Innovation

Most tech prototypes are built to be sold. These are built to break the rules of physics. Would you use this phone? When people see a fully transparent smartphone prototype, the immediate question is always: "When can I buy one?" But they are missing the point. These devices aren't consumer products—they are high-stakes, multi-million dollar experimental platforms designed to accelerate hardware R&D by a decade. Right now, transparent display technology is accelerating at an unprecedented pace: Transparent OLED (T-OLED): Currently achieving 40% to 45% transparency in active commercial and transit deployments. MicroLED: Pushing the boundaries even further, targeting over 60% transparency alongside massive leaps in nits (brightness) and energy efficiency. The Investment: Industry titans like Samsung, LG, BOE, and Lenovo are pouring billions into these concepts not for immediate retail ROI, but to solve future manufacturing bottlenecks early. The real engineering challenge, however, isn’t the screen. It’s the invisible wall behind it. The Data Behind the "Impossible" Phone A modern flagship is a dense metropolis of opaque tech. To make a phone transparent, you have to completely re-engineer components that naturally refuse to disappear: The Volume Problem: Smartphone batteries occupy 35% to 45% of a device's internal volume. There is currently no commercially viable way to make high-density lithium-ion transparent. The Thermal Load: Modern mobile SoCs regularly generate thermal loads exceeding 10W+ during intensive AI workloads. Transparent substrates are historically terrible thermal conductors, leading to rapid overheating. The Optical Barrier: Image sensors require isolated, dark optical pathways. If light leaks into the camera housing from the back of the phone, the sensor is blinded. This is why today's wildest prototypes still hide their guts in thick bottom chins, utilize opaque side rails, or drastically sacrifice battery capacity just to maintain the optical illusion. Early OLED panels were dim, expensive, and suffered from catastrophic burn-in. Early foldable hinges failed after a few thousand folds. Early EV batteries lacked the energy density for practical daily use. Iterative, aggressive prototyping solved all of them. Today, Generative AI is accelerating this hardware loop even faster. By using AI-assisted simulation, engineers can model thermal dissipation, optimize structural layouts, and test thousands of optical design concepts digitally—compressing traditional 5-year R&D cycles into months. The prototype isn't a gimmick. It’s the blueprint for the future of human-machine interaction. #Technology #Innovation #AI #Engineering #OLED #MicroLED #FutureTech #Semiconductors #SpatialComputing #IndustrialDesign

We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop. Humans are the most scalable embodiment on the planet. We discovered a near-perfect log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and this loss directly predicts real-robot success rate. Humanoid robots will be the end game, because they are the practical form factor with minimal embodiment gap from humans. Call it the Bitter Lesson of robot hardware: the kinematic similarity lets us simply retarget human finger motion onto dexterous robot hand joints. No learned embeddings, no fancy transfer algorithms needed. Relative wrist motion + retargeted 22-DoF finger actions serve as a unified action space that carries through from pre-training to robot execution. Our recipe is called "EgoScale": - Pre-train GR00T N1.5 on 20K hours of human video, mid-train with only 4 hours (!) of robot play data with Sharpa hands. 54% gains over training from scratch across 5 highly dexterous tasks. - Most surprising result: a *single* teleop demo is sufficient to learn a never-before-seen task. Our recipe enables extreme data efficiency. - Although we pre-train in 22-DoF hand joint space, the policy transfers to a Unitree G1 with 7-DoF tri-finger hands. 30%+ gains over training on G1 data alone. The scalable path to robot dexterity was never more robots. It was always us. - Website: https://lnkd.in/gxzgeP-2 - Paper: https://lnkd.in/g7PJdz_8

Grid bottlenecks are a feature — not a bug — of the energy transition. For years, we viewed economics as the main hurdle to scaling clean energy. High costs for wind, solar, heat pumps, and storage dominated the conversation. But the world has changed. Thanks to extraordinary innovation and dramatic cost reductions in renewables and electrification technologies, the bottlenecks we face today are different. They’re no longer about whether clean energy is affordable — it is. Instead, the challenge is whether our energy systems can evolve quickly enough to integrate it. A recent Financial Times piece highlights this clearly: across Europe, the rapid build-out of renewable generation now outpaces the ability of grids to move electricity to where it’s needed. Curtailment, congestion, and long queues for grid connections already cost billions annually — and without decisive action, these costs will grow. This isn’t a sign of failure. It’s a sign of success. It means the transition is happening faster than the infrastructure built for the fossil era can handle. The rise of decentralised, variable renewables and electrified heating and transport requires a fundamentally different approach to planning — one that anticipates growth rather than reacts to it. The EU’s move toward more coordinated, top-down scenario building and cross-border grid planning recognises exactly this. Better alignment between countries and system operators, faster permitting, and prioritisation of critical projects are essential steps to unlock the full value of cheap clean energy. Because every euro lost to bottlenecks is not a cost of climate action — it’s a cost of not modernising our grids fast enough. The more successful we are in deploying renewables and electrification, the more urgently we must upgrade and expand our grids. Grid constraints are not a reason to slow down. They’re a reason to speed up the transformation of an energy system that was never designed for the technologies now powering our transition.

