Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learning by example. Venture Beat brought this information to us in their article, “This is what makes deep learning so powerful.”
Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, televisions, and hands-free speakers.
The success of deep learning is fueled primarily by the availability of large data sets and computing power. It is more than the sum of its inputs, however, and deep learning is far better than any of the classical machine learning algorithms.
Deep learning is a complex composition of functions transcending layers and finding the correct mapping function from input to output. Deep learning uses this map to go from input text to output classes to neutral, positive, or negative biases.
It is unfortunate that most organizations have little knowledge regarding how artificial intelligence (AI) systems make decisions and how the results are applied. 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.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
|
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.
