Science And Technology Collaborations

Google DeepMind’s AI Co-Scientist paper was just released, and you should check it out! It represents a paradigm shift in scientific discovery, leveraging a multi-agent system built on Gemini 2.0 to autonomously generate, refine, and validate new research hypotheses. 🔹How does it work? Well the system uses a generate, debate, and evolve framework, where distinct agents called Generation, Reflection, Ranking, Evolution, Proximity, and Meta-Review, collaborate in an iterative hypothesis refinement loop. 🔹Some key innovations that pop out include an asynchronous task execution framework, which enables dynamic allocation of computational resources, and a tournament-based Elo ranking system that continuously optimizes hypothesis quality through simulated scientific debates. 🔹The agentic orchestration accelerates hypothesis validation for processes that take humans decades in some instance. For example empirical validation in biomedical applications, such as drug repurposing for acute myeloid leukemia (AML) and epigenetic target discovery for liver fibrosis, quickly helped researchers generate clinically relevant insights. What should we all get from this? 🔸Unlike traditional AI-assisted research tools, AI Co-Scientist doesn’t summarize existing knowledge but instead proposes experimentally testable, original hypotheses, fundamentally reshaping the research paradigm by acting as an intelligent collaborator that augments human scientific inquiry. Do take some time this Sunday to read! #genai #technology #artificialintelligence

I ignored 3 red flags in one single client call. And I paid the price. ❌ “There’s no budget right now, but prove yourself and maybe…” ❌ Several phone calls during our video meeting. ❌ A vague briefing with even vaguer expectations. I stayed polite. I hoped for the best. Result? Nada. In another tender we gave away our complete social selling program and step-by-step implementation process. And paid the price ❌ It was given to their preferred supplier ❌ We were never told based on what decisions were made ❌ Our contact person removed the connection on LinkedIn with us. Thanks for the free stuff. We passed it on to someone we already knew. Their mistake. Inappropriate? Sure, but above all my own mistake. And I’m not alone. Here are 4 other classic traps I know many entrepreneurs will recognize: 1. Do they say “We’re talking to a few other providers.” You need to hear “We’re fishing for free ideas.” Please ask: “What will help you decide who to work with?” If they can’t give a clear answer, it’s not a process — it’s a shopping spree. 2. Do they say “Can you just do this small thing first?” They are really telling you “We need some free work with no commitment.” Please respond: “Happy to do that — here’s a paid starter package.” If they ghost you after this? You just saved yourself 10 hours of unpaid labour. 3. Do they say “We want to collaborate, not hire.” Your BS radar should say: “You’ll be doing unpaid work while they ‘test synergies.’” Please ask “What does each of us commit to in this collaboration — in time and money?” If the commitment is one-sided, the door’s over there. Or a classic one They say  “We’ll pay after we see the results.” It means “All risks for you, your provide leads, we don’t convert, you still don’t get paid” Please respond: “Results come after partnership — not before payment.” If they don’t trust you enough to invest, they won’t trust you to deliver either. ✅ Follow your intuition. ✅ Act on it. Saying “Thanks, but I don’t think we’re a match” is more productive than wasting an hour hoping the red flags disappear. Save yourself: ⏳ Time 💭 False expectations 🤯 Frustration What’s a red flag YOU ignored that still haunts you a bit? Let’s help others dodge the same bullet in the comments

Science commercialization is often framed as lab-to-market, but the real question is: who funds the “too applied for grants, too early for VC” zone? I've seen it firsthand: a chicken-and-egg problem where VCs want traction before they'll commit, and founders need capital to create the very traction investors demand. Too often, brilliant scientists with world-changing technologies get trapped here. How science founders can navigate this valley: 1. Build your funding stack based on alignment — Grants, philanthropy, corporate partnerships, and venture capital each comes with different north stars and risk tolerances. Understand how your science fits now and in the future and plan accordingly. 2. Approach expert funders — Seek out capital providers who deeply understand your space. They’re best positioned to see the potential and impact of your work before it’s consensus. 3. Stage-gate your milestones — Show a path where $X unlocks validation, $Y proves scale, and later capital accelerates commercialization. Make each milestone reduce one major risk for follow on funders. 4. Activate alternative capital — Donor-advised funds, venture philanthropy, mission-driven corporates, and government innovation programs can back early science that’s obvious to experts but not yet to markets. Use them to build incremental validation. 5. Design for optionality — Build multiple paths forward: non-profit arms for public good research, commercial spinouts for market applications, licensing deals for near-term revenue, and strategic partnerships for distribution. 6. Create urgency — Patent deadlines, grant reporting requirements, and pilot customer commitments can become forcing functions that accelerate decisions. Use them to your advantage in funding negotiations. What strategies have you used to bridge this valley? I'd love to hear examples that others can learn from, especially creative financing structures or unexpected funding sources that worked.

