diff --git a/contents/sustainable_ai/sustainable_ai.qmd b/contents/sustainable_ai/sustainable_ai.qmd
index 4fc314ba5..8904d8a0b 100644
--- a/contents/sustainable_ai/sustainable_ai.qmd
+++ b/contents/sustainable_ai/sustainable_ai.qmd
@@ -167,7 +167,7 @@ The concept of a 'carbon footprint' has emerged as a key metric. This term refer
 
 ![Carbon footprint of large-scale ML tasks. Source: @wu2022sustainable.](images/png/model_carbonfootprint.png){#fig-carbonfootprint}
 
-Considering the carbon footprint is especially important in AI AI's rapid advancement and integration into various sectors, bringing its environmental impact into sharp focus. AI systems, particularly those involving intensive computations like deep learning and large-scale data processing, are known for their substantial energy demands. This energy, often drawn from power grids, may still predominantly rely on fossil fuels, leading to significant greenhouse gas emissions.
+Considering the carbon footprint is especially important in AI's rapid advancement and integration into various sectors, bringing its environmental impact into sharp focus. AI systems, particularly those involving intensive computations like deep learning and large-scale data processing, are known for their substantial energy demands. This energy, often drawn from power grids, may still predominantly rely on fossil fuels, leading to significant greenhouse gas emissions.
 
 Take, for example, training large AI models such as GPT-3 or complex neural networks. These processes require immense computational power, typically provided by data centers. The energy consumption associated with operating these centers, particularly for high-intensity tasks, results in notable greenhouse gas emissions. Studies have highlighted that training a single AI model can generate carbon emissions comparable to that of the lifetime emissions of multiple cars, shedding light on the environmental cost of developing advanced AI technologies [@dayarathna2015data]. @fig-carboncars shows a comparison from lowest to highest carbon footprints, starting with a roundtrip flight between NY and SF, human life average per year, American life average per year, US car including fuel over a lifetime, and a Transformer model with neural architecture search, which has the highest footprint.
 
@@ -179,7 +179,7 @@ Moreover, AI's carbon footprint extends beyond the operational phase. The entire
 
 Understanding the carbon footprint of AI systems is crucial for several reasons. Primarily, it is a step towards mitigating the impacts of climate change. As AI continues to grow and permeate different aspects of our lives, its contribution to global carbon emissions becomes a significant concern. Awareness of these emissions can inform decisions made by developers, businesses, policymakers, and even ML engineers and scientists like us to ensure a balance between technological innovation and environmental responsibility.
 
-Furthermore, this understanding stimulates the drive towards 'Green AI' [@schwartz2020green]. This approach focuses on developing AI technologies that are efficient, powerful, and environmentally sustainable. It encourages exploring energy-efficient algorithms, using renewable energy sources in data centers, and adopting practices that reduce A. I'm the overall environmental impact.
+Furthermore, this understanding stimulates the drive towards 'Green AI' [@schwartz2020green]. This approach focuses on developing AI technologies that are efficient, powerful, and environmentally sustainable. It encourages exploring energy-efficient algorithms, using renewable energy sources in data centers, and adopting practices that reduce AI's overall environmental impact.
 
 In essence, the carbon footprint is an essential consideration in developing and applying AI technologies. As AI evolves and its applications become more widespread, managing its carbon footprint is key to ensuring that this technological progress aligns with the broader environmental sustainability goals.
 
@@ -191,11 +191,11 @@ The carbon footprint of AI encompasses several key elements, each contributing t
 
 The carbon footprint varies significantly based on the energy sources used. The composition of the sources providing the energy used in the grid varies widely depending on geographical region and even time in a single day! For example, in the USA, [roughly 60 percent of the total energy supply is still covered by fossil fuels](https://www.eia.gov/tools/faqs/faq.php?id=427&t=3). Nuclear and renewable energy sources cover the remaining 40 percent. These fractions are not constant throughout the day. As renewable energy production usually relies on environmental factors, such as solar radiation and pressure fields, they do not provide a constant energy source.
 