A new 20-year analysis of satellite data shows that the Old Continent’s freshwater reserves are shrinking, silently and steadily. Satellites that weigh the Earth by tracking gravitational changes reveal 👉 Northern Europe is getting wetter. 👉 Southern and central Europe are drying fast. And what’s disappearing fastest is the water we don’t see — groundwater, the strategic reserve that keeps our taps running, our crops alive, and our economies functioning. This is #climatechange in real time. No models, no projections — observations from space. Researchers warn that Europe is barreling toward a 2°C world, and the consequences are already here: • Heavier downpours but longer, harsher dry spells • Winter recharge seasons shrinking • More runoff, less infiltration • Deep aquifers declining across the EU • Increasing pressure on public water supply and agriculture Groundwater is the backbone of Europe’s resilience. In 2022 alone: 🔹 62% of all public water supply came from groundwater 🔹 33% of agricultural demand relied on it 🔹 Groundwater abstractions increased by 6% despite lower overall water use Farmers across southern Europe are watching reservoirs drop while fruit and vegetable yields continue to fall. These are the same dynamics long documented across the Global South, now hitting Europe with unprecedented force. The old assumptions no longer hold. Europe is not water-secure. Infrastructure alone will not save us. New reservoirs arriving in 20 years are not a solution for a crisis happening today. We need: ✅ Radical efficiency — cutting leakage, modernising networks, accelerating water-smart design ✅ Water reuse at scale — separating drinking water systems from non-potable recycled streams ✅ Nature-based solutions — restoring wetlands, aquifers, and natural recharge ✅ Smarter climate-informed water governance — using the best science to guide every decision ✅ A mindset shift — rainwater harvesting, circular water systems, and demand-side management must become standard, not exceptional read the article in The Guardian 👇 https://lnkd.in/eeTsyMve

The rise of AI agents is not a sudden breakthrough, but a steady evolution through multiple layers of capability: LLM Processing Flow – Basic input-to-output transformation. LLM with Document Processing – Expanding scope to handle structured and unstructured documents. LLM with RAGs & Tools – Introducing retrieval, tool-use, and external knowledge integration. Multi-Modal Workflows – Combining text, vision, and audio with emerging memory structures. Advanced Architectures – Incorporating decision-making, orchestration of tools, and multi-level memory (short-term, long-term, episodic). Future AI Agents – Moving beyond capability toward responsibility: safety, ethics, regulation, compliance, interoperability, and human collaboration. This progression highlights a clear trajectory: from narrow assistants to autonomous, enterprise-ready agents that operate within a framework of trust, governance, and accountability. The challenge now is not whether we can reach stage six, but how we ensure safety and control while advancing toward it.

Invisible UX is coming 🔥 And it’s going to change how we design products, forever. For decades, UX design has been about guiding users through an experience. We’ve done that with visible interfaces: Menus. Buttons. Cards. Sliders. We’ve obsessed over layouts, states, and transitions. But with AI, a new kind of interface is emerging: One that’s invisible. One that’s driven by intent, not interaction. Think about it: You used to: → Open Spotify → Scroll through genres → Click into “Focus” → Pick a playlist Now you just say: “Play deep focus music.” No menus. No tapping. No UI. Just intent → output. You used to: → Search on Airbnb → Pick dates, guests, filters → Scroll through 50+ listings Now we’re entering a world where you guide with words: “Find me a cabin near Oslo with a sauna, available next weekend.” So the best UX becomes barely visible. Why does this matter? Because traditional UX gives users options. AI-native UX gives users outcomes. Old UX: “Here are 12 ways to get what you want.” New UX: “Just tell me what you want & we’ll handle the rest.” And this goes way beyond voice or chat. It’s about reducing friction. Designing systems that understand intent. Respond instantly. And get out of the way. The UI isn’t disappearing. It’s mainly dissolving into the background. So what should designers do? Rethink your role. Going forward you’ll not just lay out screens. You’ll design interactions without interfaces. That means: → Understanding how people express goals → Guiding model behavior through prompt architecture → Creating invisible guardrails for trust, speed, and clarity You are basically designing for understanding. The future of UX won’t be seen. It will be felt. Welcome to the age of invisible UX. Ready for it?

