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            <h1 class="mx-auto text-uppercase mb">ML CO<sub>2</sub> Impact</h1>
            <h2 class="mx-auto mt"> Machine Learning has a carbon footprint. <br><br>We've made a tool to help you
                estimate yours:
            </h2>

            <div class="row todo-row mt">
                <div class="col-1 todo-item">
                    1
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                <div class="col-9 col-sm-6">
                    <p>Compute your GPU's carbon emissions</p>
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            <br>
            <div class="row todo-row">
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                    2
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                    <p>Push for more transparency in our field by including the results in your publication (research
                        paper, blog post etc.)</p>
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                    3
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                <div class="col-9 col-sm-6">
                    <p>Install <strong><a href="https://github.com/mlco2/codecarbon" target="_blank"
                                rel="noopener noreferrer">codecarbon</a></strong> to integrate carbon estimations in
                        your Python
                        workfow.
                    </p>
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                <h2 class="text-white mb-4 section-title">Machine Learning Emissions Calculator</h2>
                <p class="text-white-80">Choose your hardware, runtime and cloud provider to estimate the carbon impact
                    of your
                    research.</p>
                <p class="text-white-80">This calculator will give you 2 numbers: the <strong> raw
                    </strong> carbon emissions produced and the approximate <strong> offset </strong>
                    carbon emissions. The latter number depends on the grid used by the cloud provider and we are open
                    to update our estimates if
                    anything looks inaccurate or outdated.</p>
                <p class="text-white-80"> Also, keep in mind that the estimate provided below <strong> does not </strong> take datacenter PUE 
                    (Power Usage Effectiveness) into account. To do so, you need to find your datacenter's PUE (by asking your computer provider or
                    consulting their documentation) and multiply the quantity of carbon emitted provided below by that number. </p>

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                        <em style="color: grey">
                            Missing a Hardware or a region? Open an issue or a PR on <a
                                href="https://github.com/mlco2/impact" target="_blank"
                                rel="noopener noreferrer">Github</a>
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                    <label for="compute-gpu">Hardware type</label>
                    <select class="form-control compute-input" id="compute-gpu">
                    </select>
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                    <label for="compute-hours">Hours Used</label>
                    <input class="form-control compute-input" id="compute-hours" type="number" step="0.5" value="100" />
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                    <label for="compute-provider">Provider</label>
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                    <label for="compute-region">Region of Compute</label>
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                        Carbon Efficiency (<u>k</u>g/<u>k</u>Wh)
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                        value="0.432">
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                        Offset bought (<u>%</u>)
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                            <p>How carbon-intensive is the grid your infrastructure is plugged to, in <strong><u>k</u>g
                                    CO<sub>2</sub>eq/kWh</strong></p>

                            <p>Default value is the OECD's 2014 yearly average</p>

                            <p>Sources we have used include:</p>
                            <ul>
                                <li><a href="https://www.electricitymap.org/" target="_blank"
                                        rel="noopener noreferrer">electricitymap.org</a></li>
                                <li><a href="https://www.carbonfootprint.com/docs/2018_8_electricity_factors_august_2018_-_online_sources.pdf"
                                        target="_blank" rel="noopener noreferrer">Carbon Footprint's country specific
                                        electricity factors</a></li>
                                <li><a href="https://www.sciencedirect.com/science/article/pii/S1361920916307933	"
                                        target="_blank" rel="noopener noreferrer">Electricity carbon intensity in
                                        European Member States</a></li>
                                <li><a href="https://www.epa.gov/energy/egrid-summary-tables" target="_blank"
                                        rel="noopener noreferrer">USA's EPA's eGRID</a></li>
                                <li>...</li>
                                <li><a href="https://github.com/mlco2/impact/tree/master/data" target="_blank"
                                        rel="noopener noreferrer">More sources in our data's "source" column</a></li>
                            </ul>


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                            <p>How much of your infrastructure's energy consumption do you offset, <strong>in %</strong>
                            </p>

