The A.I Megathread (LLM , GPT , Development)

[bnew]

Spoiler: thread continued

Spoiler: text

Consider me your AI tool-testing guinea pig.

Every week, I test hundreds of AI tools and report the best ones- so you don't have to.

Here are the top 10 most useful AI tools of the week (all new):
Rowan Cheung
@rowancheung
1. Inflection Pi

Pi, or 'personal intelligence' is a ChatGPT competitor designed to offer concise information, friendly advice, and conversations.

Pi, your personal AI

Hi, I'm Pi. I'm your personal AI, designed to be supportive, smart, and there for you anytime. Ask me for advice, for answers, or let's talk about whatever's on your mind.

heypi.com

·
4h
2. Nyric

A new text-to-3D world generation platform in Unreal Engine.

Soon, we'll be able to create entire games in seconds from text prompts using AI.

Lovelace Studios

4h
3. Dream3D

A new YC-backed 3D design tool making it easy for anyone to create beautiful computer graphics.

Dream3D - AI-Powered 3D Design

Dream3D is an AI-powered 3D design tool

dream3d.com

·
4h
4. Portfolio Pilot

The first verified ChatGPT plugin for investing.

Supported by hedge fund models, the plugin is already accessible to those with plugin access.

PortfolioPilot

The most advanced portfolio tracker in the world

portfoliopilot.com

·
4h
5. AudioPen

The easiest way to convert messy thoughts into clear text.

AudioPen | The easiest way to convert messy thoughts into clear text

AudioPen transcribes and summarizes unstructured voice notes into text that’s easy to read and ready to share. If you like thinking out loud, you'll love Audio Pen. It's like having a personal assistant who records and summarizes your thoughts.

audiopen.ai

https://archive.is/HUEwM

 

[Complexion]

Build web apps by describing them:

⚫️ Pico

An AI-powered text-to-app platform for entrepreneuers, consultants, and small business owners.

picoapps.xyz

Tread carefully.

 

[Silkk]

@bnew Is there one or whats the best one for text to animation/cartoon vids?


Also, You need to spoiler more. You’re killing the thread on mobile. My shyt wouldn’t even load all them twitter names.

 

[Powerline88]

I'm really try to get on the AI porn lane. There is definitely money there.

If I can get AI to create a material better than silicone for pocket p*ssy I'm rolling in dough

 

[bnew]

Artificial intelligence identifies anti-aging drug candidates targeting 'zombie' cells

A new publication in the May issue of Nature Aging by researchers from Integrated Biosciences, a biotechnology company combining synthetic biology and machine learning to target aging, demonstrates the power of artificial intelligence (AI) to discover novel senolytic compounds, a class of small...

phys.org

Artificial intelligence identifies anti-aging drug candidates targeting 'zombie' cells​

by Ten Bridge Communications

Senolytics are an emerging class of investigational drug compounds that selectively kill aging-associated se


A new publication in the May issue of Nature Aging by researchers from Integrated Biosciences, a biotechnology company combining synthetic biology and machine learning to target aging, demonstrates the power of artificial intelligence (AI) to discover novel senolytic compounds, a class of small molecules under intense study for their ability to suppress age-related processes such as fibrosis, inflammation and cancer.



The paper, "Discovering small-molecule senolytics with deep neural networks," authored in collaboration with researchers from the Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard, describes the AI-guided screening of more than 800,000 compounds to reveal three drug candidates with comparable efficacy and superior medicinal chemistry properties than those of senolytics currently under investigation.

"This research result is a significant milestone for both longevity research and the application of artificial intelligence to drug discovery," said Felix Wong, Ph.D., co-founder of Integrated Biosciences and first author of the publication. "These data demonstrate that we can explore chemical space in silico and emerge with multiple candidate anti-aging compounds that are more likely to succeed in the clinic, compared to even the most promising examples of their kind being studied today."

Senolytics are compounds that selectively induce apoptosis, or programmed cell death, in senescent cells that are no longer dividing. A hallmark of aging, senescent cells have been implicated in a broad spectrum of age-related diseases and conditions including cancer, diabetes, cardiovascular disease, and Alzheimer's disease. Despite promising clinical results, most senolytic compounds identified to date have been hampered by poor bioavailability and adverse side effects. Integrated Biosciences was founded in 2022 to overcome these obstacles, target other neglected hallmarks of aging, and advance anti-aging drug development more generally using artificial intelligence, synthetic biology and other next-generation tools.

"One of the most promising routes to treat age-related diseases is to identify therapeutic interventions that selectively remove these cells from the body similarly to how antibiotics kill bacteria without harming host cells. The compounds we discovered display high selectivity, as well as the favorable medicinal chemistry properties needed to yield a successful drug," said Satotaka Omori, Ph.D., Head of Aging Biology at Integrated Biosciences and joint first author of the publication. "We believe that the compounds discovered using our platform will have improved prospects in clinical trials and will eventually help restore health to aging individuals."


