Executive Summary
The panel discussion centered on the limitations of relying solely on Large Language Models (LLMs) for industrial AI applications, particularly in manufacturing, engineering, and design. Panelists emphasized the need for specialized AI systems that incorporate domain-specific knowledge, causal reasoning, and multiple AI modalities to address the complexities and safety requirements of these fields. They highlighted the importance of moving beyond the hype surrounding LLMs and focusing on delivering tangible results and demonstrable Return on Investment (ROI) for clients. The discussion underscored the significance of capturing and scaling implicit knowledge within organizations and the challenges of data scarcity in certain domains like CAD. Panelists presented their companies' approaches to building AI solutions that go beyond LLMs, including the use of knowledge graphs, causal AI, symbolic engines, and computer vision. They discussed the challenges of integrating AI into legacy systems and the need for industry-tailored approaches. The panelists agreed that while LLMs have a role to play, they are not a silver bullet and that specialized AI systems are essential for achieving real-world impact in industrial settings. The future outlook included the potential for AI to improve CAD modeling, optimize manufacturing processes, and drive innovation across various industries, with Switzerland playing a key role in leading this development.
Panelists
- EthonAI is an industrial AI platform that helps manufacturers produce more with less by optimizing for quality and throughput.
- They tap into various data sources, contextualize them with a knowledge graph and ontology, and use causal reasoning models to derive insights.
- Causal AI is crucial for detecting real cause-effect relationships in manufacturing processes, enabling the automation of interventions and distillation of operator knowledge.
- LLMs can be used to orchestrate industry-safe models and enrich analysis results with process descriptions and SOPs.
- Chipmind accelerates the development of microchips by providing AI solutions for top-tier European semiconductor companies.
- They address the gap between LLMs at the top and complex, legacy chip design environments at the bottom.
- Their agents leverage symbolic engines, graph data organization, and databases of repetitive tasks on top of LLMs.
- Generic AI solutions are not well-suited for chip design due to the physical, geometric nature of chip design and the lack of context in development flows.
- Raven is an AI co-pilot for CAD, accelerating model timing and making design more intuitive.
- They use symbolic engines, graph structures, and computation engines on top of LLMs to validate designs and ensure they work in reality.
- They build a database of generic, parametric models to accelerate repetitive tasks in CAD.
- They integrate vision models to understand sketches and check the visual appearance of designs.
Main Discussion Points
- The limitations of LLMs in industrial applications and the need for specialized AI systems.
- The importance of causal AI in safety-critical processes like manufacturing.
- The role of domain-specific knowledge and ontologies in building effective AI solutions.
- The integration of multiple AI modalities, including vision, reasoning, and agents, for coherent systems.
- Measuring and demonstrating the ROI of AI in specific customer fields.
- The data bottleneck in CAD and the challenges of creating generic models.
- The role of implicit knowledge in manufacturing and design and how to capture and scale it.
Key Insights
✓ Consensus Points
- LLMs alone are insufficient for solving complex problems in manufacturing, engineering, and design.
- Specialized AI systems outperform general-purpose models in specific verticals due to the need for domain-specific knowledge and safety constraints.
- There is a need to move beyond the hype and focus on tangible results and ROI in AI applications.
⚡ Controversial Points
- No points recorded
🔮 Future Outlook
- AI technology will be adopted horizontally through enterprises, not just being stuck in vertical pilots.
- Technical drawers may be disrupted within the next three years.
- The development of physical models will be a big thing.
- The rise of AI agents that can operate any tool, but this is not yet fully realized.
- The potential for AI to make CAD modeling better by incorporating optimization algorithms for energy impact and CO2 emissions.
💡 Novel Insights
- Using causal AI to distill knowledge from shop floor operators and automate interventions.
- Treating shop floor processes as a 'patient' whose health needs to be monitored and improved.
- The idea of offering AI like electricity, with endpoints to intelligence.
- The concept of Raven as an intelligence layer of CAD.
- The importance of trajectories for representing tasks in agency for chip design.