Today is April Fools’ Day, but this is no laughing matter. The U.S. is dealing with one of the most expensive accidents in its history. On March 26, a container ship lost power several times and hit the Francis Scott Key Bridge with two pilots on board. Lives were lost, and it’s estimated to take $400 million and possibly a decade to rebuild.
I’ve had a lot of experience on the water. I taught myself to sail at age 12, worked on the harbor patrol as both enforcement and fire and trained to race 17-meter boats. The largest boat I’ve helmed was a 450-foot freighter to Micronesia (I wasn’t good at it).
We could have avoided last week’s disaster if the port had been properly automated and run by AI. Let’s walk through how we might use AI to prevent this kind of disaster, why AI is potentially much safer for ports of all types, and why it is critical we begin to use AI tools more aggressively to have a better chance of preventing this kind of catastrophic outcome.
We’ll close with my Product of the Week, the HP Elite mt645 G8, a new thin client laptop from HP that could dramatically reduce support aggravation and improve security in companies and schools.
My Fantome Schooner Story
The Key Bridge disaster reminds me of when I was sailing on a much smaller 679-ton Fantome schooner. The captain of that ship told the following story.
They had moored the schooner next to a larger Norwegian Cruise Lines ship. The Fantome’s captain used to be a captain for NCL, so he wanted to show off leaving port. He ordered the sails unfurled, only to see that instead of turning like it was supposed to, the Fantome was aimed right at the Norwegian ship.
The Fantome was an ex-warship, so it had not only a solid steel bowsprit but also a bow that was protected by about three inches of battleship steel since it was designed to be able to ram. The Norwegian ship, in contrast, had about .25 inches of lighter steel protecting it amidships. The problem was that the third mate, who was supposed to have raised the foresails, which would push the boat around, was instead flirting with passengers.
The captain had a choice: fire up the huge engines and order full astern, which would likely just punch a hole the size of a house in the side of the cruise ship, or order full ahead and hope there was enough steerage to bring the ship around far enough to miss entirely.
Of course, if the ship didn’t come around, he’d likely sink both ships. He ordered full ahead but still hit the cruise ship with a glancing blow, scraping down the side and snapping the square-rigged top mast (as thick as I was tall and solid oak) in half.
Sadly, he died at sea when the Fantome sank a few months later in a massive storm — but avoiding an “almost” unavoidable accident is what an experienced skipper is capable of and well within the capability of a well-trained AI.
My point is that most captains aren’t big risk-takers, rarely have to deal with catastrophes, aren’t regularly trained or certified on simulators, and are generally unprepared for problems like the bridge collision or my Fantome story.
However, exceptional captains learn from their peers, expect problems, and execute a variety of drills to help assure positive outcomes. AI can make every captain a great captain if properly trained and implemented, and it can act at machine speeds far faster than humans.
Using AI To Prevent Another Bridge Catastrophe
While we will likely spend much of the post-collision effort looking for people to blame, in my world, the work should initially be to both understand the problem and immediately move to prevent its recurrence.
The issue appears to be that the cause of the problem wasn’t identified in a timely manner, the crew wasn’t trained on what to do when there is a catastrophic power failure in close quarters (though the two pilots evidently were), and notifications for help went out far later than they should have, which prevented most mitigation efforts other than blocking the bridge and calling for tugs — which were done — from being effective.
Modern ships have extensive sensors that report to the bridge. However, this data generally isn’t conveyed in real time to anyone who isn’t on board the vessel, unlike commercial aircraft, which also report in real time to a number of remote monitoring stations. A typical port authority generally lacks the staffing level required for effective remote monitoring, even if they were to receive this data. So, simply requiring the data flow to the port authority probably would not have changed the outcome.
This is where AI, or in this case multiple AIs, would come in.
Had the bridge crew had access to a well-trained AI, that AI would have, depending on the implementation, modeled what was likely to happen, estimated the damage and liability for each of many potential outcomes, and then recommended or executed the plan with the best potential for reducing damage and saving lives — by likely immediately dropping the anchors and ordering engine restart and full astern.
Further, it probably would have begun alerting and mitigation efforts as soon as the engines started to behave badly and well before engine failure.
The Port Authority’s AI would have, through remote monitoring, identified the same problem and responded immediately by ordering the bridge closed and evacuated for safety, alerting tugs (which could be robotic) to immediately deploy and halt and hold the container ship, and spun up both fire and patrol resources to deploy and stand by as needed.
Granted, much of this would have also required integrated communications, which evidently were not in place since, as of this writing, it appears no one notified the bridge crew before the collapse.
Such measures would have added layers of protection for the bridge, helped ensure that no lives were lost, and prevented a catastrophe that will likely cost billions, making the cost of training and deploying the AI in both the ship and the Port Authority trivial by comparison.
Just as importantly, the artificial intelligence systems could share all their actions and lessons learned from the accident with other ships and ports, further reducing the risk of similar incidents. This approach starkly contrasts with the current situation, where the knowledge gained from this disaster might never reach crews that do not visit this particular port.
For perspective, a new container ship typically costs between $50 million and $200 million. The cost to create a custom generative AI is about $100 million and is justifiable if it prevented just one accident like the Key Bridge. Once trained, this same AI could be used on multiple ships with relatively little modification. This one accident will likely incur liability in the $1 billion-plus range, supporting the argument that it is worth the money to use AI to ensure this kind of thing never happens again.
Prince Parfait