                            <p>Default value is 0</p>

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                        <div id="compute-carbon-emitted-title" class="result-title small text-uppercase text-black-50">
                            Carbon Emitted </div>
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                        <div id="compute-carbon-offset-title" class="result-title small text-uppercase text-black-50">
                            Carbon Already Offset by Provider
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                        <div style="padding-top: 15px;">Power consumption x Time x Carbon Produced Based on the Local
                            Power Grid:
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                        <div id="details-counts"></div>
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                                Visit the <strong><a href="#act" class="js-scroll-trigger">Act</a></strong>
                                section to learn about what more you can do!
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                <h3 class="pad"><span id="comparison-result-co2">0</span> kg of CO<sub>2</sub>eq. is equivalent to:</h3>
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                    <div class="comparison-column col-xs-6 col-md-4">
                        <div class="row"
                            style="width: 100%; display: flex; justify-content: center; align-items: center;">
                            <div class="col-4">
                                <div class="comparison-result-item" id="comparison-result-driven">3.4</div>
                            </div>
                            <div class="col-8">
                                <h4> Km driven by an average ICE car
                                    <small>
                                        <sup>[1]</sup>
                                    </small>
                                </h4>
                            </div>
                        </div>
                    </div>
                    <!-- <span class="separator"></span> -->
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                        style="border-left: 1px solid rgb(245, 82, 73); border-right: 1px solid rgb(245, 82, 73);">
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                            style="width: 100%; display: flex; justify-content: center; align-items: center;">
                            <div class="col-4">
                                <div class="comparison-result-item" id="comparison-result-coal">3.4</div>
                            </div>
                            <div class="col-8">
                                <h4> Kgs of coal burned
                                    <small>
                                        <sup>[2]</sup>
                                    </small>
                                </h4>
                            </div>
                        </div>
                    </div>
                    <!-- <span class="separator"></span> -->
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                            style="width: 100%; display: flex; justify-content: center; align-items: center;">
                            <div class="col-4">
                                <div class="comparison-result-item" id="comparison-result-forest">3.4</div>
                            </div>
                            <div class="col-8">
                                <h4> Tree seedlings sequesting carbon for 10 years
                                    <small>
                                        <sup>[3]</sup>
                                    </small>
                                </h4>
                            </div>
                        </div>
                    </div>
                </div>
                <div>
                    <a href="https://www.epa.gov/energy/greenhouse-gases-equivalencies-calculator-calculations-and-references"
                        target="_blank" rel="noopener noreferrer">Source: Greenhouse Gases Equivalencies Calculator -
                        Calculations and References</a> :
                    <a href="https://www.epa.gov/energy/greenhouse-gases-equivalencies-calculator-calculations-and-references#miles"
                        target="_blank" rel="noreferrer noopener"> [1] </a>
                    <a href="https://www.epa.gov/energy/greenhouse-gases-equivalencies-calculator-calculations-and-references#lbscoal"
                        target="_blank" rel="noreferrer noopener"> [2] </a>
                    <a href="https://www.epa.gov/energy/greenhouse-gases-equivalencies-calculator-calculations-and-references#seedlings"
                        target="_blank" rel="noreferrer noopener"> [3] </a>
                </div>
            </div>

        </div>

    </div>
</section>

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                <h2 class="text-white mb-4 section-title">Publish</h2>
                <p class="text-white-80">We believe that an important step towards reducing carbon emissions is the
                    generalization of emissions reporting in papers, blog posts and publications in general.
                </p>
                <p class="text-white-80">To that end, here is a LateX template you can use in your publication to report
                    the emissions you have calculated with the Calculator:
                </p>


            </div>
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                <h3 class="pad">Generated Latex Template</h3>
                <div class="pad card-body">

                    <pre>
                        <code class="language-latex" id="template-code">
\usepackage{hyperref}

\subsection{CO2 Emission Related to Experiments}

Experiments were conducted using <span id="template-provider">{cloud provider}</span><span id="template-region"> in region {region of compute}</span>, which has a carbon efficiency of <span id="template-region-efficiency"></span> kgCO$_2$eq/kWh. A cumulative of <span id="template-hours">{hours used}</span> hours of computation was performed on hardware of type <span id="template-gpu">{hardware type}</span> (TDP of <span id="template-gpu-power"></span>W).

