Podcasts about Machine vision

Welcome to The Turf Zone podcast. This episode features the article “Advancing Precision Weed Management in Turfgrass Systems with Machine Vision-Guided Targeted Spraying” Written by Brooke Heikkila – Graduate Research Assistant Navdeep Godara – Assistant Professor of Turfgrass & Forage Weed Science, Department of Crop and Soil Sciences, North Carolina State University and Pawel Petelewicz – Assistant Professor of Turfgrass Weed Science, University of Florida, Institute of Food and Agricultural Sciences, Agronomy Department Turfgrass managers are facing increasing weed challenges due to evolving regulatory framework and growing incidence of herbicide-resistant weeds. The release of the first turfgrass-specific commercial machine vision-guided sprayer (ALBA, Ecorobotix Inc.) enables automated and localized herbicide applications in turf. Although often referred to as “spot spraying” in marketing materials, “targeted spraying” is a more accurate description as it distinguishes this system from manual spot treatments and other existing precision weed management approaches. Such targeted application systems have already been successfully deployed in other crops using platforms such as the John Deere See and Spray, Agritech America WEED-IT, Verdant Robotics Sharp Shooter, Ecorobotix ARA. Using See and Spray technology, comparable weed control was observed between the broadcast and targeted spraying methods, but the targeted spraying reduced the treated acreage by up to two-thirds. In turfgrass, this technology not only offers significant herbicide savings but also opens the door for practitioners to combat herbicide-resistant weeds by incorporating alternative chemistries, including nonselective herbicides or herbicide tank mixtures combining multiple modes-of-action which are not typically feasible in broadcast applications. Overall, spot spraying is not a new concept, as many turfgrass managers already employ it to control weed escapes following broadcast herbicide applications or where selective chemistries are not an option. Manual spot spraying involves individuals walking the golf course or other turfgrass areas with a sprayer loaded with herbicide to make localized applications directly to weeds. Traditional spot spraying is labor-intensive, time-consuming, and requires applicators to accurately identify weeds, necessitating additional training and expertise. It ultimately increases application costs and is also prone to human error, often resulting in overapplication and missed weeds. However, targeted spraying systems such as ALBA, utilize artificial intelligence combined with machine vision to detect problematic weeds within turfgrass canopy in real-time to apply herbicides only to those small areas where individual weeds are present. ALBA is a tractor pull-behind unit that can operate at speeds up to 4.5 miles per hour and uses an enclosure to block ambient light and to create consistent lighting conditions to continuously scan the turfgrass canopy with its cameras to detect weeds. When a weed is spotted, an individual nozzle – one out of 108 – activates to directly target the weed with a 1.2 × 1.2-inch spray resolution per nozzle. As targeted application systems continue to advance and competing platforms emerge, it is critical to understand how to effectively integrate and leverage these sprayers within turfgrass weed management programs. Several preliminary field experiments using ALBA and its ARA-based predecessor research platform were conducted by the NC State Turfgrass Weed Science Program and the UF/IFAS Turfgrass Weed Science Program to understand the applications of this technology. Preliminary studies showed that machine-vision guided targeted spraying substantially reduces herbicide usage and treated acreage while maintaining weed control efficacy, offering both economic and environmental benefits while targeting wide variety of problematic weeds with high accuracy. Reduction in Herbicide Volume Used – In a study focused on controlling false-green kyllinga in bermudagrass fairways, machine vision-guided targeted spraying with ALBA reduced herbicide spray volume by 77% compared to broadcast treatments. False green kyllinga cover was 17% at the experimental sites during study initiation, triggering significant savings due to the weed-specific, localized targeted treatments compared to broadcast herbicide applications. Broadcast applications of standard kyllinga control products typically cost around $190 to $240 per acre, but targeted treatment can lower the cost by more than $145 per acre even when dealing with moderate level of weed infestation (~15% weed cover). Similarly, in another ongoing study, when annual bluegrass weed cover was 10% in bermudagrass fairways, targeted applications achieved a 66% reduction in herbicide spray volume compared to conventional broadcast treatments. Sulfonylurea herbicides for postemergence control of annual bluegrass cost around $140 to $185 per acre and targeted spraying can reduce the cost by at least $92 per acre when weed cover is 10% or less. Practitioners can expect greater savings at turfgrass sites with lower weed infestations, which are typical of intensively managed surfaces and when applying expensive herbicides such as PoaCure or organic herbicides during winter dormancy of warm-season turfgrasses. Targeted application system was also evaluated for control of broadleaf weeds, dallisgrass, smooth crabgrass, and tropical signalgrass in studies conducted independently or in collaboration between Mississippi State University, NCSU, Virginia Tech and UF IFAS, and observed a 53% to 95% reduction in spray volume. In all the aforementioned cases, weed control levels achieved with targeted spraying were no different from broadcast applications. Thus, these studies demonstrate that, across various problematic weed species, this novel application system can substantially reduce the herbicide volume required, lowering costs without compromising weed control efficacy. Lower Treated Acreage – During broadcast herbicide applications, substantial areas without weeds are often treated unnecessarily. Targeted applications can reduce the treated acreage, enabling practitioners to use herbicides such as MSMA, which are currently restricted to spot treatments on less than 25% of the total golf course acreage per year. Targeted spraying systems are particularly useful for herbicides with limited or no residual activity, as it allows localized treatments to weed instead of broadcast applications to turfgrass. Targeted spraying for false-green kyllinga control (17% weed cover) in bermudagrass fairways resulted in 85% reduction in treated acreage compared to broadcast spraying. In a similar study, an 80% reduction in treated acreage was found when only treating annual bluegrass in dormant bermudagrass at 10% weed cover. A study conducted by UF/IFAS Turfgrass Weed Science Program using circular, non-overlapping targets of varying patch sizes (4-10 cm diameter) to simulate random different weed densities and dispersions within the 1-20%, 21-40%, and 41-60% coverage, indicated total spray deposition of approximately 40%, 64%, and 74%, respectively. This corresponded to estimated herbicide savings of 60%, 36%, and 26%. Spray deposition increased with rising weed pressure, while the non-sprayed area, directly reflecting herbicide savings declined accordingly. These results confirm that variation in herbicide savings with targeted applications is driven primarily by weed density, with dispersion playing a secondary role, exerting stronger effects at low weed densities but negligible influence at higher densities. The reduction in treated acreage can potentially diminish the environmental impact of herbicides by minimizing overall pesticide load released into the environment, limiting off-target movement, reducing the risk of groundwater contamination, and lowering the risk of human exposure associated with pesticide applications. Targeted approaches permit treatment to a limited portion of turf, enabling the effective use of chemistries with area-use limitations. Effective reduction in area treated with targeted spraying will become increasingly important as new regulations come into effect, particularly in the context of upcoming Endangered Species Act-imposed changes. Therefore, research projects funded by the Turfgrass Council of North Carolina will focus on investigating the agronomic and environmental benefits of targeted application systems for managing problematic weed species. Alternative Herbicide Options for Resistance Management – Targeted spraying also enables selectivity at the sprayer level rather than relying only on selectivity of the herbicide used. This potentially allows turf managers to use nonselective herbicides that were previously not an option for broadcast treatment due to severe injury to actively growing turfgrasses. Broad spectrum herbicides like glyphosate, glufosinate, or flumioxazin are highly effective against a wide variety of weeds, but practitioners often wait for turfgrass to go dormant before spraying nonselective herbicides, while in some geographies, such as Florida, achieving full dormancy is not even possible. However, with this new technology, practitioners will have the option to incorporate nonselective herbicides year-round with minimal collateral damage to turfgrass. Glyphosate (Roundup Pro Concentrate) applied via broadcast application at 12 fluid ounces per acre rate reduced bermudagrass green cover significantly, but targeted spraying had similar level of green cover as nontreated plots as documented in our recent study. Likewise, glufosinate applied at 41 fluid ounces per acre (as Finale XL T&O) reduced bermudagrass cover drastically after broadcast application but had minimal effect on turfgrass after targeted spraying. Targeted spraying technology also allows use of novel admixtures that are not currently being used during regular turfgrass maintenance. Rotating or tank mixing herbicide from different modes of action are crucial for sustainable turfgrass management, as selection pressure for herbicide resistance continues to increase. For instance, practitioners can use tank mixtures of herbicides like pyridate + sulfentrazone or bentazon + halosulfuron + sulfentrazone for targeted spraying without compromising efficacy on false-green kyllinga. These novel admixtures contain multiple modes of action in a single application that could reduce selection pressure and combat herbicide-resistant kyllinga. Similar admixtures should be explored for the management of other herbicide-resistant or difficult-to-control weeds. Limitations – Like with any new technologies, there are limitations to consider when adopting a machine vision-guided sprayer. Currently, only one commercial unit (ALBA by Ecorobotix Inc.) is available, providing managers with a single option for this turfgrass-specific targeted spraying technology. Additional machine vision-guided sprayers need to be developed specifically for turfgrass systems, as interest in these technologies among turfgrass managers continues to grow and the needs across different turfgrass industry segments will vary. The cost of the equipment and the annual model subscription will be a major barrier for many turfgrass managers. Offering incentives, such as reduced subscription fees for the first few years, could help increase adoption of this technology. Alternatively, with ALBA being an example of a high-end solution maximizing performance and system sophistication, other developers may consider trade-offs to reduce equipment production and maintenance costs to improve accessibility. Although ALBA seems to demonstrate high detection accuracy on key problematic weeds, further research is needed to understand its year-round performance, considering changes in visual characteristics of weeds and turfgrass across growth stages and under varying environmental contexts. In our preliminary work, a few false positives occasionally led to herbicide applications to weed-free turfgrass. Also, we observed that in situations where weed presence (particularly grassy weeds) in the camera's path exceeded that of turfgrass, the detection system became confused, effectively reversing target and background and treating turfgrass instead of the weed. However, developers are actively addressing these shortcomings and performance of targeted applications systems by continuing to improve imagery databases, training and validation across diverse geographical regions and management contexts. There is no doubt that machine vision-guided sprayers will have a transformative impact on the turfgrass industry, however, extension efforts will be critical for adoption. Also, as this technology is still novel for turfgrass systems, ongoing research and development is critical to improve performance, reliability, and to meet industry needs. Among others, further research is needed to evaluate performance under varying travel speeds, expand applications to targeted residual treatments, and refine application thresholds to maximize herbicide savings. Authors acknowledge the Turfgrass Council of North Carolina for sponsoring ongoing research projects focused on leveraging targeted application devices for weed management in NC turfgrass systems. The authors also thank Ecorobotix Inc. for providing a commercial unit for evaluation. You have been listening to The Turf Zone Podcast. Follow The Turf Zone on X, Facebook and LinkedIn for all things turfgrass, featuring podcasts, magazines, events and more. Visit www.theturfzone.com for more. The post Advancing Precision Weed Management in Turfgrass Systems with Machine Vision-Guided Targeted Spraying appeared first on The Turf Zone.

