By Dr. Massimiliano “Max” Versace
AI executive, scientist, Neurala co-founder and former CEO; Vice President of Emergent AI at Analog Devices.
Intelligence becomes a different engineering problem when it has to operate inside a machine. A robot must respond in time. A sensor has a power budget. An industrial system must work reliably when the environment changes. These constraints have connected the different stages of my career: computational neuroscience, brain-inspired computing, robotics, building an AI company, and now semiconductor-level work on Physical AI.
I am Massimiliano Versace, usually known as Max Versace. Today, I serve as Vice President of Emergent AI at Analog Devices and as a Board Advisor to the EDGE AI FOUNDATION. Previously, I co-founded Neurala and served as its CEO for nearly two decades, working to turn neural-network research into commercial AI products.
This article brings together that trajectory and a selected archive of my talks, YouTube videos, interviews, scientific publications, contributed articles, and independent coverage. Historical titles in the archive describe my role at the time of publication.
Computational neuroscience: learning from biological intelligence
My scientific training began with a question that still shapes my work: how can a system learn from experience while remaining stable enough to retain what it already knows?
I came to the United States as a Fulbright Scholar and earned doctorates in Experimental Psychology at the University of Trieste and Cognitive and Neural Systems at Boston University. At BU, I co-founded and directed the Neuromorphics Lab. Boston University’s account of the laboratory describes our work on machines capable of learning and adapting.
My research with Stephen Grossberg included “Spikes, synchrony, and attentive learning by laminar thalamocortical circuits”, published in Brain Research in 2008. This work examined relationships between neural dynamics, attention, and learning. The broader engineering question was how insights from biological intelligence could inform artificial systems operating with limited computational resources.
That question also led toward computing hardware. In “MoNETA: A Mind Made from Memristors,” co-authored with Ben Chandler for IEEE Spectrum, we discussed brain-inspired computation and emerging hardware. The article documents an early part of my work connecting neuroscience, neural architectures, and the physical systems that implement them.
Robotics and NASA: intelligence that operates locally
For an autonomous machine, intelligence cannot always depend on a continuous connection to a remote computer. The system needs to interpret its surroundings and act under constraints imposed by communication, energy, and time.
Our NASA-supported work explored those problems in autonomous robotics. NASA traces this technology-transfer story in “A ‘Brain’ for Cars, Tested for Mars” and “From Mars Rovers to Factory Assembly Lines”. These accounts describe research for planetary exploration and its subsequent applications on Earth; they should not be confused with a claim that Neurala software flew on a Mars rover.
I discussed related ideas publicly in my 2014 TEDxFulbright talk, “Neuromorphics — Machines to Life”, and my 2016 NVIDIA GTC Europe presentation. Together, these talks provide a historical record of my interest in brain-inspired intelligence inside robots and embedded systems.
Neurala: turning AI research into a company
Co-founding and leading Neurala added another set of constraints: customers, capital, teams, product development, and the economics of deployment. A scientific result becomes a business only when it solves a problem that someone needs solved, at a cost and level of reliability they can accept.
Our work included continual learning and Neurala’s Lifelong-DNN technology, designed to support incremental learning while addressing the loss of previously learned information. I discussed the company’s scientific origins and development in this Unite.AI interview, and the application of AI to visual inspection in this Center for Data Innovation interview.
The transition from research to industrial products also informed my writing on manufacturing, explainability, supply chains, and learning at the edge. These were questions about implementation as much as algorithms: how much data a customer needs, how a system handles unfamiliar conditions, and how an AI product fits into an existing operation.
In its March 2026 Space Technology Hall of Fame announcement, the Space Foundation named the Neurala Brain among the 2026 inductees and recognized the work of Massimiliano Versace, Anatoli Gorchet, Heather Ames, and NASA’s Mark Motter. The announcement reported deployment in approximately 80 million systems worldwide. That is a dated, attributed figure for Neurala’s technology, reflecting the work of a team and its partners.
Analog Devices: Physical AI, sensing, and efficient compute
My work at Analog Devices continues the connection between intelligence and its physical implementation. ADI’s public AI overview describes its focus on Physical Intelligence and identifies my role in Emergent AI. The EDGE AI FOUNDATION’s announcement of its partnership with ADI connects that work with sensing, signal processing, and efficient edge computing.
My view is that Physical AI requires us to consider learning, sensing, memory, computation, and behavior together. A robot’s ability to act depends on much more than the size of its model. Latency, power consumption, uncertainty, and the properties of the machine itself help determine which architecture is useful.
This connects my scientific background with my experience as a founder and CEO. Evaluating an AI architecture involves asking both whether it works and whether it can become a deployable product. My executive profile develops that connection across research, company building, commercialization, and semiconductor AI.
Talks, YouTube videos, and interviews
The recordings and interviews below document different stages of that journey. Dates refer to the event or original publication where established; some accompanying website posts were published later.
Talks and video conversations
- TEDxFulbright, 2014 — Neuromorphics: Machines to Life. Brain-inspired intelligence and robotics.
- NVIDIA GTC Europe, 2016 — Max Versace’s GTC presentation. Embedded AI and robotic systems; also listed in my archive as “Brain-in-a-Box.”
- MIT Technology Review Future Compute, 2019 — How AI Is Changing the Way We Work. Conversation with David Rotman about enterprise applications and deployment. Accompanying retrospective.
- Fulbright Association, 2019 — Humanizing AI: From Myth to Reality. Organizer’s speaker listing.