Total emissions are estimated to be <span id="template-emissions">{emission}</span> kgCO$_2$eq of which <span id="template-percentage-offset">{percentage offset}</span> percents were directly offset<span id="template-text-offset"> by the cloud provider</span>.
    <!-- , corresponding to driving a regular car for {km of driving} km. -->
%Uncomment if you bought additional offsets:
%XX kg CO2eq were manually offset through \href{link}{Offset Provider}.

Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}.

@article{lacoste2019quantifying,
&nbsp;&nbsp;title={Quantifying the Carbon Emissions of Machine Learning},
&nbsp;&nbsp;author={Lacoste, Alexandre and Luccioni, Alexandra and Schmidt, Victor and Dandres, Thomas},
&nbsp;&nbsp;journal={arXiv preprint arXiv:1910.09700},
&nbsp;&nbsp;year={2019}
}
                        </code>
                    </pre>
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                <h2 class="text-white mb-4 section-title">Learn</h2>
                <p class="text-white-80">This section will present some of the important concepts to better understand
                    the carbon footprint of your ML research, including what providers do to help you offset it
                </p>
            </div>
        </div>

        <div class="row justify-content-center no-gutters mb-lg-0">
            <div class="col-lg-12">
                <div class="text-center h-100 project">
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                        <div class="learn-text w-100 my-auto text-center text-lg-left">
                            <div class="details-summary">
                                <h3 class="text-white" class="summary-header"> CO<sub>2</sub></h3>
                            </div>
                            <div class="summary-content">
                                <p class="mb-0 text-white-80">At the center of the climate crisis is a commonplace
                                    but
                                    very important concept: that of carbon dioxide (CO<sub>2</sub>), low amounts of
                                    which occur naturally in the Earth's atmosphere, but its concentration has been
                                    rapidly increasing due to human activity. This increase is dangerous because of
                                    CO<sub>2</sub>'effect as a greenhouse gas, meaning that it absorbs and emits
                                    infrared
                                    radiation in the wavelength range emitted by the Earth, which contributes to the
                                    global warming of the planet ( <a
                                        href="https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-AnnexII_FINAL.pdf"
                                        target="_blank" rel="noopener noreferrer"> IPCC Glossary </a>).
                                </p>
                            </div>
                            <hr class="d-none d-lg-block mb-0 ml-0">
                        </div>
                    </div>
                </div>
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                            <div class="details-summary">
                                <h3 class="text-white" class="summary-header"> CO<sub>2</sub> eq.</h3>
                            </div>
                            <div class="summary-content">
                                <p class="mb-0 text-white-80">Since other gases such as methane, nitrous oxide or
                                    even
                                    water vapor also have this warming effect, a standardized measure for describing
                                    how
                                    much warming a given amount of gas will have is often provided in
                                    CO<sub>2</sub>-equivalents (CO<sub>2</sub>eq), for simplification purposes. For
                                    instance, the carbon intensity of transportation is measured in grams of
                                    CO<sub>2</sub>-equivalent per person-km, whereas the carbon intensity of energy is
                                    measured
                                    in
                                    grams of CO<sub>2</sub>-equivalent per kilowatt hour.
                                </p>
                            </div>
                            <hr class="d-none d-lg-block mb-0 mr-0">
                        </div>
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                </div>
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                            <div class="details-summary">
                                <h3 class="text-white" class="summary-header">Renewable vs. Nonrenewable Energy
                                </h3>
                            </div>
                            <div class="summary-content">
                                <p class="mb-0 text-white-80"> A key distinction to be made is that between
                                    renewable
                                    and nonrenewable energy sources. Renewable energy is collected from sources
                                    which
                                    are naturally replenished on a human timescale: this includes wind, sunlight,
                                    tides,
                                    geothermal heat, etc. In contrast, nonrenewable energy sources such as coal and
                                    petrol are not naturally replenished after their usage, and can take millions of
                                    years to be formed again. Finally, not all renewable energy sources are
                                    carbon-free,
                                    since the burning of biomass (such as plants or algae) still produces
                                    CO<sub>2</sub>-equivalents.
                                </p>
                            </div>
