How Will AI Achieve Its Transformational Potential?
The continuous modernization of industrial processes is dizzying and exciting. All Systems Go is a show that intends to demystify the varying technologies—from UAVs and robotics to supply chain software and virtual reality sims—and how they will advance everything from logistics to manufacturing across the globe. AI has transformational potential, but the technology isn’t…
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The continuous modernization of industrial processes is dizzying and exciting. All Systems Go is a show that intends to demystify the varying technologies—from UAVs and robotics to supply chain software and virtual reality sims—and how they will advance everything from logistics to manufacturing across the globe.
AI has transformational potential, but the technology isn’t where many thought it would be now, especially in the realm of mobility, including autonomous vehicles and transportation. So, where do things stand on the road to transformation? Taking on the topic, All Systems Go host Ruari McCallion spoke with Michael Majster, Technology & Innovation Management Partner, Arthur D. Little.
“We are at the foundational level of what AI can mean for the industry. The uses cases are there, and the technology in the cars is close. The infrastructure is the lag,” Majster said.
What does AI need to live up to its hype? Data is the answer, but Majster agreed that this is the “disillusionment phase of the hype curve.”
“We need data to improve AI, and AI to improve data.” – Michael Majster
Majster also noted that stakeholders have to consider benefits to the general public in looking at the future of self-driving cars. “We are looking to forecast at what point it benefits congestion. We know that we need more than 50% of the cars on the roadway to be autonomous before it matters.”
In looking beyond passenger vehicles, AI has opportunities to improve supply chains. While routes aren’t programmable, Majster discussed the possibilities with railways. “Trains are suitable for high-level automation, and organizing first, and last-mile solutions that leverage AI can bring efficiencies.”
What’s hopeful about the arc of AI is that it’s constantly improving. “AI learns along the way based on contextual data during the process. It can also optimize systems with parameters that use data. Right now, the immediate usage of it could be to maximize fuel consumption or telling you when it’s the best time to charge your electric vehicle.”
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