Technology Adoption Benefits

Paper sharing- AI in Science Discovery & Product Innovation MIT researchers expanded on applying AI-driven discovery to material science, which is making discoveries happen faster than ever！ What they did was introduce an AI tool (similar to the "AI Scientist" from Sakana AI) for materials discovery to 1,800 scientists in the R&D lab of a large U.S. firm. Traditionally, scientific discovery is labor-intensive and manual—a process full of trial and error, where scientists conceptualize various potential structures and then test their properties. Here’s how AI tackles it: AI generates ideas, prioritizes promising materials, tests them, and iterates on any false positives, refining until it finds viable options. Once validated, these materials can be patented and commercialized. This entire process runs much faster, and the impact is striking. Researchers with AI assistance have discovered 44% more materials, filed 39% more patents, and seen a 177% jump in downstream product innovation. Interestingly, the benefits are more unequally divided than we might have assumed. Top researchers nearly doubled their output, while the bottom third saw little improvement. This divide is partly because AI automates 57% of idea generation, allowing top scientists to focus on testing rather than preliminary research. Another downside is that 82% of scientists reported feeling less fulfilled, citing reduced creativity and underutilized skills. References/ Paper: https://lnkd.in/g3sZdAbJ __________________ I share my learning journey here. Join me and let's grow together. For more on AI and learning materials, please check my previous posts. Alex Wang

Fuel protests in Ireland have blocked roads, public transport, ports and energy infrastructure, as protestors demand the government caps fuel prices. But the only way to durably protect hauliers - and the economy - against fossil fuel shocks is to support the electrification of trucks. The technology is available, and makes economic sense: Sales of electric trucks outsold diesel trucks in China last December. But in Ireland, so far this year, only one electric HVG was newly registered. It's possible to do this quickly, though not overnight. Electric vans already make up 5% of new light commercial vehicle sales this year, double last year's share. Whatever happens to the carbon tax, it should be our collective aim to make this our last fossil fuel crisis. For road freight, that means putting the right supports in place now: charging infrastructure for heavy trucks, depot grid connections, purchase grants or leasing support. The government has very little control over the price of oil, but it can control how dependent we remain on it.

Even when charged from relatively “dirty” power grids, battery-electric vehicles (BEVs) outperform internal combustion engine (ICE) vehicles on lifecycle emissions. In key gobal regions studied — from the U.S. and China to the UK, Germany and Japan — EVs come out cleaner over their entire lifetime. Yes — manufacturing EVs (especially batteries) remains emissions-intensive. But once on the road, BEVs rapidly recoup that initial “carbon debt.” Over 250 000 km of driving, a medium-sized BEV’s CO₂ footprint can be 21–71% lower than the equivalent ICE car — depending on driving patterns and the energy mix. That matters — we can’t afford near-term paralysis based on imperfect grids or “worst-case” assumptions. As grids continue to decarbonise, the environmental advantage of EVs will only grow. If we want to accelerate transport decarbonisation at scale, the message is clear: EV deployment must go hand-in-hand with cleaner grids — but delaying electrification until perfect conditions are met is a luxury the climate doesn’t afford.

Most quantum boardroom conversations end without an agenda. They end with a posture — "we're monitoring quantum developments," "we're taking it seriously". Neither statement produces a plan. The distinction matters because quantum creates three problem classes, each with a different urgency and a different cost of inaction. A generic posture misaddresses all three at once. The right response, for most leadership teams, has three parts. The first is to defend now. Post-quantum cryptography belongs on the enterprise risk agenda as a current priority. That means building visibility into cryptographic dependencies across the enterprise, identifying migration priorities, and mapping third-party exposure. This is the part of the quantum agenda that cannot wait. The second is to explore selectively. Most leadership teams do not need a wide portfolio of quantum pilots. They need a small number of focused efforts on high-value problems where the workload aligns with quantum's actual strengths — evaluated against the strongest available classical alternative. Each effort should be a targeted test: one specific problem, one clear classical benchmark, one honest evaluation. The third is to build options. For companies in simulation-relevant sectors — pharmaceuticals, advanced materials, energy — the right posture is modest investment in partnerships and early hardware collaborations. The goal is R&D workflows that are ready to integrate quantum subroutines when the technology matures. The companies that benefit most will not necessarily be those spending the most today. They will be the ones best positioned to move when the moment arrives. The most common failure on quantum is conflating the urgency of the three classes — treating all three as equally distant or equally immediate, when each has a different clock running. The organizations that get this right understand early which problem classes matter to their business, which ones to set aside, and what the distinction demands of them starting Monday morning. https://lnkd.in/gkymW7Xm