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                        </div>
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                            <div class="details-summary">
                                <h3 class="text-white" class="summary-header">Carbon Offsetting</h3>
                            </div>
                            <div class="summary-content">
                                <p class="mb-0 text-white-80"> Carbon offsetting is a reduction in emissions of
                                    CO<sub>2</sub>-equivalents that is made in order to compensate for the emissions
                                    made by another actor. The money paid via carbon offsetting is invested towards
                                    different types of projects, including renewable energy or energy efficiency,
                                    carbon
                                    sequestration, methane abatement, among others. These projects are run by
                                    private
                                    companies, more often than not in a different place than where the offsetting is
                                    carried out. A large part of carbon offsetting goes towards funding renewable
                                    energy
                                    projects such as building wind or solar farms, hydroelectric dams, and
                                    extracting
                                    biofuel, in the hopes of reducing the cost of renewable production.
                                </p>
                            </div>
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                        </div>
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                </div>
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                <div class="text-center h-100 project">
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                        <div class="learn-text w-100 my-auto text-center text-lg-left">
                            <div class="details-summary">
                                <h3 class="text-white" class="summary-header">Renewable Energy Credits</h3>
                            </div>
                            <div class="summary-content">
                                <p class="mb-0 text-white-80"> An indirect form of offsetting that is often used by
                                    large companies is the purchasing of Renewable Energy Credits (RECs), which
                                    involves
                                    directly purchasing quantities of energy produced by renewable sources. This is
                                    considered to be an indirect of offsetting because in order to convert RECs into
                                    offsets, the energy purchased is translated into carbon reductions under the
                                    assumption that the clean energy is displacing an equivalent amount of
                                    electricity
                                    produced by non-renewable methods. Other commonly used forms of carbon
                                    offsetting
                                    include reforestation, sequestration, replacement of legacy equipment such as
                                    coal-burning stoves by more modern ones, etc.
                                </p>
                            </div>
                            <hr class="d-none d-lg-block mb-0 ml-0">
                        </div>
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                            <div class="details-summary">
                                <h3 class="text-white" class="summary-header">Carbon Neutral</h3>
                            </div>
                            <div class="summary-content">
                                <p class="mb-0 text-white-80"> Carbon neutral is a term used to indicate a net
                                    zero
                                    carbon footprint of an individual or organization. While a small part of
                                    carbon-neutral entities rely entirely on renewable energy that is zero-carbon,
                                    the
                                    large majority of entities balance their emissions of CO<sub>2</sub>-equivalents,
                                    using
                                    approaches such as carbon offsetting or the purchase of RECs. Many major
                                    technology
                                    companies such as Google and Microsoft are carbon-neutral, matching 100% of
                                    their
                                    electricity use with renewable energy purposes (Google, 2018; Microsoft, 2018).
                                    This
                                    does not mean, however, that their operations do not produce
                                    CO<sub>2</sub>-equivalents,
                                    since
                                    this is dependent on the energy sources of the locations in which their
                                    infrastructure is located.
                                </p>
                            </div>
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                        </div>
                    </div>
                </div>
            </div>
        </div>

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            <div class="col-lg-12">
                <div class="text-center h-100 project">
                    <div class="d-flex h-100">
                        <div class="learn-text w-100 my-auto text-center text-lg-left">
                            <div class="details-summary" id="details-featured-maps">
                                <h3 class="text-white" class="summary-header">Featured Maps</h3>
                            </div>
                            <div class="summary-content">

