by Gary Mintchell | Mar 14, 2025 | Sensors, Technology
I’ve had a soft spot for visual systems ever since my introduction to the technology in the mid-1980s. Trends of more powerful video sensors plus AI have combined to form a number of interesting new products.
This news comes from a company called Cybord (that I previously wrote about here), who bills itself as “the leading provider of advanced AI-powered electronic component analytics.” They have announced the launch of its Real-Time Interception (RTI) solution, an advanced visual AI-powered software that prevents defective components from being assembled onto Printed Circuit Board Assemblies (PCBAs) in real time. By identifying and discarding faulty components within milliseconds before placement, Cybord’s RTI safeguards product quality, integrity, and compliance while significantly reducing manufacturing waste and costs. The solution, which is already integrated into Fuji’s NXT III placing machines, is currently expanding to manufacturing lines globally.
The solution provides:
- Instantaneous Detection and Rejection: The solution identifies every type of defect and discards defective or unauthorized components in real time before they are assembled onto electronic circuit boards, ensuring only top-quality and approved components are utilized.
- Seamless Manufacturing Integration: The flexible, drop-in software solution easily integrates into existing manufacturing lines to enhance quality control without disrupting production workflows.
- Data-Packed Insights: The platform provides manufacturers with crucial analytics and monitoring and ensures compliance with IPC standards.
- Value-Add for Machine Manufacturers: RTI allows machine manufacturers to empower their EMS customers with enhanced production efficiency and quality control by reducing rework and scrap. EMSs, in turn, prevent unnecessary revenue loss.
The RTI solution builds on Cybord’s successful Quality Component Inspection (QCI) and Traceability Component Inspection (TCI) offerings, addressing a critical need voiced by manufacturers: the ability to prevent faulty components from penetrating the assembly line rather than detecting them post-assembly. The RTI solution has already driven high demand from leading industry players and is currently integrated in Fuji America’s pick-and-place machines.
Powered by a database of nearly five billion components and counting, Cybord utilizes deep learning and AI algorithms to advance the next generation of AI in electronics manufacturing. During placement on the assembly line, the visual AI solution prevents defective, damaged, and counterfeit components from being assembled onto PCBA in real time with 99.9% accuracy. Cybord currently works with industry leaders including Fuji America, Siemens, and Flex.
by Gary Mintchell | Feb 28, 2025 | Technology
A PR person I’ve known for some time recently introduced me to Deepgram. This company’s application of AI is for speech-to-text. I use speech-to-text to dictate thoughts to Apple Notes on my iPhone while out walking in nature. I would certainly welcome all advancements in this area.
Deepgram announced the launch of Nova-3, its most advanced speech-to-text (STT) model to date. Nova-3 is said to be accurate in challenging audio environments. It can be customized for industry-specific needs. The company’s infrastructure includes text-to-speech (TTS) and full speech-to-speech (STS) capabilities.
Nova-3 is engineered for real-time use cases leveraging an advanced latent space architecture to encode complex speech patterns into a highly efficient representation.
Sample use cases:
- Adverse acoustic conditions – Accurately transcribes speech in distant, noisy, and multi-speaker scenarios, making it ideal for air traffic control, drive-thrus, and call centers.
- Real-time multilingual support – Enables real-time transcription across multiple languages—the first model of its kind to do so—making it ideal for emergency response, global customer service, and multilingual operations.
- Industry-specific accuracy – Recognizes domain-specific terminology for specialized fields like medical and legal transcription.
- Precision data handling – Ensures accurate numeric recognition for retail, banking, and finance while supporting real-time redaction of sensitive information for compliance and data privacy.
by Gary Mintchell | Feb 21, 2025 | Process Control, Technology
People often remind me of problems I tried to solve decades ago now made possible through technology advances and creativity.
Some 30 years ago, I tried to concept a system where we could monitor all the electrical power buses in a plant using the data to reveal potential problems with machines along the production line.
It didn’t fly.