Machine vision has long been used in factory automation for quality inspections, the final checkpoint separating acceptable parts from the rework pile or the scrap bin. Today, vision systems are no longer just inspectors. They are becoming adaptive, data-driven participants in the manufacturing process, capable of influencing outcomes rather than simply recording them. In this episode of Control Intelligence, written by contributing editor Joey Stubbs, editor in chief Mike Bacidore discusses how artificial intelligence has impacted the reliability and accuracy of vision systems.

(07:22) Brought to you by MailtrapMailtrap is a modern email delivery for developers with native SDKs support along with security compliant API & SMTP. Plus, you get 4,000 emails a month completely on their free tier! It also provides 24/7 support where you actually talk to real people, not an AI chatbot. Try Mailtrap for free at ⁠mailtrap.io⁠.What does code review mean when AI writes most of the code? The answer isn't to review more carefully. It's a fundamentally different process, one built around rules, agents, and governance rather than diffs and comments.In this episode, Itamar Friedman, founder and CEO of Qodo.ai, shares how AI is forcing a complete rethink of code review — from inline comments on code diffs to multi-agent governance systems that verify intent, architecture, and business logic at scale. He traces the evolution of code review through successive generations, explains why traditional static analysis is no longer sufficient, and lays out what a modern quality and governance layer actually looks like. Itamar also introduces the concept of “shift up” — extending quality checks into the planning phase so that technical product managers can contribute directly to shipping features — and explains how teams can move from vibe coding to viable, grounded development. The conversation also covers the race between AI labs, the role of open-source models, and a frank look at where the software developer role is heading by 2030.Key topics discussed:Why line-by-line code review doesn't scale with AI-generated PRsThe generational evolution of code review tools (Gen 1 to 3.5)How multi-agent systems surface only what needs human attentionTurning tribal knowledge into enforceable rules and skillsShift-left and shift-up: embedding quality earlier in the workflowWhat the new agentic code review UI will look likeVibe coding vs. viable coding: the governance layer in betweenWhere the software developer role is headed by 2030Timestamps:(00:00:00) Trailer & Intro(00:02:50) How Has AI Driven the Evolution of Code Review to Multi-Agent Systems?(00:07:53) How Do We Move from Vibe Coding to Viable, Grounded Development?(00:12:35) Are Traditional Static Analysis Checks Still Sufficient in the AI Era?(00:16:27) How Do We Handle Exploding PR Volume Without Sacrificing Code Review Quality?(00:22:11) How Do We Evolve Code Review from Simple Comments to Senior-Level AI Reviews?(00:28:51) What Will the New Agentic Code Review UI Look Like?(00:33:32) How Does Qodo Differentiate Itself as an AI Code Review and Governance Platform?(00:37:15) What Do Shift-Left and Shift-Up Mean for the Future of Code Quality?(00:41:23) How Do We Maintain Quality When Running Multiple AI Agents in Parallel?(00:48:11) How Are Chinese AI Models Reshaping the Open-Source vs Closed-Source Race?(00:55:25) Which AI Models Excel at Code Review, and Are We Heading Toward Specialization?(01:03:16) Will Software Developers Still Be Needed as AI Automates More of Engineering?(01:08:50) 3 Tech Lead Wisdom_____Itamar Friedman's BioItamar Friedman is the CEO and Co-Founder of Qodo, an AI code review platform used by 1M + developers. Before founding Qodo, Itamar was a founder of Visualead, which was acquired by the Alibaba Group. He then worked for Alibaba Group for 4 years as the Director of Machine Vision. Now, Itamar is dedicated to quality-first code generation.Follow Itamar:LinkedIn – linkedin.com/in/itamarfX (formerly Twitter) – @itamar_marQodo.ai – qodo.aiLike this episode?Show notes & transcript: techleadjournal.dev/episodes/257.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

Jill Walker Rettberg sits down with scholar Kishonna Gray to discuss "Synthetic Kishonna," an AI-generated likeness created from Gray's image without her consent. They explore how generative tools often prioritize superficial representation over true human embodiment, leading to a sterile homogenization that erases unique cultural voices and personal histories. References Ahmed, S. (2013) Making Feminist Points https://feministkilljoys.com/2013/09/11/making-feminist-points/ Brock, A. (2020) Distributed Blackness: African American Cybercultures DISCO Network (2025) Technoskepticism: Between Possibility and Refusal https://library.oapen.org/handle/20.500.12657/100381 Gauthier, G., Hodler, R., Widmer, P., & Zhuravskaya, E. (2026) The political effects of X's feed algorithm https://www.nature.com/articles/s41586-026-10098-2 Gray, K. L. (2015) #CiteHerWork https://kishonnagray.com/citeherwork/ Gray, K. L. (2020) Intersectional Tech: Black Users in Digital Worlds Gray, K. L. (2026) Synthetic Kishonna: What happens to your identity when your image is taken and remixed with AI? Jordan, J. (1980) Poem for South African Women Kantayya, S. (2020) Coded Bias https://www.codedbias.com/ Meme (N/A) “Who dis woman?” https://tenor.com/view/who-this-woman-harpo-who-is-this-whoisthat-colorpurple-gif-13132285Miller, N. K. (1991) Getting Personal: Feminist Occasions and Other Autobiographical Acts Noble, S. U. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism ReFiG (2015) ReFiguring Innovation in Games http://refig.ca/ Spielberg, S. (1985) The Color Purple Steele, C. K. (2021) Digital Black Feminism Ubisoft (2003) Beyond Good & Evil U.S. District Court (2024) Giuffre v. Maxwell (Unsealed Documents) https://www.courtlistener.com/docket/4355835/giuffre-v-maxwell/ Walker Rettberg, J. (2018) Machine Vision in Everyday Life

What is the scope for machine vision applications in the packaging industry? Elisabeth Skoda speaks with three experts and members of the European Machine Vision Association (EMVA): LUCID Vison Labs' Torsten Wiesinger, Murrelektronik's Simon Knapp and Advantech's Rick de Vries. Packaging Europe's podcast, featuring the leading international figures in packaging innovation, sustainability and strategy, is now weekly! Be sure to subscribe so you don't miss an episode.For more packaging news, interviews and multimedia content visit Packaging Europe.