- Immigrant Entrepreneur Video Interview Series, 2019 — AI, Immigration, and Entrepreneurship. Founding Neurala and moving from Italy to the United States. Accompanying account.
- RoboBusiness, 2019 — How to Train Your Robot’s AI. Talk page on deploying AI in robots.
- TEDxCortina, 2023 — Unlocking the AI Mirror: What Makes Us Unique?. Human and artificial intelligence.
- Robotics Summit & Expo — Max Versace speaker profile. Historical conference profile.
Interviews and podcasts
- The VentureFizz Podcast, 2019 — Max Versace, Co-Founder and CEO of Neurala. Episode 139; company building and AI commercialization, with an embedded recording.
- Unite.AI, 2020 — Max Versace interview. Neuroscience, continual learning, and entrepreneurship.
- Tech Talks Daily, 2020 — AI in Manufacturing: A Second Set of Eyes. Episode 1307; podcast page.
- Association for Advancing Automation, 2022 — Utilizing Vision AI in Manufacturing with Neurala’s Max Versace. Practical adoption of industrial AI.
- Center for Data Innovation, 2024 — 5 Q’s With Max Versace. Neural-network research and industrial visual inspection.
- Immigrant Nation, linked January 2025 — Episode 28: Replicating the Human Brain: The Future of AI with Max Versace. Accompanying post.
- The Early Stage — Max Versace on Building Brains for Bots. Neurala’s account of the interview and its early commercial development.
Forbes Technology Council: articles and contributions
These are my contributed articles, except for the explicitly labeled expert panel. They are separate from independent reporting about me or Neurala.
- Mastering AI Integration in Manufacturing: Unmasking the Myths — June 11, 2024.
- Software to Hardware: How to Shift to Building Physical Tech — March 12, 2024. Expert-panel contribution.
- Manufacturing, Sustainability and Profitability: How AI Can Make Us Greener — February 17, 2023.
- “I Doubt, Therefore I Am,” Said AI — April 7, 2022.
- Why 2022 Will Be an AI-Powered Renaissance — January 4, 2022.
- How AI and Automation Can Address America’s Broken Supply Chain — October 5, 2021.
- From One of a Million to One in a Million: The Path to Customization Passes Through AI and Automation — July 12, 2021.
- How Manufacturers Have Turned the Table on Automation — June 15, 2021.
- From Digital to Physical: The Ultimate Challenge for AI — March 25, 2021.
- Why “Explainability” Is the Next Step for AI in Manufacturing — February 22, 2021.
- Rebounding From the Pandemic: What Our Past Teaches Us About the Future — December 17, 2020.
- The Coming of Age of Industry 4.0 in a Post-Pandemic World — October 6, 2020.
- From Computer Vision to Deep Learning: How AI Is Augmenting Manufacturing — June 16, 2020.
- 2020 and the Dawn of AI Learning at the Edge — February 12, 2020.
- 2020 AI Predictions: What We Got Right in 2019 and What’s in Store for 2020 — January 8, 2020.
- The World Needs WordPress for AI — May 1, 2019.
- The Next-Generation AI Brain: How AI Is Becoming More Human — April 9, 2018.
- If You’re Worried About AI Taking Your Job, Consider This — January 26, 2018.
Scientific publications and technical writing
- Brain Research, 2008: Spikes, synchrony, and attentive learning by laminar thalamocortical circuits — Stephen Grossberg and Massimiliano Versace. DOI.
- IEEE Spectrum, 2010: MoNETA: A Mind Made from Memristors — Massimiliano Versace and Ben Chandler.
- AZoRobotics, 2011: An Introduction to Silicon Brains: Dr. Massimiliano Versace — technical thought-leadership article.
- IEEE Computer, 2011: From Synapses to Circuitry: Using Memristive Memory to Explore the Electronic Brain — collaborative research publication.
- IEEE Pulse, 2012: The Animat: New Frontiers in Whole Brain Modeling — collaborative research publication.
- The New Stack, 2017: Does Artificial Intelligence Require Specialized Processors? — contributed technical perspective.
- IEEE Spectrum, 2019: The Next Frontier in AI: Nothing — contributed article.
Independent reporting and institutional accounts
These sources document my work or Neurala’s development. Coverage of the company is identified as such; inclusion does not imply endorsement of my present views or role.
- Boston University: Pushing the Boundaries of Artificial Intelligence and The Robot as Decider.
- NASA Spinoff: Planet-Navigating AI “Brain” Helps Drones and Cars Avoid Collisions, and From Mars Rovers to Factory Assembly Lines.
- Space Foundation: 2026 Space Technology Hall of Fame announcement.
- IEEE Spectrum: Deep Learning AI for NASA Powers Earth Robots.
- TIME: Machine-Learning Programs Help Doctors and Their Patients.
- Fortune: 2017 profile and Neurala financing coverage.
- TechCrunch: Neurala’s Series A and edge machine learning.
- VentureBeat: Neurala’s $14 million financing.
- The Boston Globe: 2017 Neurala financing report, and coverage of Neurala’s mobile AI applications.
- Associated Press: Decade Since Recession: Thriving Cities Leave Others Behind.
- Inc.: This Artificial Intelligence Expert Breaks Down Your Burning Questions.
- Il Sole 24 Ore: Il ratto nel robot — March 3, 2011; Italian coverage of the MoNETA research team.
- Industrial Ethernet: Looking Ahead to 2026: AI Predictions — industry perspectives including ADI’s Max Versace and Paul Golding.
For a concise biography, see About Max Versace. For my operating and technical background, see the executive profile. For recordings and future additions to this archive, see Media & Speaking.