One of the few companies that I would expect to be able to put that sort of system into operation would be Schneider Electric. I had the opportunity to interview Manishi Tiwari, Global Director of EcoStruxure Power and Process at Schneider Electric during the recent conference held in Orlando.
Her task is to lead the teams that will enable the sort of system I had envisioned with a customer way back in 1995. The technology now exists. Now the leadership exists to bring it all together.
She told me that about half of her 17-year career at Schneider Electric was in the process business with the other half working in the electrical power business.
Industries require electrical power to operate. But those systems are not built for information exchange between it and process control. Engineers must connect these systems considering latency, reliability, and continuity. Many systems are upgrades to brownfield sites. These most likely require upgrades to electrical equipment such as circuit breakers, relays and other such equipment. How these new components interact with the process must be carefully considered.
Conversely, process system upgrades, say a change in capacity, will affect the electrical power system. These must be studied and considered at design.
Cyber security also must be engineered into the system. Then steps must be taken to improve information to the operator to make their job better.
Schneider Electric not only has expertise in power systems and process systems, but it also has the AVEVA software portfolio to finish the loop.
I must add that in 27 years of interviewing I’ve seldom had a conversation where the other person jumped right in with background, problem statement, possible solutions, and, oh, with a minimum of marketing jargon. And when I sat down to write, I could construct a logical story.
by Gary Mintchell | Jan 30, 2025 | Generative AI, Technology
[I have two websites where I write blogs. This one started in December, 2003. My other one began in 2012. Across the two sites, this is my 7,000th post.]
New companies with new ideas continue to drift into my vision. This news relates to developers and managers who see the potentials for their products if a reliable Voice AI could be added. This technology whets my old product development appetites for new products or enhanced applications. The company is called Deepgram. It has some pretty impressive credentials. If you’re an innovative thinker looking for a way to jump start your application, take a look at this.
Deepgram marketing sent a quick summary of their 2024.
AI Company Ends 2024 Cash-flow Positive with 400+ Enterprise Customers, 3.3x Annual Usage Growth Across the Past Four Years, Over 50,000 Years of Audio Processed, and Over One Trillion Words Transcribed.
The company’s tools include speech-to-text (STT), text-to-speech (TTS), and full speech-to-speech (STS) offerings.
“2024 was a stellar year for Deepgram, as our traction is accelerating and our long-term vision of empowering developers to build voice AI with human-like accuracy, human-like expressivity, and human-like latency is materializing,” said Scott Stephenson, CEO of Deepgram. “Our product strategy from founding has been to focus on deep-tech first, and the work we have done in building 3-factor automated model adaptation, extreme compression on latent space models (LSMs), hosting models with efficient and unrestricted hot-swapping, and symmetrical delivery across public cloud, private cloud, or on-premises, uniquely positions us to succeed in the $50B market for voice AI agents in demanding environments requiring exceptional accuracy, lowest COGS, highest model adaptability, and lowest latency.”
Deepgram expects to end 2025 as the industry’s only end-to-end speech-to-speech solution built to solve the four critical challenges of enterprise-ready voice AI:
- Accuracy / audio perception: Enterprise use cases require high recognition, understanding, and generation of specialized vocabulary in often challenging audio conditions. Deepgram solves this through novel, non-lossy compressions of these spaces for rapid processing paired with generation, training, and evaluation on synthetic data that precisely matches Deepgram customers’ real-world conditions.
- COGS at scale: Deepgram customers need to profitably build and scale voice AI solutions. Deepgram delivers this through its unique latent audio model with extreme compression combined with deep expertise in high-performance computing.
- Latency: Real-time conversation requires near-instantaneous responses. Deepgram achieves this using streaming state space model architectures, optimized specifically for the underlying hardware to deliver minimal processing delays.
