As I introduced earlier, AI is moving from a research endeavor to an applied art. Enterprises are starting to deploy AI to solve real-world problems. There are gaps as theory struggles to meet the challenges of practice, and these gaps lead to failed or underperforming AI and ML projects. This situation is shown in the […]
Artificial Intelligence (AI)—including Machine Learning (ML)—stands at this same precipice today as it transitions into the mass market. Yet because we are only beginning this process, there is a missing body of knowledge—you might say an unwritten book on the shelf. This is the knowledge that’s essential to developing AI applications with low risk, high value, and high adoption, across the thousands of use cases in which the technology can provide massive value to humanity worldwide.