💧 Liters per kilogram of produce. That’s the metric that will define the future of agriculture. As clean water becomes more scarce, especially in climate-stressed regions, producers have two options: react later (when water access may be significantly restricted), or invest now and lead. Controlled environment agriculture (CEA) offers a better path forward. Smart greenhouses and vertical farms use sensors, automation, and AI to optimize light, water, nutrients, and temperature—cutting water use by up to 90% while dramatically increasing yields. In tomato farming, for example, these systems have been shown to produce over 600% more than open fields. CEA approaches maximize yield, minimize risk, and conserve precious resources. While it may not be feasible to have a smart greenhouse in every field around the planet, wouldn’t it make sense to invest in more of them now, to conserve water and improve our knowledge on how to make farms around the world more resilient in the face of increasing climate volatility? 💡 It’s time to stop asking "if" and start investing in the places where smart greenhouses will make the biggest difference. The weather volatility of the last few years is signaling what’s coming. Why wait longer and risk more when we can act now to conserve water and increase profitability? #SmartFarming #AgTech #WaterEfficiency #ClimateResilience #GreenInnovation #FutureOfFood

Quantum computing is quickly moving from a theoretical promise to real impact. The challenge for leaders isn’t just what the technology can do, it’s how organizations plan to take advantage of it. This recent The Wall Street Journal article underscores that while quantum hardware and algorithms are advancing quickly, most companies are behind when it comes to readiness around talent, processes, and strategic planning. The focus for business leaders should shift from curiosity about the potential to practical questions about organizational preparedness. That means building skills, designing use cases, aligning leadership,  and engaging ecosystem partners now so that when quantum becomes commercially relevant, likely in the next few years, organizations won't be starting from zero. Read more: https://deloi.tt/4oMwpIA Scott Buchholz, Natasha Buckley, Diana Kearns-Manolatos (she/her) 

