Ashis Khan

💡 #MOCHAX #PORTFOLIO ➡️ TwinMatrix Technologies (tmt) 🦄 TwinMatrix Technologies (TMT) operates in the spatial computing sector that is underpinned by several key technologies. 🚀 Market Size and Growth: Current Market Size: The spatial computing market was valued at approximately $81.8 billion in 2022. Projected Growth: It is expected to grow to around $269.8 billion by 2030, with a compound annual growth rate (CAGR) of 23.5% from 2024 to 2031 (The Brainy Insights) (AB Newswire). #SpatialTwins #DigitalTwins #Web3 #Innovation #MOCHAX

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We are living in a world that embodies our Industrial Transformation & Supply Chain Technology investment thesis at REFASHIOND Ventures: The Industrial Transformation Fund: Escalating Geopolitical Tensions & Great Power Competition; The Climate Crisis & Severe Weather; Consumer Behavior & Demographics. OUR INVESTMENT THESIS: REFASHIOND Ventures: The Industrial Transformation Fund invests in early stage startups refashioning legacy industries through Data & AI, Advanced Materials, Advanced Manufacturing, & Next Generation Supply Chains; Defensible through economic moats. Join us. #IndustrialTransformation #SupplyChain #Innovation #Technology #Startups #VentureCapital

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2025: The Big Shift in Electronics Manufacturing – Vertical AI Agents! For decades, software and hardware evolved on parallel tracks, with occasional intersections hinting at their potential synergy. The past few decades saw tremendous growth in software, while hardware often played catch-up. But today, the convergence is real - and AI is the catalyst. I’ve had the unique experience of working at this intersection throughout my career: from interning at Siemens to projects with NEC Corporation, to years in electronics manufacturing, and building a Mobile API Platform that supported numerous manufacturers. This journey has always been about bridging these two worlds. Now, with Vertical AI Agents, the long-awaited unification of software and hardware is here. These specialized agentic systems are transforming industries, with electronics at the forefront. Unlike generic AI solutions, Vertical AI Agents tackle complex, industry-specific challenges, dramatically slashing design cycles, cutting costs, and enabling innovation at a pace we’ve never seen before. MATURING FRAMEWORKS & THE NEW HARDWARE ECONOMY This convergence is also reigniting interest in hardware as the physical and digital worlds seamlessly blend. It’s inspiring to see industry leaders recognize this shift: Jensen Huang highlighted it during his CES keynote. Erin Price-Wright explored “The New Hardware Economy” in her thought-provoking video. At Wizerr AI, we share this vision and are passionately working at this intersection to enable the next wave of transformation in electronics. By leveraging Agentic AI, we’re helping companies innovate faster, optimize component selection, costs, and rethink supply chains to shape the future of hardware and electronics. This isn’t just evolution - it’s a revolution. The convergence of software, hardware, and AI is unlocking possibilities we once only imagined. Let’s make 2025 the year we shape the future of electronics together. Follow us here to explore how Wizerr AI is driving this transformation. #AI #VerticalAI #HardwareInnovation #Electronics #AgenticAI

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New piece on autonomous vehicles: Why Waymo matched Lyft's market share in SF so quickly, and what it tells us about the limits of network effects in ride-hailing. Some thoughts on whether Uber's hybrid model or Waymo's fleet ownership will win out. #Uber #driverlesscars #autonomous #Tesla

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We are excited to back Morphing Machines. As Dr. Ranjani, CTO and co-founder says: “For the first time in India and globally, we have a processor that is truly runtime reconfigurable, catering to emerging workloads.” While Nvidia’s growth and high valuation are driven by robust demand for “infra-compute” in high-performance GPUs, spurred by Gen AI, there is a distinct need for innovative solutions tailored to a new wave of reactive applications. Enter Morphing Machines. It addresses this demand with its unique product designed for next-gen telecom, autonomous vehicles, collaborative robots, avionics, and online data analytics. This product is specifically engineered to handle heterogeneous workloads and diverse tasks with varying performance demands simultaneously, ensuring a high level of scalability. As we move from an institutionally-backed research team at IISC to productization, Golden Sparrow is collaborating with Speciale Invest and others to support Deepak Shapeti, who joined as CEO three years ago after earning his Masters at Stanford. He brings not only technical expertise but also the business acumen necessary to guide the product to commercial success. Venkat Raju Afreed Faizan Anand Rao Ritik Singh Michael Marmor

