SLM

Cisco Acquires NeuralFabric for Enterprise AI

Cisco Acquires NeuralFabric for Enterprise AI
Cisco’s intent to acquire Seattle-based NeuralFabric signals a decisive shift toward practical, domain-specific AI that meets real-world constraints around data, compliance, and infrastructure. Cisco plans to acquire NeuralFabric, an enterprise AI platform focused on building small language models (SLMs) from proprietary data with deployment across SaaS and on-premises environments. By focusing on SLMs trained on enterprise data and deployable in hybrid environments, Cisco aims to shorten time-to-value while keeping control where it belongs—inside the business. They reduce inference cost, improve latency, and can be deployed on-premises or at the edge—critical for sectors like telecom, financial services, and healthcare.

AI Arms Race and the Interplay of Tariffs with the EU AI Act

AI Arms Race and the Interplay of Tariffs with the EU AI Act
The integration of tariffs and the EU AI Act creates a challenging environment for the advancement of AI and automation. Tariffs, by increasing the cost of essential hardware components, and the EU AI Act, by increasing compliance costs, can significantly raise the barrier to entry for new AI and automation ventures. European companies developing these technologies may face a double disadvantage: higher input costs due to tariffs and higher compliance costs due to the AI Act, making them less competitive globally. This combined pressure could discourage investment in AI and automation within the EU, hindering innovation and slowing adoption rates. The resulting slower adoption could limit the availability of crucial real-world data for training and improving AI algorithms, further impacting progress.

Beyond the Hype: Unleashing the Power of Private, Affordable, Assistive AI for a Better Future

Beyond the Hype: Unleashing the Power of Private, Affordable, Assistive AI for a Better Future
AI stirs both excitement and concern. While some companies rush to take advantage of it, many are cautious due to the challenges and costs. However, there may be a better approach: using Assistive Intelligence with small, specialized models instead of Large Language Models. This method is more affordable and can benefit businesses and society. Emphasizing open-source technology respects privacy and fosters true innovation. By focusing on solving real problems, we enable growth and empower people to explore Assistive AI without high costs.

AI Meets Longevity: OpenAI & Retro’s GPT-4b Micro

AI Meets Longevity: OpenAI & Retro’s GPT-4b Micro
OpenAI and Retro Biosciences have unveiled GPT-4b micro, an AI model designed to engineer proteins for longevity science. This partnership focuses on re-engineering Yamanaka factors, which hold the potential to slow aging, regenerate organs, and add 10 healthy years to human life.

Enhancing Behavioral Intelligence AI Decision Engines with SLMs: Use Cases Across Industries

Enhancing Behavioral Intelligence AI Decision Engines with SLMs: Use Cases Across Industries
The article discusses the potential of Small, Specialized, and Symbolic Learning Machines (SLMs) in Behavioral Intelligence (BI) Artificial Intelligence (AI) decision engines. Unlike traditional machine learning models, SLMs use symbolic reasoning to make decisions and provide clear explanations for their predictions. This transparency is crucial in sensitive areas where decision-making explanations are essential. The article explores various applications of SLMs in BI AI decision engines and concludes that SLMs offer a promising pathway towards more energy-efficient and sustainable AI, reducing computational demands and enabling edge deployment while providing comparable performance for specific tasks.

Data, energy, people and planet: Some SLM balance in the polarized digital age

Data, energy, people and planet: Some SLM balance in the polarized digital age
SLMs present an exciting opportunity for creating a more energy-efficient and sustainable approach to AI. They lower computational requirements, facilitate edge deployment, and maintain similar performance levels for certain tasks, which can help lessen the environmental footprint of AI while still providing essential advantages. Additionally, prioritizing data privacy and responsible data management can greatly reduce energy use in data centers. By encouraging ethical data practices, empowering users, and promoting energy efficiency through SLMs, we can pave the way for a greener and more privacy-aware digital landscape.

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