Mark Ritzmann

Today we report a significant advance in understanding the inner workings of AI models. We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model. This interpretability discovery could, in future, help us make AI models safer. We mostly treat AI models as a black box: something goes in and a response comes out, and it's not clear why the model gave that particular response instead of another. This makes it hard to trust that these models are safe: if we don't know how they work, how do we know they won't give harmful, biased, untruthful, or otherwise dangerous responses? How can we trust that they’ll be safe and reliable?

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Although a picture is worth a thousand words, preparing visually rich, multimodal documents, such as PDFs, for a retrieval-augmented generation (RAG) workflow can be both time-consuming and error-prone. In industries where accuracy is critical, like healthcare or financial services, documents like radiology reports or financial statements often contain images or charts that provide valuable contextual information. While these visually rich elements are often left out of RAG workflows, a new approach for retrieving information from visually enhanced documents is set to streamline multimodal document preparation and transform the potential of RAG and generative AI (GenAI).

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Anthropic's Claude 3.5: A New Benchmark Leader in AI In the rapidly evolving landscape of artificial intelligence, Anthropic's Claude 3.5 has emerged as a significant contender, surpassing OpenAI's GPT-4 in several key benchmarks. This latest model demonstrates superior performance across various tasks, highlighting the continuous advancements and competitive dynamics within the AI field. Claude 3.5 excels in natural language processing, exhibiting enhanced comprehension and generation capabilities. According to recent benchmarks, Claude 3.5 achieved a 10% higher accuracy rate in contextual understanding tasks and a 15% improvement in response generation compared to GPT-4. This achievement underscores Anthropic's commitment to pushing the boundaries of AI technology. The implications of Claude 3.5's success extend beyond mere competition. Its advanced capabilities promise practical applications across industries, from improving customer service chatbots to enhancing content creation and data analysis tools. For example, companies utilizing Claude 3.5 have reported a 20% increase in customer satisfaction due to more accurate and contextually relevant interactions. With AI rapidly becoming integral to various sectors, the emergence of superior models like Claude 3.5 marks a pivotal moment in the AI race. This progress not only benefits businesses and developers but also paves the way for more sophisticated and effective AI-driven solutions. hashtag#AIInnovation hashtag#Anthropic hashtag#Claude3.5 hashtag#BenchmarkLeader hashtag#ArtificialIntelligence hashtag#TechAdvancements

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Thanks to #genai360 for the shoutout here for Consilience AI and our AlphaIQ product. Davit Buniatyan Joseph Byrum, Ph.D., MBA

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A recent article highlights how #AI #LLMs providers like #OpenAI and #Google are releasing smaller, more affordable models tailored to meet specific business needs 🤖 By prioritizing #affordability and #reliability, these companies are democratizing access to #AItechnology and paving the way for widespread adoption 🌍 As #AImodels become more accessible and cost-effective, businesses can harness their power to automate tasks, enhance customer service, and drive innovation 💪 Organizations can start approaching #GenAI adoption more strategically. Instead of continuing to focus on short-term experimentation, now is the time to create #value, #monetize, and #scale quickly. There are plenty of #LLM options available (even through single APIs) to exceed #customers' expectations and #financial forecasts 📈 The race is on, and the stakes couldn't be higher. If you want to play, you have to #innovate.. to innovate, must first #migrate ⛅️.. then watch as you scale and dominate 🚀🚀 #ToPlayHaveToInnovate #ToInnovateMustMigrate #AmazonWebServices #MicrosoftAzure #GoogleCloud #Gemini #Meta #Llama2 #Anthropic #Claude #GPT4o #CloudMigration #Partnership

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Top 5 LLM Testing and Evaluation Tools: 🔭 Galileo: An end-to-end platform for GenAI evaluation, experimentation, and observability AgentOps.ai: Build reliable AI agents with monitoring, testing, and replay analytics. Toloka: A data-centric environment to support fast and scalable AI development with the help of human insight. Gantry: Powerful observability, analytics, and evaluation for your AI-powered products. RagaAI Inc: Fix AI issues in three easy steps - Detect, Diagnose and Fix. #AI

