Innovation

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

🔥 Why DeepSeek's AI Breakthrough May Be the Most Crucial One Yet. I finally had a chance to dive into DeepSeek's recent r1 model innovations, and it’s hard to overstate the implications. This isn't just a technical achievement - it's democratization of AI technology. Let me explain why this matters for everyone in tech, not just AI teams. 🎯 The Big Picture: Traditional model development has been like building a skyscraper - you need massive resources, billions in funding, and years of work. DeepSeek just showed you can build the same thing for 5% of the cost, in a fraction of the time. Here's what they achieved: • Matched GPT-4 level performance • Cut training costs from $100M+ to $5M • Reduced GPU requirements by 98% • Made models run on consumer hardware • Released everything as open source 🤔 Why This Matters: 1. For Business Leaders: - model development & AI implementation costs could drop dramatically - Smaller companies can now compete with tech giants - ROI calculations for AI projects need complete revision - Infrastructure planning can possibly be drastically simplified 2. For Developers & Technical Teams: - Advanced AI becomes accessible without massive compute - Development cycles can be dramatically shortened - Testing and iteration become much more feasible - Open source access to state-of-the-art techniques 3. For Product Managers: - Features previously considered "too expensive" become viable - Faster prototyping and development cycles - More realistic budgets for AI implementation - Better performance metrics for existing solutions 💡 The Innovation Breakdown: What makes this special isn't just one breakthrough - it's five clever innovations working together: • Smart number storage (reducing memory needs by 75%) • Parallel processing improvements (2x speed increase) • Efficient memory management (massive scale improvements) • Better resource utilization (near 100% GPU efficiency) • Specialist AI system (only using what's needed, when needed) 🌟 Real-World Impact: Imagine running ChatGPT-level AI on your gaming computer instead of a data center. That's not science fiction anymore - that's what DeepSeek achieved. 🔄 Industry Implications: This could reshape the entire AI industry: - Hardware manufacturers (looking at you, Nvidia) may need to rethink business models - Cloud providers might need to revise their pricing - Startups can now compete with tech giants - Enterprise AI becomes much more accessible 📈 What's Next: I expect we'll see: 1. Rapid adoption of these techniques by major players 2. New startups leveraging this more efficient approach 3. Dropping costs for AI implementation 4. More innovative applications as barriers lower 🎯 Key Takeaway: The AI playing field is being leveled. What required billions and massive data centers might now be possible with a fraction of the resources. This isn't just a technical achievement - it's a democratization of AI technology.

A look at how CS50 has incorporated artificial intelligence (AI), including its new-and-improved rubber duck debugger, and how it has impacted the course already. 🦆 https://lnkd.in/eb-8SAiw In Summer 2023, we developed and integrated a suite of AI-based software tools into CS50 at Harvard University. These tools were initially available to approximately 70 summer students, then to thousands of students online, and finally to several hundred on campus during Fall 2023. Per the course's own policy, we encouraged students to use these course-specific tools and limited the use of commercial AI software such as ChatGPT, GitHub Copilot, and the new Bing. Our goal was to approximate a 1:1 teacher-to-student ratio through software, thereby equipping students with a pedagogically-minded subject-matter expert by their side at all times, designed to guide students toward solutions rather than offer them outright. The tools were received positively by students, who noted that they felt like they had "a personal tutor." Our findings suggest that integrating AI thoughtfully into educational settings enhances the learning experience by providing continuous, customized support and enabling human educators to address more complex pedagogical issues. In this paper, we detail how AI tools have augmented teaching and learning in CS50, specifically in explaining code snippets, improving code style, and accurately responding to curricular and administrative queries on the course's discussion forum. Additionally, we present our methodological approach, implementation details, and guidance for those considering using these tools or AI generally in education. Paper at https://lnkd.in/eZF4JeiG. Slides at https://lnkd.in/eDunMSyx. #education #community #ai #duck

AI is no longer just about retrieving information or generating responses—it's about autonomous systems that can plan, reason, and act on their own.  Enter the Agentic AI Stack—a multi-layered framework designed to enable AI systems to move beyond passive assistants into autonomous decision-makers.  𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗗𝗼𝘄𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘁𝗮𝗰𝗸:  1. 𝗧𝗼𝗼𝗹 & 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗟𝗮𝘆𝗲𝗿 – The foundation of any intelligent system. AI agents connect to web searches, APIs, operational data, vector databases, and business logic to retrieve relevant information.  2. 𝗔𝗰𝘁𝗶𝗼𝗻 & 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 – AI isn’t just about information retrieval; it needs to act. This layer handles task management, persistent memory, automation scripts, and event logging, allowing AI to execute decisions dynamically.  3. 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿 – The AI’s decision-making core. Using LLMs, contextual analysis, decision trees, and NLU, AI agents evaluate situations, assess outcomes, and make informed choices instead of simply reacting to prompts.  4. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 & 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿 – Continuous improvement is the key to AI evolution. AI agents integrate user feedback loops, model training, performance metrics, and self-improvement mechanisms to refine their capabilities over time.  5. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗟𝗮𝘆𝗲𝗿 – Autonomous AI must be trustworthy. This layer ensures data encryption, access control, compliance monitoring, and audit trails—critical for enterprise and real-world deployment.  𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗔𝗜: 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗟𝗲𝗮𝗽 𝗙𝗼𝗿𝘄𝗮𝗿𝗱  Most AI systems today function independently, but the real breakthrough lies in multi-agent collaboration—where multiple AI agents interact, negotiate, and coordinate tasks like human teams.  🔹 Cooperative AI – Agents collaborate towards a shared goal.   🔹 Competitive AI – Agents work independently to achieve the best outcome.   🔹 Mixed AI – A hybrid of collaboration and competition.   🔹 Hierarchical AI – AI agents follow a structured leadership system.  Why does this matter? Because the future of AI is not just about intelligence—it’s about autonomy, coordination, and adaptability.  AI that retrieves, reasons, plans, and acts—that’s the Agentic AI future.  How do you see Agentic AI shaping the next wave of automation and decision-making? Drop your thoughts below!