                                <p class="mb-0 text-white-80">
                                    Here are a few maps from Our World In Data which illustrate the complexity of carbon
                                    emissions mitigations. For a complete analysis, visit <a
                                        href="https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions"
                                        target="_blank" rel="noopener noreferrer">their webstie</a>
                                </p>

                                <br>

                                <iframe class="lozad"
                                    data-src="https://ourworldindata.org/grapher/co-emissions-per-capita"
                                    title="ourworldindata: co2 emissions per capita"
                                    style="width: 100%; height: 650px; border: 0px none;"></iframe>
                                <br>
                                <br>

                                <iframe class="lozad"
                                    data-src="https://ourworldindata.org/grapher/carbon-dioxide-co2-emissions-by-sector-or-source"
                                    title="ourworldindata: co2 emissions by sector or source"
                                    style="width: 100%; height: 650px; border: 0px none;"></iframe>

                                <br>
                                <br>
                                <img class="lozad" style="width: 100%;"
                                    data-src="https://ourworldindata.org/uploads/2018/04/Greenhouse-gas-emission-scenarios-01.png"
                                    alt="greenhouse gas scenarios">
                            </div>
                            <hr class="d-none d-lg-block mb-0 ml-0">
                        </div>
                    </div>
                </div>
            </div>
        </div>

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                    <div class="d-flex h-100">
                        <div class="learn-text w-100 my-auto text-center text-lg-right">
                            <div class="details-summary">
                                <h3 class="text-white" class="summary-header">Carbon Efficiency</h3>
                            </div>
                            <div class="summary-content">
                                <p class="mb-0 text-white-80">
                                    Carbon efficiency: the amount of CO2 emitted per unit energy (grams of CO2 emitted
                                    per kilowatt-hour). This is largely related to a countrys energy mix. An economy
                                    powered by coal-fired energy will produce higher CO2 emissions per unit of energy
                                    versus an energy system with a high percentage of renewable energy. As economies
                                    increase their share of renewable capacity, efficiency improves and the amount of
                                    CO2 emitted per unit energy falls.
                                </p>
                            </div>
                            <hr class="d-none d-lg-block mb-0 mr-0">
                        </div>
                    </div>
                </div>
            </div>
        </div>


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                <h2 class="text-white mb-4 section-title">There are things you can do</h2>
                <p class="text-white-80">With great computing power comes great responsibility</p>

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                        <h6 class="text-uppercase m-0">Choose your cloud provider wisely</h6>
                        <hr class="my-4">
                        <div class="small text-black-50">They don't all buy offsets, RECs or invest equally in clean
                            energy sources. Read their sustainability commitments and make an informed choice.</div>
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                        <h6 class="text-uppercase m-0">Choose your region</h6>
                        <hr class="my-4">
                        <div class="small text-black-50">
                            Different regions are powered by different combinations of renewable and non-renewable
                            energy. If regulations and
                            legal aspects allow, select a more sustainable region.
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                        <h6 class="text-uppercase m-0">Buy Carbon Offsets</h6>
                        <hr class="my-4">
                        <div class="small text-black-50">Many platforms provide easy ways to offset
                            your emissions. You can have a big impact by proposing offsetting within your organization.
                            It is
                            also worth doing in your personal life too, for instance by offsetting flights. </div>
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                        <h6 class="text-uppercase m-0">Don't Do Grid Search</h6>
                        <hr class="my-4">
                        <div class="small text-black-50">Hyperparameter search is a great source of ML-related carbon
                            emissions. If there is no literature to help you establish good values and you need to look
                            for
                            relevant ones, at least do it randomly, not via grid search.</div>
                    </div>
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                        <h6 class="text-uppercase m-0">Choose Clean Energy</h6>
                        <hr class="my-4">
                        <div class="small text-black-50">Your organization may have a choice in their power supply. You
                            may too. Choosing a cleaner and more sustainable source of energy will go far in decreasing
                            your
                            carbon impact</div>
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                        <h6 class="text-uppercase m-0">Push for more transparency</h6>
                        <hr class="my-4">
                        <div class="small text-black-50">Include a dedicated section about the carbon emissions of your
                            procedure whenever publishing work (be it a peer-reviewed paper or a blog post)
                            <br><br>
                            <a class="btn btn-primary js-scroll-trigger" href="#publish">Publish</a>
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                        <h6 class="text-uppercase m-0">Fill Out Our Survey</h6>
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                        <div class="small text-black-50">We'd like to understand your general AI usage and your position
                            on ML's emissions. Guaranteed &lt; 1min :)
                            <br><br>
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            <div class="col-lg-8 mx-auto">
                <h2 class="text-white mb-4 text-center section-title">About ML CO<sub>2</sub> Impact</h2>
                <p class="text-white-80">Progress in Machine Learning (ML) in recent years has been meteorical, with
                    major breakthroughs happening in domains such as machine translation, image recognition and
                    generation. While these advances are having widespread applications in many domains, detecting
                    cancers on X-rays
                    and improving the prediction of supply and demand,the carbon impact of ML training has not been a
                    central part of
                    the conversation until recently.</p>
                <p class="text-white-80">
                    Therefore, as ML practitioners who are also aware of the overall state of the environment, we find
                    that it is important to develop this conversion further and work towards building the tools we need
                    to assess the carbon emissions generated by the models we train, as well as ways to reduce those
                    emissions.</p>
                <p></p>

