> For the complete documentation index, see [llms.txt](https://bee-partners-1.gitbook.io/bee-the-machine-to-machine-learning-vector/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://bee-partners-1.gitbook.io/bee-the-machine-to-machine-learning-vector/introduction/what-is-m2ml.md).
# What is M2ML?
We previously wrote about [Industry 4.0](https://www.beepartners.vc/insights/industry40) (i4.0), and focused on the classic i4.0 domains of Cyber-Physical Systems, IoT, and Cloud Computing.
*However, we now recognize more fundamental opportunity drivers.*
**Machine-to-Machine Learning** (**M2ML**) **encompasses physical, digital, and biological domains, and is driving i4.0’s acceleration.**
**Machine Learning** **(ML)** is "broadly defined as the capability of a machine to imitate intelligent human learning mechanisms." ML is one way to use Artificial Intelligence (AI), and is the field of study that gives computers the ability to learn without explicitly being programmed. ML models for inferences are trained using large, labeled data sets. The more representative data, the better the model (source: [MIT Sloan](https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained#:~:text=Machine%20learning%20is%20a%20subfield%20of%20artificial%20intelligence%2C%20which%20is,to%20how%20humans%20solve%20problems.)).
**M2ML’s primary objective is to improve upon limitations of human performance, and, both mirror human features** (increasingly taking humans out of the loop) **and expand them** in ways (speed, precision, uptime) that have not been heretofore possible. This is made possible through modular components that execute mechanical and/or inferential tasks. The inferential components learn from other machines that may be real, virtual or simulated, to improve performance of a system.
While i4.0 has traditionally been focused on manufacturing, M2ML’s value proposition is extensible to a much wider scope of new industries and achieved by 1) automating repeatable processes in data-rich domains (e.g. healthcare, media creation) and 2) “operationalizing” discovery cycles in industries where humans power most of the value.
*M2ML innovations can be studied in three distinct waves: concept, MVP building, and go-to-market.*
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