I've watched 3 "revolutionary" healthcare technologies fail spectacularly. Each time, the technology was perfect. The implementation was disastrous. Google Health (shut down twice). Microsoft HealthVault (lasted 12 years, then folded). IBM Watson for Oncology (massively overpromised). Billions invested. Solid technology. Total failure. Not because the vision was wrong, but because healthcare adoption follows different rules than consumer tech. Here's what I learned building healthcare tech for 15 years: 1/ Healthcare moves at the speed of trust, not innovation ↳ Lives are at stake, so skepticism is protective ↳ Regulatory approval takes years usually for good reason ↳ Doctors need extensive validation before adoption ↳ Patients want proven solutions, not beta testing 2/ Integration trumps innovation every time ↳ The best tool that no one uses is worthless ↳ Workflow integration matters more than features ↳ EMR compatibility determines adoption rates ↳ Training time is always underestimated 3/ The "cool factor" doesn't predict success ↳ Flashy demos rarely translate to daily use ↳ Simple solutions often outperform complex ones ↳ User interface design beats artificial intelligence ↳ Reliability matters more than cutting-edge features 4/ Reimbursement determines everything ↳ No CPT code = no sustainable business model ↳ Insurance coverage drives provider adoption ↳ Value-based care is changing this slowly ↳ Free trials don't create lasting change 5/ Clinical champions make or break technology ↳ One enthusiastic doctor can drive adoption ↳ Early adopters must see immediate benefits ↳ Word-of-mouth beats marketing every time ↳ Resistance from key stakeholders kills innovations The pattern I've seen: companies build technology for the healthcare system they wish existed, not the one that actually exists. They optimize for TechCrunch headlines instead of clinic workflows. They design for Silicon Valley investors instead of 65-year-old physicians. A successful healthcare technology I've implemented? A simple visit summarization app that saved me time and let me focus on the patient. No fancy interface, very lightweight, integrated into my clinical workflow, effortless to use. Just solved an problem that users had. Healthcare doesn't need more revolutionary technology. It needs evolutionary technology that works within existing systems. ⁉️ What's the simplest technology that's made the biggest difference in your healthcare experience? Sometimes basic beats brilliant. ♻️ Repost if you believe implementation beats innovation in healthcare 👉 Follow me (Reza Hosseini Ghomi, MD, MSE) for realistic perspectives on healthcare technology

New findings out of UCSF! Psilocybin was safely administered to people with Parkinson’s, and the results were remarkable. In addition to improved mood, participants also saw lasting gains in motor function and cognition, with no serious side effects. This is the first time a psychedelic has been tested in a neurodegenerative disease, and the outcomes are promising enough to launch a larger clinical trial, supported by the Michael J. Fox Foundation. A powerful reminder: nature holds deep potential to heal. https://lnkd.in/gad5SGwn

I am constantly thinking about how to foster innovation in my product organization. Building teams that are experts at execution is the easy part—when there’s a clear problem, product orgs are great at coming up with smart solutions. But it’s impossible to optimize your way into innovation. You can’t only rely on incremental improvement to keep growing. You need to come up with new problem spaces, rather than just finding better solutions to the same old problems. So, how do we come up with those new spaces? Here are a few things I’m trying at Duolingo: 1. Innovation needs a high-energy environment, and a slow process will kill a great idea. So I always ask myself: Can we remove some of the organizational barriers here? Do managers from seven different teams really need to say yes on every project? Seeking consensus across the company—rather than just keeping everyone informed—can be a major deterrent to innovation. 2. Similarly, beware of defaulting to “following up.” If product meetings are on a weekly cadence, every time you do this, you are allocating seven days to a task that might only need two. We try to avoid this and promote a sense of urgency, which is essential for innovative ideas to turn into successes. 3. Figure out the right incentive. Most product orgs reward team members whose ideas have measurable business impact, which works in most contexts. But once you’ve found product-market fit, it is often easiest to generate impact through smaller wins. So, naturally, if your org tends to only reward impact, you have effectively incentivized constant optimization of existing features instead of innovation. In the short term things will look great, but over time your product becomes stale. I try to show my teams that we value and reward bigger ideas. If someone sticks their neck out on a new concept, we should highlight that—even if it didn’t pan out. Big swings should be celebrated, even if we didn’t win, because there are valuable learnings there. 4. Look for innovative thinkers with a history of zero-to-one feature work. There are lots of amazing product managers out there, but not many focus on new problem domains. If a PM has created something new from scratch and done it well, that’s a good sign. An even better sign: if they show excitement about and gravitate toward that kind of work. If that sounds like you—if you’re a product manager who wants to think big picture and try out big ideas in a fast-paced environment with a stellar mission—we want you on our team. We’re hiring a Director of Product Management: https://lnkd.in/dQnWqmDZ #productthoughts #innovation #productmanagement #zerotoone