In their new study, Integrated Biosciences researchers trained deep neural networks on experimentally generated data to predict the senolytic activity of any molecule. Using this AI model, they discovered three highly selective and potent senolytic compounds from a chemical space of over 800,000 molecules. All three displayed chemical properties suggestive of high oral bioavailability and were found to have favorable toxicity profiles in hemolysis and genotoxicity tests.

Structural and biochemical analyses indicate that all three compounds bind Bcl-2, a protein that regulates apoptosis and is also a chemotherapy target. Experiments testing one of the compounds in 80-week-old mice, roughly corresponding to 80-year-old humans, found that it cleared senescent cells and reduced expression of senescence-associated genes in the kidneys.

"This work illustrates how AI can be used to bring medicine a step closer to therapies that address aging, one of the fundamental challenges in biology," said James J. Collins, Ph.D., Termeer Professor of Medical Engineering and Science at MIT and founding chair of the Integrated Biosciences Scientific Advisory Board. Dr. Collins, who is senior author on the Nature Aging paper, led the team that discovered the first antibiotic identified by machine learning in 2020.

"Integrated Biosciences is building on the basic research that my academic lab has done for the last decade or so, showing that we can target cellular stress responses using systems and synthetic biology. This experimental tour de force and the stellar platform that produced it make this work stand out in the field of drug discovery and will drive substantial progress in longevity research."

More information: Felix Wong et al, Discovering small-molecule senolytics with deep neural networks, Nature Aging (2023). DOI: 10.1038/s43587-023-00415-z
Journal information: Nature Aging

 

[Micky Mikey]

Artificial intelligence identifies anti-aging drug candidates targeting 'zombie' cells

A new publication in the May issue of Nature Aging by researchers from Integrated Biosciences, a biotechnology company combining synthetic biology and machine learning to target aging, demonstrates the power of artificial intelligence (AI) to discover novel senolytic compounds, a class of small...

phys.org

Artificial intelligence identifies anti-aging drug candidates targeting 'zombie' cells​

by Ten Bridge Communications

Senolytics are an emerging class of investigational drug compounds that selectively kill aging-associated se


A new publication in the May issue of Nature Aging by researchers from Integrated Biosciences, a biotechnology company combining synthetic biology and machine learning to target aging, demonstrates the power of artificial intelligence (AI) to discover novel senolytic compounds, a class of small molecules under intense study for their ability to suppress age-related processes such as fibrosis, inflammation and cancer.



The paper, "Discovering small-molecule senolytics with deep neural networks," authored in collaboration with researchers from the Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard, describes the AI-guided screening of more than 800,000 compounds to reveal three drug candidates with comparable efficacy and superior medicinal chemistry properties than those of senolytics currently under investigation.

"This research result is a significant milestone for both longevity research and the application of artificial intelligence to drug discovery," said Felix Wong, Ph.D., co-founder of Integrated Biosciences and first author of the publication. "These data demonstrate that we can explore chemical space in silico and emerge with multiple candidate anti-aging compounds that are more likely to succeed in the clinic, compared to even the most promising examples of their kind being studied today."

Senolytics are compounds that selectively induce apoptosis, or programmed cell death, in senescent cells that are no longer dividing. A hallmark of aging, senescent cells have been implicated in a broad spectrum of age-related diseases and conditions including cancer, diabetes, cardiovascular disease, and Alzheimer's disease. Despite promising clinical results, most senolytic compounds identified to date have been hampered by poor bioavailability and adverse side effects. Integrated Biosciences was founded in 2022 to overcome these obstacles, target other neglected hallmarks of aging, and advance anti-aging drug development more generally using artificial intelligence, synthetic biology and other next-generation tools.

"One of the most promising routes to treat age-related diseases is to identify therapeutic interventions that selectively remove these cells from the body similarly to how antibiotics kill bacteria without harming host cells. The compounds we discovered display high selectivity, as well as the favorable medicinal chemistry properties needed to yield a successful drug," said Satotaka Omori, Ph.D., Head of Aging Biology at Integrated Biosciences and joint first author of the publication. "We believe that the compounds discovered using our platform will have improved prospects in clinical trials and will eventually help restore health to aging individuals."


In their new study, Integrated Biosciences researchers trained deep neural networks on experimentally generated data to predict the senolytic activity of any molecule. Using this AI model, they discovered three highly selective and potent senolytic compounds from a chemical space of over 800,000 molecules. All three displayed chemical properties suggestive of high oral bioavailability and were found to have favorable toxicity profiles in hemolysis and genotoxicity tests.

