Today:

• Snowflake Cost-Efficient Arctic Model

• OpenAI Boosts Moderna’s mRNA

• Synthesia Expressive Avatars

• Augment New AI Coding Tool

• Cognition AI Hits $2 Billion Valuation

BIG win for Open Source AI | Snowflake Arctic 128 Experts MoE, "Cookbook" create world-class models

Snowflake has launched Arctic, a large language model tailored for business-to-business applications, emphasizing cost efficiency and specific enterprise needs like SQL generation and coding. Unlike consumer-focused models, Arctic is designed to be less resource-intensive and more accessible, with costs dramatically lower than typical industry standards—under $2 million compared to tens or hundreds of millions. 

It is distinguished by its use of a hybrid architecture that blends dense and sparse model elements, and its open Apache 2.0 license allows free access to model weights and code, encouraging the wider AI community to develop custom solutions affordably. Snowflake's introduction of Arctic into the AI landscape highlights a shift toward open-source models, potentially increasing innovation while lowering costs across the sector.

WATCH THE VIDEO ON YOUTUBE

Moderna and OpenAI Collaborate To Advance mRNA Medicine

Moderna and OpenAI have teamed up to push the boundaries of AI in healthcare. By integrating OpenAI's generative AI into Moderna's operations, they aim to enhance efficiency and impact in mRNA medicine development. Moderna has already integrated ChatGPT Enterprise across its functions, aiding employees with personalized support. 

This collaboration reflects Moderna's commitment to innovation and scalability, crucial for their ambitious product launch plans. With over 750 deployed GPTs driving automation and productivity, Moderna's focus is on optimizing vaccine dose profiles while prioritizing safety. The partnership underscores the transformative potential of AI in shaping the future of healthcare.

MODERNA

Synthesia takes next leap in AI video with ‘expressive avatars’

London-based startup Synthesia has introduced "expressive avatars," a groundbreaking advancement in AI video technology. These avatars dynamically adjust tone, facial expressions, and body language based on context, enhancing realism in AI-generated videos. Unlike Microsoft's VASA, still in research, Synthesia's technology is live, offering customers unprecedented realism in their video content creation. 

Founded in 2017, Synthesia has seen widespread adoption in enterprises, with over 200,000 users creating 18 million videos. The introduction of expressive avatars fills a crucial gap in digital avatars' ability to understand and convey sentiment, promising more engaging and empathetic AI videos. With safety measures in place, Synthesia aims to foster positive, responsible use cases across industries, including healthcare and marketing.

VENTUREBEAT

Eric Schmidt-backed Augment, a GitHub Copilot rival, launches out of stealth with $252M

Augment, a startup backed by Eric Schmidt, emerges with $252 million in funding, aiming to revolutionize AI-driven coding. Founded by ex-Microsoft developer Igor Ostrovsky and AI scientist Guy Gur-Ari, Augment boasts a stellar team including Scott Dietzen and Dion Almaer. With industry giants like Microsoft and Google already in the AI coding space, Augment faces fierce competition. 

However, Ostrovsky claims early success with "hundreds" of developers using Augment in early access. The startup aims to address concerns around code quality and intellectual property while accelerating product development with its recent funding boost.

TECHCRUNCH

Six-Month-Old AI Coding Startup Valued at $2 Billion by Founders Fund

In just six months, Cognition AI, a startup, soared to a $2 billion valuation with a $175 million investment from Founders Fund. Despite a shaky product launch, the allure of a chatbot capable of coding and debugging attracted hefty investments. Founders Fund had previously led a Series A investment valuing the startup at $350 million. 

The startup's AI-powered coding assistant, Devin, caught investors' attention, propelling Cognition AI to unicorn status. The rapid rise underscores the growing interest and confidence in AI-driven solutions, signaling a potential shift in tech investment trends.

THE INFORMATION

🧠RESEARCH

PuLID: Pure and Lightning ID Customization via Contrastive Alignment

"PuLID: Pure and Lightning ID Customization via Contrastive Alignment" presents a new method for text-to-image generation called PuLID. It combines Lightning and diffusion branches to enhance ID customization without disturbing the original model. PuLID ensures high ID fidelity and preserves image consistency. Experimentation confirms its superior performance in ID fidelity and editability.

CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data

"CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data" introduces a novel method for pre-training vision models using web-scale image-text data. By reframing pre-training as a classification task, it eliminates the need for computationally intensive pairwise similarity computations, achieving a significant 2.7x speedup compared to contrastive learning. 

ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning

"ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning" tackles challenges in identity-preserving text-to-image (ID-T2I) generation. Existing methods struggle with maintaining accurate identity characteristics and aesthetic appeal. ID-Aligner introduces feedback learning to address these issues, leveraging rewards from face detection/recognition models and human preferences. 

Editable Image Elements for Controllable Synthesis

"Editable Image Elements for Controllable Synthesis" introduces a method to facilitate image editing within diffusion models. By encoding input images into editable "image elements," users can intuitively manipulate them for tasks like resizing, rearranging, and removing objects. The approach enables spatial editing and realistic image reconstruction.

MotionMaster: Training-free Camera Motion Transfer For Video Generation

"MotionMaster: Training-free Camera Motion Transfer For Video Generation" addresses the challenge of flexible camera motion control in video generation. Existing methods rely on training complex temporal camera modules, limiting flexibility and requiring significant computation. MotionMaster proposes COMD, a training-free model that disentangles camera and object motions, enabling flexible and diverse camera control. It achieves this through one-shot and few-shot camera motion disentanglement methods, along with motion combination techniques.

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📲SOCIAL MEDIA

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UK Set to Revise AI Oversight Amid Big Tech Data Boom

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AI designs active pharmaceutical ingredients quickly and easily based on protein structures

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