Is the Drone Industry Ready to Embrace Generative AI?

Artificial intelligence startups now focused on new forms of problem-solving could enter the aviation industry much sooner than anyone expected. For years, the public has explored the impact of artificial intelligence in aviation and aerospace when it comes to evaluating how aircraft fly, how they oversee their flights, and who has the greatest success. I’ve been How new tools, driven by the maturation of multiple technologies, are impacting the use, implementation, or connectivity of the aviation industry. There is a vast array of new problem-solving techniques that address data collected by autonomous vehicles in the air, on the ground, and in the water, rather than in flight. As most people in the UAS industry would say, the drone itself doesn’t matter. What matters is what the drone is doing. And as new technologies make drones more efficient, it will undoubtedly become as important as new forms of control and communication. This also applies to every technology part of the data capture and delivery chain: satellites, drones, trains, boats, cars, scooters, cameras, secret agents. Improving the value of captured data will have a direct effect on the development of the industry. more success; growth slows without that value.

Taking drone inspections as an example, for over a decade, power generation, distribution and transmission companies around the world have been experimenting with incorporating drone technology into their systems with great success. Industry leaders and early adopters such as Puget Sound Energy, San Diego Gas and Electric, and Dominion Energy are integrating drone data capture into their operations in a way that saves lives, reduces maintenance downtime, and ultimately costs. We were able to integrate. Otherwise it may be passed on to the customer. Drones, often DJI or Skydio systems, capture vast amounts of data and pass it to offices within an organization (or sometimes outsourced) for analysis, classification, retrieval and storage ( Varying periods of time, depending on the privacy needs and usefulness of the data). However, even from the beginning, the amount of data generated was enormous and often too large to be fully exploited. Some programs were even phased out or curtailed simply because the systems in place to absorb and analyze the data simply couldn’t handle what they were being offered. Ultimately, it wasn’t the security of the system, the passion of the people, or the strictness of the regulations that held back adoption, but the inability to integrate data in a useful way.

Drone data collection organizations are now adopting this technology as well, and the maturity of data collection and data hygiene has enabled significant integration between systems. The system itself has now evolved to meet the needs of organizations of all sizes, and is now capable of connecting across technologies in a collection-agnostic way. What was once a dream for a data scientist looking through terabytes of data and looking for specific pole numbers in maintenance images will soon leverage forms of artificial intelligence being explored via ChatGPT or Dall-E You will be able to One example is the application of generative AI to data mining, classification and analysis, but in fact it is just one example of the tremendous impact these “non-flying” related evolutions are having on advanced aviation. I’m sorry.

I’ve heard buzzwords, but what is AI?

First, I don’t claim to be an expert in AI in general, but let me explain the basics of this new form of problem solving. In its simplest form, Generative AI is a set of algorithms designed to “learn from training data containing examples of desired outputs.” To create the output, the algorithm receives the specified guidance parameters, searches for patterns and structures in the trained data, and produces the output. The more “good data examples” needed to train the algorithm, the better the output it will return in response to any prompt.

So what does this have to do with aerospace? With the recent emergence of companies like Danti, which has been designated as a Travel to any physical location on Earth and gain instant access to a wide range of information generated daily by satellites, drones, analytics companies, social media, and more. Simply put, the average user who needs to answer complex questions from a geospatial perspective will have questions that may take weeks to accurately solve with current data culling and analytical techniques across multiple platforms. , you can find the results you need in seconds. It’s not an aviation tool per se, but it greatly adds value to drone flights. Danti applies machine learning and natural language processing techniques to make it easy to access geospatial data from disparate sources and easily gain insights across your connected data.

By making data available to the general public and the average non-expert analyst, we can all make better decisions and access information faster, cheaper and more reliably. can be obtained. It may not be as flashy as the self-flying drones flying over the Paris Olympics or the advanced air mobility airliners, but it is often the combination of drones, aircraft and satellites that drives industry success and adoption. It’s technology like this that is guaranteed to help. .

A similar evolution happened just about a decade ago, fueling the rapid evolution and adoption of new technologies in the aviation industry. Just 10 years before him, we integrated flight controllers, GPS and cameras to develop the first consumer drones. Before there were flight controllers that allowed static positioning and self-balancing, if you’ve flown an old-school drone, it takes a huge amount of skill, experience, and a lot of technical knowledge to rebuild it after a crash. You know you needed it. With an advanced flight controller and intuitive AI implementation, anyone can pick up the quadcopter, fly it, or even start shooting professional-grade videos right away. This evolution will democratize the low-altitude skies in a number of ways, and similarly, implementations of generative AI may reduce the cost of access to meaningful outcomes.

So if we ask, “Will new forms of AI start to add value to the drone industry?” the only answer is yes, and it may be done in ways we never thought possible.

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