The machine learning frameworks flourished in the last decades, allowing
artificial intelligence to get out of academic circles to be applied to
enterprise domains. This field has significantly advanced, but there is still
some meaningful improvement to reach the subsequent expectations. The proposed
framework, named AI$^{2}$, uses a natural language interface that allows a
non-specialist to benefit from machine learning algorithms without necessarily
knowing how to program with a programming language. The primary contribution of
the AI$^{2}$ framework allows a user to call the machine learning algorithms in
English, making its interface usage easier. The second contribution is
greenhouse gas (GHG) awareness. It has some strategies to evaluate the GHG
generated by the algorithm to be called and to propose alternatives to find a
solution without executing the energy-intensive algorithm. Another contribution
is a preprocessing module that helps to describe and to load data properly.
Using an English text-based chatbot, this module guides the user to define
every dataset so that it can be described, normalized, loaded and divided
appropriately. The last contribution of this paper is about explainability. For
decades, the scientific community has known that machine learning algorithms
imply the famous black-box problem. Traditional machine learning methods
convert an input into an output without being able to justify this result. The
proposed framework explains the algorithm's process with the proper texts,
graphics and tables. The results, declined in five cases, present usage
applications from the user's English command to the explained output.
Ultimately, the AI$^{2}$ framework represents the next leap toward native
language-based, human-oriented concerns about machine learning framework.

提出了一个名为 AI$^{2}$ 的框架，该框架利用自然语言接口，使非专家可以从机器学习算法中受益，其主要贡献是用英语调用机器学习算法，使其界面更易于使用。

AI2：面向本地语言的可解释机器学习框架的下一个飞跃

AI2: The next leap toward native language based and explainable machine learning framework

The most recent concern of all people on Earth is the increase in the
concentration of greenhouse gas in the atmosphere. The concentration of these
gases has risen rapidly over the last century and if the trend continues it can
cause many adverse climatic changes. There have been ways implemented to curb
this by the government by limiting processes that emit a higher amount of CO2,
one such greenhouse gas. However, there is mounting evidence that the CO2
numbers supplied by the government do not accurately reflect the performance of
automobiles on the road. Our proposal of using artificial intelligence
techniques to improve a previously rudimentary process takes a radical tack,
but it fits the bill given the situation. To determine which algorithms and
models produce the greatest outcomes, we compared them all and explored a novel
method of ensembling them. Further, this can be used to foretell the rise in
global temperature and to ground crucial policy decisions like the adoption of
electric vehicles. To estimate emissions from vehicles, we used machine
learning, deep learning, and ensemble learning on a massive dataset.

本研究利用人工智能技术，提高先前繁琐的 CO2 排放估算方法，进行算法和模型比较，并利用机器学习、深度学习和集成学习方法预测全球温度上升，以此为基础制定政策，如采用电动汽车减少汽车排放。