- Context: Effective conversations are deeply contextualized. Deepgram will pass the speech Turing test thanks to its ability to train on vast bodies of data that thoroughly represent its customers’ use cases and pass that context through the entire system and interaction.
by Gary Mintchell | Jan 27, 2025 | News, Technology
Perhaps you’ve heard of the XPrize—a prize offered for teams solving audacious problems. While I was communicating with my editors in Italy for my monthly column (News from America) at Automazione Oggi (Automation Today), one asked about solutions to the problem of wildfire spread. Zoning law changes and some common sense clearing of brush would help. But the huge scope of these phenomena begs a huge solution.
Peter Diamandis, the driving force behind the XPrize, recently wrote about a project now two years into a four-year challenge on just this problem of wildfires. Wildfires are not only a California problem. Climate changes across the globe make this a world-wide problem.
The original announcement:
XPRIZE, the world’s leader in designing and operating large-scale incentive competitions to solve humanity’s grand challenges, today launched XPRIZE Wildfire, a 4-year global competition that will award $11 million prize funding to teams able to develop and demonstrate fully-autonomous capabilities to detect and extinguish wildfires.
Around the world, the severity of Extreme Wildfire Events (EWEs) is increasing, driving over 80% of fire-related damages globally and costing an approximate $350 billion in damages annually in the United States alone. EWEs spread at a faster rate and burn larger areas at higher intensities, wreak havoc on ecosystems, cause long-term global economic burdens, and often result in devastating injuries and loss of life. Despite these high environmental and economic costs, fire management technologies have not evolved significantly in decades and best practices have not changed in almost a century.
Diamandis observes:
“We have been fighting wildfires the same way for decades – it’s not working, and the destruction is getting increasingly worse. We need a radical re-invention of how we detect and battle these blazes,” said Peter H. Diamandis, Executive Chairman of the Board, XPRIZE. “The convergence of exponential technologies such as AI, robotics, drones, and sensors offer us the opportunity to detect wildfires at inception, and put them out in minutes before they spread – that’s the mission of this XPRIZE.”
XPRIZE Wildfire will incentivize teams from around the world to innovate across a wide range of technologies in two complementary tracks designed to transform how fires are detected, managed, and fought.
- In the Space-Based Wildfire Detection & Intelligence track, teams will have one minute to accurately detect all fires across a landscape larger than entire states or countries, and 10 minutes to precisely characterize and report data with the least false positives to fire managers on the ground.
- In the Autonomous Wildfire Response track, teams will need to monitor at least 1,000 km2, and autonomously suppress a wildfire within 10 minutes of detection.
- The $1M Lockheed Martin Accurate Detection Intelligence Bonus Prizewill be awarded for innovations in accurate and precise detection of wildfires.
“The reality is that we are unprepared to effectively combat the growing number of wildfires and their severity around the globe,” said Peter Houlihan, EVP, Biodiversity and Conservation, XPRIZE. “As the effects of climate change worsen, more and more communities will be at risk as dangerous wildfires increase in frequency and devastation. Thanks to the generous contributions of our sponsors and partners, XPRIZE Wildfire will accelerate innovation in detection and rapid response that will transform wildfire management practices and save lives.”
What problems are you working on solutions? Being an engineer isn’t a requirement. Creative thinking is.
by Gary Mintchell | Jan 21, 2025 | Robots, Technology, Workforce
I receive the Peter Diamandis Abundance newsletter. He’s an over-the-top optimist—but we need a dose of that in these pessimistic times. (In the “it’s a small world” category, a daughter of a couple who regularly attend a morning coffee group with me in Elgin, IL works for him.) He recently included a link to a “Metatrend Report” on humanoid robots.
Reports of robotic advances targeting human assistance have trickled my way and piqued my interest. This seems to me to be a great field for some of our best robotic engineering minds.
This is from Diamandis’s report.
I was compelled to create this Metatrend report because the coming wave of humanoid robots will have a vast impact on society that is widely underappreciated. It will transform our lives at home and work.
How Many?: In my conversations with Elon Musk, Brett Adcock, Cathie Wood, and Vinod Khosla, the predictions on how many humanoid robots we will have working alongside us by 2040 is shocking at best. At the lowest bound, the number is 1 billion (which is more than the number of automobiles on Earth) and at the upper bound, proclaimed by Musk and Adcock, the number will exceed 10 billion.
