Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. Existing research on the environmental impacts of ML has been limited to analyses covering a small number of models and does not adequately represent the diversity of ML models and tasks. In the current study, we present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision. We analyze them in terms of the energy sources used, the amount of CO2 emissions produced, how these emissions evolve across time and how they relate to model performance. We conclude with a discussion regarding the carbon footprint of our field and propose the creation of a centralized repository for reporting and tracking these emissions.

When a large IT company begins looking for smaller mid-market firms to buy (under $30M in transaction value), five key factors usually influence their decision. These factors are crucial as the larger company works to maintain its market advantage and grow its business. If you are a small, emerging growth IT company and can answer "yes" to the following questions, then you may be a desirable acquisition target for a larger firm. The questions may seem obvious – perhaps easy. But effective, compelling answers are not.