Cognaize 的动态

?? AI Week Frankfurt 2024: The fuse is lit! Watch as our city transforms into a crucible of cutting-edge innovation. Are you ready to spark the future? Dr. Holger Schmidt, Frankfurter Allgemeine Zeitung Dr Holger Schmidt is an expert and speaker on the digital economy. His core topics are platform economics, digital business models and artificial intelligence. The economist is Editorial Director Newsletter & Verticals at F.A.Z. and is responsible for the F.A.Z. PRO Digitalwirtschaft briefing. He also teaches digital transformation at TU Darmstadt, writes books and co-hosts an F.A.Z. podcast on artificial intelligence. Vahe Andonians, Cognaize Vahe Andonians is a banker, data scientist, entrepreneur and senior lecturer at the Frankfurt School of Finance & Management. In 2020, Vahe founded Cognaize. Cognaize specializes in hybrid intelligence to deliver on the promise of AI for financial services. The company’s AI platform combines proprietary deep-learning AI technologies, precise language models trained on financial documents such as loan applications, SEC filings, ESG-related documents, or trustee reports; and a unique user interface that enables the seamless engagement of human “experts in the loop” throughout the entire document automation process. Johannes Wiese, Mercedes-Benz AG After successfully completing his Bachelor's degree in Industrial Engineering and Management at TU Dortmund University, Johannes worked as a Junior Consultant at SCMT GmbH, helping Mercedes-Benz to harness the power of data through the introduction of big data and AI. In his extra-occupational Master of Business Engineering, he specialized in analyzing data-driven companies and driving the development towards one. As an employee of Mercedes-Benz, he developed a global system for the provision and analysis of time series data during long-term testing. In his current role as Product Owner for EV Analytics, he supports data-based product decisions to pave the way for e-mobility. ???Tickets: Discount Code: AI_HUB_COMMUNITY_EARLYBIRD -25% off all tickets! Ticket Options: Full Week Pass or Individual Day Passes. Link: https://lnkd.in/gxjWEyEQ ?? Sponsors & Partners: All of this would not be possible without the support of our amazing sponsors, including Google, Microsoft, HP, Merck Gruppe, DekaBank Deutsche Girozentrale, Bankhaus Metzler, statworx, SPARK Europe, new office, STATION, Dataiku, Projektwerk Unternehmensberatung AG, #zeroG, DataRobot, SAS and KI Bundesverband - more incoming! #aiweek2024 #aiweek #aiweekfrankfurt #aiw24

HybridRAG: A Hybrid AI System Formed by Integrating Knowledge Graphs and Vector Retrieval Augmented Generation Outperforming both Individually https://lnkd.in/dhGXbmFT Financial data analysis plays a critical role in the decision-making processes of analysts and investors. The ability to extract relevant insights from unstructured text, such as earnings call transcripts and financial reports, is essential for making informed decisions that can impact market predictions and investment strategies. However, this task is complicated by the specialized language and varied formats within these documents, posing significant challenges to traditional data extraction methods.....

I'm excited to announce the launch of Tecton 1.0, a significant milestone for our team. Tecton now has unified support for predictive ML and generative AI workflows, enabling organizations to build smarter AI applications. From the beginning, our vision has been to address a critical challenge in AI development: effectively leveraging enterprise-specific data. During our time at Uber, my co-founder Kevin and I observed that the most impactful AI systems are those that utilize data unique to the enterprise. Implementing this, however, is often more complex than it appears. This challenge is evident in both predictive ML and generative AI. LLMs are trained on general knowledge but lack specific enterprise data, limiting their effectiveness in production environments. Getting models to a usable level of performance for most use cases requires providing the right inputs—features, tailored prompts, and domain-specific embeddings—that supply necessary context. Our mission at Tecton is to simplify this process by removing the data engineering complexities involved in building, managing, and operationalizing these inputs. This makes it easier to bring smarter applications into production. We started this journey five years ago with a robust feature store. Today, we've evolved into a comprehensive AI data platform that addresses end-to-end data challenges across the AI lifecycle. I'm proud of our team for reaching this milestone and grateful to our customers who are already leveraging rich enterprise context to power intelligent experiences. This launch is a significant step forward for Tecton and our customers. I invite you to check out Tecton 1.0! Learn more here: https://lnkd.in/gs6AvyQ4

