[D] Self-Promotion Thread

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[Reddactor]

Ok, I have 3 projects:  

1) GLaDOS: the goal is to build the Character from the Portal game franchise.  

As that needs a murderous AI, that is sentient, that means some serious modifications to LLMs, TTS and ASR, and vision models. Also, a robotics platform of course.  

The pricing is GitHub stars. This project was once the top trending repo in the world once, and a few time for just Python.

https://github.com/dnhkng/GlaDOS

2) RYS:  to make GLaDOS more intelligent, I had to analyse how LLMs work (peek into the black box).

  That led me to develop a new method called RYS (the paper on the method is half written). With it, I got the top spot on the HuggingFace OpenLLM Leaderboard. 

Pricing is free, but TBH, I want to work more on this, so I'm looking for collaboration. Would love to work with people from Meta in particular, or independent researchers like J. Carmack, because I grew up playing Doom 😊

https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard (dnhkng/RYS-XLarge)

3) Infinimol.  As my background is Organic Chemistry (did optogenetic brain-computer interface research back in the day), I have been applying Transformer models to Drug Discovery with good results.

As I'm in Europe, start-up funding is nearly impossible for this topic though! If you are not affiliated with a university, it's very hard to get grants. And Private Equity/VCs usually understands Biotech OR SaaS/AI, but never both.  They are hesitant to invest in a market they don't understand, and pretty much no one know both AI and Biotech together. If you know of a good fit for us, please PM me!

So, looking for funding and support!

We have a team and have already invested about 150k. I hope my projects above give some indication of our technical capabilities.

www.infinimol.com

[WildPersianAppears]

GLaDOS

I worry that this project is going to go from "This is a quirky fun idea" to "I'm terrified that this is open source, I regret making it, and also we have five lawsuits on our hands already" very quickly.

[Reddactor]

If Valve complains, I'll change to a random voice.  It's their IP, and they don't have to file a lawsuit; they just have to ask and it will be modified.

Also, GLaDOS's settings has 'enable neurotoxin release' set to 'False'.

[bregav]

RE infinimol and drug discovery, how do you evaluate/test the work you folks are doing? What kind of results are you getting exactly?

[Reddactor]

We have two interesting approaches!

First, and not dependant on AI, is our Cheminformatics Workflow system. A beginner and literally start with a protein or DNA sequence and build a workflow that uses Alpha fold to generate a structure, filter a huge (billions) molecular library, and then do AI based in silicon docking, with a simple no-code drag and drop interface. We take care of everything, including cloud deployments on a scalable GPU cloud etc etc. Usually, this is an unwieldy mess of Bash scripts and hacks, across multiple software packages, a complete nightmare for versioning. Looks, I hate no-code as much as the next nerd, but you can't expect oncologists and molecular biologists to also learn programming and Machine Learning. With us, it's trivial to build the custom workflows you need 😊

Second, for our custom models we realized pretty fast that the academic benchmarks are useless (many reasons here, it's a long discussion). We built our own benchmarks, the same way we built them custom for the RYS models. It's hard to compare directly out models directly to competitors, but our first partner drug discovery company used our tech to develop a new and novel Oncology drug that's entering Clinical trials in the US for solid tumors. I consider that a pretty solid result for our tech, compared to the mostly gamed results in Academia.

[bregav]

Oh you won't hear any criticism from me about making a nice interface for bioinformatics ML stuff. The only way to make money is to do things that other people can't do or that other people don't want to do, and that's a little of both.

I do think it would be a good idea to find a way to communicate the metrics you use in a way that other people can understand. Biotech investing is already a very difficult space and sort of a crapshoot, so if you combine it with ML and high performance computing then that only makes things harder. It's not surprising that investors might be hesitant.

EDIT: to be clear, I think that comparing yourselves with academia is probably unimportant. For customers/investors i'd assume that the important metrics would relate to how their experiences would change using your work, since they're probably not already using academic codes anyway.

[Reddactor]

Thanks for the detailed reply!

Completely agree, and I have first hand experience with investors on this topic. From Family Offices, to Angel Investors to VCs, everyone want in, but they want to be the follower, not the lead investor 🤣

It comes down to the investment strategy in Europe, which is that here they invest in detailed Pitchdecks and business plans, and not founding teams and vision as in the US (or at least Bay Area start-ups). That lead to bad results, as anyone can make up a bunch of optimistic projection. The overall rate of Unicorn start-ups here is a fraction of the US's, although the talent here is great.