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More groundbreaking methods in forecasting! In conjunction with the many model frameworks for forecasting that I shared earlier this week, the team has also released an innovative approach to forecasting using AI. LLMs are built on a transformer architecture. The team has used this same transformer architecture to perform time-series forecasting. This gives it the ability to leverage mountains of disparate information for forecasting. Most commercial forecasting packages won’t be able to do transformer based forecasts for years, if ever. It requires a different architecture. But this is seamless with Databricks. AND…it works with the many models framework. You can include this as a type of forecast to test along with other models. Great work by Ryuta, Bryan, and Puneet!

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5 questions with Marcello Cacciato (Metyis) on bringing Generative AI to life Marcello Cacciato, Director in the Data Science practice of Metyis, is an expert Generative AI, having advised multiple organisations on the development and implementation of their Generative AI stra https://lnkd.in/efSSGj3g to-life

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💡#Microsoft banking big on #Copilot with the launch of Agentic workflows. While organizations still try to understand ways to leverage LLMs, albeit cautiously, Microsoft continues to ramp up their R&D at a phenomenal speed with enterprise grade applications. 💡The potential of agents to automate tasks is significantly higher than a stand alone LLM, signalling towards a major layoff trend across many industries with #Dell planning to cut down massive workforce (around 10% of it's sales workforce) amidst the rise of AI technologies... 🛑Tough times ahead? I'm still optimistic but would love to hear your thoughts 💭 https://lnkd.in/dFMw5jM8

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#Agentic_AI, the #next_frontier in #generative_AI #IBM_describes_Agentic_AI as a #system that can #autonomously perform tasks on #behalf of a user or #another_system by #designing its #workflow and using #available_tools . https://lnkd.in/ghxNuU3t

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Retrieval Augmented Generation (RAG) is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information repositories, enhancing their ability to generate accurate and contextually relevant responses. Make sure RAG is embedded into any AI solution under consideration.

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Smaller is better The need for powerful #LLMs has fueled their exponential growth. However, large LLMs come with a cost - they require significant computational resources and energy, leading to increased response latency. The largest models today have over 100 billion parameters and can take over 200 gigabytes to load. Industry applications and #AI giants are now shifting their focus towards smaller and faster models, prioritizing efficiency over maximum performance. Learn more:

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For GenAI enthusiasts: How to Build AI Agents with LlamaIndex, MongoDB, and Claude 3.5 Sonnet. https://lnkd.in/gwcfbadD

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#GenAI is the new #cloud 🤖🤝🌥 #Amazon’s CEO shared a bold perspective on #generativeAI growing even faster than #cloudcomputing did in its early days. This acceleration speaks volumes about the shift in the tech landscape. As innovation speeds forward, we're seeing a massive embrace of tools that don't just improve efficiency but are reshaping the very nature of our work. Generative AI's potential spans across industries, from transforming the #customerexperience to driving #operationalefficiencies at unprecedented scales. For leaders and businesses, staying adaptable isn’t just a choice anymore—it’s a requirement. As Amazon demonstrates, those who prioritize #agility and #innovation are positioned to set the pace in this new era 🚀🚀 🔗 in the comments 👇 #AmazonWebServices #AWS #SageMaker #Bedrock #AmazonQ #LLM #AI #ML #RAG #AIassistant

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The OCI Generative AI RAG Agent is an advanced #AI solution, offering exceptional response quality, relevance, and advanced data handling capabilities. Learn more: https://lnkd.in/e5FeN3pw #GenAI

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📊 The Rise of Chief AI Officers in the C-Suite 🤖 A recent survey by West Monroe reveals that nearly 40% of young business leaders expect to see more Chief AI Officers (CAIOs) in the next five years, while the role of Chief Transformation Officer may fade away. This shift reflects the growing importance of AI in business strategy. However, opinions differ on the need for a dedicated AI exec. Some argue for integrating AI across all departments rather than isolating it. What do you think about the future of AI leadership? #AI #BusinessInnovation #Csuite #Leadership #Technology #FutureOfWork #ChiefAIOfficer

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