We're funding the same project again and again. And calling it innovation. Last month, a donor showed me dozens of AI-for-good proposals. Different teams. Different logos. Nearly identical solutions. All competing for the same pot of funding. Here's the thing: nobody's the villain. Every team is solving real problems with limited resources. The issue isn't the people - it's the system that rewards duplication over coordination. At the UN General Assembly this year, there were dozens of AI sessions. No shared calendar. People missed critical conversations because nobody coordinated. If we can't sync a Google Doc, how will we coordinate AI models across continents? I just wrote about this for Fast Company. The reality: 👉Grant structures still favor tidy, solo asks 👉We build ten pilots when we need one system and ten deployments 👉Some collaborative funds are backing orgs that cracked cooperation -with up to $2-3B annually At Tech To The Rescue, we've learned this the hard way. Some projects became shared infrastructure. Others became well - intentioned duplicates. The difference was always coordination - not capability. This fall, we're testing a minimum viable ecosystem for AI for good - shared tech services, pooled fundraising, collective accountability. Not another white paper. Actual infrastructure that scales what works. To donors: I know joint proposals are messier to evaluate. But check your scoring systems - are you accidentally punishing the collaboration you claim to want? Fund backbone work. Fund integration. Fund multi-year commitments and collective effort. To builders: If your next sprint could serve ten deployments instead of one, design it to be shared. That's what AI-native means - systems built once, deployed everywhere. We have the tools. We have the talent. Now we need the humility to share credit and the governance to coordinate. Full piece in comments 👇 #TechForGood #AINative #ImpactAtScale

Universities spends $108 billion on research. But only 25-40% ever reaches the market. Universities are sitting on massive untapped potential. While traditional tech transfer has built critical infrastructure, there's a fundamental gap between laboratory proof-of-concept and market-ready applications. The emerging solution? University-attached venture studios. These systematic company creation platforms are demonstrating average net IRRs of 60% compared to 33% for top-quartile traditional VC. But the real value goes beyond returns. MIT Proto Ventures shows how this works: "We need a new, proactive model for research translation—one that breaks down silos and bridges deep technical talent with validated market needs." The results speak for themselves: Enhanced research impact through real-world application Universities build sustainable innovation infrastructure Students gain hands-on entrepreneurial experience Faculty research becomes more industry-relevant Early implementations at UNC's Eshelman Innovation, John Carroll's Blue Streak Ventures, and Arizona State's partnership with Idealab prove the model works across different institutional contexts. For university leadership, technology transfer professionals, and institutional investors: the question isn't whether venture studios represent superior research commercialization, but how quickly you can capture this opportunity. The institutions that act decisively will establish sustainable advantages that benefit their communities for generations. What's your university doing to bridge the innovation gap?

NEW: Google introduces AI co-scientist. It's a multi-agent AI system built with Gemini 2.0 to help accelerate scientific breakthroughs. 2025 is truly the year of multi-agents! Let's break it down: What's the goal of this AI co-scientist? It can serve as a "virtual scientific collaborator to help scientists generate novel hypotheses and research proposals, and to accelerate the clock speed of scientific and biomedical discoveries." How is it built? It uses a coalition of specialized agents inspired by the scientific method. It can generate, evaluate, and refine hypotheses. It also has self-improving capabilities. Collaboration and tools are key! Scientists can either propose ideas or provide feedback on outputs generated by the agentic system. Tools like web search and specialized AI models improve the quality of responses. Hierarchical Multi-Agent System AI co-scientist is built with a Supervisor agent that assigns tasks to specialized agents. Apparently, this architecture helps with scaling compute and iteratively improving scientific reasoning. Test-time Compute AI co-scientist leverages test-time compute scaling to iteratively reason, evolve, and improve outputs. Self-play, self-critique, and self-improvement are all important to generate and refine hypotheses and proposals. Performance? Self-improvement relies on the Elo auto-evaluation metric. On GPQA diamond questions, they found that "higher Elo ratings positively correlate with a higher probability of correct answers." More results: AI co-scientist outperforms other SoTA agentic and reasoning models for complex problems generated by domain experts. Performance increases with more time spent on reasoning, surpassing unassisted human experts. How about novelty? Experts assessed the AI co-scientist to have a higher potential for novelty and impact. It was even preferred over other models like OpenAI o1. Real-world experiments: "AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML)." There is more: "AI co-scientist identified epigenetic targets grounded in preclinical evidence with significant anti-fibrotic activity in human hepatic organoids..."

