Artificial Intelligence and Drones

Right now, many questions are being asked about artificial intelligence (AI). The topic is hyped and makes headlines in the media every day. I have been working with AI since the late 1980s.

At first, we asked fundamental questions about semantics and possible architecture. In the early 1990s, we then considered possible applications on the LISP machines designed for AI development at the time. Even in the early stages, there were different definitions of AI and, consequently, different approaches. On the one hand, attempts were made to describe the basic principles of AI using dialectical laws (Georg Wilhelm Friedrich Hegel and Friedrich Engels), while other approaches were based on mathematical and cybernetic models. The crucial question, however, was whether a machine-based AI system could achieve self-awareness. Put simply, we asked: Can a machine learn?

This question is still hotly debated today. It starts with the question: What does learning mean? Currently, AI systems are based on data collections. These can be texts and/or links between mathematical and textual contexts. Image information can also represent a data collection.

Today's AI systems are rule-based. Put simply, these systems are often based on “if-then relationships” or pattern comparisons. An AI system can perform tasks and classify the result as successful or unsuccessful, again according to corresponding rules. Good AI systems can refine and adapt their own rules according to the results. However, this is not yet learning, as the component “realisation” is missing.  According to Duden, insight is defined as follows: “Insight is knowledge gained through the mental processing of impressions and experiences.” However, AI systems are not yet able to generate “insight.” Because this component is (still) missing, we refer to it as “simple AI.” Cross-references to simulations based on mathematical models are not entirely wrong.

But what is the biggest problem with the simple AI currently available? This simple AI makes mistakes, crucial mistakes.

A self-experiment

I then conducted a self-experiment and asked various AI systems about the future of medical logistics with drones. This is a subject area I am familiar with, so I can evaluate the results professionally. First, I asked AI image generators (Midjourney, Dall-e 2) to develop a drone for medical logistics of the future. As an engineer, I have some reservations about the proposed results in terms of flight characteristics, to put it mildly. Nevertheless, it is interesting and, in some cases, funny.

When we feed an AI-driven text generator (ChatGPT) with the same question, the result is a fairly general, but not fundamentally incorrect text:

Medical logistics using drones has made significant progress in recent years and will continue to evolve in the future. Here are some possible developments:

• Improved technologies: Drone technologies are becoming increasingly advanced and reliable, which will lead to faster and safer deliveries.
• Broader applications: Drones are increasingly being used in new areas of application, such as disaster relief, disease surveillance, and the provision of medical care in remote areas.
• Regulation: Regulatory authorities are expected to adapt their regulations for the use of drones in medical logistics to ensure that deliveries can be carried out safely and efficiently.
• Data analysis: The use of data analysis will increase to improve the efficiency of medical logistics and to understand how it can be better tailored to the needs of patients.
• Integration with other technologies: In the future, drones may be integrated with other technologies such as artificial intelligence and the Internet of Things (IoT) to enable even more efficient medical logistics.

Overall, medical logistics using drones will continue to grow in importance and play a vital role in providing fast and reliable medical care.

(ChatGTP)

A brief assessment

From a technological point of view, the AI text generator's answer is not incorrect, but crucial elements in assessing the future viability of the application are not mentioned or are formulated very imprecisely.

• Regulations are an important issue. These primarily concern licensing requirements and the regulation of airspace. This topic could be covered by the third point of the AI text generator's answer. This statement would be acceptable for a newspaper article, but it is definitely not sufficient as a basis for a strategic decision in a company.
• Data analysis is not a new element associated with drones. We do this every day with our software for our ground transportation. The topic will not be treated differently in terms of quality compared to ground transportation.
• The question of economic efficiency is not addressed. However, this is the crucial question. Can drone-based logistics keep up with its competitor, the "car," or even undercut it in terms of costs? So far, drone projects – insofar as figures on economic efficiency have been published at all – have been subsidized or are still operating at a high loss.

Conclusion

AI is already exciting. For example, AI can be used to create funny image conversions and text generators can generate interesting phrases. But I doubt that (real) AI is already sufficient for use in everyday practice, and not just because of the examples shown here. AI developments have potential, even if it will certainly take several years before these (real) AI systems achieve acceptable accuracy and relevance. Simple AI can already help in the preparation of decisions, but AI-generated information should not be relied upon under any circumstances. In some cases, such as agriculture, positive effects can already be achieved with the simple AI available today. In other areas, such as military applications or self-driving cars, there is (still) a need for an expert who can classify and evaluate the information generated.