🌍🔬 Oregon State University is transforming oceanic research at the Edge with the power of Dell AI Factory and Dell #PowerScale! Here's how they're making waves in science and climate research mitigation: 1️⃣ Scale research affordably with AI labs across more vessels. 2️⃣ Share data and insights securely with the global science community. Empowering researchers to accelerate discovery and amplify impact - that’s the potential of AI in action. 3️⃣ Leverage high-performance computing for unparalleled ROI. 🌏✨ Full case study here: https://shr.bi/834A626l

The “AI era” feels big. But no era lasts forever. Three futures that could follow: 1. BI (Biotech Intelligence): where we code biology instead of software. 2. CI (Collective Intelligence): networks of humans + machines thinking together. 3. DI (Distributed Intelligence): smart systems embedded in everything, everywhere. AI isn’t the destination. It’s just the current chapter.

A weekly look at the signals shaping deep technology, from quantum computing and AI to the expanding space economy. 👇 We monitor real-time developments, funding movements, and the practical shifts helping decision-makers turn emerging science into strategic advantage. Below: the latest insights from The Quantum Insider, AI Insider, and Space Insider. To explore how deep tech intelligence can inform your strategy, contact our Expert Team at Resonance team at hello@resonce.holdings or dm us. Get the full story via the links in the comments below. #DeepTech #QuantumComputing #TechUpdates #AiInsights #SpaceNews

Neuromorphic computing isn't just another technology wave - it's a fundamental shift in how organizations will need to structure their decision intelligence. The gap between today's linear decision flows and tomorrow's neural-inspired architectures is wider than most realize. The organizations that will thrive in the neuromorphic era are already evolving their decision systems. They're moving from rigid hierarchies to dynamic neural networks of expertise. From isolated knowledge silos to interconnected intelligence webs. From sequential approvals to parallel processing of insights. The challenge isn't the technology - it's rewiring how we think about organizational intelligence. Just as biological neural networks process information through countless parallel pathways, future decision systems will need to handle multiple streams of insight simultaneously. Is your organization still stuck in linear decision flows? Are you preparing your teams to think in neural networks rather than flowcharts? The time to evolve your decision architecture is before the neuromorphic wave arrives, not after. What steps are you taking today to reshape your decision systems for the neural-inspired future? #Decidra #NeuromorphicComputing #DecisionIntelligence #OrganizationalTransformation #EmergingTech

As distributed AI systems increasingly integrate across virtual and physical domains, a hidden risk may be emerging — one we are not yet discussing widely. There is likely a critical coupling threshold inherent to all complex, interconnected AI systems: a point where their level of interconnection could trigger a phase transition in behavior. This phenomenon appears throughout nature — in physics, biology, and ecosystems — though its form varies with context and is not always immediately obvious. If such a threshold exists in AI networks, it represents not just a technical challenge but a safety concern. We may need to develop a new discipline at the intersection of engineering and physics to study how coupling dynamics and phase transitions operate within highly integrated AI systems. Our move into the AI age does not need to be difficult or chaotic — if we proceed with intention, awareness, and humility toward complexity. Below is a link to my exploratory paper, which explains this concept in greater detail and outlines its potential implications for AI safety and complex systems research. https://lnkd.in/eDZYrBqB

𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗜 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗗𝗲𝗯𝗮𝘁𝗲 This week brought thought-provoking insights on AI's trajectory from two prominent figures, offering perspectives that appear contradictory on the surface but reveal a nuanced reality. Julian Schrittwieser of Anthropic published an essay titled "Failing to Understand the Exponential, Again," challenging what he characterizes as the naïve 'AI bubble' narrative. https://lnkd.in/gPec6snq He argues that critics fundamentally misunderstand AI's potential for exponential growth over the next 18 months. His key predictions include:   • 𝗔𝘁 𝗹𝗲𝗮𝘀𝘁 𝗼𝗻𝗲 𝗺𝗼𝗱𝗲𝗹 𝗮𝗰𝗵𝗶𝗲𝘃𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻 𝗲𝘅𝗽𝗲𝗿𝘁-𝗹𝗲𝘃𝗲𝗹 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗰��𝗼𝘀𝘀 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 𝗯𝘆 𝗲𝗻𝗱 𝗼𝗳 𝟮𝟬𝟮𝟲   • 𝗠𝗼𝗱𝗲𝗹𝘀 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝘁𝗹𝘆 𝘀𝘂𝗿𝗽𝗮𝘀𝘀𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝘁 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗻 𝗻𝘂𝗺𝗲𝗿𝗼𝘂𝘀 𝘁𝗮𝘀𝗸𝘀 𝗯𝘆 𝗲𝗻𝗱 𝗼𝗳 𝟮𝟬𝟮𝟳 In contrast, Richard Sutton—the Turing Award winner and father of reinforcement learning—offered a more cautious view in his recent Dwarkesh Patel podcast appearance. https://lnkd.in/gNVM6PNm Sutton contends that imitation learning through next-word prediction, as employed by current LLMs, faces significant limitations. His central thesis: 𝘄𝗲 𝗻𝗲𝗲𝗱 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗻𝗲𝘄 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 𝘁𝗼 𝗲𝗻𝗮𝗯𝗹𝗲 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹, 𝗼𝗻-𝘁𝗵𝗲-𝗷𝗼𝗯 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴. My perspective aligns with a synthesis of these views as aptly expressed by Erik Brynjolfsson. Two realities can coexist:   1. 𝘾𝙪𝙧𝙧𝙚𝙣𝙩 𝙇𝙇𝙈 𝙖𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙡𝙤𝙣𝙚 𝙬𝙞𝙡𝙡 𝙣𝙤𝙩 𝙖𝙘𝙝𝙞𝙚𝙫𝙚 𝙜𝙚𝙣𝙚𝙧𝙖𝙡 𝙞𝙣𝙩𝙚𝙡𝙡𝙞𝙜𝙚𝙣𝙘𝙚   2. 𝙀𝙫𝙚𝙣 𝙬𝙞𝙩𝙝𝙤𝙪𝙩 𝙖𝙙𝙙𝙞𝙩𝙞𝙤𝙣𝙖𝙡 𝙗𝙧𝙚𝙖𝙠𝙩𝙝𝙧𝙤𝙪𝙜𝙝𝙨, 𝙇𝙇𝙈𝙨 𝙬𝙞𝙡𝙡 𝙛𝙪𝙣𝙙𝙖𝙢𝙚𝙣𝙩𝙖𝙡𝙡𝙮 𝙩𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢 𝙤𝙪𝙧 𝙚𝙘𝙤𝙣𝙤𝙢𝙮 I am excited about advances in modern hardware and brain-inspired computing paradigms that will bridge current gaps. 𝗡𝗲𝘂𝗿𝗮𝗹 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗺𝗮𝘆 𝗲𝘃𝗲𝗻𝘁𝘂𝗮𝗹𝗹𝘆 𝗯𝗲 𝗮𝗰𝗵𝗶𝗲𝘃𝗲𝗱 𝗼𝗻 𝗽𝗵𝗼𝘁𝗼𝗻𝗶𝗰, 𝗯𝗶𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹, 𝗰𝗵𝗲𝗺𝗶𝗰𝗮𝗹, 𝗾𝘂𝗮𝗻𝘁𝘂𝗺, 𝗼𝗿 𝗼𝘁𝗵𝗲𝗿 𝗲𝗻𝘁𝗶𝗿𝗲𝗹𝘆 𝗻𝗼𝘃𝗲𝗹 𝘀𝘂𝗯𝘀𝘁𝗿𝗮𝘁𝗲𝘀. There is no better time to be working on compute, silicon and AI. Excited for the future of hardware and AI evolution. #silicon #ai #computing #llm #artificialintelligence #model #neuralnetworks #machinelearning

For the past two years, we’ve been quietly working on something that bridges two of the most powerful forces in technology — AI and Quantum Computing. Next week, we’ll finally share what we’ve been building and why we believe it changes how the world will develop, test, and accelerate quantum systems — from algorithms to hardware performance. #QuantumComputing #AI #DeepTech #Innovation

Day 1: AI Research Breakthroughs, Industry & Infrastructure We’re one week away. Here’s a snapshot of what’s to come. Day 1 brings together leading researchers from Google DeepMind, Microsoft, University of Oxford, UCL, NVIDIA, Nscale, and more, exploring breakthroughs in AI research, the future of industry-led research, and how the UK can lead in AI infrastructure. Keynotes include: • Dr Raia Hadsell (Google DeepMind) • Prof. Csaba Szepesvari (Google DeepMind) • Prof. Jakob Foerster (University of Oxford) • Prof. Andrew Saxe (UCL) • Prof. David Silver (Google DeepMind & UCL) • Dr. Laura Gilbert CBE (Tony Blair Institute for Global Change) Speakers: • Dr James Whittington (University of Oxford) • Dr Leon Chlon, PhD (Hassana Labs) • Dr Lion Schulz (Bertelsmann SE & Co. KGaA) • Dr Jonathan Richard Schwarz (Thomson Reuters) • Dr Felix Sosa (NTT) • Max Beverton-Palmer (NVIDIA) • Karl Havard (Nscale) • Mike Mattacola (CoreWeave) Read more on our website below #AI #Research #Infrastructure #ThinkingAboutThinking #OpenProblemsAI

Quantum computing expert Scott Aaronson published a new paper that he revealed had a key technical step come from GPT-5-Thinking. In my opinion this is huge and isn't getting enough attention. I frequently hear people complain about GPT-5 or AI in general and claim that it's *all* slop. The fact that we're seeing ANY occurrences of novel breakthroughs in the most technically-advanced fields is astounding and I'll be shocked if it's not a sign of things to come.

It. Is. 2025. We live in an era where AI can compose symphonies and forecast markets, but selecting Washington, D.C. and/or District of Columbia from a dropdown menu??? Apparently, that’s still TOO advanced for many systems. 😂 When I’m forced to manually type it in, or worse, select “Washington State” and pray the system will understand the context, I can’t help but chuckle at the irony. We’ve entered the age of quantum computing, but apparently, address recognition remains a mystery. A perfect reminder that while AI may be getting smarter each day, some systems still just need a good ol' fashioned human/COMMON SENSE update!