𝐖𝐞 𝐬𝐩𝐞𝐧𝐭 𝐲𝐞𝐚𝐫𝐬 𝐫𝐚𝐜𝐢𝐧𝐠 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐋𝐋𝐌 𝐌𝐨𝐝𝐞𝐥𝐬 𝐛𝐢𝐠𝐠𝐞𝐫. Turns out, we were solving the wrong problem. While everyone was obsessing over parameter counts, a quiet revolution started at the opposite end: Small Language Models. Small Language Models (SLMs) are not just "LLMs lite" they are a fundamentally different approach to deploying AI at scale. 𝐓𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫: • Up to 100x+ cheaper inference depending on workload. • 96% API savings • Ultra-low latency • Runs on a laptop But here is what really excites me as a tech lead: 𝟏. 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐛𝐲 𝐃𝐞𝐬𝐢𝐠𝐧 No cloud dependency means sensitive data never leaves your infrastructure. For healthcare, finance, and enterprise this is not a nice-to-have, it is a requirement. 𝟐. 𝐄𝐝𝐠𝐞-𝐅𝐢𝐫𝐬𝐭 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 IoT devices, mobile phones, manufacturing floors AI that works where your users actually are, not where your data center is. 𝟑. 𝐑𝐞𝐚𝐥 𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜𝐬 When you are processing millions of requests, that 280x cost reduction is not incremental it is the difference between viable and impossible. 𝐓𝐡𝐞 𝐓𝐞𝐜𝐡 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞: DeepSeek R1: Open-source, controversial but effective Llama 4 Scout: 17B parameters with active MoE routing Mixture of Experts: Resource-friendly, efficient architecture 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐒𝐋𝐌: • Lower attack surface vs cloud-only models • Deterministic behavior tuning is easier • Better multi-agent concurrency due to lightweight compute needs 𝐖𝐡𝐞𝐫𝐞 𝐒𝐋𝐌𝐬 𝐬𝐡𝐢𝐧𝐞: ✅ Smart cities (traffic management) ✅ Manufacturing (quality control, predictive maintenance)   ✅ Real-time decision support ✅ Edge computing (on-device processing) 𝐌𝐲 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧? By 2026, most production AI systems will use a hybrid approach: SLMs for real-time, edge, and privacy-sensitive tasks + LLMs for complex reasoning and training. 𝐓𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬 𝐧𝐨𝐭 "𝐒𝐋𝐌 𝐯𝐬 𝐋𝐋𝐌" 𝐢𝐭'𝐬 "𝐰𝐡𝐢𝐜𝐡 𝐦𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐰𝐡𝐢𝐜𝐡 𝐣𝐨𝐛?" For tech leads building now: Start experimenting with SLMs for latency-critical and privacy-sensitive features. The cost savings alone justify the POC. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐒𝐋𝐌𝐬? 𝐀𝐫𝐞 𝐲𝐨𝐮 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐝𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐭𝐡𝐞𝐦 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/esF52fm5 #AI #TechLeadership #SmallLanguageModels #EdgeComputing #AIArchitecture #EnterpriseAI

To Feed the World, A Rethink in Agriculture is a Must: Harnessing Modern Technology for Food Security!! With the global population expected to surpass 9.7 billion by 2050, the challenge of feeding the world has never been more pressing. The current agricultural system, strained by climate change, declining soil health, and unsustainable practices, is ill-equipped to meet this demand. According to the UN's Food and Agriculture Organization (FAO), global food production must increase by 70% to feed the projected population—a daunting task under existing farming methods. A comprehensive rethink of agriculture is essential, and technology must play a pivotal role in this transformation. Modern agriculture is no longer just about growing crops; it's about growing them sustainably, efficiently, and in harmony with our planet's limitations. Digital Technologies are revolutionizing how we farm. The use of AI, machine learning, and data analytics allows farmers to make smarter decisions—whether it's about planting, irrigation, or crop protection. According to a McKinsey report, precision farming technologies can increase farm productivity by 60-70%, significantly boosting yields while reducing resource consumption. In India, startups using digital platforms to provide real-time advice and market insights can help farmers increase income by 20-30%. Biotechnology offers another vital solution. By developing genetically modified crops resistant to pests, drought, and disease, we can ensure higher yields in increasingly unpredictable environments. The success of Bt cotton in India, which led to a 24% increase in yield, is just one example. Biotechnology also enhances nutritional content, with biofortified crops like Golden Rice tackling malnutrition in developing countries. Controlled Environment Agriculture (CEA)—from greenhouses to vertical farming—allows for year-round cultivation in any climate, with minimal water and land use. CEA systems can produce up to 10 times more yield per acre compared to traditional farming. Companies like Plenty and Bowery are already proving that urban vertical farms can be part of the solution, growing crops sustainably with 95% less water and no pesticides. If we are to feed the world, embracing these modern technologies is not just a choice—it’s a necessity. Agriculture must evolve to meet the challenges of the future, and the integration of digital technologies, biotechnologies, and controlled environment farming is the pathway toward sustainable global food security. The future of food is here, and it demands our attention today.