Structural and biochemical analyses indicate that all three compounds bind Bcl-2, a protein that regulates apoptosis and is also a chemotherapy target. Experiments testing one of the compounds in 80-week-old mice, roughly corresponding to 80-year-old humans, found that it cleared senescent cells and reduced expression of senescence-associated genes in the kidneys.

"This work illustrates how AI can be used to bring medicine a step closer to therapies that address aging, one of the fundamental challenges in biology," said James J. Collins, Ph.D., Termeer Professor of Medical Engineering and Science at MIT and founding chair of the Integrated Biosciences Scientific Advisory Board. Dr. Collins, who is senior author on the Nature Aging paper, led the team that discovered the first antibiotic identified by machine learning in 2020.

"Integrated Biosciences is building on the basic research that my academic lab has done for the last decade or so, showing that we can target cellular stress responses using systems and synthetic biology. This experimental tour de force and the stellar platform that produced it make this work stand out in the field of drug discovery and will drive substantial progress in longevity research."

More information: Felix Wong et al, Discovering small-molecule senolytics with deep neural networks, Nature Aging (2023). DOI: 10.1038/s43587-023-00415-z
Journal information: Nature Aging

Crazy to think where we'll be by the end of the decade. Ray Kurzweil may be on to something.

 

[bnew]

Spoiler: thread continued

https://archive.is/poV0e

 

[bnew]

https://archive.is/7lcgZ

 

[bnew]

https://archive.is/cyvks

 

[bnew]

 

[bnew]

 

[bnew]

GitHub - VainF/Awesome-Anything: General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX

General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX - GitHub - VainF/Awesome-Anything: General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX

github.com

Awesome-Anything​

A curated list of general AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, etc.

Contributions are welcome!

• Awesome-Anything
  • AnyObject - Segmentation, Detection, Classification, Medical Image, OCR, Pose, etc.
  • AnyGeneration - Text-to-Image Generation, Editing, Inpainting, Style Transfer, etc.
  • Any3D - 3D Generation, Segmentation, etc.
  • AnyModel - Any Pruning, Any Quantization, Model Reuse.
  • AnyTask - LLM Controller + ModelZoo, General Decoding, Multi-Task Learning.
  • AnyX - Other Topics: Captioning, etc.
  • Paper List

 

[bnew]

Resolution-robust Large Mask Inpainting with Fourier Convolutions

Resolution-robust Large Mask Inpainting with Fourier Convolutions

advimman.github.io

Resolution-robust Large Mask Inpainting with Fourier Convolutions​

LaMa generalizes surprisingly well to much higher resolutions (~2k️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e.g. completion of periodic structures.

Abstract​

Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an ef-fective receptive field in both the inpainting network andthe loss function. To alleviate this issue, we propose anew method called large mask inpainting (LaMa). LaM ais based on:​

• a new inpainting network architecture that uses fast Fourier convolutions, which have the image-widereceptive field
• a high receptive field perceptual loss;
• large training masks, which unlocks the potential ofthe first two components.

Our inpainting network improves the state-of-the-art across a range of datasets and achieves excellent performance even in challenging scenarios, e.g.completion of periodic structures. Our model generalizes surprisingly well to resolutions that are higher than thoseseen at train time, and achieves this at lower parameter & compute costs than the competitive baselines.

 

[Suge Shot Me]

I don't know if it's already been posted, but this Google MusicLM music generating app looks really dope.

 

[bnew]

Bark: Real-time Open-Source Text-to-Audio Rivaling ElevenLabs

If the trend of improvement continues, we can expect that the newly available H100 GPUs will be able to perform real-time inference for every type of audio synthesis.

neocadia.com

BARK: REAL-TIME OPEN-SOURCE TEXT-TO-AUDIO RIVALING ELEVENLABS​

May 14, 2023

Bark is a transformer-based text-to-audio model created by Suno. Bark can generate highly realistic, multilingual speech as well as other audio – including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints, which are ready for inference and available for commercial use.

https://neocadia.com/wp-content/uploads/2023/05/002_As_the_sun_depa-23-0513-2151-25-SPK-female_david_attenborough.wav

https://neocadia.com/wp-content/uploads/2023/05/018_trek_1sent_atten_topp_601.wav

GitHub - suno-ai/bark: 🔊 Text-Prompted Generative Audio Model

🔊 Text-Prompted Generative Audio Model. Contribute to suno-ai/bark development by creating an account on GitHub.

github.com

About​

Text-Prompted Generative Audio Model

​

Bark is a transformer-based text-to-audio model created by Suno. Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints, which are ready for inference and available for commercial use.

bark/README.md at main · suno-ai/bark

🔊 Text-Prompted Generative Audio Model. Contribute to suno-ai/bark development by creating an account on GitHub.

github.com

demo site:

Bark - a Hugging Face Space by suno

Discover amazing ML apps made by the community

huggingface.co