Autonomous Flying Boats: How Technology Can Create a Step Function Change in Marine Vessel Efficiency
Bibliographic record
Abstract
In the past, our waterways were the dominant form of transit for passengers and goods. The advent of the automobile and light rail led to the downfall of our coastal transportation network as bridges and tunnels could whisk passengers across the water cheaper and more efficiently than slow, lumbering steamships. As congestion in coastal cities keeps increasing we see a variety of new mobility solutions: air taxis, scooters, and so on. Yet, the waterways are a free piece of infrastructure that is severely underutilized as the cost of operating small marine vessels is too high. The cost of operating a small vessel is driven by two cost buckets: fuel and labor. In total, small vessels are 15X more expensive than driving a car. With today’s technology, it is possible to radically transform the unit economics of running small marine vessels. Electric hydrofoiling technology reduces fuel costs by 90% whereas autonomy eliminates labor cost. This massive cost reduction opens up a whole new range of possibilities for next generation marine vessel applications. In this talk we shall discuss the evolution of marine transportation, and deep dive into the technology aspects of autonomous electric hydrofoiling boats, and the new opportunities they will create. Presenter Bio Dr. Sampriti Bhattacharyya is a roboticist, founder and CEO of Navier, a Bay Area startup working on technologies for next generation marine vessels. Dr. Bhattacharyya received her Ph.D. at MIT where she worked on design and dynamic modeling of underwater vehicles under hydrodynamic ground effect. Prior to MIT, Sampriti received her Masters in Aerospace Engineering and a bachelors in Electrical Engineering. Bhattacharyya is a Forbes 30 under 30 recipient, and her work has spanned across many fields of engineering—from working on underwater drones (Hydroswarm), marine networks, autonomous flight control at NASA to Accelerator Driven Subcritical Reactors while at Fermilab. Reo Baird is the CTO of Navier. Baird holds an M.Eng. in Electrical Engineering & Computer Science from MIT with a focus on autonomy. He has owned over 35 motor vessels in a variety of types and configurations and has logged over 10,000 ocean miles. And, through his professional experience at McKinsey & Co and Ocean Networks Canada, Reo has engaged on a variety of maritime topics in both recreational and commercial segments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".