-The variability of renewable energy production has been an ongoing challenge in the widespread use of these sources. Looking at @fig-energyprod, which shows data for the European grid, we see that it is supposed to be able to produce the required amount of energy throughout the day. While solar energy peaks in the middle of the day, wind energy shows two distinct peaks in the mornings and evenings. Currently, we rely on fossil and coal-based energy generation methods to supply the lack of energy during times when renewable energy does not meet requirements,
+The variability of renewable energy production has been an ongoing challenge in the widespread use of these sources. Looking at @fig-energyprod, which shows data for the European grid, we see that it is supposed to be able to produce the required amount of energy throughout the day. While solar energy peaks in the middle of the day, wind energy has two distinct peaks in the mornings and evenings. Currently, we rely on fossil and coal-based energy generation methods to supplement the lack of energy during times when renewable energy does not meet requirements.
 
-Innovation in energy storage solutions is required to enable constant use of renewable energy sources. The base energy load is currently met with nuclear energy. This constant energy source does not directly emit carbon emissions but needs to be faster to accommodate the variability of renewable energy sources. Tech companies such as Microsoft have shown interest in nuclear energy sources [to power their data centers](https://www.bloomberg.com/news/newsletters/2023-09-29/microsoft-msft-sees-artificial-intelligence-and-nuclear-energy-as-dynamic-duo). As the demand of data centers is more constant than the demand of regular households, nuclear energy could be used as a dominant source of energy.
+Innovation in energy storage solutions is required to enable constant use of renewable energy sources. The base energy load is currently met with nuclear energy. This constant energy source does not directly produce carbon emissions but needs to be faster to accommodate the variability of renewable energy sources. Tech companies such as Microsoft have shown interest in nuclear energy sources [to power their data centers](https://www.bloomberg.com/news/newsletters/2023-09-29/microsoft-msft-sees-artificial-intelligence-and-nuclear-energy-as-dynamic-duo). As the demand of data centers is more constant than the demand of regular households, nuclear energy could be used as a dominant source of energy.
 
-![Energy sources and generation capabilities. Source: [Energy Charts.](https://www.energy-charts.info/?l=en&c=DE).](images/png/europe_energy_grid.png){#fig-energyprod}
+![Energy sources and generation capabilities. Source: [Energy Charts](https://www.energy-charts.info/?l=en&c=DE).](images/png/europe_energy_grid.png){#fig-energyprod}
 
 Additionally, the manufacturing and disposal of AI hardware add to the carbon footprint. Producing specialized computing devices, such as GPUs and CPUs, is energy- and resource-intensive. This phase often relies on energy sources that contribute to greenhouse gas emissions. The electronics industry's manufacturing process has been identified as one of the eight big supply chains responsible for more than 50 percent of global emissions [@challenge2021supply]. Furthermore, the end-of-life disposal of this hardware, which can lead to electronic waste, also has environmental implications. As mentioned, servers have a refresh cycle of roughly 3 to 5 years. Of this e-waste, currently [only 17.4 percent is properly collected and recycled.](https://www.genevaenvironmentnetwork.org/resources/updates/the-growing-environmental-risks-of-e-waste/). The carbon emissions of this e-waste has shown an increase of more than 50 percent between 2014 and 2020 [@singh2022disentangling].
 
@@ -214,7 +214,7 @@ Did you know that the cutting-edge AI models you might use have an environmental
 
 The current focus on reducing AI systems' carbon emissions and energy consumption addresses one crucial aspect of sustainability. However, manufacturing the semiconductors and hardware that enable AI also carries severe environmental impacts that receive comparatively less public attention. Building and operating a leading-edge semiconductor fabrication plant, or "fab," has substantial resource requirements and polluting byproducts beyond a large carbon footprint.
 
-For example, a state-of-the-art fab producing state-of-the-art chips like in 5nm can require up to [four million gallons of pure water each day](https://wccftech.com/tsmc-using-water-tankers-for-chip-production-as-5nm-plant-faces-rationing/). This water usage approaches what a city of half a million people would require for all needs. Sourcing this consistently places immense strain on local water tables and reservoirs, especially in already water-stressed regions that host many high-tech manufacturing hubs.
+For example, a state-of-the-art fab producing chips like those in 5nm may require up to [four million gallons of pure water each day](https://wccftech.com/tsmc-using-water-tankers-for-chip-production-as-5nm-plant-faces-rationing/). This water usage approaches what a city of half a million people would require for all needs. Sourcing this consistently places immense strain on local water tables and reservoirs, especially in already water-stressed regions that host many high-tech manufacturing hubs.
 