This conversation was originally released in February of 2025. We're replaying this episode because Cognex sits right at the intersection of AI and robotics. As the market focuses more on physical AI and automation in 2026, machine vision is becoming an increasingly important part of that story. Today we are breaking down Cognex, the leader in machine vision. Cognex builds the cameras, sensors, and software that allow factories and logistics systems to see. Their technology inspects products, detects defects, reads barcodes, and guides robots across manufacturing lines and warehouses around the world. Cognex is not your typical recurring revenue story. It is a cyclical industrial business that has grown by repeatedly finding new “S-curves” in automation. From early semiconductor inspection to modern logistics systems and AI-driven vision, the company has spent decades expanding the applications of machine vision across industries. Our guest today is Brett Larson from NZS Capital. Brett walks us through the history of machine vision, Cognex's unique culture and founder story, and the company's position inside the broader automation ecosystem. We also discuss how Cognex sells into factories, the competitive dynamics with companies like Keyence, and why new technologies like deep learning could unlock the next wave of growth. For the full show notes, transcript, and links to the best content to learn more, check out the episode page here.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- This episode is brought to you by⁠⁠⁠⁠ ⁠Portrait Analytics⁠⁠⁠⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at ⁠⁠⁠⁠⁠portraitresearch.com⁠⁠⁠⁠⁠ ----- Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps (00:00:00) Sponsor: Portrait Analytics (00:01:42) Update on Cognex  (00:02:53) Welcome to Business Breakdowns  (00:03:41) Episode Intro  (00:05:09) What is Cognex and What They Do  (00:07:10) Hardware vs Software and Human Interaction  (00:07:58) Market Size of Machine Vision  (00:08:59) Cognex's Market Share and Positioning  (00:13:01) Sales Channels and Customer Types  (00:14:17) History and Origin of Cognex  (00:17:49) Deep Learning vs Rules-Based Programming Examples  (00:22:18) Customer Stickiness and Sales Contracts  (00:27:41) Understanding S-Curves and CapEx Cycles  (00:29:35) Culture and Leadership  (00:40:08) Valuation and Risks  (00:44:42) Key Lessons from Cognex

In this episode of the Workforce 4.0 podcast, host Ann Wyatt engages with Sadiq Panjwani, SVP of Machine Vision Cameras Group at Teledyne FLIR, to discuss the transformative impact of machine vision and automation in manufacturing. They explore the challenges and opportunities presented by emerging technologies, the rise of physical AI, the importance of human-robot collaboration, and the evolving skill sets required for the future workforce. Sadiq emphasizes the need for empathy and agility in organizations to successfully integrate new technologies and retain talent. The conversation also touches on the significance of interoperability and standardization in manufacturing processes. In This Episode:-00:00: Introduction to Machine Vision and Manufacturing-00:30: Welcoming Sadiq Panjwani, Teledyne-04:51: Breaking Down The Future Of Automation And Manufacturing-09:39: The Rise Of Physical AI and Human-Robot Collaboration-14:31: AI In Manufacturing: Unpacking The Timeline Shift-19:02: Connecting The Data Dots In Real Time-28:30: Uncovering Generational Challenges For Most Legacy Manufacturers-31:37: Closing Thoughts And Point of Contact-32:30: Workforce 4.0 OutroMore About Sadiq:Sadiq Panjwani has extensive work experience in various leadership roles within prominent companies. Sadiq currently serves as the Vice President and General Manager of the Machine Vision Cameras Group at Teledyne FLIR, where he has helped lead the global business division for integrated imaging solutions. Before joining Teledyne, Sadiq worked at GE, where he held several senior positions, including the Senior Commercial Director at GE Digital. Above all, Sadiq is committed to delivering decisive action in responding to evolving customer needs, uncovering market trends and mobilizing resources to deliver best-in-class and cost-effective technology solutions. This includes designing and driving initiatives that increase productivity, competitive differentiation and customer engagement while reducing costs and creating disruptive strategies. To learn more about Sadiq, connect with him here.

The last few decades have seen an incredible amount of change in the egg industry. That's especially true of egg grading, where we've gone from machines that can process 200 cases per hour, to systems that can do 800. And where advances in vision automation mean we no longer need people on the line manually inspecting eggs for dirt or cracks. Craig England was formerly the President of Sanovo Technology USA and also served as the President of MOBA USA, two of the world's foremost egg processing technology companies. Today, he walks us through the timeline of how automation transformed the breaking and grading process, and the profound impact that had on the broader industry. 

AI-driven machine vision systems are becoming essential in mechanical engineering applications such as fastener classification, yet their increasing connectivity exposes them to adversarial cyberattacks. Model evasion attacks like FGSM can subtly alter input images and cause misclassification, raising concerns about reliability in automated manufacturing.This talk focuses on the role of Explainable AI and human-in-the-loop strategies in detecting and mitigating such attacks. In the presented case study, an EfficientNet-B0 fastener classification model is examined using Grad-CAM visualizations to determine whether shifts inactivation patterns can reveal adversarial manipulation. The study evaluates how FGSM-generated images affect model accuracy and confidence while assessing the XAI system's ability to highlight abnormal regions of attention and the potential for human-in-the-loop approaches to be utilized with XAI techniques as a practical path to strengthening the resilience of AI-based machine vision systems in manufacturing. About the speaker: Dr. Vijayanth Tummala is a Researcher in Cybersecurity and Human-AI Interaction. His research spans artificial intelligence and cybersecurity across interdisciplinary areas, including AI and Cybersecurity leadership, AI literacy, and computer vision applications. He was one of only seven recipients to receive the Best Paper Award in the AI track at ASME's IMECE conference held in November 2024, which features over 2,400 submissions annually. Previously, he held key leadership roles, including leading the NSA CAE-CD designation, launching graduate programs as part of a $1.5 million EDA grant received by his previous employer, and partnering with the Allen County High-Tech Crimes Unit.

Grain grading is one of the last highly subjective processes in the ag supply chain and that makes it ripe for transformation, says Kyle Folk, founder and CEO of Ground Truth Ag. Folk says his Saskatchewan-based company is using machine vision and AI to reduce inconsistency and improve speed at the grading stage, from farm... Read More

OpenMV has recently launched a Kickstarter campaign. The OpenMV N6 is the first python-powered machine vision module capable of running a Yolov8n image detection model at more than 30 frames per second, thanks to its STM32N6. #STAuthorizedPartner #STPartnerProgram

Curious how someone ends up building a career around machine vision and industrial automation? Nikki sits down with Kerry Pierce, discussing the winding path that led her from computer engineering to becoming a machine vision integrator, the invaluable lessons learned along the way, and how she's helping demystify the field for newcomers. -------This episode is brought to you by SPS Atlanta.  Smart Production Solutions is the world's premiere industrial automation trade show is coming to the US!Register today:  https://www.xpressreg.net/register/SPSA0925/landing.asp?sc&aban&hkey&iq&vip&tm----------Connect with Nikki on LinkedIn: https://www.linkedin.com/in/nikki-gonzales/ Connect with Ali on LinkedIn: https://www.linkedin.com/in/alicia-gilpin-ali-g-process-controls-engineering/ Connect with Courtney on LinkedIn: https://www.linkedin.com/in/courtneydfernandez/ Connect with Kerry on LinkedIn: https://www.linkedin.com/in/kspierce/Support the show__________________________________________________________________Co-Hosts are Alicia Gilpin Director of Engineering at Process and Controls Engineering LLC, Nikki Gonzales Director of Business Development at Weintek USA, and Courtney Fernandez Robot Master at FAST One Solutions. Follow us on Linkedin and YouTube for live videos, demos, and other content!Subscribe to our weekly newsletter for episode updates, job announcements, and more!Get in touch with us at automationladies.io!P.S. - Help our podcast grow with a 5-star podcast review if you love us!