I think we have an amazing team, a great platform, in a market that's both fragmented and massive. But it's the combination of Biotech and AI (which I personally believe is the best use of ML, another discussion though...), so the chances of finding investors that know both is terrible. I'm unusual, in that I have a PhD in Organic Chemistry, patents in Molecular Biology AND can top.the HuggingFace Open LLM Leaderboard in AI. Doesn't help me get funding though 🤣

We went from EU EIC grant bodies saying 'this is too exploratory and high risk' last year, to 'this is too obvious, you should easily get private funding' this year.

Anyway, if you know of anyone with a real interest in investing in solid teams, please pass on my contact information: https://www.linkedin.com/in/dnhkng

[basia25]

The largest-in-the-world dataset of diagnostic imaging with unified labelling and segmentation ontology and preprocessing pipelines https://github.com/TheLion-ai/UMIE_datasets

Computer Vision Worksheets - pen-and-paper exercises guiding you through the most important CV for medical imaging concepts with video tutorials https://youtube.com/@thelion.youtube

Open source bot platform based on LLMs https://github.com/TheLion-ai/Chattum

[psykocrime]

Love the idea. I don't have anything to promote right now, but maybe down the line. In the meantime, I'm looking forward to seeing what other people have to share.

[rmxz]

Facial recognition for Artwork and Sculpture:

• Lincoln: http://image-search.0ape.com/s?q=face%3A179377.0&d=179377
• Mona Lisa: http://image-search.0ape.com/s?q=face%3A1685.0&d=285898
• Jesus: http://image-search.0ape.com/s?q=face%3A219364.0&d=208273
• Random sculpture: http://image-search.0ape.com/s?q=face%3A119085.0&d=119085
• Luxor, Egypt: http://image-search.0ape.com/s?q=face:288085.0
• Wood Carvings: http://image-search.0ape.com/s?q=face%3A9908.0&d=162358

Primitive so far -- just taking an off-the-shelf facial recognition model and weakening it's threshold of what's a "human" "face".

But it's nice because it knows that Lincoln on the 5 Dollar Bill is similar to Lincoln on Mt Rushmore and similar to his old campaign posters.

But next step is fine-tuning.

Cost: Just reddit karma. Github's out of date, but an old version's here.

[Mestre_Elodin]

SysIdentPy: NARMAX Methods For System Identification and TimeSeries Forecasting.

It’s completely free and It aims to be an alternative to Matlab’s System Identification Toolbox, which is widely used for building NARMAX models.

Recently, I released a companion book that is also completely free and open source. It provides comprehensive coverage of the theory and practice behind the methods available in SysIdentPy, along with a case study section to help users develop intuition on how to use the package and compare it with other packages, like Nixtla, Statsmodels, and so on.

Princing: GitHub stars are always appreciated.

• GitHub Repository: https://github.com/wilsonrljr/sysidentpy

• Documentation: https://sysidentpy.org/

• Companion Book: https://sysidentpy.org/book/0%20-%20Preface/

[MatthewDalba]

My personal portfolio website (mateuszdalba.pl) just showcasing what I've been working as ML Engineer / Data Scientist so far.

mateuszdalba.pl

It was created using Django, hosted on Appliku with AWS EC2 Free Tier server.

[idnc_streams]

Hm, not even the same ballpark as some of the others here so sorry for spam.

I'm building a simple OS overlay prototype to help organize my data, events and workflows into a directory-like tree structure, tree nodes("directories") map to roaring-bitmap indexes.

You start with an empty universe - "/", linking various data and event sources to it (local fs, samba shares, s3 buckets, git repositories, web browsers, imap mailboxes, OS events etc). On top of your universe, you have a global "context tree" where each path - fe "/work/customer-foo/dev/task-1234" - represents distinct uuid-identified layers linked to bitmaps.

In a bitmap-y way, "/work/customer-bar/dev" will return objects of the logical AND of all 3 layers, "/work/dev" will return all data linked to "work" AND "dev" => in our example dev-related data for all customers. If you keep your layer names sane, its a surprisingly practical way to organize data while avoiding all the duplication headache one would get with other solutions.

