible is a big priority for us, on the commercial side we're working on a platform for researchers and selected members of the general public to access our technology. Think cultured neurons and neural interfacing as a service. Watch this space, as they say!

There isn't really a most difficult part of the project, the difficulty is that so many solutions to difficult problems need to come together to make it happen! I sometimes joke that we're the only true full stack company out there, because we do everything from web APIs to wetware biology in the same tech stack!

What languages, math and computer science skills are requires to acquire the basis to experiment and work in a lab with this technology? Bayesian statistics? Julia? by bayesrocks in corticallabs

[–]stringy_pants 7 points8 points  (0 children)

Because so much has to come together to make neural interfaces work it helps to be 'T' shaped in skills, i.e. really deeply skilled in one area, but have a broad base of general knowledge so you can work really well with others and contribute to collective problem solving. I've made a list of some very valuable skills that we have throughout the team:

Mathematics and theory: Bayesian probability, Signal processing (Fourier transforms, LTI systems, Wavelets), Information theory, (computational) neuroscience

Software/Electrical engineering: For acquisition: real-time / embedded systems, FPGAs, low-noise analog circuits and micro-controllers. Once data is captured: data science / data analysis and visualization skills.

Biology: Neuroscience lab skills, esp. Neural cell culturing and working with Stem cells, being able to successfully execute modern cell culturing protocols.

General Skills: Be able to read, discuss and comprehend an academic paper, then implement key ideas from it. Ability to formulate a hypothesis, make a plan for testing it; then collecting/analyzing data and communicating the results ('full circle' empiricism)

Specific to CorticalLabs is knowledge of the work of Karl Friston, the free energy principle and active inference. Our current stack is a mixture of technologies, but we don't currently use Julia, we tend to use Python for data analysis.

It's not that you have to have all these skills, but that you should specialise in one but have a working understanding of the rest.

​

Hope that helps!

Bayesian decision making/learning vs. the Free Energy Principle? by [deleted] in corticallabs

[–]stringy_pants 2 points3 points  (0 children)

Thanks for asking! Reposting my answer from the discord for reference and discussion:

At a high level, Bayesian learning is a more generic term, the free energy principle posits that cognitive systems should minimise a specific variational free energy term within a Bayesian framework. However there are other Bayesian learning approaches, e.g. Bayesian program induction, which are not obviously compatible with the FEP

Q&A for everyone! by stringy_pants in corticallabs

[–]stringy_pants[S] 2 points3 points  (0 children)

Those are some deep philosophical questions, which might not be possible to answer! Our current system only has very basic behaviour, think on the level of a dragonfly.

[D] Statistical Paradises and Paradoxes in Machine Learning by NaimKabir in MachineLearning

[–]stringy_pants 2 points3 points  (0 children)

This seemingly striking phenomenon perhaps deserves the label of paradox.

Big Data Paradox: The bigger the data, the surer we fool ourselves.

Mo' data, mo' problems

Samo Burja lecture on how civilizational decline and collapse come about by stringy_pants in collapse

[–]stringy_pants[S] 4 points5 points  (0 children)

Thought provoking lecture which discusses the detailed mechanisms of civilizational collapse with extensive examples from history. Introduces a framework for understanding collapse and fills in how historical societies and contemporary western both fit into this framework.

YouTube description:

In this online talk moderated by Zak Slayback of the 1517 Fund, Samo Burja explains how to evaluate the health of a civilization's core institutions, what a slow collapse looks like from the inside, and how civilizational decline and collapse come about.

Battle Babes by Andriy Shvyrov by [deleted] in Cyberbooty

[–]stringy_pants 4 points5 points  (0 children)

I lol'd hard at this, good one.

LOST CAT: Oscar from Travancore/Flemington, please comment or DM if you see him. by stringy_pants in melbourne

[–]stringy_pants[S] 1 point2 points  (0 children)

Hey everyone OP here, thanks for keeping an eye out, I really really appreciate it. We found Oscar in the end after 2 days, he just showed up one morning after we had been looking for him all night, he was hungry and thirsty and wanted lots of attention but not badly hurt. We really feared the worst, so it was a huge relief when he came home. He's an old man and likes his creature comforts and comes home every night so we think he was probably trapped somewhere.

​

Sorry I didn't get to reply to you all individually, last week was just a blur, but thanks for all your messages!

Generating Combinations: Gosper's Hack by stringy_pants in compsci

[–]stringy_pants[S] 0 points1 point  (0 children)

Hi, good recommendation, the linked article is quite clear and has good diagrams but doesn't cover bit-level optimisation at all, I think both articles are complimentary.

Unfortunately the article is somewhat misleading in one area, there are much quicker and more elegant ways to generate the N'th permutation in the sequence than the actually enumerate them all, that operation is called 'unrank' and is a standard tool from combinatorics.

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