Today, Velocitor Solution's Rudy Nemeth talks about the evolution of fleet technology from digitizing basic processes to integrated platforms that enhance safety and efficiency! Rudy highlights how utilizing AI in fleet management helps identify safety issues, existing data challenges in logistics companies, cash flow constraints faced by small businesses, and the benefits of technology advancements!   About Rudy Nemeth As the VP of Sales at Velocitor Solutions, Rudy has over 25 years of experience in new business development for mobile software solutions and managed mobile services. His expertise includes Enterprise Cross-Platform Mobile Apps, Mobile Back-End, and ERP Integration. He also manages a fleet management system that includes GPS tracking, telematics monitoring, FMCSA-compliant ELD system, driver score cards, and in-cab video. His vertical markets include Consumer Packaged Goods, Field Service, Transportation and Logistics, Manufacturing, Retail, and Field Sales. He's passionate about delivering innovative and effective mobile solutions to various industries and helping businesses optimize their operations and performance.   Connect with Rudy Website: https://velocitor.com/  LinkedIn: https://www.linkedin.com/in/rudynemeth/  

Join us in this episode as we explore the complex world of machine vision AI applications with Maria Greicer, the VP of Partnerships at Keymakr. With more than 18 years of experience in AI-driven technology startups across the EU, USA, and Israel, Maria is a seasoned executive who combines her entrepreneurial mindset and medical background to bring a unique perspective to the industry as a whole.  Maria has a BA from Reichman University, where she specialized in Entrepreneurial Management and Information Technologies. Since then, she has led strategic growth initiatives in multiple leadership roles – including CEO, VP of Partnerships, and other executive positions…  The conversation covers: What makes medical AI technology so cutting-edge and important.  How AI is changing our understanding of traditional medicine.  The ways that AI can improve the efficiency of ultrasounds and other similar procedures. The learning process of machine vision AI applications. Wondering how Maria is helping innovate emerging technologies, AI advancements, and continuous learning? Hit play to find out now! Be sure to follow along with Maria and her work with Keymakr here! Episode also available on Apple Podcasts: http://apple.co/30PvU9

In this episode of the Share PLM Podcast, we are joined by Patrick Hillberg, an adjunct professor at Oakland University, where he teaches a graduate course in engineering management (called "Product Lifecycle Management") and is an Industry Advisor to the Department of Industrial and Systems Engineering. Patrick has decades of industry experience in designing, developing and leading teams in Product Lifecycle Management (PLM), Digital Twins, Digital Manufacturing, Process Planning, Robotics, and Machine Vision applications in Aerospace, Shipbuilding, Automotive, Construction, Packaging, and other industries. Join us as we dive deep into these topics:⚉ Product Lifecycle Management (PLM) and Sustainability⚉ Engineering ethics and catastrophic product failures⚉ The role of culture in engineering and business decisions⚉ Engineering Change Management: People vs. Process⚉ What does a solution architect do?⚉ Solution Architect vs. Project Manager⚉ Agile approaches and communication in engineering projects⚉ Are meetings a waste of time?⚉ The rise of software-defined vehicles and new safety challenges⚉ Traditional waterfall project management vs. agile methodology⚉ Traditional waterfall approach vs. agile systems thinking in academia⚉ Balancing finance, learning, and uncertainty⚉ PLM approaches in the US vs. Germany⚉ The role of human resources in PLM implementationCONNECT WITH PATRICK:Linkedin: https://www.linkedin.com/in/patrickhillberg/ CONNECT WITH SHARE PLM:Website: https://shareplm.com/ Join us every month to listen to fascinating interviews, where we cover a wide array of topics, from actionable tips, to personal experiences, to strategies that you can implement into your PLM strategy.If you have an interesting story to share and want to join the conversation, contact us and let's chat. We can't wait to hear from you!

Send us a textIn this engaging episode of The Digital Executive Podcast, host Brian Thomas sits down with Maria Greicer—a seasoned executive with over 18 years of experience in AI-driven startups spanning Israel, Europe, and the USA—to explore the transformative world of machine vision technology. With a unique blend of entrepreneurial acumen and a medical background, Maria offers listeners an insider's view into how emerging AI innovations are reshaping industries worldwide.Throughout the conversation, Maria delves into the distinctive AI ecosystems of different regions. She highlights Israel's dynamic and risk-friendly environment, where a strong startup culture fuels rapid innovation and frequent experimentation. In contrast, she explains that while the U.S. benefits from abundant capital, its approach is somewhat more conservative. European markets, on the other hand, prioritize stability and predictable growth, which can sometimes slow the pace of breakthrough innovations. These regional nuances, Maria notes, have significantly influenced her own approach to leadership and strategic decision-making in the tech industry.Maria further explains her role at Keymakr, where she leads initiatives to create and optimize high-quality training data for machine vision AI applications. She details the company's proprietary annotation platform, Keylabs AI, which underpins their rigorous in-house process for developing training data sets tailored to diverse applications—from autonomous vehicles and in-cabin safety systems to precision agriculture and medical imaging. Emphasizing the importance of stringent quality control, Maria outlines how every project undergoes a four-tier review process to ensure the highest data integrity and model reliability.The discussion also touches on the challenges posed by global data privacy regulations. Maria recounts real-world scenarios where navigating varying international data protection laws—such as GDPR in Europe and similar stringent standards in the U.S.—requires a meticulous approach to data consent, security, and processing. Her insights reveal how Keymakr's commitment to ethical data practices and in-house processing not only safeguards sensitive information but also positions the company at the forefront of compliance and innovation.Ultimately, this episode offers a comprehensive look into the evolving landscape of machine vision AI, blending technical insight with real-world experience. Listeners gain a deeper understanding of how strategic risk-taking, cutting-edge technology, and robust data governance are key to driving innovation in today's fast-paced digital environment.

Today, we're diving into the fascinating world of artificial intelligence and coding with Itamar Friedman, CEO & Co-Founder of Qodo, a groundbreaking generative AI coding platform.About Itamar : With a rich background in technology and innovation, including a pivotal role as Director of Machine Vision at Alibaba, Itamar now leads Qodo, which has transformed the way over a million developers write code.Episode Highlights:Qodo's Genesis: Itamar shares the story of Qodo's inception and its mission to revolutionize coding through AI.Ethical AI: Delving into the significance of ethical AI, Itamar discusses how Qodo ensures its technology enhances rather than hinders human effort.AI and the Future of Work: Itamar offers insights into how AI is reshaping job roles and industries, predicting significant shifts in the tech landscape.Challenges and Triumphs: Hear about the obstacles Qodo faced and the milestones they've achieved under Itamar's leadership.Advice for Tech Innovators: Itamar provides valuable advice for aspiring tech entrepreneurs and innovators looking to make a mark in the AI space.Interested in learning more about how AI is transforming coding? Check out Qodo's platform.

Say the words "artificial intelligence" or simply, "A.I." in an art setting, and people think of either cutting-edge, new media art, or of misinformation., hallucination, and plagiarism. But there's a case to be made that those words should prompt you to think about very old art and about very new technology's use in finding out what's real. My colleague at Artnet, Jo Lawson-Tancred has a new book out called A.I. and the Art Market, that serves as an accessible guide to a range of ways that artificial intelligence and machine learning are impacting the art market. There's a lot in the book about valuing art, about selling art, and about navigating the intellectual property challenges around A.I., but we thought we'd drill down into the question of art authentication, which has drawn plenty of headlines and controversy in recent years, all on its own. After all, huge amounts of money hinge on the question of whether a given piece of paint on canvas is actually considered to be by a particular old master painter. The art market has an entire robust world of art historical expertise built up around art authentication, which is revered, but sometimes also viewed with suspicion as corruptible and subjective. Then, here come various forms of A.I. art authentication with its own jargon and new kinds of suspicion aimed at it. So who should you trust? Jo has spent a lot of her time talking to various players to help begin to answer that question, and today we dig into the thorny question.