 Additionally, over 250 unique hazardous chemicals are utilized at various stages of semiconductor production within fabs [@mills1997overview]. These include volatile solvents like sulfuric acid, nitric acid, and hydrogen fluoride, along with arsine, phosphine, and other highly toxic substances. Preventing the discharge of these chemicals requires extensive safety controls and wastewater treatment infrastructure to avoid soil contamination and risks to surrounding communities. Any improper chemical handling or unanticipated spill carries dire consequences.
 
@@ -283,7 +283,7 @@ While modern semiconductor fabs aim to contain air and chemical discharges throu
 
 As contaminants permeate local soils and water sources, wildlife ingesting affected food and water ingest toxic substances, which research shows can hamper cell function, reproduction rates, and longevity--slowly poisoning ecosystems [@hsu2016accumulation].
 
-Likewise, accidental chemical spills and improper waste handling, which release acids, BODs, and heavy metals into soils, can dramatically affect retention and leeching capabilities. Flora, such as vulnerable native orchids adapted to nutrient-poor substrates, can experience die-offs when contacted by foreign runoff chemicals that alter soil pH and permeability. One analysis found that a single 500-gallon nitric acid spill led to the regional extinction of a rare moss species in the year following when the acidic effluent reached nearby forest habitats. Such contamination events set off chain reactions across the interconnected web of life. Thus, strict protocols are essential to avoid hazardous discharge and runoff.
+Likewise, accidental chemical spills and improper waste handling, which release acids and heavy metals into soils, can dramatically affect retention and leaching capabilities. Flora, such as vulnerable native orchids adapted to nutrient-poor substrates, can experience die-offs when contacted by foreign runoff chemicals that alter soil pH and permeability. One analysis found that a single 500-gallon nitric acid spill led to the regional extinction of a rare moss species in the year following when the acidic effluent reached nearby forest habitats. Such contamination events set off chain reactions across the interconnected web of life. Thus, strict protocols are essential to avoid hazardous discharge and runoff.
 
 ## Life Cycle Analysis {#life-cycle-analysis}
 
@@ -344,7 +344,7 @@ While electronic waste generation levels can be estimated, specifics on hazardou
 
 The need for fine-grained data on computational resource consumption for training different model types makes reliable per-parameter or per-query emissions calculations difficult even for the usage phase. Attempts to create lifecycle inventories estimating average energy needs for key AI tasks exist [@henderson2020towards; @anthony2020carbontracker], but variability across hardware setups, algorithms, and input data uncertainty remains extremely high. Furthermore, real-time carbon intensity data, critical in accurately tracking operational carbon footprint, must be improved in many geographic locations, rendering existing tools for operational carbon emission mere approximations based on annual average carbon intensity values.
 
-The challenge is that tools like [CodeCarbon](https://codecarbon.io/) and [ML $\textrm{CO}_2$](https://mlco2.github.io/impact/#compute) but these are ad hoc approaches at best. Bridging the real data gaps with more rigorous corporate sustainability disclosures and mandated environmental impact reporting will be key for AI's overall climatic impacts to be understood and managed.
+The challenge is that tools like [CodeCarbon](https://codecarbon.io/) and [ML $\textrm{CO}_2$](https://mlco2.github.io/impact/#compute) are just ad hoc approaches at best, despite their well-meaning intentions. Bridging the real data gaps with more rigorous corporate sustainability disclosures and mandated environmental impact reporting will be key for AI's overall climatic impacts to be understood and managed.
 
 ### Rapid Pace of Evolution {#rapid-pace-of-evolution}
 
@@ -465,10 +465,10 @@ By transparently publishing detailed energy usage statistics, adopting rates of
 
 To curb emissions from their rapidly expanding AI workloads, Google engineers systematically identified four best practice areas--termed the "4 Ms"--where optimizations could compound to reduce the carbon footprint of ML:
 