The FirstMark 2024 Machine Learning, AI and Data Landscape isn't just a bunch of tech logos organized in different boxes. It's a rich and insightful analysis of the trends and subtrends that are shaping data infrastructure, #analytics use cases, and #AI opportunities. Some observations that I found particularly interesting:? Efforts to build structured data pipelines are waning, as more enterprises steer their IT investment toward AI-ready unstructured data stacks that can feed proprietary #GenAI solutions; Correspondingly, generative AI is likely to completely upend the analytics interaction model, and democratize data access with truly conversational interfaces; And finally, the #LLM competition is yet to fully play out as big tech, open source and startup players continuously leapfrog each other, surface new use cases and look for market-leading opportunities in the value chain. It's a long read, but totally worth it! https://lnkd.in/e4uPT7xq

The relationship between data infrastructure and AI is pivotal in today's rapidly evolving tech landscape. As we celebrate a decade of observing the MAD (ML, AI & Data) ecosystem, it's clear that the integration of data and AI has never been more promising. In the past decade, we've seen data digitization on a massive scale. Modern tools have made it possible to store, process, and analyze data swiftly and inexpensively. This is essential because AI models rely heavily on vast datasets to generate meaningful insights, predictions, and creative outputs. What's fascinating is how the MAD ecosystem has transitioned from a niche, technical field to a mainstream powerhouse impacting not just businesses but also societal and geopolitical arenas. This transformation is driven by two major waves: the decade-long data infrastructure cycle and the more recent surge in ML/AI, particularly with Generative AI. The symbiotic relationship between data infrastructure and AI is vividly illustrated in the MAD landscape, showcasing over 2,000 companies. Despite the crowded space, the integration of these technologies is akin to an assembly line—data is collected, stored, processed, and ultimately used to deliver value through analytics and applications. As we look forward to 2024, it's crucial to recognize the importance of this partnership. While the journey is ongoing, the potential for innovation and disruption is immense. Let's continue to explore how data infrastructure and AI can work hand-in-hand to drive advancements across industries and society at large. ?? #DataInfrastructure #AIFuture ?? https://lnkd.in/g5gm2Z6m

?? Day 17 of 100 Days, 100 Open Source Libraries! ?? ? LlamaIndex: Enhancing Language Models with Intelligent Data Integration ?? As AI continues to evolve, efficient data integration has become a cornerstone of building smarter, scalable applications. Enter LlamaIndex—a powerful open-source library designed to bridge the gap between language models and structured data. Why LlamaIndex is a game-changer: 1?? Effortless data ingestion and management. 2?? Seamless integration with Large Language Models (LLMs). 3?? Enables intelligent decision-making through advanced data querying. ?? Full details in the blog: ?? https://lnkd.in/dnUKpJrN ?? Explore the repo: ?? https://lnkd.in/dxudcvXe ?? Pro Tip: Dive into this tool and unlock new possibilities for your AI applications. ? If you found this valuable, don’t forget to star the repo, share your insights, and let’s discuss your favorite use cases! ====================== Experts predict 2025 will be the defining year for Gen AI implementation. Want to be ahead of the curve? Join Build Fast with AI’s Gen AI Launch Pad 2025 - your accelerated path to mastering AI tools and building revolutionary applications. Register now: https://lnkd.in/dgCEJ9Ed #LlamaIndex #AIInnovation #DataIntegration #OpenSource #100DaysOfAI #BuildFastWithAI