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I've been thinking a lot about how Tesla's vehicle infrastructure can leverage inference compute, distributing computational load and reducing data center energy usage. Dr. John Gibb does a nice job of summarizing these concepts in his video (Starting around 8:10). Inference Arbitrage: A Game Changer for AI Compute As AI continues to evolve, the demand for computational power is skyrocketing. Giants like OpenAI, Meta, and Google are pouring billions into training and inference compute, facing looming shortages of transformers and electricity. Here's where inference arbitrage, a novel concept leveraging Tesla's infrastructure, comes into play. The Challenge: Soaring AI Compute Costs Leading AI companies are investing billions to power their AI programs. However, the energy and computational needs are outpacing infrastructure capabilities. This bottleneck isn't just financial; it's physical. The growing demand for AI services could soon exceed available power and hardware. The Solution: Inference on the Edge xAI, in collaboration with Tesla, offers a groundbreaking solution: using Tesla vehicles for AI inference tasks. Tesla's fleet, equipped with powerful hardware and large batteries, can perform AI inference during idle times (about 90% of the time), reducing strain on central data centers How Inference Arbitrage Works Distributed Computing: Instead of relying solely on massive, centralized compute farms, inference requests (e.g., generating an email or image) are processed by idle Tesla vehicles. This distributed approach taps into the existing infrastructure efficiently. Energy Optimization: Tesla vehicles can perform inference tasks using their batteries during peak electricity demand times, avoiding high energy costs. At off-peak times, they recharge at lower costs, ensuring energy-efficient operations. Commercial Benefits: This model allows Tesla owners to earn money by utilizing their vehicle's idle time for AI computations. Tesla benefits from additional revenue streams with minimal overhead, and xAI gains a scalable, cost-effective solution for AI service delivery. Impact on the AI Industry By integrating AI inference with Tesla's distributed compute network, xAI can deliver services faster and more economically than competitors. This innovative use of existing resources not only addresses the infrastructure bottleneck but also offers a sustainable path forward for AI growth. Conclusion Inference arbitrage provides a strategic edge for xAI and Tesla, combining AI innovation with smart energy use. As the demand for AI compute continues to rise, this approach could redefine the landscape, positioning xAI and Tesla as leaders in efficient, scalable AI service delivery. Stay tuned as this exciting development unfolds and reshapes the future of AI. #ElectricVehicles #InferenceCompute #DataCenters #Electrification #AI #FrontierTechnology #FutureFrontier

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The purpose of the Agile Dashboard is summarized in this WSJ article: "In 1988, former Intel CEO Andy Grove published a book called Only the Paranoid Survive, which highlighted the ways that companies have to be vigilant about what’s coming next, and be willing to disrupt themselves and pursue new technologies. What he intended as a warning to all companies has since become a prophecy foretelling Intel’s current difficulties. The book is literally about the importance of not missing strategic inflections, and then Intel proceeds to miss every single strategic inflection since, says O’Laughlin." #theagiledashboard

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Very good interview with Avijit Sinha, President of Wind River and Amazon Web Services (AWS) at their #reinvent event, on the importance of #edgeAI and their investment in this area - including a nice shout out for EDGE AI FOUNDATION …! Bill Podrasky Efren Mercado Michael Kuptz Rosina Haberl Evgeni Gousev Walter Sullivan Sandeep Modhvadia

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An IEEE SA whitepaper authored by ADWG members have been published. -Simulation, Testing, Verification, and Validation (STV2) of Autonomous Driving. It can be downloaded at https://lnkd.in/gvVsF5P3.

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This is what I mean when I say AI x DEEPTECH A revolution is coming. Upending entire swaths of our economy that have been previously ignored. Nearly $10 Trillion of our GDP rebuilt with AI and software.

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Silicon is the New Oil: UnityGrid is Refining it. Vision to Propel the U.S. to #1 in Semiconductor Manufacturing by 2032 At UnityGrid AI, we are committed to propelling the U.S. to the forefront of semiconductor manufacturing. Our mission extends beyond innovation to raising awareness and fostering workforce development in the AI and semiconductor industries. Our podcast series, "Silicon is the New Oil," reflects this mission by exploring the transformative potential of AI and highlighting the critical need for skilled professionals to drive these advancements. The AI chip restrictions imposed by the U.S. have triggered significant shifts in global semiconductor supply chains, particularly affecting China's access to advanced technologies. These changes are reshaping the industry landscape and prompting strategic adaptations globally. Key Points: - Impact on China: Restricted access to U.S. AI chips, leading to increased domestic investment and the rise of homegrown AI chip suppliers. - Global Impact. : Strategic shifts in production by companies like Samsung and SK Hynix, and development of self-reliant AI solutions in China. - Challenges and Opportunities. : Higher costs of domestically produced chips, potential global tech landscape bifurcation, and continued market opportunities. Let's make this vision a reality together. Kudos to the entire NSTC team for their dedication. Read more about these seismic shifts in our latest article and join the conversation on our upcoming podcast series on how we can drive innovation and self-reliance in the tech sector. #AI #Semiconductors #Innovation #WorkforceDevelopment #UnityGridAI #SiliconIsTheNewOil #RFIC https://lnkd.in/ezWwAXBf

