Artificial intelligence (AI) is the field that has achieved immense success in recent years and accelerated our technological and digital boom. Everyone knows about the recommendations on Amazon and YouTube and how exactly the image or keyword search is carried out. We know that robots can serve as domestic help or support airport services. But do we know what is happening in the background of the search? Do we know how AI works? And do we even know its limits? This interesting topic came to us from Machine Learning Times in their article, “AI And The Limits Of Language.”
From medical imaging and language translation to facial recognition and self-driving cars, examples of AI are everywhere. And let’s face it: although not perfect, AI’s capabilities are pretty impressive.
Unfortunately, most organizations have little knowledge on how AI systems make their decisions, and as a result, how the results are applied in the field. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, changing search to found.
