Dave Brace

AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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Interesting notes from Intuit's recent webinar on LLMs. What evaluation processes do you deploy? at tasq.ai our approach is to integrate the AI models with diverse, global human guidance, to support #responsibleai faster and near real time.

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AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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Interested in #GenAI and its impact on #softwaredevelopment? Read EQengineered's Principal Technical Consultant/Architect Ed Lyons' recent essay. #techinsights #softwareengineering #digitaltransformation #GenAI #LLMs #EQengineered

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Awesome to see the Meta #Llama 3.2 models on #Azure #AI Model Catalog through managed compute inferencing #MaaS and soon on serverless API deployments while MACC Eligible providing customers with Enhanced Security and Compliance, Content Safety Integration, Simplified Assessment of LLM flows, Client integration, and Simplified Deployment and Inference. https://lnkd.in/g4Zpb3Dm

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Some freshness in the LLMs arena! LLM can be accurate if you follow SOTA practices!

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AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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Excellent advice from Kjell Carlsson if you want a quick takeaway from the article below: “ Enterprise leaders should seize this opportunity to proactively address AI risks and protect their AI initiatives now. Rather than wait for regulation to dictate safety measures, organizations should enact robust AI governance practices across the entire AI lifecycle: establishing controls over access to data, infrastructure and models, rigorous model testing and validation, and ensuring output auditability and reproducibility."

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The first step to delivering trusted AI is having the right type of data. Explore the dark age of data W/ @rwang0 on episode 1 of The AI Forecast w/ @xthestreams. Watch it here: https://buff.ly/4fBpC0V @cloudera #Data #AI

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Turns out #GenAI takes some basic common sense: (1) use the right Model for the right Use-Case (2) use Open models to leverage the Open eco-system (3) run models - where they should run - based on data availability, proximity, latency, operational efficiency, etc... On Tuesday, July 23, #Meta announced the launch of the Llama 3.1 collection of multilingual large language models (#LLMs). The instruction-tuned Llama 3.1-405B, which figures to be the largest and most powerful open-source language model available today and competitive with the best proprietary models on the market. So, if you have a use-case that benefits from this large scale OPEN model - it is now available on IBM® #watsonx.ai - where it can be deployed on the IBM cloud, in a hybrid cloud environment or on-premises. #watsonx provides a consistent common platform for use with your models, open models, and our models.... Read the announcement here: https://ibm.biz/BdKqta

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Organizations have insufficient technical capabilities for the speed and scale of their ML model implementations. My colleague Sumit Agarwal just published a very helpful research with a capability view and sample vendors for defining a modular, flexible and scalable MLOps solution. https://lnkd.in/gnB_T9bq #MLOps #LLMOps #AI

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🚀 Unlocking the Power of Hybrid Datasets for LLM Fine-Tuning! If you’re in the world of #AI and #NLP, you know that quality data is the bedrock of high-performance models. Bitext dives into the importance of data fine-tuning for Large Language Models (LLMs) and highlights how hybrid datasets—those that combine synthetic and real-world data—can supercharge LLM accuracy and applicability. By blending curated real-world samples with synthetic data, hybrid datasets bring robustness and nuance, especially in specialized domains. Whether you're tackling multilingual applications, industry-specific jargon, or complex use cases, this approach is a game-changer. Read our blog post to learn more about how hybrid datasets are shaping the future of NLP and the performance of LLMs! #AI #MachineLearning #DataScience #FineTuning #LanguageModels #HybridDatasets #Bitext

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Unlocking the full potential of AI for your business isn't just about having the latest LLM. It's about seamlessly integrating your proprietary data with these powerful models. Enter RAG - the key to making AI truly work for your unique business needs. Are you leveraging RAG to stay ahead in the AI race? #ArtificialIntelligence #BusinessInnovation #RAG #DataIntegration #AIStrategy

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The Elephant in the Room (of AI): Memory Match Up! Can you remember where you left your water bottle last week? Elephants, remember! These brainiacs with billions of neurons can remember watering holes from years ago. Talk about an impressive memory! AI is no elephant (yet!), but GenAI applications built with Karini AI are mastering some fascinating techniques. We don't rely on biological memory but on a unique 'semantic cache' that acts as GenAI's memory jog. It stores recently used information, so when a question resurfaces, it's like a light bulb moment! I remember that!' Just like an elephant recalls a familiar watering hole. Here's the twist: the elephant's memory constantly learns and adapts based on experiences and emotions. Our semantic cache, on the other hand, relies on data and algorithms. It's clever but not quite as "elephantastic." So, who wins the memory battle? It's a tie! Elephants are champions at the long game, while GenAI shines with lightning-fast retrieval. Together, they demonstrate the incredible potential of both nature and technology, inspiring us with the possibilities of AI. And guess what? Karini AI's managed semantic cache takes things a step further. By efficiently managing and optimizing the storage and retrieval of information, GenAI applications run even faster and more intelligently, saving you money. Pretty cool. 😎 Ready to dive deeper and unlock the power of AI memory? You can find out more about what we're doing at Karini AI. The possibilities are endless. ➡ https://lnkd.in/eSpifX5a

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Exciting news for ML teams managing hybrid infrastructure: Run:ai now integrates with Amazon SageMaker HypePod from @Amazon Web Services, making it easier than ever to seamlessly extend AI infrastructure across both on-premises and public/private cloud environments. 1. Unified GPU Resource Management Across Hybrid Environments 2. Enhanced Scalability and Flexibility 3. Resilient Distributed Training 4. Optimized Resource Utilization Check out all the entails in our blog and stay tuned for more collaboration with AWS, including our joint reference architecture, outlining best practices and configurations for managing hybrid environments Link to blog in first comment below #AIInfrastructure #MachineLearning #AWS #AmazonSageMaker

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Prompting for scale will soon be obsolete... i think...i hope. Prompting is an error-prone, discovery-based process, unsuitable for scalable AI. Many folks rely on black-box LLMs behind API walls (e.g., Claude, GPT-4), lacking direct control over LLM model weights. Programmatically custom-pruned LLMs will be the future of controllable, production AI, not prompting. hint--> #steeringvectors

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