『Forging The Future with Chris Howard』のカバーアート

Forging The Future with Chris Howard

Forging The Future with Chris Howard

著者： Chris Howard
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今ならプレミアムプランが3カ月 月額99円

2026年5月12日まで。4か月目以降は月額1,500円で自動更新します。

概要

 Join Chris Howard, Founder and CEO of Softeq, as he interviews knowledgeable leaders in the innovation spectrum, including CEOs, CTOs, R&D professionals, and start-up founders. Real conversations, technology, and processes of bringing new ideas to market.© 2022 All rights reserved. "Forging the Future" podcast, content, title, and logo owned by Softeq. Unauthorized use prohibited. Contact: ftf@speakerboxmedia.com. Respect our creativity. 経済学
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  • The “SaaS-pocalypse” Is Here: Equipt.ai’s Amanpreet Kaur on How AI Is Rebuilding Software

    The “SaaS-pocalypse” Is Here: Equipt.ai’s Amanpreet Kaur on How AI Is Rebuilding Software
    2026/04/09
    Episode Summary: Amanpreet Kaur, Chief AI and Technology Officer of Equipt.ai, breaks down the so-called “SaaS-pocalypse” and argues that SaaS isn’t dying, it’s evolving into smarter, AI-embedded systems of execution. Amanpreet explains how traditional SaaS created fragmentation and operational friction, and how Equipt.ai is rebuilding the stack by unifying workflows, data, and decision-making into a single AI-driven execution layer. Drawing from her experience in asset-heavy industries like energy, Aman shares how their platform replaces multiple disconnected tools while enabling real-time, guided operations from quote to cash. She also reflects on the early startup journey, from landing their first enterprise customer to navigating investor skepticism and refining their positioning. A key shift is undoubtedly taking place: the future belongs to agentic SaaS platforms that reduce human intervention and turn software into an active driver of business outcomes. 🎧 Episode Highlights [02:09]: SaaS solved infrastructure but created fragmentation [04:59]: “Dumb SaaS is dying:” AI-powered execution rises [06:17]: Replacing 10-20 tools with one platform [10:44]: AI as “steroids” for SaaS, not replacement [14:38]: First enterprise client before having a product [23:13]: Embedding AI across the full workflow 🔑 Key Takeaways: The “SaaS-pocalypse” isn’t about SaaS disappearing, it’s about a shift from systems of record to systems of execution. Traditional SaaS created fragmented workflows and heavy reliance on human coordination, while AI-enabled platforms are now unifying data and driving real-time decisions directly within operations. AI’s real value is not replacing software, but embedding intelligence into every layer of it. From automation and optimization to predictive insights, the winning approach is combining machine learning, simple automation, and context-aware AI to reduce manual work and enable more deterministic, reliable outcomes. Startups that succeed in this shift will focus on solving real operational problems, not just adding AI for hype. Equipt.ai’s journey shows that deep industry understanding, clear pain points, and delivering immediate value to customers matter more than technology trends, especially when building trust and scaling from early enterprise clients. 👤 Guest Spotlight:Amanpreet KaurAmanpreet Kaur is the Chief AI and Technology Officer and co-founder of Equipt.ai, where she is building AI-powered operational platforms for asset-intensive industries. With a background in energy and industrial technology, she previously led the development and commercialization of digital solutions, including enterprise-scale platforms and digital twin systems. Kaur brings deep domain expertise and a product-first mindset to redefining how businesses move from fragmented SaaS tools to AI-driven systems of execution. Stay Connected: https://www.softeq.com/ https://www.linkedin.com/in/techris/ https://www.linkedin.com/in/amanpreet-hon-doc/ https://www.equipt.ai/ Stay inspired and ahead of the curve by subscribing to Forging the Future. Share your thoughts on this episode with the hashtag #ForgingTheFuture or tag us online!
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    33 分
  • What If AI Worked More Like the Human Brain? ft. Chris Eliasmith of Applied Brain Research

