Join us this Friday for a fireside chat with Debadyuti Roy Chowdhury, VP of Product at InfinyOn. We'll discuss data streaming, its importance for artificial intelligence and Infinyon's vision for their open source project Fluvio.
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In a previous video, we introduced Scitara’s new FAIR data streaming capability. When we talk with customers about it, we ask them what use cases come to mind. Most haven’t thought about detailed use cases, so in this video we give some ideas about how you could leverage FAIR lab data that is centralized in a repository. But we would also love to hear your ideas on the subject. #DigitalLab #LabData #FAIRData #ScitaraDLX #LaboftheFuture
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Retrieval Augmented Generation (RAG) is becoming popular to preventing your large language model from hallucinating. Giving it access to real-time, contextualized and trustworthy data. Learn how you can accomplish this with data streaming in this lightboard video! #datastreaming #genAI #RAG
The key to preventing your large language model from hallucinating? Making sure it has access to real-time, contextualized, and trustworthy data—that's where retrieval-augmented generation (RAG) comes in. In this lightboard video, Global Field CTO Kai Waehner explains how RAG and a data streaming platform with #ApacheKafka and #ApacheFlink help ensure your GenAI application generates the most reliable outputs. Watch it below! ⬇️
Retrieval Augmented Generation (RAG) with Data Streaming
https://www.youtube.com/
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Great light board presentation! The reason why #LLM #RAG is not enough. You need 1) updated domain context and 2) access to transactional data in real-time. This is only possible with a real-time data streaming platform. I actually built this architecture into my Collaborative Intelligence Platform, but I have also called it "EventGPT" for short. What is missing in the ChatGPT RAG architecture is an event-driven architecture. By combining the "brain" with a "Central Nervous System" you get a complete solution for event orchestration and collaborative between humans and AI in real-time.
The key to preventing your large language model from hallucinating? Making sure it has access to real-time, contextualized, and trustworthy data—that's where retrieval-augmented generation (RAG) comes in. In our 🆕 lightboard video, Global Field CTO Kai Waehner explains how RAG and a data streaming platform with #ApacheKafka and #ApacheFlink help ensure your GenAI application generates the most reliable outputs. Watch it below! ⬇️
Retrieval Augmented Generation (RAG) with Data Streaming
https://www.youtube.com/
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AI will be everywhere, and it's going to based on open source.
"I am not one trying to predict the future of technology, but I think this is a safe prediction. AI won't be built by a single vendor. It isn't going to revolve around a single monolithic model. Your choice of where to run AI will be everywhere, and it's going to be based on open source." -Red Hat President and CEO, Matt Hicks Watch the full keynote from the #RHSummit stage on Red Hat TV, Red Hat's always-on streaming platform: https://red.ht/3UOR6a7 #AnsibleFest
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Area Vice President & Country Manager India at Confluent | X Servicenow | X HP Software | Hindi Poet | Berkeley Haas School of Business| Views expressed are purely personal.
Are your #LLMs hallucinating? If yes, you need #Confluent to plug the issue with realtime, trustworthy and contextualised data. Please follow the post below to learn more. #AI #data #dataengineering #realtime #flink #Kafka #datastreamingplatform #datastrategy
The key to preventing your large language model from hallucinating? Making sure it has access to real-time, contextualized, and trustworthy data—that's where retrieval-augmented generation (RAG) comes in. In our 🆕 lightboard video, Global Field CTO Kai Waehner explains how RAG and a data streaming platform with #ApacheKafka and #ApacheFlink help ensure your GenAI application generates the most reliable outputs. Watch it below! ⬇️
Retrieval Augmented Generation (RAG) with Data Streaming
https://www.youtube.com/
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The key to preventing your large language model from hallucinating? Making sure it has access to real-time, contextualized, and trustworthy data—that's where retrieval-augmented generation (RAG) comes in. In this lightboard video, Global Field CTO Kai Waehner explains how RAG and a data streaming platform with #ApacheKafka and #ApacheFlink help ensure your GenAI application generates the most reliable outputs. Watch it below! ⬇️
Retrieval Augmented Generation (RAG) with Data Streaming
https://www.youtube.com/
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The future of AI is #open source! learn more here:
"I am not one trying to predict the future of technology, but I think this is a safe prediction. AI won't be built by a single vendor. It isn't going to revolve around a single monolithic model. Your choice of where to run AI will be everywhere, and it's going to be based on open source." -Red Hat President and CEO, Matt Hicks Watch the full keynote from the #RHSummit stage on Red Hat TV, Red Hat's always-on streaming platform: https://red.ht/3UOR6a7 #AnsibleFest
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The key to preventing your large language model from hallucinating? Making sure it has access to real-time, contextualized, and trustworthy data—that's where retrieval-augmented generation (RAG) comes in. In our 🆕 lightboard video, Global Field CTO Kai Waehner explains how RAG and a data streaming platform with #ApacheKafka and #ApacheFlink help ensure your GenAI application generates the most reliable outputs. Watch it below! ⬇️
Retrieval Augmented Generation (RAG) with Data Streaming
https://www.youtube.com/
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In this episode, we discuss Speculative Streaming: Fast LLM Inference without Auxiliary Models by Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason, Mohammad Rastegari, Mahyar Najibi. The paper introduces Speculative Streaming, a method designed to quickly infer outputs from large language models without needing auxiliary models, unlike the current speculative decoding technique. This new approach fine-tunes the main model for future n-gram predictions, leading to significant speedups, ranging from 1.8 to 3.1 times, in tasks such as Summarization and Meaning Representation without losing quality. Speculative Streaming is also highly efficient, yielding speed gains comparable to complex architectures while using vastly fewer additional parameters, making it ideal for deployment on devices with limited resources.
arxiv preprint - Speculative Streaming: Fast LLM Inference without Auxiliary Models
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🔬 April is #CitizenScience Month and PREDICT-6G is joining in! We believe that the basis for progress in science is the sharing of knowledge, so we want to explain the concepts on which PREDICT-6G is based. 5️⃣ Time-sensitiveness: Networks do not have any concept of “time” and therefore, they cannot provide time precision nor time synchronisation. Time-sensitiveness refers to the ability of delivering a message within a timeframe, which is essential for reliable and timely #DataExchange between domains. 💡 This is essential in many applications such as audio and video, for example, to enjoy your favourite show in streaming. Did you know about this concept? Let us know!
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