Most people will hear an idea and say, “Wow, what a great pitch!” just to be polite. Others might have no clue what they’re talking about and still tell you why you’ll fail. Both of these opinions? Noise. I’ve spent my career searching for ideas that have the potential to transform industries. Those ideas are rarely obvious, but one of the signs you might be on to something big is thoughtful pushback from people who truly understand the industry you’re working in. That’s what makes an idea contrarian. It’s not about swimming against the tide for the sake of being different. It’s about challenging conventional wisdom with a well-reasoned theory of the game you’re playing, and why your idea will win. The key is balancing confidence in your vision with humility—being willing to listen, learn, and refine your idea based on feedback from those same smart skeptics. Their pushback isn’t just a roadblock; it’s a signal. And if you can navigate that tension, you might just have something extraordinary on your hands.

We’ve called efficiency the unsung hero of the energy transition in the past. While the energy transition will happen first through the transition of energy usages, like the shift with transport, from internal combustion engines to electric vehicles, or from fuel or gas boilers to heat pumps, we cannot ignore the utmost priority of the energy transition: efficiency. Efficiency is the greatest path to reduce our energy use, our impact on the world’s climate through CO2 emission reduction, and very importantly, the best way to make solid and practical savings. In its most historical form, energy efficiency is about better insulation, to reduce heating (or cooling) loss in buildings like family homes, warehouses, office high rises, and shopping malls. This is useful, but expensive and tedious to realize on existing installations. Digitizing home, buildings, industries and infrastructure brings similar benefits at a much lower cost and a much higher economic return. The combination of IoT, big data, software and AI can significantly reduce energy use and waste by detecting leaky valves, or automatically adjusting heating, lighting, processes and other systems to the number of people present at any given time, using real-time data analysis. It also allows owners to measure precisely progress, report automatically on their energy and sustainability parameters, and benefit from new services through smart grid interaction. And this is just the energy benefit. Automation and digital tools also optimize the processes, safety, reliability, and uptime leading to greater productivity and performance.

Ever heard of the Lippitt-Knoster Model for Managing Complex Change? It's a classic in the change management world, laying out the essential pieces needed to navigate big transformations. Taking a cue from that, I've adapted it to fit the world of digital transformation. There are seven key elements you can't afford to miss: Vision, Strategy, Objectives, Capabilities, Architecture, Roadmap, and Projects & Programs. Skip any one of these, and you're asking for trouble. Here’s why each one matters: • 𝐕𝐢𝐬𝐢𝐨𝐧: This is the 'what' of your transformation. A clear vision gives everyone a target to aim for, aligning all efforts and keeping the team focused. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Think of this as the 'why' and 'how.' A solid strategy explains the logic behind your vision, showing how you plan to get there and why it's the best route. It’s designed to guide everyone in the company on how to make decisions that support the vision, aligning all efforts and keeping the team focused. • 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: These are your milestones. Clear, specific objectives make it easy to measure success and ensure everyone knows what's important. Without them, you can easily veer off course and waste resources. • 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: These are what your company will now be able to do that it wasn't able to before in order to achieve the objectives. These can be organizational capabilities (like improved decision-making), technical capabilities (such as real-time operational visibility), or other types like enhanced customer engagement or streamlined processes. • 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: A robust architecture ensures all your tech works together smoothly, preventing inefficiencies and costly headaches. This includes various types of architecture such as data architecture, IT infrastructure architecture, enterprise architecture, and functional architecture. Effective architecture is central to reducing technical debt and aligning software with broader business transformation goals. • 𝐑𝐨𝐚𝐝𝐦𝐚𝐩: Your roadmap is the game plan. It lays out the sequence of actions, helping you avoid uncertainty and missteps. It's your guide to getting things done right. • 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 & 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬: These are where the rubber meets the road. Actionable projects and programs turn your strategy into reality, making sure your plans lead to real, tangible outcomes. From my experience, I think '𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬' and '𝐑𝐨𝐚𝐝𝐦𝐚𝐩' are the two most overlooked. What do you think? ******************************************* • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas