Eli Gild

We’re thrilled today to introduce the OCI Generative AI Agents platform to create, deploy, and manage custom agents on OCI as part of the OCI Generative AI Agents Service. Along with this platform release, we are also launching a new pre-built tool for interacting with structured data through SQL as well as major enhancements to our existing Retrieval-Augmented Generation (RAG) tool. Check it out and let us know what you think, we are always looking for feedback and ideas for additional features. https://lnkd.in/gHq9kBkr

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I'm excited to share three research papers from our talented team at Together AI that are advancing the frontiers of efficient and effective AI systems: ## 🚀 Accelerating Large Model Inference Our researchers have developed "Ladder-residual," a novel parallelism-aware architecture that significantly accelerates large model inference through communication overlapping. This innovation addresses one of the key bottlenecks in deploying large language models at scale, making AI systems faster and more cost-effective. 📄 Read more: [Ladder-residual: parallelism-aware architecture for accelerating large model inference with communication overlapping](https://lnkd.in/g825uRRz) ## 🤝 Enhancing Model Alignment Through Collective Intelligence By leveraging the collective intelligence of open-source models, we've developed new methods to improve AI alignment without the computational expense of traditional alignment techniques. This research demonstrates how cooperation between diverse models can lead to superior performance and better alignment with human values. 📄 Read more: [Improving Model Alignment Through Collective Intelligence of Open-Source Models](https://lnkd.in/g9FZ83P8) ## 📱 Optimizing On-device and Cloud Collaboration Our collaboration with researchers from Stanford introduces cost-efficient methods for on-device and cloud language model cooperation. This work enables more powerful AI capabilities on edge devices while intelligently leveraging cloud resources, resulting in better user experiences and reduced costs. 📄 Read more: [Cost-efficient Collaboration between On-device and Cloud Language Models](https://lnkd.in/ggM6v3R3) These papers represent our commitment to developing AI that is more efficient, aligned, and accessible. I'd love to hear your thoughts on these research directions or discuss potential collaborations! #TogetherAI #MachineLearning #AI #ResearchAndDevelopment #LLM #EdgeComputing #AIAlignment

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Shardul Shah's post on our Series A captures something important about how we build product at 7AI. He wrote: "Its multi-agent architecture learns each environment's unique context, enabling faster, more accurate, and more consistent investigations than humans or rule-based systems." That's the product vision in one sentence. Not a one-size-fits-all solution. A platform that learns your environment, your context, your tools, and delivers outcomes specific to you. Shardul called 7AI potentially "the defining agentic security company of this new era." That's the standard our team is building toward. Every day.

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What if “smaller” turns out to be the smarter bet in AI? Three takeaways/insights: (1) SLMs (Small Language Models) trained by LLMs are now performing at levels comparable to LLMs from last year, (2) SLMs can enable new use cases (& business models) where efficiency and focus matter the most: on-device AI, domain-specific enterprise applications, and privacy-sensitive deployments. (3) Not either-or: SLMs and LLMs can co-exist and complement each other, with SLMs focused on specialized, frequent tasks https://lnkd.in/ghNZft2a #AIrevolution #ArtificialIntelligence #LargeLanguageModels #GenerativeAI

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This interview breaks down the secret sauce behind how Intuit is bringing data, AI, and human intelligence to life for consumers and businesses. As Chairman and CEO Sasan Goodarzi puts it, the real value of AI is unlocked when it’s applied to solve the customer problems that really matter. That’s where we lead. Read the full interview here: https://lnkd.in/gwCArm_q

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When most people hear "solid-state," they think storage. But in Santa Clara, xMEMS Labs just gave that term new resonance, literally. The company just raised $21M in Series D funding led by Boardman Bay Capital Management, with Cloudview Capital, CDIB-TEN Capital, Harbinger Ventures, Susquehanna Asia Venture Capital, and other strategic investors tuning in. This isn't another round of capital, it's an #amplifier for a company turning silicon into a new language of #sound and #cooling for the AI era. Founded in 2018 by Joseph Jiang and Jemm Liang, xMEMS spun out of piezoelectric R&D at TSMC and turned what was once wafer-level theory into commercial hardware. Joseph Jiang, a veteran of Knowles, Fortemedia, and InvenSense, leads with precision and swagger, while Jemm Liang, who built Ultrachip from the ground up, drives the engineering with surgical focus. Co-Founder & CLO Wei-Fu Hsu, the former MediaTek exec and IP expert, ensures xMEMS' innovation is as protected as it is disruptive. Their play? A monolithic MEMS platform that transforms voltage into vibration, silicon into sound. Unlike legacy coil speakers and fans, xMEMS tech doesn't move parts, it moves markets. Over 500K MEMS speakers shipped in 1H'24, backed by >250 global patents. The Cypress & Sycamore lines redefine what "small" can do: 1mm-thin, full-range, and capable of sub-bass response once thought impossible in earbuds. Their XMC-2400 µCooling chip, 1mm-thin, fan-on-a-chip, inaudible, moves 39cc/sec of air at just 30mW. That's not cooling, that's choreography at a molecular level. Dr. Chester Hwang, named CTO on Oct 15, 2025, joins to push that curve further. Alongside veterans Mike Housholder (VP Marketing & Biz Dev), Jim Wargnier (VP N.A. & EU Sales), James Lee (VP & Korea GM), Steven P. Bentley (VP Sales), and Martin Lim (VP MEMS Tech), xMEMS is scaling at semiconductor speed. They're already in BleeqUp's AI sports glasses and teamed with Dongguan Rayking Electronics for TWS modules. When OEMs talk "next-gen," they're talking xMEMS-level integration. For investors like Boardman Bay's Will Graves, this isn't just sound engineering, it's signal intelligence. With AI pushing devices thinner, faster, and hotter, xMEMS' piezoMEMS platform sits at the intersection of #acoustics and #thermals, where every millimeter matters. Backing them is a bet that the next wave of #sensorytech won't just compute, it'll feel alive. So what's next? Scaling production, expanding capacity, and embedding xMEMS' silicon pulse into everything from earbuds to AI #datacenters. In a world chasing speed, silence, and fidelity, xMEMS isn't following the beat, they're building the frequency the future will hum to. #Startups #StartupFunding #VentureCapital #SeriesD #AI #Sound #SoundEngineering #Semiconductors #Data #DataDriven #Technology #Innovation #TechEcosystem #StartupEcosystem #Hiring #TechHiring If software engineering peace of mind is what you crave, Vention is your zen.

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Barbarians at the Gate (https://lnkd.in/gkEBX79s) is a very interesting new paper from some great folks at Berkeley, with some exciting results showing the potential for AI in systems research. But I think the authors aren't quite asking the hardest problem about where this takes systems as a field. I wrote a new blog post about it: https://lnkd.in/gxS3s4v4 The core point of the paper is that AI can be effective at automatically driving the 'number go up' part of systems research. Given an evaluator, AI can automate progress on implementation. I believe this is 100% correct. The paper also talks about how more focus will have to go into developing evaluators. Again, I 100% agree, but think they're understating their case. Building evaluators (basically tests) is, I believe, going to become the focus of systems and software over the next decade. Which leaves researchers with the hard problem of choosing which problems to go after. That's never been easy in academic systems, and is only going to become more of a challenge. But the best systems research is driven by choosing problems well! This vision for AI could lead to lots more of the least interesting systems research. The "number go up" kind. Which could make it harder than ever to identify the truly innovative and visionary work. We're already seeing that elsewhere in science. So, mostly, I think the paper is right, but not yet asking the biggest question: what should systems research be?

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"And we are also excited at the potential for AI to help reproduce packages: Build and release processes are often described in natural language documentation which, while difficult to utilize with discrete logic, is increasingly useful to language models. Our initial experiments have demonstrated the approach's viability in automating exploration and testing, with limited human intervention, even in the most complex builds." https://lnkd.in/gUsC4ZJw