We all know that work has changed a lot over the last decade. But through every technological revolution and market cycle, one powerful truth has remained constant: technology alone doesn't drive innovation and growth. PEOPLE drive innovation and growth. Our Work Change Report looks at data from more than 1 billion professionals and 69 million companies to offer a deeper look into how AI is changing work and what you can do to navigate it. A few takeaways that make me optimistic: 1. Human skills have grown in importance by 10% since 2018. As AI takes on the more operational aspects of work, these skills will be integral to how our everyday work gets done, and are what’s going to give our organizations an advantage. 2. The data particularly resonates at the leadership level – C-suite executives have increased their emphasis on human skills by 31% between 2018-2023. This reflects a growing recognition that modern leadership requires exceptional adaptability and emotional intelligence. 3. The path forward is clear: investing in your people’s human capabilities will be the true catalyst for innovation. The skills that make us uniquely human–empathy, compassion, communication- are becoming our most valuable assets. It's encouraging to see that 77% of HR leaders are already prioritizing upskilling initiatives for 2025, whether that means online learning, mentorship, personalized coaching, or all of the above. We’re living through an incredible moment where technology and human potential are coming together in ways we’ve never seen before. I’d love to hear your thoughts - what findings echo your experience? Read the full report here: https://lnkd.in/erpVwSRA

Researchers have made a significant breakthrough in AI hardware with a 3D photonic-electronic platform that enhances efficiency and bandwidth, potentially revolutionizing data communication. Energy inefficiencies and data transfer bottlenecks have hindered the development of next-generation AI hardware. Recent advancements in integrating photonics with electronics are poised to overcome these challenges. 💻 Enhanced Efficiency: The new platform achieves unprecedented energy efficiency, consuming just 120 femtojoules per bit. 📈 High Bandwidth: It offers a bandwidth of 800 Gb/s with a density of 5.3 Tb/s/mm², far surpassing existing benchmarks. 🔩 Integration: The technology integrates photonic devices with CMOS electronic circuits, facilitating widespread adoption. 🤖 AI Applications: This innovation supports distributed AI architectures, enabling efficient data transfer and unlocking new performance levels. 📊 Practical Quantum Advancements: Unlike quantum entanglement for faster-than-light communication, using quantum physics to boost communication speed is more feasible and practical. This breakthrough is long overdue, but the AI boost might create a burning need for this technology. Quantum computing might be seen as a lot of hype, but using advanced quantum physics to enhance communication speed is more down-to-earth than relying on quantum entanglement for faster-than-light communications, which is short-lived #AI #MachineLearning #QuantumEntanglement #QuantumPhysics #PhotonicIntegration #SiliconPhotonics #ArtificialIntelligence #QuantumMechanics #DataScience #DeepLearning

We are excited to announce the release of our "Guide to Integrating Generative AI for Deeper Literacy Learning" - a collaboration between AI for Education and Student Achievement Partners. We co-developed the guide with SAP, experts in high quality instruction, with an understanding that both the technology and its educational applications are at it's earliest stages. We also know that many teachers, leaders, and students are concerned about the impact the tools will have on learning. We want this guide to act as a jumping off point for educators that are trying to determine if GenAI can positively intersect with high quality instruction in the literacy classroom. The Key Principles of the Guide: •  GenAI tools should support, not circumvent, productive struggle for students •  AI literacy should come before the Integration of GenAI tools •  GenAI should augment educators’ pedagogical expertise, content knowledge, and knowledge of students •  Integration when appropriate should enhance, not replace, proven instructional practices •  Usage should align with students’ developmental readiness and literacy goals Highlights: • A framework for distinguishing productive vs. counterproductive struggle in literacy classrooms • Practical strategies for using AI to enhance student engagement without replacing critical thinking for students •  Best practices for enhancing cognitive lift and what strategies to avoid that offload cognitive lift • Detailed GenAI use cases across foundational skills, knowledge building, and writing instruction • Elementary-specific guidance emphasizing teacher-led AI implementation and modeling • Comprehensive worked examples with Chatbot transcripts that illustrate these practices This is just the beginning, which is why we're actively gathering educator feedback to refine and expand these resources through a survey in the guide. Thank you so much to Carey Swanson and Jasmine Costello, PMP from SAP for being such wonderful partners in this work! You can access the full guide or watch the accompanying webinar in the link in the comments! #ailiteracy #literacy #GenAI #K12