News from the AI & ML world
DeeperML - #deepmind
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LangChain@LangChain Blog
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LangChain Blog
, www.marktechpost.com
The LangGraph Platform, an infrastructure solution designed for deploying and managing AI agents at scale, has announced its general availability. This platform aims to streamline the complexities of agent deployment, particularly for long-running, stateful agents. It offers features like one-click deployment, a suite of API endpoints for creating customized user experiences, horizontal scaling to manage traffic surges, and a persistence layer to maintain memory and conversational history. The platform also includes Native LangGraph Studio, an agent IDE, to facilitate debugging, visibility, and iterative improvements in agent development.
The LangGraph Platform addresses challenges associated with running agents in production environments. Many AI agents are long-running, prone to failures, and require durable infrastructure to ensure task completion. Additionally, agents often rely on asynchronous collaboration, such as interacting with humans or other agents, requiring infrastructure that can handle unpredictable events and preserve state. LangGraph Platform aims to alleviate these concerns by providing the necessary server infrastructure to support these workloads at scale. The platform also boasts a native GitHub integration for simplified one-click deployment from repositories. Alongside the LangGraph Platform, the "LangGraph Multi-Agent Swarm" has been released, a Python library designed to orchestrate multiple AI agents. This library builds upon the LangGraph framework, enabling the creation of multi-agent systems where specialized agents dynamically hand off control based on task demands. This system tracks the active agent, ensuring seamless continuation of conversations even when users provide input at different times. The library offers features like streaming responses, memory integration, and human-in-the-loop intervention, allowing developers to build complex AI agent systems with explicit control over information flow and decisions. Recommended read:
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@Google DeepMind Blog
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Google DeepMind has introduced AlphaEvolve, a revolutionary AI coding agent designed to autonomously discover innovative algorithms and scientific solutions. This groundbreaking research, detailed in the paper "AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery," represents a significant step towards achieving Artificial General Intelligence (AGI) and potentially even Artificial Superintelligence (ASI). AlphaEvolve distinguishes itself through its evolutionary approach, where it autonomously generates, evaluates, and refines code across generations, rather than relying on static fine-tuning or human-labeled datasets. AlphaEvolve combines Google’s Gemini Flash, Gemini Pro, and automated evaluation metrics.
AlphaEvolve operates using an evolutionary pipeline powered by large language models (LLMs). This pipeline doesn't just generate outputs—it mutates, evaluates, selects, and improves code across generations. The system begins with an initial program and iteratively refines it by introducing carefully structured changes. These changes take the form of LLM-generated diffs—code modifications suggested by a language model based on prior examples and explicit instructions. A diff in software engineering refers to the difference between two versions of a file, typically highlighting lines to be removed or replaced. Google's AlphaEvolve is not merely another code generator, but a system that generates and evolves code, allowing it to discover new algorithms. This innovation has already demonstrated its potential by shattering a 56-year-old record in matrix multiplication, a core component of many machine learning workloads. Additionally, AlphaEvolve has reclaimed 0.7% of compute capacity across Google's global data centers, showcasing its efficiency and cost-effectiveness. AlphaEvolve imagined as a genetic algorithm coupled to a large language model. Recommended read:
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@www.analyticsvidhya.com
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Google's DeepMind has achieved a significant breakthrough in artificial intelligence with its Dreamer AI system. The AI has successfully mastered the complex task of mining diamonds in Minecraft without any explicit human instruction. This feat, accomplished through trial-and-error reinforcement learning, demonstrates the AI's ability to self-improve and generalize knowledge from one scenario to another, mimicking human-like learning processes. The achievement is particularly noteworthy because Minecraft's randomly generated worlds present a unique challenge, requiring the AI to adapt and understand its environment rather than relying on memorized strategies.
Mining diamonds in Minecraft is a complex, multi-step process that typically requires players to gather resources to build tools, dig to specific depths, and avoid hazards like lava. The Dreamer AI system tackled this challenge by exploring the game environment and identifying actions that would lead to rewards, such as finding diamonds. By repeating successful actions and avoiding less productive ones, the AI quickly learned to navigate the game and achieve its goal. According to Jeff Clune, a computer scientist at the University of British Columbia, this represents a major step forward for the field of AI. The Dreamer AI system, developed by Danijar Hafner, Jurgis Pasukonis, Timothy Lillicrap and Jimmy Ba, achieved expert status in Minecraft in just nine days, showcasing its rapid learning capabilities. One unique approach used during training was to restart the game with a new virtual universe every 30 minutes, forcing the algorithm to constantly adapt and improve. This innovative method allowed the AI to quickly master the game's mechanics and develop strategies for diamond mining without any prior training or human intervention, pushing the boundaries of what AI can achieve in dynamic and complex environments. Recommended read:
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@www.analyticsvidhya.com
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Google's DeepMind has achieved a significant milestone in artificial intelligence by developing an AI system, named Dreamer, that has mastered Minecraft without any human instruction or data. The Dreamer AI system successfully learned how to mine diamonds, a complex and multi-step process, entirely on its own through trial and error. This breakthrough highlights the potential for AI systems to generalize knowledge and transfer skills from one domain to another, marking a major step forward in the field of AI development.
