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

[bnew]

https://blog.google/products/bard/google-bard-try-gemini-ai/

BARD

Bard gets its biggest upgrade yet with Gemini​

Dec 06, 2023
2 min read

You can now try Gemini Pro in Bard for new ways to collaborate with AI. Gemini Ultra will come to Bard early next year in a new experience called Bard Advanced.

Sissie Hsiao
Vice President and General Manager, Google Assistant and Bard

Today we announced Gemini, our most capable model with sophisticated multimodal reasoning capabilities. Designed for flexibility, Gemini is optimized for three different sizes — Ultra, Pro and Nano — so it can run on everything from data centers to mobile devices.

Now, Gemini is coming to Bard in Bard’s biggest upgrade yet. Gemini is rolling out to Bard in two phases: Starting today, Bard will use a specifically tuned version of Gemini Pro in English for more advanced reasoning, planning, understanding and more. And early next year, we’ll introduce Bard Advanced, which gives you first access to our most advanced models and capabilities — starting with Gemini Ultra.

Try Gemini Pro in Bard​

Before bringing it to the public, we ran Gemini Pro through a number of industry-standard benchmarks. In six out of eight benchmarks, Gemini Pro outperformed GPT-3.5, including in MMLU (Massive Multitask Language Understanding), one of the key leading standards for measuring large AI models, and GSM8K, which measures grade school math reasoning.

On top of that, we’ve specifically tuned Gemini Pro in Bard to be far more capable at things like understanding, summarizing, reasoning, coding and planning. And we’re seeing great results: In blind evaluations with our third-party raters, Bard is now the most preferred free chatbot compared to leading alternatives.

We also teamed up with YouTuber and educator Mark Rober to put Bard with Gemini Pro to the ultimate test: crafting the most accurate paper airplane. Watch how Bard helped take the creative process to new heights.

6:19

You can try out Bard with Gemini Pro today for text-based prompts, with support for other modalities coming soon. It will be available in English in more than 170 countries and territories to start, and come to more languages and places, like Europe, in the near future.

Look out for Gemini Ultra in an advanced version of Bard early next year​

Gemini Ultra is our largest and most capable model, designed for highly complex tasks and built to quickly understand and act on different types of information — including text, images, audio, video and code.

One of the first ways you’ll be able to try Gemini Ultra is through Bard Advanced, a new, cutting-edge AI experience in Bard that gives you access to our best models and capabilities. We’re currently completing extensive safety checks and will launch a trusted tester program soon before opening Bard Advanced up to more people early next year.

This aligns with the bold and responsible approach we’ve taken since Bard launched. We’ve built safety into Bard based on our AI Principles, including adding contextual help, like Bard’s “Google it” button to more easily double-check its answers. And as we continue to fine-tune Bard, your feedback will help us improve.

With Gemini, we’re one step closer to our vision of making Bard the best AI collaborator in the world. We’re excited to keep bringing the latest advancements into Bard, and to see how you use it to create, learn and explore. Try Bard with Gemini Pro today.

 

[bnew]

 

[IIVI]

Pair programming using A.I would be insane

 

[bnew]

https://archive.is/x0O8e

 

[bnew]

Spoiler: larger image

Spoiler: more tweets

https://storage.googleapis.com/deepmind-media/AlphaCode2/AlphaCode2_Tech_Report.pdf

 

[bnew]

 

[bnew]

https://archive.is/PkfDs

 

[bnew]

https://archive.is/EibnO

 

[bnew]

https://archive.is/TUk47

https://archive.is/pcbpV

 

[bnew]

GitHub - aganoob/vid2densepose: Convert your videos to densepose and use it on MagicAnimate

Convert your videos to densepose and use it on MagicAnimate - aganoob/vid2densepose

github.com

About​

Convert your videos to densepose and use it on MagicAnimate

Vid2DensePose​

Overview​

The Vid2DensePose is a powerful tool designed for applying the DensePose model to videos, generating detailed "Part Index" visualizations for each frame. This tool is exceptionally useful for enhancing animations, particularly when used in conjunction with MagicAnimate for temporally consistent human image animation.

Key Features​

• Enhanced Output: Produces video files showcasing DensePosedata in a vivid, color-coded format.
• MagicAnimate Integration: Seamlessly compatible with MagicAnimate to foster advanced human animation projects.

Prerequisites​

GitHub - Flode-Labs/vid2densepose: Convert your videos to densepose and use it on MagicAnimate

Convert your videos to densepose and use it on MagicAnimate - GitHub - Flode-Labs/vid2densepose: Convert your videos to densepose and use it on MagicAnimate

github.com

 

[bnew]

https://archive.is/kkMO9

https://archive.is/0EHpB

 

[bnew]

https://archive.is/MbU3w

 