In this episode, we talked to Jill Walker Rettberg, Professor of Digital Culture at the University of Bergen in Norway. In this wide-ranging conversation, we talk about machine vision's origins in polished volcanic glass, whether or not we'll actually have self-driving cars, and that famous photo-shopped Mother's Day Photo released by Kate Middleton in March, 2024. 

In this episode of Control Intelligence, written by contributing editor Rick Rice, editor in chief Mike Bacidore explains the differences between machine vision and computer vision.

Itamar Friedman, CEO of Qodo on the evolution of AI in software development, and the changing philosophy of AI.  The former director of Machine Vision, which was bought out by Alibaba, established Codium AI before ChatGPT, predicts a future in which increasingly AI will think like humans, and argues that System  2 Thinking could even improve human reasoning. He gives us an insight into many of the tools that his own company is creating, and explains that jobs for developers will grow rather than fall victim to AI. The need for coders and software engineers, he says, will increase, but their jobs will expand and operate at a higher level. #Business #Leadership #AI #SoftwareEngineer

If you're planning to use machine vision to assist with quality control or automate other visual inspections, there are some things you need to account for when designing your system. In the latest episode of the Industrial Automation Insider podcast, two engineers from Tri-Phase Automation explain what you should think about when spec'ing out your vision system to avoid headaches later on and make sure you get the results (and return on investment) you want.  

Today's episode features a conversation recorded live in May at The King's Festival of Artificial Intelligence in London. The event featured as the launch of Cinema and Machine Vision: Artificial Intelligence, Aesthetics and Spectatorship, a new book by Daniel Chávez Heras from Edinburgh University Press. Before a live audience, Daniel and Will chat about the themes and topics covered in the book, the intersections of AI and Film Studies, and answer audience questions. To learn more about Daniel and his work, click here. Daniel has also agreed to give away two copies of the book to listeners! Learn more here. Follow the show on ⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠. Learn more at the pod's ⁠⁠⁠⁠⁠website⁠⁠⁠⁠⁠. Get the ⁠⁠⁠⁠⁠free newsletter⁠⁠⁠⁠⁠. Music by Ketsa.

They say there's two sides to every story. Well, now there's at least two viable machine vision solutions to every problem when you invite both seasoned and early career vision system engineers to the table, and that's making it easier to see the right path forward, according to two engineers. Find out why they feel this way, and what diversity of thought means for the impact of vision technology investments on your business. 

From building machines to building Apple iPhones, Roman Piszcz (Founder of Quotebeam) has a lot of experience in creating products and helping businesses find real solutions. It was from his personal passion for creating a better way for companies to purchase products. This week on Workforce 4.0, host Ann Wyatt sits down with Roman and Nikki Gonzales to discuss in more detail about his passion for entrepreneurship, what it means to hire "intraprenuers" and how start up companies can leverage the interviewing process to identify and hire leading talent. Roman and Nikki also share their unique perspectives on the benefits of remote work, how automation and machine learning apps empower people to focus on more meaningful work and why providing tools for ownership is crucial to organizational success in the digital era. In This Episode:-03:06 Roman's Journey and QuoteBeam's Origin -18:03 Exploring The Spirit of Entrepreneurship -29:59 Evaluating Candidates Via An Interviewing Question About Climate Change -31:03 Unlocking Interview Tactics To Assess Critical Thinking-31:56 Hiring Fresh Graduates: A Unique Approach -36:05 The Future of Workforce and Technology -38:57 The Evolution of Communication -55:47 Building and Retaining High-Performing Teams More About Roman Piszcz:Throughout his career, Roman always designed machines using two principles: simplicity and performance. These two principles brought about the inception of Quotebeam. Although the machines he was designing were using state-of-the-art technologies, Roman realized that the process of designing them was extremely fragmented and inefficient. Recognizing these engineering and procurement gaps in parts search, sourcing, and vendor collaboration marked his first efforts in automating these processes. During the five years before taking Quotebeam full time in early 2021, Roman led a team at Apple's iPhone & Core Technologies manufacturing operations. That experience amplified his belief of the importance of data transparency and supplier collaboration, and led to the beginning of Quotebeam as a company. Learn more and connect with Roman here. About Nikki Gonzales:Nikki has a unique mix of skills including growing SaaS startups and direct experience as a field Sales Engineer in industrial automation for Festo and Keyence. After a series of meetings over a few years from Silicon Valley to Seattle, a couple of babies, a global pandemic, and a move to Texas, finally the stars aligned for Nikki to join Quotebeam full time! She's always loved problem solving and building things, so it led to a career in engineering sales spanning technologies such as Machine Vision, Pneumatics & Mechatronics, Computational Electromagnetics, and AI & Data Analytics, to name a few. Learn more and connect with Nikki here. The Future of Work (and this Episode) Is Brought To You By Secchi:Secchi is a revolutionary workforce engagement tool created for organizations to make data-driven frontline decisions in real-time. By measuring and combining multiple people-related lead indicators, Secchi provides in-the-moment visibility into individual frontline employee performance, team performance, engagement/turnover risks, and positive employee behaviors all while removing the traditional barriers of administrative burden on leaders. To learn more about Secchi, check them out here.

Noel Buckley founded Knolly Bikes, a highly respected mountain bike brand based in British Columbia, Canada. He has a background in physics from the University of British Columbia and worked as an engineering physicist for a decade before starting Knolly in 2006.Before Knolly, he worked in machine vision systems for industrial forestry applications and hydrogen hybrid power systems. This experience gave him insights into developing advanced products for harsh environments and real-world testing methods. As an avid mountain biker from Vancouver's North Shore riding scene, he applied these principles to launch Knolly and design bikes optimized for aggressive riding. ___Get your copy of Personal Socrates: Better Questions, Better Life Connect with Marc >>> Website | LinkedIn | Instagram | Twitter Drop a review and let me know what resonates with you about the show!Thanks as always for listening and have the best day yet!*A special thanks to MONOS, our official travel partner for Behind the Human! Use MONOSBTH10 at check-out for savings on your next purchase. ✈️*Special props

Quality spoke with David Dechow of Machine Vision Source following his recent presentation at The Quality Show South about vision solutions for quality applications and integration that drives application success with current and emerging technologies.

(3:40) - The Secret to Super-Fast Robot Vision This episode was brought to you by Murata, the market leader in leading-edge electronics components. Click HERE to learn more about Murata's contributions to the future of autonomous vehicles.   Become a founding reader of our newsletter: read.thenextbyte.com As always, you can find these and other interesting & impactful engineering articles on Wevolver.com.

In this episode, Nikki  interviews Yamini Vattipalli, Director of Operations at Easy Automation Systems. Yamini shares her journey from being an image processing engineer to her current role. They discuss the challenges and importance of vision applications, the transition to system integration, and the role of AI in machine vision. Support the Show.Co-Hosts are Alicia Gilpin Director of Engineering at Process and Controls Engineering LLC, and Nikki Gonzales Head of Partnerships at Quotebeam Follow us on Linkedin for live videos, demos, and other contentMusic by Samuel JanesAudio Editing by Laura MarsilioLeave us an audio message or get in touch at automationladies.io

Ashley Harris is an experienced Operations Leader used to working in fast-growing and high-pressure companies. He spent 5 years working at Tesla, where he rose to a management role for European Operations and Logistics. He's currently Director of Operations for Clearview, a pioneering company in the Machine Vision space, where he works with the Managing Director and Senior Leadership Team to help deliver strategic goals (serving as "Integrator", using the Entrepreneurial Operating System (EOS)).In this episode of the First-time Founders Podcast, Ash compares and contrasts the style of Elon Musk, one of the greatest entrepreneurial Visionaries of our time, with a good small business leader, running on EOS. We talk about aligning teams around a Vision; recruiting and managing People; use of Data; understanding Issues at their root; if, when and where Processes are necessary; and quarterly operating rhythms. We explore what it takes as an entrepreneurial leader to really 'put a dent in the universe'!Interested viewers can reach Ash via LinkedIn - https://www.linkedin.com/in/ashley-w-harris/ - and Rob (https://www.linkedin.com/in/robertliddiard/) at Rob@mission-group.co.uk (or to book some free time with Rob, visit https://www.eosworldwide.com/rob-liddiard).

