DEC - FEBCIOAPPLICATIONS.COM8CXO InsightsIN MY VIEWJOE ZIRILLI, VICE PRESIDENT, ARTIFICIAL INTELLIGENCE, PARSONSam here to tell you that you are not alone in trying to understand what artificial intelligence (AI) is and what it means to you in your daily life. I have developed countless AI applications and I still cannot produce a comprehensive definition. I have tried to define it so many times I have just given up! I have asked many software development professionals to tell me their definition of AI and what I have learned is their answers usually depend on when they were first introduced to it. The first time I became interested in AI was in college in the 80s. As a software engineer, I immediately thought AI was writing a computer program that could think and act like us. So, I started researching what was out there and soon realized that technology was nowhere near what the words imply. I became extremely interested after learning about artificial neural networks while getting my master's degree I got excited again! Wow, artificial neural networks are like building an artificial brain. I soon realized that is not the case. The artificial neuron is a very crude representation of the neurons in our brain, and the learning mechanisms are slow, but that does not mean they are not useful. We have recently seen the explosion in deep learning and large language model (LLMs) (e.g., Generative pre-trained transformer, GPT) and have seen that these implementations are exceptionally good and in many cases are better than a human at some tasks. My point is what we call AI changes as the technology morphs. I could make the case that Charles Babages's Analytical Engine designed in the 1800s is AI.Now that we have that behind us, let us try to understand how AI can be applied. AI is algorithm, and as such, they are implemented in software and hardware. AI algorithms are fundamentally different than traditional algorithms because traditional algorithms transform data into desired goals, while AI algorithms transform data and desired goals into rules. The development process is quite different but the implementation in production can be similar, although there is a host of new issues surrounding the maintenance of the models (rules) developed by AI algorithms. Development of traditional software is the manual creation of algorithms that create the desired outputs (goals). Development of AI software requires the development of algorithms that learn to produce rules based on the given data and desired goals. This gives the old adage "garbage in garbage out (GIGO)" a new twist. With AI it is not just GIGO but its "garbage in garbage learned (GIGL)" which is much harder to diagnose when something goes wrong. With traditional software you can perform comprehensive testing and ensure that your software performs good enough for its intended use. With AI software it learns to generalize, and that generalization can lead to issues in production. When given data similar to what is was trained on, it should perform quote well, but when unexpected data is introduced, the results can appear normal but be far outside what is intended. Traditional software will produce errors or warnings and it will be obvious the data is outside intended ranges, but for AI that is not the case. When errors are introduced in the data the outputs can be catastrophic.Any sufficiently advanced technology is indistinguishable from magic." - Arthur C. ClarkeMAKING SENSE OF ARTIFICIALINTELLIGENCEJoe ZirilliI
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