[bnew]

https://arxiv.org/abs/2302.01649

Structure-informed Language Models Are Protein Designers​

Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei YE, Quanquan Gu

This paper demonstrates that language models are strong structure-based protein designers. We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs), that have learned massive sequential evolutionary knowledge from the universe of natural protein sequences, to acquire an immediate capability to design preferable protein sequences for given folds. We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. During inference, iterative refinement is performed to effectively optimize the generated protein sequences. Experiments show that LM-Design improves the state-of-the-art results by a large margin, leading to up to 4% to 12% accuracy gains in sequence recovery (e.g., 55.65%/56.63% on CATH 4.2/4.3 single-chain benchmarks, and >60% when designing protein complexes). We provide extensive and in-depth analyses, which verify that LM-Design can (1) indeed leverage both structural and sequential knowledge to accurately handle structurally non-deterministic regions, (2) benefit from scaling data and model size, and (3) generalize to other proteins (e.g., antibodies and de novo proteins)

Comments:	10 pages; ver.2 update: added image credit to RFdiffusion (Watson et al., 2022) in Fig. 1F, and fixed some small presentation errors
Subjects:	Machine Learning (cs.LG)
Cite as:	arXiv:2302.01649 [cs.LG]
	(or arXiv:2302.01649v2 [cs.LG] for this version)
	[2302.01649] Structure-informed Language Models Are Protein Designers Focus to learn more

Submission history​

From: Zaixiang Zheng [view email]
[v1] Fri, 3 Feb 2023 10:49:52 UTC (5,545 KB)
[v2] Thu, 9 Feb 2023 14:14:05 UTC (5,496 KB)

https://arxiv.org/pdf/2302.01649.pdf

---

A.I generated partial summary:

Simplified version of the abstract:​

This paper demonstrates that language models can be powerful tools for protein design. We present a novel method called LM-DESIGN that leverages the strengths of both language models and protein structure prediction methods. LM-DESIGN achieves state-of-the-art performance on protein sequence design tasks, outperforming previous methods by a large margin. Additionally, LM-DESIGN is shown to be data-efficient, flexible, and generalizable to diverse protein families.

Key points of the paper:​

• Protein design: LM-DESIGN is a new method for designing protein sequences based on their desired structures.
• Language models: LM-DESIGN uses the power of large language models to learn the complex relationships between protein sequences and their structures.
• State-of-the-art performance: LM-DESIGN outperforms previous protein design methods by a large margin.
• Data-efficient: LM-DESIGN requires less data than previous methods to achieve high accuracy.
• Flexible: LM-DESIGN can be easily adapted to different protein families and structures.
• Generalizable: LM-DESIGN can be used to design proteins that are not found in nature.

Potential applications of LM-DESIGN:​

• Developing new drugs and therapies: LM-DESIGN could be used to design new proteins with desired functions, such as targeting specific diseases.
• Improving protein engineering: LM-DESIGN could be used to improve the properties of existing proteins, such as their stability or activity.
• Understanding protein evolution: LM-DESIGN could be used to study how proteins have evolved over time.

Overall, LM-DESIGN is a promising new method for protein design with a wide range of potential applications.​

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Another A.I generated summary:

Protein Designers Got a Big Boost: Meet LM-DESIGN!​

Researchers just unveiled a game changer in protein design: LM-DESIGN! It's a system that lets protein language models, like the ones that help computers understand your emails, play a key role in crafting new proteins.
Think of it like this: protein language models are like master chefs with years of experience cooking up protein sequences. They know all the right ingredients and how to combine them to create delicious, functional proteins. Now, imagine giving these chefs access to a map of a protein's structure, like a blueprint. That's what LM-DESIGN does!
With this map, the chefs can design even more precise and functional protein sequences, opening up a world of possibilities. LM-DESIGN has already achieved some incredible results:

• It can design protein sequences with over 55% accuracy, which is way better than existing methods.
• It can handle tricky regions in protein structures, like loops and exposed surfaces.
• It can even create brand new protein types, like antibodies and proteins that haven't been seen before.

This is just the beginning for LM-DESIGN. It has the potential to revolutionize protein design, leading to:

• New drugs: Imagine designing proteins that can target specific diseases and cure them!
• Better enzymes: Enzymes are nature's tiny factories, and LM-DESIGN could help us create even more efficient ones for industrial processes.
• A deeper understanding of proteins: By studying the sequences LM-DESIGN creates, scientists can unlock the secrets of how proteins work.

Of course, LM-DESIGN is still young and has room to grow. But with its impressive start, it's clear that this system is a major leap forward in protein design. So next time you see a protein, remember, there might just be a bit of LM-DESIGN magic in its recipe!

 

[Micky Mikey]

seems like LLMs have reached their plateau. from what I've seen Gemini isn't that much more powerful than ChatGPT4. Google has had ample time and compute to make something far more powerful than ChatGPT4 and the results, while better than ChatGPT aren't ALL that impressive. There is already talk of Google misrepresenting and being deceitful about Gemini's capability.

 

[bnew]

seems like LLMs have reached their plateau. from what I've seen Gemini isn't that much more powerful than ChatGPT4. Google has had ample time and compute to make something far more powerful than ChatGPT4 and the results, while better than ChatGPT aren't ALL that impressive. There is already talk of Google misrepresenting and being deceitful about Gemini's capability.

it's practically at toddler stage maybe even infancy. have you see the research papers put out to improve the underlying tech. still have a lot of room to improve inference speed, context window size along with accuracy. multi-modality , input token size, reasoning etc.



they dropping all these llms for free cause they know it's just the beginning.