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🚀 The Speed of Thought: Harnessing the 𝐅𝐚𝐬𝐭𝐞𝐬𝐭 𝐋𝐋𝐌 𝐰𝐢𝐭𝐡 𝐆𝐫𝐨𝐪’s LPU The world of artificial intelligence is witnessing a seismic shift, where the speed and power of Large Language Models (LLMs) are reaching unprecedented levels. At the forefront of this revolution stands Groq, whose Language Processing Unit™ (LPU) is shattering records and redefining the future of AI language applications. Groq is relentlessly pushing the boundaries of LLM technology. We can expect even greater performance gains and wider applications as they continue to refine their LPU technology. The possibilities in AI language processing seem endless! Ready to dive deeper into the future of AI language processing? Join the upcoming webinar, "Revolutionizing LLM Performance with 𝐆𝐫𝐨𝐪’𝐬 𝐋𝐏𝐔 , The register link is in the comment below. Thanks to SingleStore for sharing these Knowledge sessions with the communities. Decoding Data Science #ai #data #tech #llm

Investigating various inference platforms for Large Language Models (LLMs) involves a thorough understanding of critical optimization strategies, ranging from diverse Quantization methods to paged attention, alongside different batching techniques and their support within specific inference engines. This knowledge is essential for making informed, long-term decisions about selecting an inference engine. It's encouraging to witness a resurgence of Computer Science within the ML/AI sphere, emphasizing computational optimization. Interestingly, there was a period when a study suggested that conversational AI systems should incorporate intentional delays in their responses to mimic human conversation speeds, supposedly because users preferred the illusion of speaking with a human. This concept gained traction among some developers who began implementing such delays in their systems. However, I have yet to find the original study that quantified this preference for slower interactions. Personally, I'm very skeptical of the claim that users would favor slower over faster, more seamless experiences.

💡 Not in the mood for a long read? We used AI to break down AI disruptions on power grids in a fun, engaging way. Check it out! Two AI speakers (from NotebookLM) discussed our recent work (with Mariam Mughees, Yize Chen and Yunwei Ryan Li, project link: https://lnkd.in/gK77yKK4), on how recent breakthroughs in large language models (LLMs) are impacting power grids, with a particular focus on the abrupt, transient behaviour of AI infrastructures and the unique challenges brought to power grid reliability and resilience. 📄 P.S. NotebookLM is not 100% correct all the time. For those interested in diving deeper into the technical details, you can also explore our latest paper: https://lnkd.in/gcn5sXjG. Would love to hear your thoughts and feedback! 💬 #AI #PowerGrids #Innovation #EnergySystems #Research #ArtificialIntelligence #DataCenter #Decarbonization #AI #UniversityofAlberta https://lnkd.in/g4MyyNYR

Missed the European Conference on Computer Vision (ECCV) last month? Have no fear, we have collected some of the best research from the show into a series of online events in November. We are excited to announce we have our first speakers for ECCV 2024 Redux: Day 4 including: * Zero-shot Video Anomaly Detection: Leveraging Large Language Models for Rule-Based Reasoning - Yuchen Yang at John Hopkins University * Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models- Xiaoyu Zhu at Carnegie Mellon University Register here 👉: https://lnkd.in/gRTUKv2Z #ECCV2024 #ECCV24 #ECCV #VisualAI #FiftyOne #computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

The LLM Technical Essentials Level 3 credential recognizes acquisition of enhanced Generative AI and large language model technical knowledge and skills including completion of the following learning offerings: - Developer Co-pilots - Evaluation/Performance metrics/Hallucination - Ethical Considerations in Generative AI 

Excellent paper on the illusion of considering AI as a 'superhuman collaborator' instead of a tool intended to help human performing better ... Without denying the immense interest in the development of AI and its capacity to have a very positive impact on scientific progress, the illusion of its 'superpower' must not affect scientists. This paper takes a very objective look at the components of this illusion, its dangers, and how to avoid falling into this trap. https://lnkd.in/e_Bbm9hF

Excited to share an insightful analysis of AI model performance from Galileo's latest report! The 2024 LLM Hallucination Index dives deep into the performance of various large language models, highlighting the rise of open-source models and the impact of context length on Retrieval-Augmented Generation systems. Discover how models like Claude 3.5 Sonnet and Qwen2-72b-instruct are leading the way. A must-read for anyone interested in AI and machine learning advancements! Read the full article here: https://lnkd.in/ePbQH6FJ #AI #MachineLearning #LLM #TechInnovation #AIResearch #DataScience #ArtificialIntelligence

Building your AI strategy to gain marketshare, return dollars to shareholders and increasing you NPS is dependent upon making sure you get AI's into production faster, safer and better. #beAIresponsible #AIgovernance #AIvelocity

Enhancing LLM Logical Reasoning with Symbolic Chain-of-Thought Groundbreaking research from Jundong Xu et al. demonstrates how integrating symbolic expressions and logic rules with chain-of-thought prompting can significantly boost the logical reasoning capabilities of large language models (LLMs). Their proposed framework, Symbolic Chain-of-Thought (SymbCoT): 1. Translates natural language into symbolic format 2. Derives a step-by-step reasoning plan using symbolic logic rules 3. Verifies the translation and reasoning chain SymbCoT outperforms standard chain-of-thought methods on 5 logical reasoning benchmarks, setting new state-of-the-art results. Notably, it enables LLMs to perform more faithful, flexible, and explainable logical reasoning. As an AI strategy consultant, I see immense potential for SymbCoT to expand the applicability of LLMs to domains that heavily rely on formal logic, such as: - Mathematics - Formal verification - Legal reasoning - Financial contracts - Scientific research While many have viewed symbolic AI and neural networks as competing paradigms, SymbCoT demonstrates the power of intelligently combining their strengths. I believe we'll see more AI systems leverage neuro-symbolic fusion to unlock new capabilities. This research provides an exciting glimpse into the future of faithful and transparent machine reasoning. I look forward to seeing how the community builds upon this innovative SymbCoT framework. Paper - https://lnkd.in/gMib2B2b #AI #MachineLearning #LogicalReasoning #SymbolicAI #ChainOfThought #LLMs

MiniCTX is a new method that enhances the ability of large language models (LLMs) to solve mathematical proofs. It does this by breaking down proofs into smaller parts and using a "sliding window" technique to keep track of the important information. This allows LLMs to solve more complex problems while using less computing power. MiniCTX has been shown to improve performance on various mathematical proof benchmarks, indicating its potential to advance artificial intelligence (AI) in mathematical reasoning.