Demanding AI Labels

August 16, 2017

Artificial intelligence has become a standard staple in technology driven societies.  It still feels like that statement should still only be in science-fiction, but artificial intelligence is a daily occurrence in developed nations.  We just do not notice it.  When something becomes standard practice, one thing we like to do is give it labels.  Guess what Francesco Corea did over at Medium in his article, “Artificial Intelligence Classification Matrix”?  He created terminology to identify companies that specialize in machine intelligence.

Before we delve into his taxonomy, he stated that if the framework for labeling machine intelligence companies is too narrow it is counterproductive to the sector’s purpose of maintaining flexibility.    Corea came up with four ways to classify machine intelligence companies :

i) Academic spin-offs: these are the more long-term research-oriented companies, which tackle problems hard to break. The teams are usually really experienced, and they are the real innovators who make breakthroughs that advance the field.


  1. ii) Data-as-a-service (DaaS): in this group are included companies which collect specific huge datasets, or create new data sources connecting unrelated silos.


iii) Model-as-a-service (MaaS): this seems to be the most widespread class of companies, and it is made of those firms that are commoditizing their models as a stream of revenues.


  1. iv) Robot-as-a-service (RaaS): this class is made by virtual and physical agents that people can interact with. Virtual agents and chatbots cover the low-cost side of the group, while physical world systems (e.g., self-driving cars, sensors, etc.), drones, and actual robots are the capital and talent-intensive side of the coin.

There is also a chart included in the article that explains the differences between high vs. low STM and high vs. low defensibility.  Machine learning companies obviously cannot be categorized into one specific niche.  Artificial intelligence can be applied to nearly any field and situation.

Whitney Grace, August 16, 2017


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