Multi-Agent Systems(MAS)

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In partnership with

In partnership with

Microsoft has released three new AI models in its Phi-3.5 series, including a lightweight instruction model, a multimodal vision model, and a Mixture of Experts (MoE) model. These models deliver near state-of-the-art performance on various benchmarks and are available under an open-source MIT license.

In California authors are starting a lawsuits against Anthropic for copyright infringement. The authors and journalists Andrea Bartz, Charles Graeber and Kirk Wallace Johnson, said that Anthropic used pirated versions of their works and others to teach Claude to respond to human prompts.

Anthropic has made the move to open access to Claude Artifacts to nearly all users.

California has built an AI wildfire detection system that has been named one of the best inventions of the year.

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Cerebras launched fastest Llama3.1 inference API with Llama 3.1 8B at 1,700 tokens/sec.

Cartesia has launched a model with a hybrid Mamba-2 SSM backbone, and tailored to on-device inference.

Zyphra to release Zamba2-mini, a state-of-the-art small language model for on-device applications.

These cannabis gummies keep selling out in 2024

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Open Funnel, CRM for developer lead marketing.

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Creatify, An AI-powered app that generates high-quality marketing videos from a simple product link or a description.

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Multi-Agent Systems

Multi-Agent Systems (MAS), a groundbreaking approach that's reshaping how we think about and manage complex supply networks. By harnessing the power of autonomous, intelligent agents working in concert, MAS is enabling businesses to achieve new levels of efficiency, adaptability, and resilience in their supply chains.

Whether you're a supply chain veteran or new to the field, understanding MAS could be the key to unlocking your organization's competitive edge in the digital age.

A Multi-Agent System (MAS) is a network of intelligent, autonomous software agents that interact to solve problems too complex or distributed for a single agent to handle effectively. In the context of supply chain management, these agents can represent various entities such as suppliers, manufacturers, distributors, and retailers, each with its own goals, constraints, and decision-making capabilities.

How does MAS work in supply chain management

The decentralized nature of MAS aligns perfectly with the distributed structure of supply chains, allowing for localized decision-making and rapid adaptability to changing conditions.

Intelligent agents collect and analyze data from various sources, including sales records, social media trends, and economic indicators. Using advanced machine learning algorithms, these agents can predict future demand patterns with increasing accuracy.

Inventory management and order fulfillment also benefit significantly from MAS implementation. Warehouse agents monitor stock levels in real-time, automatically triggering reorders based on demand forecasts and predefined rules.

MAS also alters supplier selection and risk management in supply chains. Intelligent agents continuously evaluate suppliers based on multiple criteria, including cost, quality, reliability, and sustainability. These agents can perform automated negotiations and dynamically adjust supplier ratings based on performance.

By breaking down silos and enabling seamless communication between different parts of the supply chain, MAS creates a more cohesive and responsive system. This approach allows businesses to achieve system-wide optimization, balancing potentially conflicting objectives across the entire supply chain and ultimately leading to improved overall performance, increased agility, and a stronger competitive position in the market.