Machine Intelligence, which tool for which usage ?
It’s starting to be complex to select a Machine Intelligence solution. Not only because of the difficulty to estimate usage fees, but mostly because of the huge number of targeted intelligence types, behind these technologies.
Machine Learning ? Data Capture ? Natural Language ? Image Recognition ?
Many different application fields, and even worst, many different providers.
Indeed, many many companies, startups and big ones, propose everyday their additional technologies. Each one uses its own marketing, and its own wording, creating constant and on-purpose marketing confusion.
it’s getting harder to dive into these technologies.
Technology accessing cost is increasing, and quick and cheap solutions are getting less and less accessible everyday. Complexity in concepts, in tooling selection, in implementation, and now Machine Intelligence technologies are transforming them-self in venture-only technologies. Strange situation where it’s going to be impossible to really drive some machine intelligence project without millions ?
This is obviously a non-sense.
Technologies are supposed to be everyday cheaper, and everyday more accessible. This solutions confusion just demonstrates the lack maturity of these market’s offers.
Some classification, tooling segmentation and shared methodology are definitively required, in order to reduce knowledge entry cost. And there were several months yet, I didn’t found some syntactical classification.
But I found one now ! Thanks to Bloomberg BETA, find here this segmentation.
The truth is I don’t know exactly the status of this document, in term of licensing and diffusion authorization, but it seems (following this article) that this classification is openly available.
I’m pretty happy to share such powerful knowledgeable table with you.
Find it here, I’m pretty sure it can make you spare some hours. Enjoy.