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                    <div class="row about-content-row">
                        <div class="col-md-6 col-sm-12 about-content-text">
                            <h3 class="text-white mb-4" style="width:100%">Read our paper</h3>
                            <p class="text-white mb-4">We have published this short paper to the
                                <a href="https://www.climatechange.ai/NeurIPS2019_workshop.html" target="_blank"
                                    rel="noopener noreferrer">Climate Change AI
                                    workshop</a> at NeurIPS 2019.</p>
                        </div>
                        <div class="col-md-6 col-sm-12 about-content-img">
                            <a href="https://arxiv.org/abs/1910.09700" target="_blank" rel="noopener noreferrer">
                                <img class="lozad"
                                    data-src="https://raw.githubusercontent.com/mlco2/impact/master/img/measuring-im.png"
                                    alt="paper outline">
                            </a>
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                            <h3 class="text-white mb-4" style="width:100%">Look at the Data</h3>
                            <p class="text-white mb-4">The data is hosted on Github to allow for external contributions
                            </p>
                        </div>
                        <div class="col-md-6 col-sm-12 about-content-img">
                            <a href="https://github.com/mlco2/impact/tree/master/data" title="Go see the data on Github"
                                target="_blank" rel="noopener noreferrer">
                                <img class="lozad"
                                    data-src="https://raw.githubusercontent.com/mlco2/impact/master/img/data.png"
                                    alt="data presentation">
                            </a>
                        </div>
                        <div class="col-12" style="margin-top: 3rem">
                            <p class="text-white-80">
                                The sources are available in the csv file above. Use issues and pull-requests to suggest
                                modifications/additions. The data used for the providers' offset ratio (how much of
                                their
                                emissions they offset) comes from these sources:
                                <a href="https://storage.googleapis.com/gweb-sustainability.appspot.com/pdf/24x7-carbon-free-energy-data-centers.pdf"
                                    target="_blank" rel="noopener noreferrer">Google</a>,
                                <a href="https://aka.ms/beyond" target="_blank" rel="noopener noreferrer">Microsoft</a>,
                                <a href="https://aws.amazon.com/about-aws/sustainability/#progress" target="_blank"
                                    rel="noopener noreferrer">Amazon</a>. For the latter we assumed non-carbon neutral
                                regions were not offset at all.
                            </p>
                            <p class="text-white-80">
                                We have looked for data about the main cloud providers. If you'd like to contribute and
                                add more providers, please follow <a
                                    href="https://github.com/mlco2/impact/blob/master/data/Readme.md" target="_blank"
                                    rel="noopener noreferrer">these guidelines</a>.
                            </p>
                        </div>
                    </div>
                    <br>
                    <br>
                    <h3 class="text-white mb-4" style="width:100%">Ongoing work: how to help</h3>
                    <p class="text-white-80">
                        We're currently working towards enabling you to compute your emissions when using a <strong>
                            private infrastructure</strong>. This would require either mapping cities to carbon emissions of
                        local grids or expecting our users (you) to input such a value. The mapping does not (?) exist for now (and for free), and we feel like
                        most users would not know the carbon efficiency of their electricity grid. This is why this is not yet available.
                    </p>
                    <p class="text-white-80">
