The field of AI development is focused on creating machines that can think and react like humans. It involves using complex algorithms to help them learn and grow on their own, enabling them to perform tasks ranging from speech recognition to autonomous driving.
The term “artificial intelligence” was coined in the 1940s, and scientists developed programs that could play games, do simple pattern recognition and machine learning, and even create their own language to communicate. In the 1950s, a computer scientist named Arthur Samuel created the first self-learning program, a checkers-playing robot that learned the game through observation and practice. By the 1960s, IBM had created its first industrial robot Unimate to operate assembly lines and weld die casings—a dangerous job for humans.
As the technology progressed, so did the number of industries and institutions adopting it to improve their operations. Today, there are thousands of successful AI applications across a broad range of industries and fields of expertise. AI can be found in energy storage, medical diagnosis, military logistics, applications that predict the outcome of judicial decisions or foreign policy, and even help farmers identify crops, classify livestock pig calls to determine emotions, operate agricultural robots, monitor soil moisture and conserve water.
However, as more and more companies adopt AI, they must also address issues of security and transparency. Threat actors can potentially access and manipulate data used to train and run AI models, affecting their accuracy, reliability and performance. They can do this by tampering with the model’s architecture, weights or parameters—the core components that influence a system’s behavior, functionality and performance.
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