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EnzymeCAGE: A Deep Learning Framework Designed to Predict Enzyme-Reaction Catalytic Specificity by Encoding both Pocket-Specific Enzyme Structures and Chemical Reactions Enzymes are indispensable molecular catalysts that facilitate the biochemical processes vital to life. They play crucial roles across metabolism, industry, and biotechnology. Despite their importance, there are significant gaps in our knowledge of these catalysts. Out of the approximately 190 million protein sequences cataloged in databases like UniProt, fewer than 0.3% are curated by experts, and less than 20% have experimental validation. Furthermore, 40-50% of known enzymatic reactions remain unlinked to specific enzymes, often termed “orphaned” reactions. Read the full article: https://lnkd.in/exsyYX_z Paper: https://lnkd.in/eKJkSu9Z

Salesforce Unveils Agentforce 2.0: An Advanced Digital Labor Platform for Enterprises Customer service teams face significant challenges in today’s fast-paced business environment. They must handle a growing number of customer inquiries while maintaining a high standard of service. Balancing these demands is difficult, especially with tools that lack integration and real-time support. The result is often delays, inefficiencies, and unmet customer expectations. As businesses scale, equipping service teams with effective tools becomes increasingly complex. Salesforce’s latest release, Agentforce 2.0, aims to address these pressing issues. Read the full article: https://lnkd.in/gxhTjK-7

Google Released State of the Art ‘Veo 2’ for Video Generation and ‘Improved Imagen 3’ for Image Creation: Setting New Standards with 4K Video and Several Minutes Long Video Generation Video and Image generation innovations are improving the quality of visuals and focusing on making AI models more responsive to detailed prompts. AI tools have opened new possibilities for artists, filmmakers, businesses, and creative professionals by achieving more accurate representations of real-world physics and human movement. AI-generated visuals are no longer limited to generic images and videos; they now allow for high-quality, cinematic outputs that closely mimic human creativity. Read the full article: https://lnkd.in/erRYaa_7

Meta AI Releases Apollo: A New Family of Video-LMMs Large Multimodal Models for Video Understanding While multimodal models (LMMs) have advanced significantly for text and image tasks, video-based models remain underdeveloped. Videos are inherently complex, combining spatial and temporal dimensions that demand more from computational resources. Existing methods often adapt image-based approaches directly or rely on uniform frame sampling, which poorly captures motion and temporal patterns. Moreover, training large-scale video models is computationally expensive, making it difficult to explore design choices efficiently. Read the full article: https://lnkd.in/gwPwGdnN Paper: https://lnkd.in/e9H_KCsi

This AI Paper from Microsoft and Novartis Introduces Chimera: A Machine Learning Framework for Accurate and Scalable Retrosynthesis Prediction Chemical synthesis is essential in developing new molecules for medical applications, materials science, and fine chemicals. This process, which involves planning chemical reactions to create desired target molecules, has traditionally relied on human expertise. Recent advancements have turned to computational methods to enhance the efficiency of retrosynthesis—working backward from a target molecule to determine the series of reactions needed to synthesize it. Read the full article: https://lnkd.in/gs7drk5p Paper: https://lnkd.in/evUKBEDg

UBC Researchers Introduce ‘First Explore’: A Two-Policy Learning Approach to Rescue Meta-Reinforcement Learning RL from Failed Explorations Reinforcement Learning is now applied in almost every pursuit of science and tech, either as a core methodology or to optimize existing processes and systems. Despite broad adoption even in highly advanced fields, RL lags in some fundamental skills. Sample Inefficiency is one such problem that limits its potential. In simple terms, RL needs thousands of episodes to learn reasonably basic tasks, such as exploration, that humans master in just a few shots (for example, assume a kid finally figuring out basic arithmetic in high school). Read the full article: https://lnkd.in/gJYA4NHb Paper: https://lnkd.in/eYrKZiBz

BiMediX2: A Groundbreaking Bilingual Bio-Medical Large Multimodal Model integrating Text and Image Analysis for Advanced Medical Diagnostics Recent advancements in healthcare AI, including medical LLMs and LMMs, show great potential for improving access to medical advice. However, these models are largely English-centric, limiting their utility for non-English-speaking populations, such as those in Arabic-speaking regions. Furthermore, many medical LMMs need help to balance advanced medical text comprehension with multimodal capabilities. Read the full article: https://lnkd.in/eMK_juFm Paper: https://lnkd.in/e59SjMET

Nexa AI Releases OmniAudio-2.6B: A Fast Audio Language Model for Edge Deployment Audio language models (ALMs) play a crucial role in various applications, from real-time transcription and translation to voice-controlled systems and assistive technologies. However, many existing solutions face limitations such as high latency, significant computational demands, and a reliance on cloud-based processing. These issues pose challenges for edge deployment, where low power consumption, minimal latency, and localized processing are critical. Read the full article: https://lnkd.in/g-vQ-qVm

Meta AI Proposes Large Concept Models (LCMs): A Semantic Leap Beyond Token-based Language Modeling Large Language Models (LLMs) have achieved remarkable advancements in natural language processing (NLP), enabling applications in text generation, summarization, and question-answering. However, their reliance on token-level processing—predicting one word at a time—presents challenges. This approach contrasts with human communication, which often operates at higher levels of abstraction, such as sentences or ideas. Read the full article: https://lnkd.in/guqis22g Paper: https://lnkd.in/e_5qVwda

InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal AI System for Long-Term Streaming Video and Audio Interactions AI systems are progressing toward emulating human cognition by enabling real-time interactions with dynamic environments. Researchers working in AI aim to develop systems that seamlessly integrate multimodal data such as audio, video, and textual inputs. These can have applications in virtual assistants, adaptive environments, and continuous real-time analysis by mimicking human-like perception, reasoning, and memory. Read the full article: https://lnkd.in/g2Y5ExaD Paper: https://lnkd.in/gtWdSKVU