                        <strong >Any help with this issue is welcome, please get in touch.</strong>
                    </p>
                    <p class="text-white-80">
                        <strong >If you wish to add a cloud provider to our list, <a href="https://github.com/mlco2/impact/tree/master/data#adding-a-provider" target="_blank" rel="noopener noreferrer">here's how</a>.</strong>
                    </p>
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                            <h3 class="text-white mb-4" style="width:100%">Victor Schmidt</h3>
                            <p class="text-white mb-4"> Victor is a PhD candidate at Mila with Prof. Yoshua
                                Bengio. He focuses on different areas where AI can help mitigate Climate Change. Amongst
                                others, his main focus is on creating visualizations of Climate Change's impact to
                                help the public better understand it. Prior to his PhD, Victor worked as a <a
                                    href="https://entrepreneur-interet-general.etalab.gouv.fr/defis/2018/hopkins.html"
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                            <p class="text-white mb-4">Sasha is a Postdoctoral Researcher working
                                on AI for Humanity initiatives at Mila - Quebec AI Institute, under the supervision of
                                Yoshua Bengio. She obtained her PhD in Cognitive Computing from UQAM in 2018 and spent
                                two years working in applied ML, specifically in applying deep learning and NLP to
                                different industrial applications. She is highly involved in community initiatives,
                                serving on the Research and Policy Committee of Women in Machine Learning (WiML) and on
                                the Advisory board of Kids Code Jeunesse.</p>
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                            <h3 class="text-white mb-4" style="width:100%">Alexandre Lacoste</h3>
                            <p class="text-white mb-4">
                                Alexandre is a research scientist at <a href="https://www.elementai.com/"
                                    target="_blank" rel="noopener noreferrer">Element AI</a>. His research interests
                                revolve around multi-task transfer learning, probabilistic machine learning and causal
                                inference. Prior to Element AI, he worked at Google for 3 years on large scale question
                                answering systems using machine learning. He obtained his PhD in theoretical machine
                                learning where he developed bridges between PAC-Bayes and Bayes theories.</p>
                        </div>
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                            <img class="lozad"
                                data-src="https://raw.githubusercontent.com/mlco2/impact/master/img/alex.png"
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                                After working a few years in the field of energy and climate change (GRAME), Thomas
                                Dandres completed a Ph.D. in CIRAIG (with Prof. Rejean Samson, Polytechnique Montreal)
                                in life cycle assessment (specialization: energy policy analysis) and conducted
                                postdoctoral researches in Synchromedia (with Prof. Mohamed Cheriet, Ecole de
                                technologie superieure) on sustainable ICT (green cloud computing and smart buildings).
                                From 2015 to 2019, he has been a research officer with CIRAIG/Synchromedia and worked on
                                projects related to energy, industry 4.0, ICT and machine learning (energy predictive
                                models). Since 2019, he has started working for Hydro-Quebec (Direction environnement)
                                as an external environmental specialist and CIRRELT as the scientific coordinator of the
                                Chair of transport transformation (Prof. Bernard Gendron, University of Montreal).
                            </p>
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                            <img class="lozad" data-src="https://ivado.ca/wp-content/uploads/2019/02/Thomas.jpg"
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