Cedric Vidal’s Post

In this new blog post, I explore open-source alternatives to LANDING AI, discussing how to navigate the open-source landscape, choose state-of-the-art models, and why #ReductStore is a great on-premise option compared to LandingEdge. Check it out if you're interested in #ai, #mlops, and #edgecomputing.

🚀 Exciting News! Microsoft introduces GPT-4o, OpenAI's multimodal model on Azure AI. Explore its text and vision capabilities in the preview playground. Efficient, cost-effective, and ready for advanced analytics! #AI #GenerativeAI #AzureAI #OpenAI #GPT4o

#Technology #Innovation #Data From chaos to creation: How data labeling drives success in generative AI: Training data is the cornerstone of AI algorithms – the quality of outputs is contingent on the data the AI model was trained on. The data determines the success of AI models, underscoring the importance of data labeling. Data labeling plays a crucial role in generative AI by providing context and meaning to the data… Read More »From chaos to creation: How data labeling drives success in generative AI The post From chaos to creation: How data labeling drives success in generative AI appeared first on Data Science Central. #AI #BigData #Cloud

Azure AI Document Intelligence introduces game-changing features for advanced document processing 📊📑: - Hierarchical document structure analysis to segment documents semantically. - Figure detection with bounding regions, spans, and elements properties to connect figures to textual content. - Integration with Azure OpenAI Service GPT-4 Turbo with Vision (GPT-4V) for enriched insights. Key benefits include: - Enhanced comprehension and navigation of document structures. - Efficient information retrieval with structured markdown output. - Improved understanding of complex information through visual and textual context linkage. Discover the full potential of document processing with Azure AI 🚀. For more info: https://lnkd.in/daGPCFDk #AzureAI #DocumentIntelligence #OpenAIService

Llama 3 is now available in Azure 🥇 Meta-Llama-3-70B pre-trained and instruction fine-tuned models are geared towards content creation and conversational AI, providing deeper language understanding for more nuanced tasks, like R&D and enterprise applications requiring nuanced text summarization, classification, language modeling, dialog systems, code generation and instruction following. https://lnkd.in/ee3Dc43R

This report has some great information. Here are a few takeaways from a conversation with my personalized custom GPT: Training Costs Insight: Training costs for AI models have skyrocketed. OpenAI’s GPT-4 required $78 million in compute resources, while Google’s Gemini Ultra cost $191 million . Significance: This highlights the escalating resource demands for developing state-of-the-art AI. These costs pose barriers to entry, concentrating power among tech giants. Implications: As AI tools become more critical to organizations, tracking cost trends can inform decisions about adopting existing platforms versus developing in-house solutions. Generative AI Growth Insight: Generative AI investment nearly octupled, reaching $25.2 billion in 2023, even as overall AI investments declined. Significance: The rapid rise of generative AI reflects its transformative potential across industries, from content creation to operational efficiency. Implications: Focus on generative AI tools to enhance productivity or fill gaps in your organization’s capabilities. Stay ahead of emerging tools and evaluate their business impact. Global AI Leadership Insight: The U.S. leads in producing notable AI models, contributing 61 in 2023, compared to China’s 15 and the EU’s 21. Significance: The U.S. dominance is a marker of its innovative ecosystem, but global competitors are catching up. Implications: If your organization operates globally, monitor regulatory and innovation trends in these regions to align strategies with regional capabilities and laws. Public Perception of AI Insight: Global awareness of AI’s impact has risen; 66% believe AI will significantly affect their lives in 3–5 years, but 52% express nervousness. Significance: Public skepticism about AI reflects concerns about its societal effects, from job displacement to misinformation. Implications: Integrating AI tools into your workplace may require clear communication about their benefits and ethical usage to gain employee and stakeholder trust. Responsible AI and Risks Insight: AI systems lack standardized evaluations for responsible use, complicating the mitigation of risks like misinformation, bias, and misuse . Significance: Without robust accountability frameworks, the risks of deploying AI at scale increase. Implications: Advocate for and implement governance structures in your organization to ensure responsible AI use, reflecting industry best practices. Impact on Labor Insight: AI improves productivity and narrows skill gaps, but improper use can reduce performance. AI adoption across organizations rose from 50% in 2022 to 55% in 2023. Significance: The nuanced impact of AI on labor highlights its potential to both empower and disrupt workforces. Implications: As organizations increasingly adopt AI, prioritize workforce training and oversight to maximize benefits and minimize disruptions. #ai #genai

🎉 Exciting News! 🎉 I am thrilled to share that I have successfully completed the #GCPBoleh program and passed 7 advanced AI courses through Google Boost! 🚀📚 Check my Profile :)) https://lnkd.in/gWe3Vp5u Looking forward to the next steps in my AI journey! If you're passionate about AI and technology, let's connect and discuss how we can collaborate and create impactful solutions together. #AI #MachineLearning #DeepLearning #DataEngineering #GoogleBoost #LifelongLearning #CareerGrowth #TechInnovation

#Tech #FutureTech #Automation From chaos to creation: How data labeling drives success in generative AI: Training data is the cornerstone of AI algorithms – the quality of outputs is contingent on the data the AI model was trained on. The data determines the success of AI models, underscoring the importance of data labeling. Data labeling plays a crucial role in generative AI by providing context and meaning to the data… Read More »From chaos to creation: How data labeling drives success in generative AI The post From chaos to creation: How data labeling drives success in generative AI appeared first on Data Science Central. #AI #BigData #Cloud

From chaos to creation: How data labeling drives success in generative AI: Training data is the cornerstone of AI algorithms – the quality of outputs is contingent on the data the AI model was trained on. The data determines the success of AI models, underscoring the importance of data labeling. Data labeling plays a crucial role in generative AI by providing context and meaning to the data… Read More »From chaos to creation: How data labeling drives success in generative AI The post From chaos to creation: How data labeling drives success in generative AI appeared first on Data Science Central. #AI #BigData #Cloud