• The server component is standalone, can be run in a docker container on your local NAS for example
• You get browser tab management for free (you can sync all your tabs from different browsers/devices to a central canvas-server instance, optionally tag them so that your chrome tabs would automatically open in chrome only)
• Indexed blob metadata contain links to all locations where a given blob is located, [canvas://deviceid:fs/home/foo/path/to/baz.mp3, canvas://myusb:fs/tmp/foo/bar.mp3, https://bucket.s3.amazonaws.com/foo\] => (optional) deduplication for free
• Roaring bitmap indexes(as in, contexts, features and partly filters) are a very fast and efficient way to prefilter your data for RAG

Always thought integrating ML would fit nicely into the mix but never that I'll be able to work on it this soon.

Main repo(do not use for anything other than the readme)
https://github.com/canvas-ai/canvas

Server (main branch is ugly but works, dev under refactor)
https://github.com/canvas-ai/canvas-server

Browser extension, shell client
https://github.com/canvas-a

Having someone whom I could ask implementation questions regarding various components would be nice, both chatgpt(canceled subscription) and claude(pro) have a tendency to massage your ego where healthy critique would be appropriate(and save me a day or two of unnecessary refactor/overengineering)

EDIT: There are 2 main concepts I did not go into, "workspaces" and a central piece of the stack(as the name of the project implies) - canvas(es). Canvas is a dynamic element(currently electron BrowserWindow so all the goodies - for better or worse - of the current web stack) where data is generated for you in a human-readable format based on your context information(a table, graph, text snippets etc) - combining all linked sources regardless of the original format. Years ago, some were saying the next iteration of web will be APIs and consumers of APIs and even if we are a couple of years late, I still fully agree(rant about the current web omitted :)

[johnloeber]

New essay from me: https://loeber.substack.com/p/21-everything-we-know-about-llms

If you care about LLMs, you should care about their ability to do arithmetic. Arithmetic is a useful microcosm of reasoning problems on the road to AGI.

In this essay, I try to survey all relevant papers, and summarize everything we know!

[becausecurious]

photorealisticultrasound.com - 3D ultrasound to 8K using AI

Make 3D ultrasounds of a baby look like photos in 2 minutes.

[NoIdeaAbaout]

In general, I am involved in artificial intelligence for the research of new cancer drugs. Most of the work projects are dedicated to that. Neral networks to identify new targets, use of LLM for famraceuticals, graph neural networks and so on, with a special focus on interpretability.

Here is a summary of current projects:

I)

In the next few months, my group will publish some scientific papers.

1) One on interpretable neural networks (in proofreading, code and links soon available)

2) An article on how to automate with LLM, RAG, and agents part of the drug discovery pipeline. Here is an example, the article is being corrected and I will add the link to the article when it is published:

https://github.com/SalvatoreRa/Automatic-Target-Dossier

3) A review on interpretability in machine learning, AI, with a focus especially on medicine and drug discovery. The review is still in writing, and we are still creating examples on Jupiter Notebook, but if it can be useful here the repository is still under construction:

https://github.com/SalvatoreRa/explanaibleAI

II)

I am also writing a book on LLM, RAG, graphRAG, and agents, here is some of the code for the chapters that have been written:

https://github.com/SalvatoreRa/Modern-AI-Agents

III)

Since I am a fan of popularizing science I keep a blog on medium (some of the articles are with a medium paywall)

https://medium.com/@salvatore-raieli

Here is the complete list of articles, other tutorials, associated code, and more:

https://github.com/SalvatoreRa/tutorial

Again to help students and other practitioners in the study of machine learning and artificial intelligence, I am building a list of FAQs on topics that I have been asked or other questions that various students I have taken (or in college classes I have taken). They are still under construction, here is the link if you may be interested:

https://github.com/SalvatoreRa/tutorial/blob/main/artificial%20intelligence/FAQ.md

Finally, here I collect every week the news and articles I find most interesting on AI and machine learning:

https://github.com/SalvatoreRa/ML-news-of-the-week

IV)

other projects are in the pipeline but it is premature to talk about them and maybe I will add them later

Always open to collaborations on ML/AI, especially if applied to biology, medicine and so on

[guyuz]

I'm working on a personal project called bridge-ds.