?? **The Power of Staying Task-Specific with GenAI** ?? I enjoy articles like this one. Not only does it speaks to GenAI pros, but I believe it is also applicable to our careers and projects. The TechCrunch article highlighted that many companies face challenges with GenAI due to data overload and overly ambitious implementations. The key takeaway? **Focusing on smaller, specific goals makes a huge difference.** When companies embrace task-specific generative AI, they can unlock clearer, more tangible value without getting bogged down by complexity. Here are three big benefits of this approach: - **Greater Accuracy and Relevance** ?? Task-specific GenAI zeroes in on what's essential, providing outputs that are highly relevant and minimizing distractions from unrelated data. - **Efficiency and Cost Savings** ?? Narrowing the scope means processing only what's necessary, reducing the resources required and cutting down costs—important wins for any AI initiative. - **Simplified Maintenance and Scalability** ?? With a task-focused GenAI model, fine-tuning and updates are more targeted, which means better adaptability and sustainable performance over time. The message is clear: If you’re starting or scaling GenAI, don’t aim for “everything at once.” Start with the essentials, keep it focused, and build from there. #GenerativeAI #AI #TechCrunch #AIBenefits #DataManagement

“Agentic AI”, one of the newest concepts in the evolution of AI, offers the potential to autonomously solve complex, multi-step problems. But, the term itself is causing waves. Industry experts are concerned about the term being another buzzword without a consistent and clear definition. One such expert, Warren Powell, recently pointed out that “‘AI’ has become such an empty phrase, and adding adjectives such as “agentic” simply adds more jargon.” The term “Agentic” doesn’t clearly describe a product or functionality and sounds like something already in existence – an “optimization model”. Or as our good friend noted, a “cron job” (which has been around since 1975 to automate/schedule repetitive tasks… you read that right – 1975!). We have seen so many buzzwords in our space over the years, with many causing businesses significant confusion. Remember “Big Data”? With the expansion of the digital age in the early 2000s, the term was created to describe datasets that were too large and complex to manage using traditional data processing tools, such as data from web traffic (think terabytes). Since "Big Data" was a vague term, we came across stakeholders that thought their 1 million row Excel spreadsheet was big data – clearly, there was confusion on what the term really meant. One thing we guarantee at ES is that we will not throw random buzzwords at you. We will partner to understand your requirements, use clear and transparent language, and ensure the proposed solution makes sense for your needs. And, if a proposed solution includes something like “Agentic AI”, we’ll ensure you know exactly what that means in practice.? See comments for Warren Powell’s post. #AI #machinelearning #datascience #business

?? Embracing the Generative AI Revolution: From Data to Insight to Action*?? The potential of Generative AI (GenAI) is limitless, reshaping the way businesses operate and make decisions. ???? As highlighted in the article from CIO, we are on the brink of an AI-driven transformation where data moves seamlessly to insights, context, and actionable decisions. ?? Why Now? GenAI enables companies to process vast amounts of data, providing context-rich insights that allow teams to act faster and with more precision. This shift is especially critical as businesses look to streamline operations and enhance customer experience. ?? Key Benefits: 1. Data to Insight: AI automates the analysis of unstructured and structured data, uncovering hidden patterns and insights. 2. Insight to Context: AI algorithms now provide contextual understanding, making predictions more relevant. 3. Context to Action: Real-time, actionable insights help businesses pivot quickly, ensuring a competitive edge in today’s fast-paced world. ?? For Leaders: The question is no longer "if" AI will impact your business, but "how" prepared you are to harness its full potential. Now is the time to invest in "AI strategies" that move beyond just data analysis and empower "end-to-end decision-making". Is your business ready to leverage the full spectrum of AI, from data to action ? Let’s discuss the next steps in transforming your organization with AI-powered tools. #GenerativeAI #AIInnovation #BusinessTransformation #DataDriven #CIO #TechTransformation https://lnkd.in/gP2B4n7A

The future of capital markets is here, and it's powered by Generative AI! Experts hold the key to unlocking endless potential in this rapidly evolving landscape. Dive into the real-world applications of AI and discover how it's transforming the financial sector. The time to embrace innovation is now! Join the conversation at https://okt.to/6RfIQe. Let's accelerate together!