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Increase the efficiency of data center compute by focusing on reimagining chip architecture, leading to significantly lower power consumption while packing more compute into a smaller space. #DataCenterEfficiency #ChipArchitecture #PowerConsumption #ComputeOptimization #DataCenterManagement At Grow Scale Win™, we're all about using AI to enhance productivity and efficiency. That's why we've built the AI Growth System™, a truly holistic approach to growing your business. You get a personalized growth strategy and a full suite of AI powered tools to generate leads, nurture prospects, and close sales. Learn more at www.aigrowthsystem.com or growscalewin.com. DM us with any questions. Read more about this post here: https://lnkd.in/eRMWksBf

The #Gartner "Hype Cycle for AI in Software Engineering, 2024" helps software engineering leaders stay updated on the latest AI technologies and their impact on software development amidst the rapid evolution of generative AI. deepset is proud to be named as a vendor in the Generative AI (GenAI) application orchestration framework space that's rising up the curve. See a demo to see why: https://lnkd.in/gRRN9SvT The full report details how platforms like deepset are enable connect together the vital components of data and memory, prompt chaining, model chaining, interfacing with external APIs, plus templates, monitoring, evaluation and deployment capabilities for new GenAI applications.  This is the recipe our successful customers are using to build high-quality, custom AI into their mission-critical applications. Read on here – Link for Gartner clients: https://lnkd.in/gwq5_wwm Thank you Arun Chandrasekaran and team for this valuable research!

😎 We made it!! This was a dream for us in the early deepset days - being a category shaper and showing up in the Gartner Hype Cycle one day - well, here we are 🎯 deepset is one of the category creators and leading vendors of "#GenAI orchestration frameworks" which are featured in Gartner's "Hype Cycle for AI in Software Engineering, 2024." Why are "GenAI orchestration frameworks" needed? • They provide standard interfaces to #LLMs. • They are essential as most #AI applications are multi-component systems. • The iterative nature of GenAI application development requires the ability to swap out models, components, and customize chains of components. What made me goosebumps: We are one of the the 7 vendors in this category, alongside Big Tech players such as Amazon Web Services (AWS), Microsoft, and Google, as well as three other challengers.

Is the era of software investment coming to an end? As AI reshapes the tech landscape, Deep Tech emerges as the new frontier for transformative investments. Dive into this latest analysis on why investors should look beyond software to a more 'physical' future. https://lnkd.in/eadH_9Um #DeepTech #TechInvestment #AIImpact #FutureOfTech

As we say often at DataStax, people and process are as important as technology during a tech transformation. In Generative AI, many enterprises are having challenges scaling from POC to production. Lenovo's most recent CIO survey showed that more than 70% of companies have less than 30% of their projects in production. Why? A big culprit is likely not enough focus on People and Process during the Proof of Concept phase. In this quick video I layout a new way to look at the PoC (and thanks to Krishnan Hariharan who alerted me to the challenge). PoC = PoT + PoPP. Take a look! 

Sequoia believes there is a $600B debt owed by Web2 AI and its reaching a tipping point. The web2 AI narrative is moving towards how revenue will be earned by AI. The obvious route is how the application layer will evolve. (Evident by people's expectations @EthCC ). Sequoia believes there is a $600B shortfall to revenues earned (Right now Web2 AI is about $4B ARR, with @openAI earning a majority $3.4B) Perhaps we should focus our attention on how today's problems can be solved with AI instead of just developing AI for the sake of AGI... With more applications built with AI we will have a future with AGI thats supported by thriving businesses. https://lnkd.in/dQEXvQvH

Do you want to know how to connect the engineering dots through an enterprise-scale AI platform? Listen to my colleague @Soumen Saha talk about his work with @TE Connectivity at #AWSreInvent and learn how you can accelerate your research and development. I was impressed by how @Capgemini helped 8,000 engineers: ✅ Accelerate technical research ✅ Streamline millions of documents ✅ Improve productivity by 10x. Join this Lightning Talk at #AWSreInvent to hear how to transform your R&D: https://bit.ly/4fspeRQ #ConversationalAI #ProductDevelopment Soumen Saha Pankaj Welankiwar John Broghammer Trent Pooley Michael McLachlan Sylvia Winker Cruz, MBA Joanna Ostaszewski Alex MirZabeigi Darshan Naik Sandeep Kamath Prateek Agrawal Joe Briggs Shashi Gupta April Scoville Karina Shatsman Zide Chapin Hough

Built on Open Platform for Enterprise #AI (OPEA), our RAG solution improves enterprise #LLMs and delivers more specificity and accuracy for end-users. Listen to Bill Pearson, VP, Software Solutions and Ecosystem of the Data Center and AI Group at #Intel, discuss how we're enabling collaboration within the industry to build out RAG solutions for LLMs using enterprise data. Learn more here: https://intel.ly/3YS9kv4 #GenAI

Yesterday's Productionize GenAI at Enterprise Scale webinar by 🔭 Galileo was really thought provoking - Thanks to Nidhi Wadmark for sharing. The contributions from thought leaders like Ya Xu, Rama Mahajanam, Anupam Singh and Surojit Chatterjee were on the money... striking a good balance between optimism in the art of what is possible and the challenges we face doing this at scale. 3 Key takeaways for me were: * 2024 is a year when the models become commoditised - there will be lots of thought around model selection: proprietary v open, larger models v more cost effective smaller domain specific ones. Scaling to production is very much a trade off between cost, latency and accuracy * As we move into production, response evaluation is still a big challenge across the industry and generally seems to be solved by throwing manpower at the problem, so automation here is key moving forward. * Successful deployments 'take a village', and success invariably depends upon a cross functional collaborative effort. If you're starting out down a path to production start small on a tangible use case. Make sure that you can measure ROI and have a clear strategy wrt evaluation as you iterate. #GenAIProductionize2024

The rise of artificial intelligence (AI) has been a focal point for investors over the past few decades, with Nvidia emerging as a key player in this revolution. However, concerns have been raised about Nvidia's future growth prospects, leading some prominent investors to reduce their stakes in the company. Meanwhile, other hypergrowth AI stocks like CrowdStrike Holdings and Snowflake have captured the attention of billionaire investors, offering potential opportunities for growth and competitive advantages in the evolving AI landscape. #AIRevolution #InvestmentOpportunities #Nvidia #CrowdStrike #Snowflake #HypergrowthStocks #BillionaireInvestors #ArtificialIntelligence #StockMarket #InvestingInsights #TechInvestments