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Learn how to develop machine learning applications on the embedded edge from ideation to deployment. Carlos Davila introduces a new framework called Cross-Industry Standard Process for Embedded Machine Learning or CRISP-EML as a new methodology for embedded ML development. He also guides readers through the ML development process by taking into account various stakeholder and persona perspectives. #machinelearning #embeddededge #development #SiMa

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Just witnessed the most epic impromptu panel discussion take-over by none other than John Chen (VP of Technology at NVIDIA) at Monte Jade West Science & Technology Association talk with Brandon Wang, Wen H. Hsieh, Ph.D., Jay Lee, Achin Bhowmik and Wei Li: GPUs are far ahead of all the rest of the infrastructure behind AI/LLM datacenters. It’s time to invest in the next frontier of AI infrastructure folks! Not just Cloud compute, hybrid cloud+edge, networking, power, edge RL, edge inference and edge firmware orchestration just to name a few areas we’re excited about!

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New Tesla Patent Reveals Clues to an Exciting Future of Humanoid Robots and AI Integration In a interesting discussion, Herbert Ong and Scott Walter dive deep into Tesla's recently published patent application WO2024072984A1 (link in comments). Entitled "Actuator and actuator design methodology," the patent primarily focuses on controlling robot movement through strategic actuator placement. However, the document also unveils important clues about the potential for end-to-end neural net training of humanoid robots, reminiscent of Tesla's approach to training its vehicles with FSD. Ong and Walter explore the concept of personalized humanoid skills. Imagine purchasing a bot with built-in capabilities, then having the option to augment and customize those skills through a "Skills Store" akin to Apple's App Store. This opens up a world of possibilities for tailoring robots to specific tasks and preferences. The discussion also touches on the potential for various robot form factors, each designed to excel at particular skills. Picture a tennis-playing robot that comes pre-trained with basic volleying instructions, but requires a "Skills Upgrade" to perform at a master level. This raises questions about the need to manufacture and train a diverse range of robot body types and sizes, mirroring how humans possess ideal physiological characteristics for different activities. Later, Ong and Walter discuss a scenario in which Tesla builds the hardware – including automobiles, humanoids, and other bot form factors – while xAI develops the brains. This arrangement opens up opportunities for licensing agreements between the two companies, that may help the companies navigate future antitrust concerns as these technologies are deployed at scale. The convergence of advanced robotics and artificial intelligence holds the potential to catalyze a profound societal transformation. As Tesla and xAI continue to push the boundaries of what's possible, I can envision a future where intelligent, adaptable robots become integral to various aspects of our lives – from manufacturing and transportation to healthcare, recreation, and beyond. The seamless integration of cutting-edge hardware and software will revolutionize the way we live and work, ushering in an era of unprecedented efficiency, productivity, and innovation. While there are still challenges to overcome and ethical considerations to address, the prospect of these combined technologies driving positive change on a global scale is awe-inspiring. #FrontierFrontier #Humanoids #AI #Robots

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AI's relentless march continues in 2024, with breakthroughs that push the boundaries of what's possible. From healthcare to climate tech, these innovations are reshaping industries and challenging our understanding of human-machine collaboration. What's your take on AI's rapid evolution? Are we prepared for the societal impacts? #ArtificialIntelligence #TechInnovation #FutureOfWork #AIEthics #DigitalTransformation Share your thoughts below!

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There is a lot of talk about how #GenAI is driving the demand for compute to stratospheric levels and is resulting in an increase in energy consumption and GHG emissions from a growing number of data centers. The flipside of that coin is the following: We appreciate Fast Company's coverage of how AI is being applied by a number of companies to make a dent in the carbon emissions of the built environment - one of the top 5 categories of emissions globally no matter how you slice it. With smaller data sets being ingested by AI models and the resulting reductions energy consumption in buildings, the trade off between the added compute of using AI versus the reduced emissions is heavily in favour of the latter. Read the full article here: https://lnkd.in/eRVpCTTu or here: https://lnkd.in/ejvjE7aE Look out for the latest addition to our AI tool set for the built environment: ARIA, the virtual engineer for your building (https://lnkd.in/eeJGDd2E) Patrick Sisson, BrainBox AI, Jean-Simon Venne, Omar Tabba, Frank Sullivan #aiforgood #autonomousbuildings #sustainablebuildings

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For deep tech enthusiasts like me, Alan Kay’s 1982 quip still rings true: “People who are really serious about software should make their own hardware.” This principle is core to Cerebras Systems’ approach. From day one, they've embraced hardware-software co-design, crafting their systems to address the unique demands of large-scale neural networks. Sean Lie, co-founder and CTO, explains why this holistic strategy is more than just an engineering choice. It’s the path to the next big leap in AI’s capabilities. Our full conversation here: https://lnkd.in/gWZCi4zE

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