How Much?: But equally impressive as the sheer number of robots is the price point, predicted to be between $20,000 to $30,000 which translates to a leased cost on the order of $300 per month, for a robot helper working 24 hours per day, 7 days per week.
Why Now?: The first question to ask is why now? Why are we seeing such an explosion of activity in the humanoid robot field now? Beyond any single technical advancement, the convergence of 5 major technological areas are super-charging this field: multimodal generative AI, high-torque actuators, increased compute power, enhanced battery life, cameras and tactile sensors.
This, in combination with AI voice recognition, is transformative: As Brett Adcock recently told me, “We can literally talk to our robot and it can implement the tasks you request — the end-state for this is you really want the default UI to be speech.”
Impact on Jobs: Naturally, the prospect of billions of humanoid robots raises questions about their impact on jobs and society. According to Adcock: “Our goal is to really be able to do a lot of the jobs that are not desirable by humans.” As of Q3 2024, there are nearly 8 million US job openings — jobs that people just don’t want to do.
Creating a Future of Abundance: As Musk has commented regarding a future involving humanoid robots: “This means a future of abundance, a future where there is no poverty, where people, you can have whatever you want, in terms of products and services. It really is a fundamental transformation of civilization as we know it.” Adcock echoes this vision, “You can basically create a world where goods and services prices are trending to zero in the limit and GDP spikes to infinity … You basically can request anything you would want and it would be relatively affordable for everybody in the world.”
Included in the introduction are 7 Key Takeaways:
1 Market Explosion: The humanoid robots market is poised for exponential growth, with projections ranging from $38 billion by 2035 (Goldman Sachs) to a staggering $24 trillion (Ark Invest). In the U.S. alone, at the lower-bound, Morgan Stanley estimates 63 million humanoid robots could be deployed by 2050, potentially affecting 75% of occupations and 40% of employees. On the upper bounds, Brett Adcock and Elon Musk predict as many as 1 billion to 10 billion humanoid robots by 2040.
2 Technological Convergence: The rapid advancement of humanoid robots is driven by converging breakthroughs in AI, hardware components (actuators, sensors), and battery technology. Multimodal generative AI in particular is enhancing robots’ adaptability and decision-making capabilities, while hardware costs are plummeting.
3 Labor Shortage Solution: Humanoid robots are emerging as a critical solution to global labor shortages, particularly in elderly care, manufacturing, and dangerous jobs. By 2030, the U.S. is projected to have a 25% “dependency ratio” of people over 70, driving demand for robotic assistance in healthcare and social care. In China and other parts of Asia and Europe, an aging population and lower birth rates make humanoid robotics critical for their economy.
4 Cost Reduction Trends: The cost of humanoid robots is plummeting rapidly, with high-end models dropping from $250,000 to $150,000 in just one year: a 40% decrease compared to the expected 15-20% annual decline. Ambitious targets, such as Tesla’s goal of a $20,000 selling price for its Optimus robot, suggest mass adoption will become feasible across various sectors.
5 Investment Opportunities: The humanoid robot sector is attracting significant investment, exemplified by Figure AI’s recent $675 million funding round at a $2.6 billion valuation. Morgan Stanley’s “Humanoid 66” list provides a roadmap for investors interested in both robotics developers and potential beneficiaries across various industries.
6 Broad Societal Impact: The widespread adoption of humanoid robots has the potential to usher in an era of unprecedented abundance, dramatically reducing the cost of goods and services while freeing humans to focus on creative and fulfilling pursuits. This transformation could reshape our concept of work and fundamentally alter the structure of our economy and society.
7 Job Disruption: The speed at which multimodal generative AI and humanoid robot development is progressing, paired with the lack of public discourse on this subject, indicates that there will be significant job disruption and societal upheaval. Mechanisms to address these concerns such as universal basic income (UBI), will need to be addressed. Some have proposed funding such UBI programs by taxing companies which utilize “robots and AIs” to displace previously human-filled jobs.