In this second episode of season two, Scott is joined by Jill Walker Rettberg, co-director of the Center for Digital Narrative to talk about her book on Machine Vision and how algorithms are changing the way we see the world.  Sign up for ⁠⁠the CDN newsletter⁠⁠ here. References Rettberg, Jill Walker. 2024. Machine Vision: How Algorithms Are Changing the Way We See the World. Cambridge, UK: Polity Press.  Vertov, Dziga, director. 1929. Man with a Movie Camera. All-Ukrainian Photo Cinema Administration. 68 minutes. https://en.wikipedia.org/wiki/File:Man_With_A_Movie_Camera_(Dziga_Vertov,_1929).webm   Hayles, N. Katherine. 1999. How We Became Posthuman: Virtual Bodies in Cybernetics, Literature and Informatics. Chicago: University of Chicago Press.  Ring LLC. 2017. Neighbors by Ring. Android & iOS. Shusterman, Neal. 2018. Thunderhead. Simon & Schuster. Kronman, Linda. 2019. The deception of an infinite view – exploring machine vision in digital art. BCS Learning and Development Ltd. http://dx.doi.org/10.14236/ewic/POM19.11.

This is a repost of Episode 149, originally aired on April 2, 2023. This conversation was the 3rd most popular Future of Mobility episode in 2023. ... Angus Pacala serves as the CEO at Ouster, which he co-founded in 2015 to make lidar both digital and ubiquitous. Key topics in this conversation include: The fundamentals of digital LiDAR, and the advantages over analog technology Why Ouster and Velodyne merged The potential for digital LiDAR to transform smart infrastructure Ouster's strategic approach to modular product development, and how it enables them to explore various application segments How Ouster is providing LiDAR solutions for L2 and L3 systems, as well as fully automated driving systems Links: Show notes: ⁠http://brandonbartneck.com/futureofmobility/anguspacala⁠ ⁠https://www.linkedin.com/company/ouster/⁠ ⁠https://twitter.com/ousterlidar?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor⁠ ⁠https://www.linkedin.com/in/apacala/⁠ ⁠https://ouster.com/⁠ Angus's Bio: Angus Pacala serves as the CEO at Ouster, which he co-founded in 2015 to make lidar both digital and ubiquitous. Mr. Pacala has watched self-driving technology evolve from the early days of the DARPA Grand Challenge to today where he has a unique window into the future of automation through Ouster's work with approximately 700 customers spanning the automotive, industrial, robotics, and smart infrastructure industries. Prior to Ouster, Mr. Pacala co-founded lidar company Quanergy Systems and served as the Director of Engineering. Before that, he was a Battery Engineer at Amprius, Inc. He received his Bachelor and Master degrees in mechanical engineering from Stanford University. About Ouster Ouster (NYSE: OUST) is a leading global provider of high-resolution scanning and solid-state digital lidar sensors, Velodyne Lidar sensors, and software solutions for the automotive, industrial, robotics, and smart infrastructure industries. Ouster is on a mission to build a safer and more sustainable future by offering affordable, high-performance sensors that drive mass adoption across a wide variety of applications. With a global team and high-volume manufacturing, Ouster supports over 850 customers in over 50 countries. Ouster is headquartered in San Francisco, CA with offices in the Americas, Europe, Asia-Pacific, and the Middle East. Future of Mobility: The Future of Mobility podcast is focused on the development and implementation of safe, sustainable, effective, and accessible mobility solutions, with a spotlight on the people and technology advancing these fields. ⁠linkedin.com/in/brandonbartneck/⁠ ⁠brandonbartneck.com/futureofmobility/

EPISODE 1880: In this KEEN ON show, Andrew talks to Jill Walker Rettberg, author of MACHINE VISION, about how algorithms are changing the way we see and are seen by the worldJill Walker Rettberg is Professor of Digital Culture and Co-Director of the Center for Digital Narrative (CDN), a Norwegian Center of Research Excellence that has received a €15 million grant from the Norwegian Research Council (2023-2033). She is also Principal Investigator of the ERC project Machine Vision in Everyday Life: Playful Interactions with Visual Technologies in Digital Art, Games, Narratives and Social Media (2018-2024). Rettberg is currently developing new research on how new language-based AI is impacting the kinds of stories we tell and that spread online. She argues that generative AI has deep cultural biases that are less easy to spot than the biases that are evident in, for example, facial recognition. This emerging work draws upon the research on AI and visual technologies in the Machine Vision project as well as on Rettberg's decades of narratological research into digital genres of storytelling, such as electronic literature, blogging and transmedia narrative.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.

Quality control is one of those things that only a select few people pay attention to—until something goes wrong, then everyone cares. That's especially true in the drug manufacturing industry, where episodes like cross-contamination in a drug factory can shut down a production line and create instant shortages of important medicines. And if a contaminated medicines ever does get shipped out to clinics or stores, people's lives can be at stake. So drug makers are usually pretty receptive toward any new technology that can help them detect manufacturing problems before they get out of hand.That's the market opening that Harry's guest Taylor Chartier says she saw back in 2020, during the coronavirus pandemic. Chartier watched the stories about the Baltimore company Emergent BioSolutions, which was manufacturing vaccines for Johnson & Johnson and AstraZeneca and had to throw out millions of doses of both vaccines due to suspected cross-contamination, and thought: there has to be a better way. So she started her own company. And today her startup Modicus Prime is partnering with top pharma companies to use new machine vision and AI capabilities to catch drug manufacturing problems faster.For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:1. Open the Podcasts app on your iPhone, iPad, or Mac. 2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.3. Scroll down to find the subhead titled "Ratings & Reviews."4. Under one of the highlighted reviews, select "Write a Review."5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.7. Once you've finished, select "Send" or "Save" in the top-right corner. 8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out. 9. After selecting a nickname, tap OK. Your review may not be immediately visible.That's it! Thanks so much.

The Impressionist painter Claude Monet wrote that he was driven ‘wild with the need to put down what I experience'. In his long career he revolutionised painting and made some of the most iconic images of western art. The art critic Jackie Wullschläger's biography of Monet looks at the man behind the famous artist. Monet's late series of paintings of water lilies became less and less concerned with a conventional depiction of nature. The artist Mat Collishaw's latest works also draw on evocative imagery from the natural world, including use of AI technology. At an exhibition at Kew Gardens (until April 2024) Collishaw takes inspiration from 17th century still life paintings of flowers, but on closer inspection the viewer sees the flowers morph into layers of insects. Humans have always used technology to expand our limited vision, from the stone mirror 8,000 years ago to facial recognition and surveillance software today. Jill Walker Rettberg is Professor of Digital Culture at the University of Bergen. In her book, Machine Vision, she looks at the implications of the latest technologies, and how they are changing the way we see the world. Producer: Katy Hickman

Lex chats with Sam Bobley, founder and CEO of Ocrolus - a fintech infrastructure company that powers underwriting processes for lenders like SoFi, Lending Club, and Enova. In this episode Sam starts off by sharing how he got started in entrepreneurship at a young age and the influence of his father, who was a serial entrepreneur. Bobley explains how Ocrolus focuses on document automation using OCR technology and the challenges they faced in the early days. He also discusses the evolution of machine vision and the improvements in handling semi-structured and unstructured documents. Bobley highlights the importance of vertical-specific solutions and the integration of AI technologies into financial services. He shares the company's experience during the COVID-19 pandemic and the pivot they made to focus on the Paycheck Protection Program. Bobley emphasizes the need for transparency, focusing on the big picture, and having a support system to navigate the challenges of entrepreneurship. He rounds off the episode discussing the trends he sees in the industry, including the rise of large language models and the adoption of cashflow-based underwriting. MENTIONED IN THE CONVERSATION Ocrolus's Website: https://bit.ly/3QHYoeNSam's LinkedIn profile: https://bit.ly/3SrC7TU Topics: artificial intelligence, ai, machine vision, OCR, LLM, machine learning, automation, fintech, embedded banking Companies: Ocrolus, Plaid, OpenAI, Google, Amazon, AWS, OnDeck ABOUT THE FINTECH BLUEPRINT 

In this episode of the Brains and Machines podcast, EE Times regular Sunny Bains talks to Dr Yulia Sandamirskaya, who has just created the Neuromorphic Computing Group at Zurich University of Applied Sciences. We discuss the role that dynamical systems theory plays in robotics, her work at with Intel's Loihi platform, and what she plans to do in her new position at ZHAW, particularly related to vision. After that, Sunny discusses the interview with Giulia D'Angelo from the Italian Institute of Technology and Ralph Etienne-Cummings from Johns Hopkins University.

