Josh Wong is the Founder and CEO of ThinkLabs AI. He previously worked at GE Vernova as a General Manager, Grid Orchestration. Josh Wong attended the University of Waterloo.
ThinkLabs AI is a specialized AI development and deployment company. Its mission is to empower critical industries and infrastructure with trustworthy AI geared toward achieving global energy sustainability. The corporate is developing its flagship product, ThinkLabs Copilot, a digital assistant that comprehends the actual world through proprietary physics-informed AI digital twins, providing a foundational model for engineering systems.
Are you able to tell us more concerning the vision behind ThinkLabs AI and what inspired its creation?
The vision behind ThinkLabs is a reliable, sustainable, and reasonably priced energy infrastructure powered by trustworthy AI. We understand that the grid stays at the middle of the energy transition. To decarbonize we must electrify. To affect we’d like the grid, and the grid really must modernize. We imagine the intersection of electrical power systems engineering, AI, and cloud computing is the answer.
How does ThinkLabs AI differentiate itself from other AI startups within the grid management sector?
The grid is complex, and a lot in order that AI in itself cannot learn concerning the complex power flows and operational processes that exist within the grid space. ThinkLabs mix the wealthy history and confidence of traditional power systems engineering with AI, as trustworthy physics-informed AI, for confidence in scaled, automated inferencing and decision support for critical infrastructure. It also takes greater than technology, but an experienced team that understands the nuances of the grid and the way utilities and regulators think. Our team comes from the electrical power systems space with proven track record, including founder Josh Wong who has sold his previous company Opus One Solutions to GE, and stands on the intersection of engineering, AI, and cloud computing.
What specific challenges in grid management does ThinkLabs AI aim to unravel?
Automated analytics and suggestions for real time situational awareness across the grid, large scale simulations, and continuous learning and suggestions to mitigate grid constraints and optimize grid performance. Specific functional areas include:
- Insights – near real time state estimation of grid power flow, detecting congestions, voltage violations and the way capital assets are literally utilized.
- Solutions – optimal dispatch recommendations, including switching, grid devices and DERs, for congestion relief, mitigate DERs interconnections, reduce losses, restore outages, etc.
- Model validation – validation and corrections in utility source data sets for grid models, saving OpEx and increasing operator confidence for grid operations.
- Operator’s Copilot – operator dispatch recommendations trained with grid physics, business rules, standard procedures, and operational experience, empowering workforce training and upskilling.
What’s the ThinkLabs Copilot, and the way does it enhance grid planning and operations?
ThinkLabs Copilot is a digital assistant that that understands the actual world with proprietary physics-informed AI digital twins that provide a foundation model for engineering systems. It really works with utility planners and operators, to model the grid into its “AI digital twin”, perform high speed and enormous scale analytics including in near real time, and make recommendations on grid operations, plans, and designs.
Are you able to explain what a physics-informed AI digital twin is and the way it advantages grid reliability?
AI by itself can’t learn such a fancy system because the grid with measurement data only. AI digital twins of the actual world are trained by, work for, and work with engineering systems, hence “physics-informed”. Training is finished using large amounts of synthetic data generated from engineering simulation. Traditional physics-only, impedance-based digital twins are deterministic and mathematically optimized, yet challenged by data quality, high computing power needed, and slow response time. Conversely, general AI techniques promise speed, yet sparse data, hallucinations, and “black box” effects concerns mission critical grid operations. A physics-informed AI digital twin offers transparent and trustworthy analytics, resilient and robust against bad data, fast response and motion suitable for real time operations, preparedness with large pre-trained operating scenarios, and a closed-loop, continuous learning and improvement process.
How does ThinkLabs AI make sure the reliability and accuracy of its AI models in real-world scenarios?
Nature of physics-informed AI keeps AI grounded, tied to the actual world, and bounded by the actual world. We also do continuous learning and monitoring of model performance.
What makes your AI technology particularly suited to coping with the complexities of recent electrical grids?
Being trained by determine engineering models, but handling the imperfect data quality of real world operations. AI also bring a wealth of optimization and generative techniques unmatched by traditional engineering mathematics.
How does ThinkLabs AI’s technology integrate with existing grid management systems like ADMS and DERMS?
ThinkLabs integrate as a Copilot with existing ADMS, DERMS, and AEMS, which can remain as the basic communications and control platform, while ThinkLabs will layer on additional intelligence and automation as much like a vehicle’s driving assistance system.
What does the recent $5M seed investment mean for the longer term of ThinkLabs AI?
This seed investment has enabled us to spinoff and launch from GE, partner with a gaggle of world class investors, spend money on our team and product, coming to market with our first industrial Copilot, and work with quite a lot of channel partners to bring this into the hands of our customers. That is the primary foundational step to subsequent expansion and scale.
How do you envision the role of AI evolving in grid management and other critical infrastructures?
We see grid management and other critical infrastructure as being increasingly “AI first”, especially with physics-informed AI. Open up far greater understanding, situational awareness, and increasing automation decision making and orchestration of critical actions. Yet, all the time remain humble and trustworthy as AI, being true to the muse laws of physics and engineering design.