Researchers programmed the Dreamer AI to play Minecraft by setting up a system of rewards, particularly for finding diamonds. The AI explores the game on its own, identifying actions that lead to in-game rewards and repeating those actions. The AI was able to reach an expert level within just nine days. The results are a good sign that AI apps can learn to improve its abilities over a short period of time, which could give robots the tools they need to perform well in the real world. Recommended read:
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@Google DeepMind Blog
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Google DeepMind has released a strategy paper outlining its approach to the development of safe artificial general intelligence (AGI). According to DeepMind, AGI, defined as AI capable of matching or exceeding human cognitive abilities, could emerge as early as 2030. The company emphasizes the importance of proactive risk assessment, technical safety measures, and collaboration within the AI community to ensure responsible development. They are exploring the frontiers of AGI, prioritizing readiness and identifying potential challenges and benefits.
DeepMind's strategy identifies four key risk areas: misuse, misalignment, accidents, and structural risks, with an initial focus on misuse and misalignment. Misuse refers to the intentional use of AI systems for harmful purposes, such as spreading disinformation. DeepMind is also introducing Gemini Robotics, which it touts as its most advanced vision-language-action model. Gemini Robotics aims to allow robots to comprehend something in front of them, interact with a user, and take action. Recommended read:
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@Google DeepMind Blog
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Google DeepMind is intensifying its focus on AI governance and security as it ventures further into artificial general intelligence (AGI). The company is exploring AI monitors to regulate hyperintelligent AI models, splitting potential threats into four categories, with the creation of a "monitor" AI being one proposed solution. This proactive approach includes prioritizing technical safety, conducting thorough risk assessments, and fostering collaboration within the broader AI community to navigate the development of AGI responsibly.
DeepMind's reported clampdown on sharing research will stifle AI innovation, warns the CEO of Iris.ai, one of Europe’s leading startups in the space, Anita Schjøll Abildgaard. Concerns are rising within the AI community that DeepMind's new research restrictions threaten AI innovation. The CEO of Iris.ai, a Norwegian startup developing an AI-powered engine for science, warns the drawbacks will far outweigh the benefits. She fears DeepMind's restrictions will hinder technological advances. Recommended read:
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@www.infoq.com
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AI ? SiliconANGLE
, www.infoq.com
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Google DeepMind has unveiled TxGemma, an AI designed to improve drug discovery and clinical trial predictions. TxGemma, built upon the Gemma model family, aims to streamline the drug development process and accelerate the creation of new treatments. This announcement comes as Isomorphic Labs, an Alphabet spinout, secured $600 million in funding to further develop its AI drug design engine, which reduces manual labor and speeds up drug development.
Isomorphic Labs' engine uses AI to design small molecules with therapeutic applications, predicting their effectiveness in attaching to disease-causing proteins and mapping properties like solubility. This is powered by models like Google's AlphaFold 3, which can predict the shape of proteins, DNA, and RNA, crucial for drug development. The funding will accelerate Isomorphic Labs' research and development efforts, expand its team, and advance its programs, including those focused on oncology and immunology, toward clinical development. Recommended read:
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Maria Deutscher@AI ? SiliconANGLE
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Isomorphic Labs, an Alphabet spinout focused on AI-driven drug design, has secured $600 million in its first external funding round. The investment, led by Thrive Capital with participation from Alphabet and GV, will fuel the advancement of Isomorphic Labs' AI drug design engine and therapeutic programs. The company aims to leverage artificial intelligence, including its AlphaFold technology, to revolutionize drug discovery across various therapeutic areas, including oncology and immunology. This funding is expected to accelerate research and development efforts, as well as facilitate the expansion of Isomorphic Labs' team with top-tier talent.
Isomorphic Labs, founded in 2021 by Sir Demis Hassabis, seeks to reimagine and accelerate drug discovery by applying AI. Its AI-powered engine streamlines the design of small molecules with therapeutic applications and can predict the effectiveness of a small molecule's attachment to a protein. The company's software also eases other aspects of the drug development workflow. Isomorphic Labs has already established collaborations with pharmaceutical companies like Eli Lilly and Novartis, and the new funding will support the progression of its own drug programs into clinical development. Recommended read:
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Synced@Synced
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References:
Synced
, www.theguardian.com
DeepMind has announced significant advancements in AI modeling and biomedicine, pushing the boundaries of what's possible with artificial intelligence. The company's research is focused on creating more effective drugs and medicine, as well as understanding and protecting species around the world.
