Artificial intelligence (AI), with all its advantages and perks, has limitations. For example, a lot of people would be surprised at the extent to which AI is inherently domain-specific. This interesting topic came to us from Protocol in their article, “What can make even the best AI strategy fail?”
Many organizations may try out the latest and greatest general-purpose algorithms available, but even the best AI will fail without the proper context and data tuning for your specific scenarios.
There is a surprisingly straightforward reason so many companies struggle with AI-driven transformation. Most have little visibility and knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields.
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 potential biases. We are the intelligence and the technology behind world-class explainable AI solutions.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.