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EvoAgent: An Innovative Approach to Automatically Advance Professional Agents for Multi-Agent Systems Using Evolutionary Algorithms

Large Language Models (LLMs) have achieved considerable success in various tasks related to language understanding, reasoning, and generation. Currently, researchers are focusing on creating LLM-based autonomous agents for more diverse and complex real-world applications. However, many situations in the real world pose challenges that cannot be overcome by a single agent. Hence, engineers are developing…

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Active Inheritance Enhances AI Cohesion in Large Language Models (LLMs): Guiding Artificial Data Creation for Optimum Efficiency and Minimized Bias

Generating synthetic data is becoming an essential part of machine learning as it allows researchers to create large datasets where real-world data is scarce or expensive to obtain. The created data often display specific characteristics that benefit machine learning models' learning processes, helping to improve performance across various applications. However, the usage of synthetic data…

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Exploring AI Representatives: The Three Primary Elements – Dialogue, Sequence, and Representative

AI agents, systems designed to autonomously perceive their environment, make decisions, and act to achieve specific goals, have become increasingly important in the world of artificial intelligence applications. These agents function through three primary components: Conversation, Chain, and Agent, each playing a critical role. The Conversation component refers to the interaction mechanism for AI agents, allowing…

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Comprehending AI Agents: The Three Central Elements – Dialogue, Sequence, and Representative

Artificial Intelligence (AI) agents are now a significant component of AI applications. AI agents are systems designed to understand their environments, make decisions, and act autonomously to achieve specific goals. Understanding how AI agents work involves exploring their three main components: Conversation, Chain, and Agent. Conversation, the interaction mechanism, is the portal through which AI agents…

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Spectrum: A Technique Powered by AI that Boosts LLM Training by Precisely Focusing on Layer Modules According to their Signal-to-Noise Ratio (SNR)

The development and deployment of large language models (LLMs) play a crucial role in natural language processing (NLP), but these models pose significant challenges due to their high computational cost and extensive memory requirement. This makes the training process laborious and inefficient and could inhibit broader application and research. As a result, developing efficient methods…

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Spectrum: An Artificial Intelligence Technique that Enhances LLM Training by Specifically Focusing on Layer Modules Depending on their Signal-to-Noise Ratio (SNR)

Large language models (LLMs) are essential for natural language processing (NLP), but they demand significant computational resources and time for training. This requirement presents a key challenge in both research and application of LLMs. The challenge lies in efficiently training these huge models without compromising their performance. Several approaches have been developed to address this issue.…

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NASA and IBM scientists present INDUS: A collection of large, domain-specific language models designed for sophisticated scientific research.

Large Language Models (LLMs) have proven highly competent in generating and understanding natural language, thanks to the vast amounts of data they're trained on. Predominantly, these models are used with general-purpose corpora, like Wikipedia or CommonCrawl, which feature a broad array of text. However, they sometimes struggle to be effective in specialized domains, owing to…

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Researchers from NASA and IBM Present INDUS: A Collection of Specific Large Language Models (LLMs) for Progressive Scientific Research.

Large Language Models (LLMs) are typically trained on large swaths of data and demonstrate effective natural language understanding and generation. Unfortunately, they can often fail to perform well in specialized domains due to shifts in vocabulary and context. Seeing this deficit, researchers from NASA and IBM have collaborated to develop a model that covers multidisciplinary…

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A Basic Model-Free Open-Loop Baseline for Reinforcement Learning Mobility Tasks that Does Not Require Sophisticated Models or Computational Resources

Deep Reinforcement Learning (DRL) is advancing robotic control capabilities, albeit with a rising trend of algorithm complexity. These complexities lead to challenging implementation details, impacting the reproducibility of sophisticated algorithms. This issue, therefore, necessitates the need for simpler machine learning models that are not as computationally demanding. A team of international researchers from the German Aerospace…

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Scientists from the University of Toronto have unveiled a deep-learning model that surpasses the predictive capabilities of Google’s AI system for peptide structures.

Peptides are involved in various biological processes and are instrumental in the development of new therapies. Understanding their conformations, i.e., the way they fold into their specific three-dimensional structures, is critical for their functional exploration. Despite the advancements in modeling protein structures, like with Google's AI system AlphaFold, the dynamic conformations of peptides remain challenging…

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Introducing BricksAI: An open-core AI Gateway designed to assist developers in incorporating all fundamental features required in any GenAI initiative.

Enterprise-level software often grapples with managing large language models (LLMs) due to a lack of robust methods in regulating such models' usage. Regularizing these expenditures per use, project, environment or feature can be tricky as it requires a detailed and intricate method for monitoring LLMs. In many cases, this could mean a diversion of technical…

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15 Practical Instances of LLM Usage in Various Sectors

Large Language Models (LLMs) have become crucial in various industries owing to their proficiency in natural language processing, content generation, and data analysis. They offer an array of applications for businesses, offering transformative impact across different sectors. More than ever, companies are harnessing LLMs in real-world scenarios. Netflix, for instance, has transitioned from traditional rule-based classifiers…

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