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🚀 𝐀 𝐫𝐞𝐜𝐚𝐩 𝐨𝐟 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞𝐬𝐭 𝐀𝐥𝐩𝐬𝐆𝐞𝐧𝐀𝐈 𝐄𝐱𝐜𝐡𝐚𝐧𝐠𝐞 & 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐃𝐚𝐲 Just before the summer break, we concluded another 𝐡𝐢𝐠𝐡-𝐢𝐦𝐩𝐚𝐜𝐭 𝐞𝐝𝐢𝐭𝐢𝐨𝐧 of the AlpsGenAI Exchange, where AI leaders explored how 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐡𝐮𝐦𝐚𝐧 𝐞𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞 to transform customer and business experiences. 👉 𝐁𝐨𝐨𝐤𝐢𝐧𝐠.𝐜𝐨𝐦 (𝐍𝐞𝐭𝐡𝐞𝐫𝐥𝐚𝐧𝐝𝐬) Pranav Pathak, Director of Product Machine Learning, shared how Booking.com has successfully prioritized 𝟐𝟎𝟎+ 𝐆𝐞𝐧𝐀𝐈 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬. Their AI models now deliver 𝟏𝟎𝟎𝐌+ 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 and 𝟐𝟎𝟎𝐁+ 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧𝐬 𝐝𝐚𝐢𝐥𝐲. Booking.com is a 𝐭𝐫𝐮𝐞 𝐀𝐈-𝐟𝐢𝐫𝐬𝐭 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞, showcasing how Generative AI drives value at scale. 👉 𝐒𝐞𝐧𝐭𝐢𝐨𝐂𝐗 (Netherlands) Ronald Rubens shared how SentioCX orchestrates 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬 𝐭𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧𝐬 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐡𝐮𝐦𝐚𝐧 𝐞𝐱𝐩𝐞𝐫𝐭𝐬. Unlike traditional systems that escalate into static queues, SentioCX aligns 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐯𝐚𝐥𝐮𝐞 𝐰𝐢𝐭𝐡 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧, leveraging real-time signals such as churn prediction or complex intent detection to ensure 𝐟𝐢𝐫𝐬𝐭-𝐜𝐨𝐧𝐭𝐚𝐜𝐭 𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧. Ronald and Pranav had a compelling discussion on the enduring importance of the 𝐡𝐮𝐦𝐚𝐧 𝐞𝐱𝐩𝐞𝐫𝐭 in digital customer channels. They emphasized that 𝐬𝐞𝐚𝐦𝐥𝐞𝐬𝐬 𝐭𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧𝐬 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐡𝐮𝐦𝐚𝐧 𝐞𝐱𝐩𝐞𝐫𝐭𝐬 𝐜𝐚𝐧 𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐞 𝐚𝐥𝐥 𝐟𝐫𝐢𝐜𝐭𝐢𝐨𝐧 in digital customer journeys—ensuring customers feel supported, understood, and valued at every interaction. 🌟 According to Deloitte Digital and Shopify, 𝐩𝐨𝐨𝐫 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞𝐬 𝐜𝐨𝐬𝐭 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝟐𝟏% 𝐨𝐟 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐬𝐚𝐥𝐞𝐬. Human-AI orchestration has become an essential driver of sustainable growth. 👉 𝐖𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐥𝐢𝐤𝐞 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐦𝐨𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐡𝐨𝐰 𝐒𝐞𝐧𝐭𝐢𝐨𝐂𝐗 𝐜𝐚𝐧 𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 𝐟𝐫𝐢𝐜𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧? 𝐏𝐥𝐞𝐚𝐬𝐞 𝐫𝐞𝐟𝐞𝐫 𝐭𝐨 𝐭𝐡𝐞 𝐚𝐭𝐭𝐚𝐜𝐡𝐦𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧. Rudolf Schuler Dr. Dominic Oertly Uria Fiano Phan Eustachon Jeffrey Van der Eijk Sheryl Jumawid Purnima Wijendra Daniel Graffe Businessincub Scale Advice 2p team Ronald Rubens Dimitar Bobev Rob Lucassen Iram Amjad Stefan van Eerde Booking.com #AI #GenerativeAI #SupplyChain #CustomerExperience #AILeadership #HumanInTheLoop #AIInnovation #AlpsGenAI

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"Graphs make provenance first-class. Instead of burying "where did this come from?" inside a dense vector, a graph records sources, activities, and agents as nodes and edges you can query..." https://lnkd.in/efAdV9dx

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