    What If AI Worked More Like the Human Brain? ft. Chris Eliasmith of Applied Brain Research
    2026/03/19
    At CES 2026, we sat down with Chris Eliasmith, CTO of Applied Brain Research, to discuss how brain-inspired AI is enabling fast, low-power voice interfaces that run directly on edge devices. Drawing on research modeling the hippocampus, his team developed new neural network architectures that significantly improve efficiency and accuracy for tasks like speech recognition and text to speech. These advances allow devices such as AR glasses, robots, and wearables to respond to voice commands in under 300 milliseconds, creating interactions that feel natural and conversational. Eliasmith also explains the tradeoffs between model size, accuracy, and power consumption, and how running AI at the edge can reduce costs and reliance on the cloud. He ultimately envisions a future where complete AI agents run locally on small devices, making technology simpler and more accessible for everyday users. 🎧 Episode Highlights: ●[01:59]: Introducing ultra-low-power voice AI at the edge ●[03:27]: Why 300ms latency is critical for natural conversations ●[09:06]: Brain-inspired neural networks modeled after the hippocampus ●[15:02]: Tiny AI chips for AR glasses, robotics, and wearables ●[20:25]: Cutting cloud costs with local speech processing ●[27:54]: The future of full AI agents running at the edge 🔑 Key Takeaways: ● By modeling neural networks after how parts of the brain like the hippocampus process time-based information, researchers can build AI systems that achieve higher accuracy with far fewer parameters. This approach allows models to process speech and other signals more efficiently, making advanced AI practical even on small, resource-constrained devices. ● For voice interfaces to feel natural, responses must happen within roughly 300 milliseconds, the same timing humans expect in conversation. Designing AI systems that meet this latency requirement changes how models are built and deployed, pushing developers to prioritize real-time performance rather than relying on slower cloud-based processing. ● Low-power AI that operates directly on devices reduces reliance on internet connectivity, lowers operational costs, and improves responsiveness. As models become efficient enough to run locally, entire AI agents could operate on wearables, robotics platforms, and AR devices, simplifying technology and making intelligent interfaces accessible to more users. 👤 Guest Spotlight: Chris Eliasmith Chris Eliasmith is the Director of the Centre for Theoretical Neuroscience at the University of Waterloo and holds the Canada Research Chair in Theoretical Neuroscience. He is also the CTO and co-founder of Applied Brain Research, where he works on low-power AI technologies for machine learning, robotics, and edge computing. Eliasmith is the co-inventor of the Neural Engineering Framework, the Nengo software platform, and the Semantic Pointer Architecture, and is the author of How to Build a Brain (Oxford University Press) and Neural Engineering (MIT Press). Stay Connected: ●https://www.softeq.com/ ●https://www.linkedin.com/in/techris/ ●https://www.linkedin.com/in/chris-eliasmith/ ●https://www.linkedin.com/company/applied-brain-research/
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    30 分
  • The Race to Ultra-Efficient, Low-Power AI with Edge Impulse and Nordic Semiconductor

    The Race to Ultra-Efficient, Low-Power AI with Edge Impulse and Nordic Semiconductor
    2026/03/06
    At CES 2026 in Las Vegas, Brandon Shibley of Edge Impulse and Thomas Soderholm of Nordic Semiconductor join Chris to explore the exciting shift of AI from the cloud to the edge. Brandon shares how streamlined, low power machine learning models are unlocking new possibilities in health wearables, industrial inspection, and agriculture by bringing fast, responsive intelligence directly onto devices. Thomas highlights how Nordic’s latest ultra low power chips with built-in neural processing are making this next wave of innovation possible. Together, they paint an optimistic picture of AI that is faster, smarter, and more accessible, running right where data is created. 🎧 Episode Highlights ●[01:02]: Why AI is moving from cloud to edge ●[08:08]: Wearables and health monitoring on-device ●[11:08]: Industrial and agricultural vision at the edge ●[22:25]: Bluetooth Low Energy and ultra low power design ●[28:02]: New Nordic chips with built-in neural processing 🔑 Key Takeaways: ●Edge AI is about efficiency, not scale for the sake of it. Smaller, purpose-built models running directly on devices can reduce latency, preserve privacy, and dramatically lower power consumption while still delivering high impact outcomes in health, agriculture, and industrial settings. ●Hardware innovation is unlocking the next wave of on-device intelligence. Ultra low power chips with integrated neural processing units and advanced Bluetooth Low Energy connectivity make it possible to run meaningful machine learning workloads on wearables and battery-driven products. ●The future of AI is distributed by design. Instead of relying entirely on massive cloud models, intelligence will live closer to where data is created, balancing performance, cost, and connectivity to create scalable and practical real world solutions. 👤 Guest Spotlight: Brandon Shibley Brandon Shibley is a Founder at Edge Delivery and Senior Staff Engineer at Edge Impulse, a Qualcomm company, where he helps developers and enterprises build and deploy machine learning models on edge devices. With a background spanning CTO, founder, and innovation leadership roles, he has led full-stack IoT and edge computing strategies across industrial, robotics, and embedded systems markets. Brandon specializes in bringing intelligent software closer to the physical world, enabling scalable, low power AI solutions that run directly on devices. Thomas Soderholm Thomas Soderholm is the Vice President of Business Development at Nordic Semiconductor, where he helps drive the company’s strategy in ultra low power wireless connectivity and edge AI. With deep roots in Bluetooth Low Energy innovation, he works at the intersection of hardware, software, and connectivity to enable smarter battery-driven devices. Thomas focuses on advancing integrated solutions that bring efficient machine learning and secure connectivity to wearables and connected products worldwide. Stay Connected: ●https://www.softeq.com/ ●https://www.linkedin.com/in/techris/ ●https://www.linkedin.com/in/shibley ●https://www.linkedin.com/company/nordic-semiconductor/ Stay inspired and ahead of the curve by subscribing to Forging the Future. Share your thoughts on this episode with the hashtag #ForgingTheFuture or tag us online!
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    43 分
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