Write Once, Run Anywhere: The IoT Machine Learning Shift From Proprietary Technology To Data »

While early artificial intelligence (AI) programs were a one-trick pony, typically only able to excel at one task, today it's about becoming a jack of all trades. The goal is to write one program that can solve multi-variant problems without the need to be rewritten when conditions change--write once, run anywhere. Digital heavyweights--notably Amazon, Google, IBM, and Microsoft--are now open sourcing their machine learning (ML) libraries in pursuit of that goal as competitive pressures shift focus from proprietary technologies to proprietary data for differentiation. Machine learning is the study of algorithms that learn from examples and experience, rather than relying on hard-coded rules that do not always adapt well to real-world environments. ABI Research forecasts ML-based IoT analytics revenues will grow from 2 billion in 2016 to more than 19 billion in 2021, with more than 90% of 2021 revenue to be attributed to more advanced analytics phases.