Lessons from Women’s Cooperatives in Senegal’s Irrigation Journey I recently visited a women’s farming cooperative in Senegal that had received a donor-funded solar-powered pump and drip irrigation system. The transformation was evident: - Water use dropped by half. - Yields of vegetables more than doubled. - Labor hours for irrigation fell dramatically and profitability of agriculture from each farmer’s small area of allocated land soared. But here’s the challenge: I crossed the project boundary line to talk to the neighboring farmer. He was part of another scheme but refused to adopt the same technologies that he could see worked!  He had a small parcel in a competing donor project and had learned how to use those new technologies and adopt those practices, but on his own farm he still irrigates relies on rainfall and plows his fields with a donkey. The donor’s budgets are usually exhausted in the pilot, and there is no financing model to expand. Its a pattern I’ve seen across several countries: - Donor-driven pilots deliver impressive results but lack a pathway to scale. Neighboring farmers see the benefits but can’t access the technology on their own land. - Private suppliers hesitate to invest in sales and marketing and aftersales service without aggregated demand or financing for farmers. Scaling will almost always stall if there is no sustainable financing mechanism for replication. The use of excessive grants or subsidies in pilots to “demonstrate” technology hides true costs farmers face, and equipment remains donor-dependent. The World Bank Group is now designing and delivering our new projects with a combination of common sense farming, small business principles  and lower cost AgTech solutions: - Low cost solar-powered pumps with pay-as-you-go financing. - Modular irrigation systems that can be expanded as farmers grow. - Digital aggregators that use technology to consolidate products from multiple farmers and deliver them to wholesale buyers - Agribusiness supply chains providing an exchange of inputs, services and technologies for a secure supply of crops Where farmers are dispersed and cannot be supported through physical extension services, AI solutions are also playing an increasing role. These tools matter because they reduce risk for farmers and financiers: - AI scheduling ensures water is used only when needed, lowering costs. - IOT enabled Pay-as-you-go models spread capital costs over time, making adoption feasible. To design for scalability, we need to co-design solutions with farmers — not just for them. That means our advisory teams - Listen, learn and think like a farmer first, - Understand if farmers will bundle technology, financing and market access. - Aggregate demand to incentivize supply. My questions to you: - Have you seen irrigation projects in Africa successfully scale beyond the pilot phase? - What financing, aggregation, or market strategies made it work? #IFC #Agtech #AI4Agriculture #WBG

Petrol and diesel prices have just gone up by ₹3 today. It was imminent. For most people, this will feel like a small inconvenience, something that barely registers in their monthly expenses. But for a delivery rider who earns daily, it shows up as a big cut in his earnings. If you look at his day closely, he is on the road for 10 to 12 hours, covering close to 200 kilometres, completing anywhere between 30 to 50 deliveries. By the time he gets home, he has already burned through roughly 4 litres of petrol in a single day. That’s ₹400 gone out of his hard earned money. That’s where the ₹3 hike starts to matter, not as a headline, but as a slow leak in what he takes home every month. And this is what aches me the more as I meet so many of them on streets. What makes this harder is that none of it is in his control. The cost of his work is decided by factors far away from him, oil prices moving globally, currency shifts, government decisions, and market fluctuations that have nothing to do with the effort he puts in every day. Yet all of it quietly changes how much he earns for the exact same amount of work. This is the real structural issue with petrol-based mobility. The effort stays constant, but the earnings don’t, and that gap slowly eats into stability for someone whose income depends entirely on daily movement and volume. This is where EV changes the equation, not just as an environmental statement, but as a financial one too. A rider on an EV ends up saving over ₹5,000 every month through lower energy cost, predictable cost per kilometre, and reduced maintenance. Same work, same hours, but a far more stable take-home. When this fuel hike happened today, our EV riders didn’t feel it at all, because their earnings are no longer tied to oil prices. What they earn is simply a function of how much they ride and how many deliveries they complete. And the interesting part is, the riders who switched early didn’t “predict the future.” They just chose better math when it made sense. Today, that same math looks a lot more obvious. #goelectric #savefuel #savegigeconomy