Joe Gugliotti, president, R.J. Wilson, Inc., describes the importance of good images.

In this first episode of the podcast, Scott Rettberg is joined by his co-director and partner Jill Walker Rettberg. They talk about what has inspired them over the years to start this research Center of Excellence, the concept of "algorithmic narrativity," and some of the research that the Center will focus on including digital narratives in electronic literature, computer games, social media, computational narrative systems, AI, XR and more. Sign up for the newsletter coming soon. References University of Bergen. n. d. “Center for Digital Narrative.” https://www.uib.no/en/cdn.   Infocom. 1977. Zork. Personal Software. PDP-10 mainframe computer. https://www.pcjs.org/software/pcx86/game/infocom/zork1/.   Gillespie, W., Rettberg, S., Stratton, D., & Marquadt, F. 1999. The Unknown [Hypertext fiction]. Web. http://unknownhypertext.com/.   ELMCIP. n. d. “Electronic Literature Knowledge Base.” https://elmcip.net/.   Strachey, Christopher. 1952. M.U.C. Love Letter Generator. HTML.   ELMCIP. n. d. “Digital Arts and Culture 1998 Conference.” https://elmcip.net/event/digital-arts-and-culture-1998-conference.   Kittler, Friedrich, Dorothea von Mücke, and Philippe L. Similon. 1987. “Gramophone, Film, Typewriter.” October 41: 101–18. https://doi.org/10.2307/778332.  Jhave Johnston, David. 2019. ReRites. Anteism Books, Montreal. http://glia.ca/rerites/.   Wittig, Rob. 2022. Netprov: Networked Improvised Literature for the Classroom and Beyond. Amherst College Press. https://doi.org/10.3998/mpub.12387128.  University of Bergen. n. d. “Machine Vision.” https://www.uib.no/en/machinevision.  

Harpreet Sahota, a data science expert and deep learning developer at Deci AI, joins Jon Krohn to explore the fascinating realm of object detection and the revolutionary YOLO-NAS model architecture. Discover how machine vision models have evolved and the techniques driving compute-efficient edge device applications. This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au), by https://WithFeeling.ai, the company bringing humanity into AI, and by Modelbit (https://modelbit.com), for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • What is machine vision? [07:02] • Object detection and YOLO architectures [13:00] • Deci's YOLO-NAS: Optimal object detection model architecture [23:39] • Developer Relations [1:00:16] • Harpreet's 'top-down' approach to learning Deep Learning [1:06:50] Additional materials: www.superdatascience.com/693

Angus Pacala serves as the CEO at Ouster, which he co-founded in 2015 to make lidar both digital and ubiquitous. Key topics in this conversation include: The fundamentals of digital LiDAR, and the advantages over analog technology Why Ouster and Velodyne merged The potential for digital LiDAR to transform smart infrastructure Ouster's strategic approach to modular product development, and how it enables them to explore various application segments How Ouster is providing LiDAR solutions for L2 and L3 systems, as well as fully automated driving systems Links: Show notes: http://brandonbartneck.com/futureofmobility/anguspacala https://www.linkedin.com/company/ouster/ https://twitter.com/ousterlidar?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor https://www.linkedin.com/in/apacala/ https://ouster.com/ Angus's Bio: Angus Pacala serves as the CEO at Ouster, which he co-founded in 2015 to make lidar both digital and ubiquitous. Mr. Pacala has watched self-driving technology evolve from the early days of the DARPA Grand Challenge to today where he has a unique window into the future of automation through Ouster's work with approximately 700 customers spanning the automotive, industrial, robotics, and smart infrastructure industries. Prior to Ouster, Mr. Pacala co-founded lidar company Quanergy Systems and served as the Director of Engineering. Before that, he was a Battery Engineer at Amprius, Inc. He received his Bachelor and Master degrees in mechanical engineering from Stanford University. About Ouster Ouster (NYSE: OUST) is a leading global provider of high-resolution scanning and solid-state digital lidar sensors, Velodyne Lidar sensors, and software solutions for the automotive, industrial, robotics, and smart infrastructure industries. Ouster is on a mission to build a safer and more sustainable future by offering affordable, high-performance sensors that drive mass adoption across a wide variety of applications. With a global team and high-volume manufacturing, Ouster supports over 850 customers in over 50 countries. Ouster is headquartered in San Francisco, CA with offices in the Americas, Europe, Asia-Pacific, and the Middle East. Future of Mobility: The Future of Mobility podcast is focused on the development and implementation of safe, sustainable, effective, and accessible mobility solutions, with a spotlight on the people and technology advancing these fields. linkedin.com/in/brandonbartneck/ brandonbartneck.com/futureofmobility/

MLOps Coffee Sessions #138 with Dattaraj Rao, Explainability in the MLOps Cycle co-hosted by Vishnu Rachakonda. // Abstract When it comes to Dattaraj's interest, you'll hear about his top 3 areas in Machine Learning. What he sees as up and coming, what he's investing his company's time into and where he invests his own time. Learn more about rule-based systems, deploying rule-based systems , and how to incorporate systems into more systems. there is no difference between ML systems and deploying models. It's just that this machine learning model is much smarter than traditional rule based models. // Bio Dattaraj Jagdish Rao is the author of the book “Keras to Kubernetes: The Journey of a Machine Learning Model to Production”. Dattaraj leads the AI Research Lab at Persistent and is responsible for driving thought leadership in AI/ML across the company. He leads a team that explores state-of-the-art algorithms in Knowledge Graphs, NLU, Responsible AI, MLOps and demonstrates applicability in Healthcare, Banking, and Industrial domains. Earlier, he worked at General Electric (GE) for 19 years building Industrial IoT solutions for Predictive Maintenance, Digital Twins, and Machine Vision. Dattaraj held several Technology Leadership roles at Global Research, GE Power, and Transportation (now part of Wabtec). He led the Innovation team out of Bangalore that incubated video track inspection from an idea into a commercial Product. Dattaraj has 11 patents in Machine Learning and Computer Vision areas. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Keras to Kubernetes: The Journey of a Machine Learning Model to Production book: https://www.amazon.com/Keras-Kubernetes-Journey-Learning-Production/dp/1119564832 Responsible Data Science Research | Talk @ VLDB 2022| Dattaraj Rao https://www.youtube.com/watch?v=5_19KvSiy8s Operationalizing AI/ML: Journey of an ML Model to Production | Masterclass by Dattaraj Rao https://www.youtube.com/watch?v=Zk3RiiG07Us Dattaraj Rao presenting workshop on MLOps at VISUM 2021 https://www.youtube.com/watch?v=wonUvbMDTUA --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Dattaraj on LinkedIn: https://www.linkedin.com/in/dattarajrao/

We're back for Part 2 from the 2022 Vision Show, hosted by A3, the Association for Advancing Automation.