When science is under attack and budgets are shrinking, “publish and pray” isn’t a strategy—it’s a risk to public health. Our new Nature Portfolio piece—“Maximizing researcher–policymaker engagement in global public health”—sets out a practical playbook so every pound/dollar of research translates into policy impact. As researchers who have held high level roles in the #UN (including #WHO) and in government-facing roles, we wrote this for researchers who need to move evidence beyond journals and into decisions—now. What the paper offers (ready to use): 👉 A 6-question framework (Why, What, With whom, When, Where, How) to plan engagement from day zero—not after publication. 👉 Mechanisms you can deploy immediately: concise policy briefs; rapid “science-on-demand” syntheses; deliberative dialogues and roundtables; embedded advisors/knowledge brokers; advocacy coalitions that combine diverse skills and networks; and digital evidence hubs. 👉 Timing & politics: how to spot policy windows, manage trade-offs, and show contribution (not just attribution). 👉 Roles for funders & universities: ring-fence time/budget for engagement; reward policy outcomes alongside citations. Do this in the next 90 days: 1.    Map your decision-makers & calendars (who decides, when). 2.    Turn your latest findings into a 2-page brief + 10-minute deck. 3.    Convene a small roundtable with policy leads and one civil-society partner. 4.    Join or form an advocacy coalition for your topic: identify 1–2 civil-society groups, a policy entrepreneur, and a comms ally; agree a shared objective (e.g., wording in a guideline), split roles (research, convening, media, legislative outreach), and set a 12-week action plan. Shaping policy is hard work, and far from a science, but if publicly funded research stops at publication, it underserves the public. Let’s fix that—together. Read the paper: Maximizing researcher–policymaker engagement in global public health https://lnkd.in/eruJ_d-R J. Jaime Miranda David Berlan Camila Corvalan Taufique Joarder Arpita Raja Raja Yoong Khean Khoo Sunoor Verma Brig Gen Prof Dr Mohd Arshil Moideen (Rtd) Anne Marie Thow Helena Legido-Quigley David Peiris Rogers Kanee PhD, MPH, CSCA Ertila Druga MD MBA PhD Adeeba Kamarulzaman

AI’s exponential energy appetite is quietly rebooting America’s nuclear industry. In 2024, Big Tech had a critical realization: artificial intelligence isn’t just a software revolution - it’s a thermodynamic one. Training a single GPT‑4‑class model consumes ~500 MWh, that’s enough to power ~15 U.S. homes for a year. But inference is the real sinkhole. It’s always-on, everywhere, all at once. AI server racks consume >100 kW per rack, 10x more than traditional racks. Renewables can’t keep up. The sun sets. The wind stalls. Batteries are expensive, and at this scale, insufficient. Clean power isn’t the same as reliable power. And for 24/7 inference, only one option checks every box: nuclear - clean, constant, controllable baseload power. So what do trillion-dollar firms do when they realize their business model runs on electrons? They start buying the grid. ▪️ Microsoft partnered with Constellation Energy to restart Three Mile Island Unit 1 by 2028, supplying 835 MW of baseload power to its AI data centers - the first large-scale restart of a decommissioned U.S. reactor. Oh, and it’s betting on fusion too: Microsoft’s backing Helion, targeting the first commercial fusion prototype by 2028. When you have Microsoft money, you can place moonshots on the sun. ▪️Google is doing what Google does: building a portfolio. It inked a deal in October 2024 with Kairos Power for molten-salt SMRs (6–7 reactors by 2035, first demo 2030). Two weeks ago, it added Elementl Power - 1.8 GW of advanced nuclear capacity. ▪️Amazon Web Services (AWS) locked down up to 1.9 GW from Talen Energy's Susquehanna plant and, last year, dropped $650 million to buy a nuclear-powered data center campus outright. ▪️Meta finally joined the party last week, signing a 20‑year Purchase Agreement with Constellation to draw 30 MW from the Clinton nuclear plant in Illinois. The capacity is modest, but it signals a strategic shift - away from carbon offsets and toward operational baseload coverage. Even Meta sees the writing on the grid. This isn’t a hypothetical future - it’s happening now.  3 major nuclear PPAs signed within 2 weeks. Soaring federal support. Billions in private bets. What began as a GPU arms race is now an energy land grab. The next big AI breakthrough might not be a model, it might be a reactor.