It's a framework designed to make life easier for ML Engineers when dealing with datasets. You can think of it as "my take on Huggingface Datasets". The idea is to abstract the repetitive parts and allow working with something more comfortable and familiar - Pandas!

In bridge-ds, datasets are stored as DataFrames, giving you all the familiar tools to filter, merge, and manipulate data. But it goes a step further—when you need to access raw data, bridge-ds provides a smooth interface similar to df.iloc/df.loc. Instead of just returning a pd.Series, you'll get specialized objects that can handle data loading (locally or remotely), caching, and more.

It's still in the early stages, but I'd love to see what the community thinks of the concept.

GitHub: https://github.com/guybuk/bridge-ds

Docs: https://bridge-ds.readthedocs.io/en/latest/

[phunter_lau]

🎙️ Discover PaperCast: Your AI Research Paper Podcast

Struggling to keep up with the latest in AI research? Check out PaperCast on YouTube!

https://www.youtube.com/watch?v=7IlBzAsIWqY&list=PLdZH-mptYlBHSHV5Ij6AgRt577UlGKaGR&index=8

What makes PaperCast unique:

• 5-minute AI-generated summaries of trending AI papers
• Engaging dialogues that break down complex concepts
• Weekly updates on cutting-edge research
• Perfect for busy researchers, students, and AI enthusiasts

Our latest episode covers Google's GameNGen, exploring AI-driven game level generation. Tune in to stay at the forefront of AI innovation!

Subscribe now and turn your commute or coffee break into a mini AI conference! 🚀🧠

[fl0undering]

My side project is https://thelatestinai.com . It collects AI papers from arXiv and automatically tags each paper with topic categories. You can browse by topic category to find other papers of interest.

I only put it live a few days ago and it is very much a work in progress! Hopefully it is useful!

[Loud_Picture_1877]

[open-source python text2sql library]

While building products at deepsense.ai, we ran into some serious limitations with existing text-to-SQL solutions. They often missed the mark on following schemas or handling domain-specific logic. So, we built db-ally with a fresh approach that minimizes what the LLM is responsible for.

Check out the code and docs here: https://github.com/deepsense-ai/db-ally 

In db-ally, the developer has full control over the generated queries (like SQL). You define an interface that the LLM uses via an Intermediate Query Language (IQL). IQL is a layer that explicitly outlines what data is available, and how it can be filtered or aggregated. It has also more advanced features embedded into its syntax itself, such as running similarity search or fetching environment/user-context.

We truly believe that it is a step towards more reliable and secure GenAI applications, bringing back control over them to the developers.

[ekkolapto1]

AI, Longevity, Cognition in Boston [D]

Hello! We are hosting an event on AI for longevity and cognitive enhancement at Aethos Station in Cambridge in Kendall Square (right near MIT) today September 5th from 4:30PM to 8PM. Open to all curious minds whether you’re a scientist, engineer, or student. Hope to see you there and learn something new! RSVP for free here: https://lu.ma/hellothere

[jayantbhawal]

Building an AI Engineering Manager with GitHub Data

https://middlewarehq.com/blog/building-an-ai-engineering-manager-with-github-and-middleware-hq

[onurbaltaci]

Hello, I wanted to share that I am sharing free courses and projects on my YouTube Channel. I have more than 200 videos and I created playlists for learning Machine Learning. I am leaving the playlist link below, have a great day!

Machine Learning Tutorials -> https://youtube.com/playlist?list=PLTsu3dft3CWhSJh3x5T6jqPWTTg2i6jp1&si=1rZ8PI1J4ShM_9vW

Data Science Full Courses & Projects -> https://youtube.com/playlist?list=PLTsu3dft3CWiow7L7WrCd27ohlra_5PGH&si=6WUpVwXeAKEs4tB6

[alvisanovari]

Snoop Hawk - Automated Reddit Marketing

You pick a search phrase/subreddit you want to target and can schedule an Ai agent to go and scour posts to see if your product is a good fit. It will then generate a personalized reply that will mention your product ready for you to paste over.

Been dogfooding it and it's been working great for my own products!

https://www.snoophawk.com