In October 2022, we ventured to Boston, MA for the Association for Advancing Automation's annual Vision Show and Autonomous Mobile Robots and Logistics Conference (AMRL). In the past, these had been 2 separate events, but this year, A3 brought them together under one roof.You really can't have robotics without imaging and you can't have mobile robotics without machine vision. The tie-ins are endless. It just made sense to bring these events together and it was great being part of this weeklong extravaganza.This is somewhat of an anniversary episode since we covered the AMR and Logistics Conference last year with Jake Hall, the Manufacturing Millennial. We have another 2-part bonus episode for you this week containing over 10 interviews with leaders in robotics and machine vision, including:-Erik Nieves, Founder of Plus One Robotics-Jeremy Bergh, President, North America at IDS Imaging-Joe Gemma, VP of Sales and Marketing at Calvary Robotics-Theng Kuoch of CMES Robotics-Rajesh Iyengar, Founder & CEO of Lincode Labs-Denise Stafford of KUKA Robotics-Patty Katsaros of Locus Robotics-Laura Hoffman, Automation Industry Insider-Matt Charles, VP of Mobile Robotics at RoBEX-Melonee Wise, VP Robotics Automation at Zebra Technologies-Alex Shikany, VP Membership & Business Intelligence at A3Make sure to visit ManufacturingHappyHour.com for detailed show notes and a full list of resources mentioned in this episode. Stay Innovative, Stay Thirsty.

Where does the time go? Let's talk Machine Vision and build upon Rev27-Rev28 about Artificial Intelligence Engineers and catch you up on everything that has been happening! Get your hands on our popular Engineering Notebooks! https://www.engineeringinreallife.com/engineering-notebooks Learn more about Obsidian Breakers on The Sandbox https://www.engineeringinreallife.com/obsidian-breakers My Interview on the Engineering Career Coach Podcast: https://www.youtube.com/watch?v=4_PrQA4bBnw&t=212s&ab_channel=EngineeringManagementInstitute Links to AI and Machine Vision videos: https://www.youtube.com/watch?v=1L0TKZQcUtA&list=PLQdwCecMFVkusNxjnD95c94xTYp5jEWSU Get free online access to the engineering book 10+1 Steps to Problem Solving - An Engineers Guide from a Career in Operational Technology and Control Systems at our website here: https://www.engineeringinreallife.com/book Don't forget to subscribe for more and share the engineering podcast. Head to https://www.engineeringinreallife.com and become a member of the Engineering IRL Community. If you're excited to see what the video has to offer you can watch this on the Engineering IRL Youtube Channel https://www.youtube.com/@engineering_irl/ And to support us consider purchasing an Engineering Notebook covered with inspirational quotes for engineers. It could be a great gift for an Engineer. The other way to support the Engineering IRL podcast is to subscribe and share the show with your friends! I want this to be a top engineering podcast and will be working hard to make that happen. Facebook: www.Facebook.com/engineerIRL Twitter: www.twitter.com/engineering_irl TikTok: https://www.tiktok.com/@engineering_irl Instagram: https://www.instagram.com/engineeringinreallife To know more about our partnerships and how to get in touch with the show visit the top engineering podcast. #Engineering #EngineeringNotebooks #ArtifiialIntelligence #ChatGPT

Guest BioI am a knowledgeable source of factory automation techniques when using machine vision systems, robotics, sensors, and non-contact measurement devices on production lines. My work has used the most influential brands of intelligent cameras, such as Cognex, Keyence, Banner, Omron, Matrox, Dalsa, and more. Most recently, my experience with the Elementary ML platform has shown me what machine learning vision systems can offer, and I am on a path to learning much about the next wave of AI-backed vision systems. My experience reflects 22+ years of hands-on use of machine vision equipment placed directly into production environments.How are people typically using Vision Systems?How can we use them better?Join us as Greg McEntyre is giving us a deep dive into all things Vision Systems.Have questions about or comments on vision systems?Manufacturing Hub Episode 84. Recommended Materials Lord of the Rings Purple Cow Connect with Us Greg McEntyre Vlad Romanov Dave Griffith Manufacturing Hub Let Us Know What You ThinkIf you enjoyed the show, it would mean the world to us if you could leave us a review: https://podcasts.apple.com/us/podcast/manufacturing-hub/id1546805573#manufactuing #automation #digitaltransformation #machinevision

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Using artificial intelligence (machine vision) to increase the effectiveness of human-wildlife conflict mitigations could benefit WAW, published by Rachel Norman on October 28, 2022 on The Effective Altruism Forum. 1. Overview This report explores using artificial intelligence (AI) to increase the effectiveness of human-wildlife conflict (HWC) mitigations in order to benefit wild animal welfare (WAW). Two concrete examples are providing more funding, research and direct work into reducing fatalities due to 1) collisions between bats and wind turbines, and 2) culling crop-raiding starlings. The report aims merely to raise awareness of this topic and introduce the idea for discussion, but not yet strongly suggest it is a cost-effective intervention on par with other interventions - see uncertainties, limitations, and potential for harm. What's the problem profile? HWC is increasing due to human expansions and climate change, (Gross et al., 2021) and is starting to be considered in government strategies and policy. The expected future impact of innovative and effective solutions to HWC could be even larger than currently appreciated. Lethal control or other methods which significantly impact animal welfare are still widely used (such as culling), despite preventative non-lethal strategies growing in more recent wildlife management approaches. Currently deployed AI systems directed towards HWC could be expanded further within the next 10-20 years as they become more reliable, more effective, and cheaper. We should not assume they will prioritize WAW concerns, or be widely used for animals of WAW concern, so this should be embedded before they are potentially rolled out at scale. There are already companies working on AI solutions for specific problems involving endangered species, such as protected areas using AI assisted technology for poacher detection. There is already proof-of-concept of an NGO-backed early warning AI system, ‘WildEyes', with this type of solution being invested in by a local governmental department in Tamil Nadu, India. Buy-in from a range of stakeholders (especially when it benefits humans and profits too) offers a way in with conservationists and researchers who may not otherwise consider WAW. Research and development (R&D) on AI-assisted HWC mitigations would likely attract researchers who would not otherwise consider or be motivated by WAW concerns. What should we be doing differently? A very tentative theory of change: if machine vision-based methods prevent HWC, they could be adopted, even on a small scale helps drop prices allowing for systems to be more widely adopted leads to more support and R&D continued price drops and adoption could create space for legislation to ban harmful or lethal methods of animal control preventing HWC could reduce apathy and antagonism towards “problem species” and make it easier for people to consider the welfare of animals, while also directly reducing negative WAW effects of HWC. This report highlights two examples of HWC where advocates could influence AI-assisted mitigation to directly affect substantial numbers of animals, and spread welfare considerations in software and norms: Wind turbine collisions are a leading anthropogenic cause of bat deaths and cause a significant number of bird deaths (600,000 to 949,000 bats and 140,000 to 679,000 birds annually in North America). We should expect fatalities to increase due to expansions in wind power. Culling of crop-raiding species. In one year, the USDA's Wildlife Services culled 1,028,642 European starlings responsible for agricultural crop damage, because other mitigations are ineffective. Despite this, starlings still cause extensive damage each year. More effective mitigation measures would hold value and could prevent culls. There are a number of r...

aboutGOLF's award-winning simulators are tailored for your specific home or business needs. aboutGolf simulators offer unbeatable accuracy and world-class customer service. On this episode of The Wednesday Match Play Podcast presented by MemberText, Ken Reynolds gives us an overview of aboutGOLF, talks about Machine Vision and the technology they are using, the cost associated with putting a simulator in your home or business and how to find a golf simulator near you. He also explains the Ryder Cup Sim Series and talks about Michael Breed's involvement and the MB 360 Range. This was an educational conversation and an honor having Ken on this show. Let's tee off.

Angelo Stekardis is a Senior Computer Vision Engineer at Rivian, an electric vehicle manufacturer known for their high tech adventure vehicles and Amazon delivery vans. Angelo is a UK Grad who used his love for technology and cars to eventually begin working on cutting edge computer vision technology that helps cars drive themselves. Our discussion covers his journey from UK to Rivian, artificial intelligence, electric vehicles, and ways UK can help more students land high tech jobs. Connect with Angelo on LinkedIn Visit us at MiddleTech.com Twitter Instagram Facebook LinkedIn Logan's Twitter Nate's Twitter Middle Tech is proud to be supported by: Our presenting sponsor, KY Innovation Bolt Marketing Render Capital Endeavor Midwest