In this episode, I sit down with Konstantinos Kyriakopoulos, CEO of DeepSea, to discuss the transformative world of AI-powered shipping optimization. DeepSea focuses on enhancing vessel performance, fuel efficiency, and overall logistics management in the shipping and logistics industry. Konstantinos has been a key figure in advocating for digitalization in the maritime sector, pushing for technologies to streamline processes, cut costs, and reduce environmental impact.
In our conversation, Konstantinos shares the captivating journey behind DeepSea's inception, revealing how its AI-driven solutions emerged from a desire to revolutionize the shipping industry's efficiency and environmental impact. We explore the intricate use of machine learning to predict fuel consumption, optimize vessel operations, and navigate the shift toward decarbonization.
Gain insights into the intricacies of data architecture, the critical role of scalability, measuring impact, the future vision of the company, and much more. Don't miss out on discovering the cutting-edge applications of AI that are steering the shipping industry toward a more sustainable future with Konstantinos Kyriakopoulos. Tune in now!
Key Points:
- Background about Konstantinos and DeepSea's inception.
- How AI is reshaping shipping efficiency and vessel operations.
- The role of DeepSea in the shipping industry and mitigating climate change.
- Insights into the challenges and hurdles of an evolving shipping industry.
- How DeepSea leverages AI, inputs into the model, and the overall aim.
- Approaches the company implements to ensure the integrity of its products.
- Why the explainability of machine learning models is critical.
- He shares DeepSea’s approach to model validation.
- Measuring impact: CO2 reduction and cost savings for clients.
- Konstantinos offers valuable advice for leaders of AI-powered startups.
- What the company has planned for the future.
Quotes:
“If you really want to create impact, it’s not enough to just show people what’s happening and give them analytics, but you also have to, in some way, produce a tangible ROI.” — Konstantinos Kyriakopoulos
“The most important thing is to evaluate performance, so to make sure that the proof of performance is constantly being tested and you have good benchmarks and analytics.” — Konstantinos Kyriakopoulos
“It’s really important to also be able to check internally what is going on but also how the customer wants to see what’s created.” — Konstantinos Kyriakopoulos
“For us, the impact is actually very straightforward. It’s dollars and the metrics tonnes of CO2.” — Konstantinos Kyriakopoulos
“I think what I always say when people talk to me about starting an AI company is to focus on your data architecture early.” — Konstantinos Kyriakopoulos
Links:
Resources for Computer Vision Teams:
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