In the last installment we discussed generative AI from a theoretical standpoint. I suggested it may have some utility in Amateur radio. So how?
Well let’s ask an AI chatbot. How about Perplexity for a start..
Question: How can AI be useful in Amateur Radio?
Response: (Copied and Pasted)
The integration of AI and Amateur Radio is still developing. While many
of these technologies are being adopted through external software and
services rather than directly built into transceivers, advances in cloud
connectivity and hardware will likely increase their direct integration
in the future.
AI can be highly useful in Amateur Radio (ham
radio) by automating tasks, enhancing signal processing, improving
prediction accuracy, and supporting both beginner and advanced
operators. Key applications include noise reduction, signal analysis,
smarter logging, propagation forecasting, troubleshooting, and
educational resources.Detailed examples of AI’s usefulness in Amateur Radio include:
Automatic Signal Processing: AI can analyze and
filter radio signals in real time, helping operators efficiently find
and tune the right frequencies, especially in crowded band conditions or
during contests.
Noise Reduction and Interference Mitigation:
Machine learning algorithms can remove unwanted band noise using neural
networks, making communications clearer and more reliable.
Propagation Prediction: AI can analyze vast amounts
of historical and real-time data (like solar activity and time of day)
to give more accurate and personalized predictions for when and where
radio signals will propagate best, helping operators plan contacts more
effectively.
Voice Recognition and Control: AI-driven voice
recognition enables hands-free equipment operation and can convert text
to speech or vice versa, improving accessibility for users with
disabilities.
Automated Logbook and QSO (contact) Management: AI
can automatically log contact details, organize QSO records, and provide
operator performance analytics.
Troubleshooting and Technical Support: AI-powered
tools can guide operators through repairs or optimizations, assist with
antenna design, explain complex concepts, and offer tailored advice—even
for newcomers studying for licensing exams.
Contest and DX Cluster Assistance: AI can analyze
real-time data to recommend rare stations to contact during contests,
and suggest optimal strategies to maximize scores.
Remote and Automated Operation: AI allows remote
station setup and can automate routine tasks like antenna adjustment,
enabling operation from anywhere with internet access.
CW (Morse Code) Decoding: AI is particularly
promising for decoding Morse code (CW), especially given its ability to
learn from diverse sending styles and tempos.The adoption of AI in Amateur Radio is poised to
enhance efficiency, accessibility, and enjoyment for users while
preserving the hobby’s innovative spirit.
Not a bad answer. But there are many more. Like antenna design, troubleshooting, including circuit analysis and schematic interpretation and the list goes on.
Remember though, this technology is in perhaps the 2nd or 3rd inning. There are some inherit limitations of transformer based generative AI. Besides being limited by the training data used, there is another issue that remains somewhat illusive; so called “hallucination”. That means AI can makes things up that are not true (with great authority)! We have all known people like that. If you can’t produce that facts, baffle them with BS..
Depending on the underlying model used, there may be limitations. Particularly when it comes to math and calculations. Some models are better than others in this regard. The bottom line is always double check. If you are making an antenna designed by AI, get a second opinion. Try another AI chatbot, try another model. etc. Like the old carpenters rule: measure twice and cut once.
K7DMK