DeepMind's JetFormer, a novel Transformer model, is designed to directly model raw data, eliminating the need for pre-trained components. JetFormer can understand and generate both text and images seamlessly. This model leverages normalizing flows to encode images into a latent representation, enhancing the focus on essential high-level information through progressive Gaussian noise augmentation. JetFormer has demonstrated competitive performance in image generation and web-scale multimodal generation tasks. Additionally, DeepMind is exploring how studying honeybee immunity could offer insights into protecting various species. The company's AlphaFold continues to revolutionize biology, aiding in the design of more effective drugs. AlphaFold, which uses AI to determine a protein's structure, has been used to solve fundamental questions in biology, awarded the Nobel prize (in chemistry – to Demis Hassabis and John Jumper) and revolutionised drug discovery. There are approximately 250,000,000 protein structures in the AlphaFold database, which has been used by almost 2 million people from 190 countries. Recommended read:
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@Google DeepMind Blog
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References:
Google DeepMind Blog
, THE DECODER
Researchers are making strides in understanding how AI models think. Anthropic has developed an "AI microscope" to peek into the internal processes of its Claude model, revealing how it plans ahead, even when generating poetry. This tool provides a limited view of how the AI processes information and reasons through complex tasks. The microscope suggests that Claude uses a language-independent internal representation, a "universal language of thought", for multilingual reasoning.
The team at Google DeepMind introduced JetFormer, a new Transformer designed to directly model raw data. This model, capable of both understanding and generating text and images seamlessly, maximizes the likelihood of raw data without depending on any pre-trained components. Additionally, a comprehensive benchmark called FACTS Grounding has been introduced to evaluate the factuality of large language models (LLMs). This benchmark measures how accurately LLMs ground their responses in provided source material and avoid hallucinations, aiming to improve trust and reliability in AI-generated information. Recommended read:
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Nathan Labenz@The Cognitive Revolution
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References:
Google DeepMind Blog
, Windows Copilot News
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DeepMind's Allan Dafoe, Director of Frontier Safety and Governance, is actively involved in shaping the future of AI governance. Dafoe is addressing the challenges of evaluating AI capabilities, understanding structural risks, and navigating the complexities of governing AI technologies. His work focuses on ensuring AI's responsible development and deployment, especially as AI transforms sectors like education, healthcare, and sustainability, while mitigating potential risks through necessary safety measures.
Google is also prepping its Gemini AI model to take actions within apps, potentially revolutionizing how users interact with their devices. This development, which involves a new API in Android 16 called "app functions," aims to give Gemini agent-like abilities to perform tasks inside applications. For example, users might be able to order food from a local restaurant using Gemini without directly opening the restaurant's app. This capability could make AI assistants significantly more useful. Recommended read:
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@Google DeepMind Blog
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Google is pushing the boundaries of AI and robotics with its Gemini AI models. Gemini Robotics, an advanced vision-language-action model, now enables robots to perform physical tasks with improved generalization, adaptability, and dexterity. This model interprets and acts on text, voice, and image data, showcasing Google's advancements in integrating AI for practical applications. Furthermore, the development of Gemini Robotics-ER, which incorporates embodied reasoning capabilities, signifies another step toward smarter, more adaptable robots.
Google's approach to robotics emphasizes safety, employing both physical and semantic safety systems. The company is inviting filmmakers and creators to experiment with the model to improve the design and development. Veo builds on years of generative video model work, including Generative Query Network(GQN),DVD-GAN,Imagen-Video,Phenaki,WALT,VideoPoetandLumiere— combining architecture, scaling laws and other novel techniques to improve quality and output resolution. Recommended read:
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Ben Lorica@Gradient Flow
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References:
Gradient Flow
DeepSeek is making significant strides in the AI landscape, particularly within the healthcare sector in China. The AI solution is being rapidly adopted across China's tertiary hospitals to improve clinical decision-making and operational efficiency. Its rollout began in Shanghai, with hospitals like Fudan University Affiliated Huashan Hospital, and has expanded nationwide. DeepSeek is being used in areas such as intelligent pathology to automate tumor analysis, imaging analysis for lung nodule differentiation, clinical decision support for evidence retrieval, and workflow optimization to reduce patient wait times.
DeepSeek has also open-sourced several code repositories to give competitors a scare on the journey toward transparency and the advancement of the AI community. This move puts the firm ahead of the competition on model transparency and the open source nature allows hospitals to customize the programs. This level of openness is a further step than other AI competitors such as Meta’s Llama, which has only open-sourced the weights of its models. DeepSeek's deployment focuses on practical applications within hospital intranets, ensuring data security while improving accuracy and generalization through hierarchical knowledge distillation, reducing computational costs. Recommended read:
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