This year, India’s defense sector unveiled advancements in AI that are reshaping military strategies & boosting national security. Here’s what the data tells us: --> AI is now central to defense modernization. --> Collaboration across sectors is driving innovation. Let’s explore these in detail. 1️⃣ AI-Powered Technologies Transforming Defense India’s armed forces are deploying AI across critical areas: ➤ Autonomy in operations: AI-enabled systems like swarm drones & autonomous intercept boats enhance mission precision, reduce human risk, & improve tactical outcomes. ➤ Intelligence, Surveillance, & Reconnaissance (ISR): AI-based motion detection & target identification systems provide real-time alerts for better situational awareness along borders. ➤ Advanced robotics: Silent Sentry, a 3D-printed AI rail-mounted robot, supports automated perimeter security & intrusion detection. Example: Swarm drones use distributed AI algorithms for dynamic collision avoidance, target identification, & coordinated aerial maneuvers, providing versatility in both offensive & defensive tasks. 2️⃣ Collaboration as the Catalyst for Innovation India’s AI advancements are the result of partnerships between the government, private industries, & research institutions. ➤ Indigenous solutions: 100% indigenously developed systems like the Sapper Scout UGV for mine detection. ➤ Startups and SMEs: Innovative contributions from tech firms and startups have fueled projects like AI-enabled predictive maintenance for naval ships and drones. ➤ Global export potential: Systems like Project Drone Feed Analysis and maritime anomaly detection tools are export-ready, positioning India as a major global defense tech player. 3️⃣ The Data-Driven Case for AI ➤ Efficiency: AI-driven systems exponentially improve surveillance coverage and reduce operational time. For example, the Drone Feed Analysis system decreases mission costs while expanding surveillance areas. ➤ Safety: Predictive AI systems in vehicles and maritime platforms enhance safety by identifying potential risks before failures occur. ➤ Economic impact: AI-powered predictive maintenance for critical assets like naval ships and aircraft maximizes uptime while minimizing costs. Real Impact ➤ Swarm drones: Affordable, scalable, and capable of BVLOS operations, offering precision in combat. ➤ AI-enabled maritime systems: Detect anomalies in vessel traffic, securing trade routes and protecting economic interests. ➤ AI-driven mine detection: Enhances soldier safety while automating high-risk tasks. What does this mean for defense organizations? AI isn’t just modernizing defense; it’s placing it firmly in the global defense innovation market. With bold policies, dedicated budgets, and a growing ecosystem of public and private sector players, this will help lead the next wave of AI-driven defense technologies. But the question remains: How do we ensure these technologies are deployed ethically and responsibly? Agree?

🎯 TATA AUTOCOMP – ŠKODA GROUP JV: DRIVING INDIA’S RAIL TECHNOLOGY REVOLUTION Banner Caption: When Automotive Excellence Meets Rail Innovation India’s railway modernization drive has taken a significant leap forward with the new joint venture between Tata AutoComp Systems and Škoda Group. The partnership will manufacture rail propulsion systems and critical components domestically, marking a decisive moment in India’s transition from a rail-assembly economy to a technology-producing nation. 🚆 1️⃣ Localization of Advanced Propulsion Technology Škoda’s proven European traction technology will now be produced in India, reducing import dependency and enhancing supply-chain resilience. This move aligns with Atmanirbhar Bharat and Make in India, enabling local production of traction converters, drive systems, and control electronics—core elements of modern rolling stock. Outcome: India gains not just assembly capability, but engineering sovereignty. ⚙️ 2️⃣ Diversification of Tata AutoComp’s Portfolio Traditionally a major automotive and EV-component supplier, Tata AutoComp’s entry into rail systems represents a strategic diversification into multi-modal mobility. The company’s expertise in high-precision manufacturing, coupled with Škoda’s century-old traction know-how, creates a self-reliant ecosystem for locomotives, EMUs, metros, and high-speed trainsets. Outcome: India builds a cohesive supply chain spanning automotive, EVs, and railways. 💼 3️⃣ Economic, Employment & Sustainability Impact This JV will drive technology transfer, job creation, and MSME participation across castings, forgings, and electronic sub-systems. Localized propulsion solutions will enhance energy efficiency and reduce carbon footprint, reinforcing India’s sustainable mobility goals under Mission Net Zero. Outcome: A self-sustaining, green industrial ecosystem. 📈 4️⃣ Supporting India’s Rail Modernization Push With Indian Railways investing in Vande Bharat, RRTS, and Metro expansion, this JV ensures that propulsion technology is available locally, at scale, and with global standards. It bridges India’s infrastructure push with domestic manufacturing strength—fueling both industrial growth and national confidence. Outcome: Integration of engineering innovation with nation-building. 🌍 In Perspective This collaboration is more than a business arrangement—it’s a technological alliance that positions India as an exporter of advanced rail propulsion technology. It symbolizes the fusion of Indian scale with European precision, setting a template for future Indo-European partnerships in transport engineering. #TataAutoComp #ŠkodaGroup #MakeInIndia #AtmanirbharBharat #RailwayTechnology #SustainableMobility #RailModernization #TataGroup #Innovation #RailwaysOfIndia