Harinderpal (Hans) Hanspal

Materials Science is a key part of all of our futures UCR Group is at the forefront of materials innovation, pioneering a scalable, low-energy Plasma Electrolytic Oxidation (PEO) process that unlocks the creation of complex, intelligent high-entropy ceramics with unparalleled resilience and adaptability. These breakthrough materials can be engineered to withstand extreme conditions, from ultra-high temperatures and oxidative environments to dynamic stresses, addressing long-standing challenges in ultra-high temperature ceramics and thermal protection systems. Integration of multiple elements in high-entropy configurations enable UCR’s ceramics with “smart” properties that enhance performance, reduce degradation, and extend component lifespans, dramatically cutting costs and environmental impact across critical sectors. In Aerospace and Defense, these capabilities will enable hypersonic vehicles and protective systems that push the boundaries of speed and durability; In Medical applications, they can power advanced medtech devices with biocompatible, long-lasting implants; In Marine environments, they can provide corrosion-resistant coatings for offshore structures and vessels; and In countless other industries like energy, automotive, and sustainability, they will drive efficiency gains of 20% or more, fostering a new era of breakthrough technologies that were once deemed impossible. At Corpora.ai we are pleased to be helping UCR Group to push the frontiers of both HEC and the equally incredible low energy PEO process they have developed. UCR Group represents an outstanding opportunity for materials scientists looking to be involved at the very forefront of one the most significant field of science, physics, chemistry and quantum information management. #HEC #PEO #MATERIALS #DEFENSE #AEROSPACE #MEDTECH #EMP #HYPERSONIC

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Bain: Skyrocketing AI demand creates unattainable CapEx requirement at current compute cost; major technological innovation is needed to reduce cost of compute to keep up. (Spoiler: Investing in some of this at Jumpspeed Ventures ...) https://lnkd.in/dcS8Xddy

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As Agentic AI moves to the [literal] edge, designing for trust, resilience and performance requires addressing a paradox: cutting edge reasoning and highly performant inference on constrained compute, power and connectivity. Today, our Google Cloud CISO and CTO teams jointly published some design principles we've found useful when enabling and securing Agentic AI at the extreme edge. You can find them here: https://lnkd.in/gFnr6u4w As an aside, we've found more and more that deploying Agentic AI successfully, capturing the opportunities, and managing the unique risks requires ignoring traditional functional silos; case in point, the collaboration across our CISO and CTO teams at Google Cloud has increased dramatically over the past few years. With that in mind, special thanks to Thiébaut Meyer, Antoine Larmanjat and Seth Rosenblatt for taking the time to document and share these insights. Chris Betz Michael Bachman Francis deSouza Brad Calder

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Innovation is fundamentally a contrarian bet —- it would not be innovative otherwise. John Martinis explains how to build a contrarian quantum computer ! #qolab #quantumcomputing

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Capturing machine level data to power predictive maintenance is the holy grail of AI in manufacturing. Great podcast Hugo Dozois-Caouette #AI #manufacturing #digitaltransformation https://lnkd.in/gDWT2Df4

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Harvesting robots are essential in greenhouses! The accompanying article was published last fall in Global Newswire. Various studies predict that the market for harvesting robots alone will grow at a CAGR of over 20% to a size of approximately USD 4.8 billion (!) in 2032. With an expected concentration in Europe. A mega interesting market! Over the past 5-10 years, many companies have been working on harvesting robots for tomatoes, peppers, asparagus, strawberries, mushrooms, and cucumbers, among other things. Logically, the focus is on harvesting robots in greenhouses, because the existing infrastructure (tube rail systems) offers advantages and can be connected to existing logistics systems. In fact, the current cultivation system is taken as the starting point. We are also seeing successful systems in which plants are brought to the robot via mobile cultivation systems. The cultivation system is then adapted to automation. This has been happening for about 10 years with lettuce/baby leaf and cress on mobile cultivation systems. Building a harvesting robot is apparently very complicated. Many companies have misjudged the difficulty and/or have even gone bankrupt. Apart from speed and success rate, the costs of harvesting must also be acceptable. Currently, the harvesting robot has to compete with employees, who earn roughly EUR 25/hour in the Netherlands. The big advantage of the harvesting robot could be that the working day can be extended, multiple features can be added, and color recognition by AI can always be the same. A harvesting robot must be integrated into the company's logistics system and process. This requires adjustments and also mental flexibility on the part of management. Growers are often reluctant to embrace new developments because margins are small and setbacks are difficult to absorb. In greenhouse cultivation, tomatoes are the most important target for harvesting robotization, accounting for 35% of the acreage, according to Rabobank, despite the fact that there are many varieties of tomatoes. Apart from mobile cultivation systems for lettuce/baby leaf and cress, there are no successful examples yet, but I believe we are getting closer. #TTAxISO is currently fine-tuning its harvesting robot for commercial launch in 2026. I am curious to see how this develops; the market is ready, and the demand from growers is evident! #harvesting #horticulture #vegetables #fruit #automation #robotics #food #agri

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I’ve been working on the theory of why small, shippable factories are the perfect inversion to the centralized prefab factory model. It all collapsed into one line of math (credit to Gilles Retsin): Factory Efficiency = Usage × (Throughput × Product Value ) / ( CapEx + Deployment OpEx) The idea is that the closer you can drive Usage to 100 % while keeping the denominator tiny, the faster the flywheel spins.  A shippable microfactory tackles this by primarily focusing on a high Usage factor. Because the microfactory can be packed up afterwards and reused, its CapEx gets amortized over multiple projects, effectively raising its overall Usage across the year. If one project alone doesn’t fully occupy the factory, it can simply roll over to another job to maintain high Usage. Its CapEx is often an order of magnitude lower than a centralized factory (< $1M vs. $10-50M min), so the breakeven throughput is more achievable on a small pipeline of work. The real breakthrough here is portability. There is near-zero stranded capital and almost no idle time. To flesh this theory out, I wrote 3000 words on the topic for Brad Hargreaves and Thesis Driven. The article breaks down: - why “fixed factories, shipped goods” is being inverted to shipped factories, fixed goods - what a sub‑$1 M robotic cell does to CapEx per home versus a £45 M off‑site plant - early production/cost data on a shippable microfactory from Mollie Claypool, Gilles Retsin, Sam Baker and the Automated Architecture (AUAR) team - the hybrid reality of robots tackling the heavy structural components and human crews assembling faster and cheaper - technical constraints to pull this model off and future considerations on the optimal business model (own vs. rent) Major shoutout to Gilles Retsin for helping me pull this together. https://lnkd.in/eRTnbZPa

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Smart manufacturing startup CloEE | Berkeley SKYDECK'24 has raised $600,000 in Pre-Seed funding to reinvent industrial efficiency with AI. CloEE helps manufacturers unlock profit from every process by bridging machines, data systems, and frontline workers. The company's plug-and-play AI platform delivers seamless connectivity and analyzes data directly from industrial machines to provide real-time reports for continuous improvement, boosting overall equipment effectiveness (OEE). The round was co-led by Miro Vertanen and the Innovestor Angel Co-fund, with participation from Cariplo Iniziative and members of the Finnish Business Angels Network (FiBAN). The new funding will focus on R&D to explore how AI Agents can transform manufacturing platforms and will help CloEE expand sales in the Nordics, Italy, and the US. Congratulations to the co-founding team of Oleksandr Zadorozhnyi, Julia Sabitova, and Nick Gushchin. Tech.eu: https://lnkd.in/diDTqDst #IndustrialTech #Manufacturing #AI #IIoT #SmartFactory #PreSeed #Funding #VentureCapital #FinnishTech

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