-* Model - Selecting efficient AI model architectures can reduce computation by 5-10X with no loss in model quality. Google has extensively researched developing sparse models and neural architecture search to create more efficient models like the Evolved Transformer and Primer.
-* Machine—Using hardware optimized for AI over general-purpose systems improves performance per watt by 2-5X. Google's Tensor Processing Units (TPUs) led to 5-13X better carbon efficiency versus GPUs not optimized for ML.
-* Mechanization—By leveraging cloud computing systems tailored for high utilization over conventional on-premise data centers, energy costs are reduced by 1.4-2X. Google cites its data center's power usage effectiveness as outpacing industry averages.
-* Map - Choosing data center locations with low-carbon electricity reduces gross emissions by another 5-10X. Google provides real-time maps highlighting the percentage of renewable energy used by its facilities.
+* **Model:** Selecting efficient AI model architectures can reduce computation by 5-10X with no loss in model quality. Google has extensively researched developing sparse models and neural architecture search to create more efficient models like the Evolved Transformer and Primer.
+* **Machine:** Using hardware optimized for AI over general-purpose systems improves performance per watt by 2-5X. Google's Tensor Processing Units (TPUs) led to 5-13X better carbon efficiency versus GPUs not optimized for ML.
+* **Mechanization:** By leveraging cloud computing systems tailored for high utilization over conventional on-premise data centers, energy costs are reduced by 1.4-2X. Google cites its data center's power usage effectiveness as outpacing industry averages.
+* **Map:** Choosing data center locations with low-carbon electricity reduces gross emissions by another 5-10X. Google provides real-time maps highlighting the percentage of renewable energy used by its facilities.
 
 Together, these practices created drastic compound efficiency gains. For example, optimizing the Transformer AI model on TPUs in a sustainable data center location cut energy use by 83. It lowered $\textrm{CO}_2$ emissions by a factor of 747.
 
@@ -504,7 +504,7 @@ Tiny computers, microcontrollers, and custom ASICs powering edge intelligence fa
 
 End-of-life handling of internet-connected gadgets embedded with sensors and AI remains an often overlooked issue during design. However, these products permeate consumer goods, vehicles, public infrastructure, industrial equipment, and more.
 
-#### E-waste {#e-waste}
+### E-waste {#e-waste}
 
 Electronic waste, or e-waste, refers to discarded electrical equipment and components that enter the waste stream. This includes devices that have to be plugged in, have a battery, or electrical circuitry. With the rising adoption of internet-connected smart devices and sensors, e-waste volumes rapidly increase yearly. These proliferating gadgets contain toxic heavy metals like lead, mercury, and cadmium that become environmental and health hazards when improperly disposed of.
 
@@ -514,7 +514,7 @@ Developing nations are being hit the hardest as they need more infrastructure to
 
 The danger is that crude handling of electronics to strip valuables exposes marginalized workers and communities to noxious burnt plastics/metals. Lead poisoning poses especially high risks to child development if ingested or inhaled. Overall, only about 20% of e-waste produced was collected using environmentally sound methods, according to UN estimates [@un2019circular]. So solutions for responsible lifecycle management are urgently required to contain the unsafe disposal as volume soars higher.
 
-#### Disposable Electronics {#disposable-electronics}
+### Disposable Electronics {#disposable-electronics}
 
 The rapidly falling costs of microcontrollers, tiny rechargeable batteries, and compact communication hardware have enabled the embedding of intelligent sensor systems throughout everyday consumer goods. These internet-of-things (IoT) devices monitor product conditions, user interactions, and environmental factors to enable real-time responsiveness, personalization, and data-driven business decisions in the evolving connected marketplace.
 
@@ -524,7 +524,7 @@ The problem accelerates as more manufacturers rush to integrate mobile chips, po
 
 While offering convenience when working, the unsustainable combination of difficult retrievability and limited safe breakdown mechanisms causes disposable connected devices to contribute outsized shares of future e-waste volumes needing urgent attention.
 
-#### Planned Obsolescence {#planned-obsolescence}
+### Planned Obsolescence {#planned-obsolescence}
 
 Planned obsolescence refers to the intentional design strategy of manufacturing products with artificially limited lifetimes that quickly become non-functional or outdated. This spurs faster replacement purchase cycles as consumers find devices no longer meet their needs within a few years. However, electronics designed for premature obsolescence contribute to unsustainable e-waste volumes.
 
@@ -624,9 +624,7 @@ The key idea is that equitable participation in AI systems targeting environment
 
 As public sector agencies and private companies alike rush towards adopting AI tools to help tackle pressing environmental challenges, calls for transparency around these systems' development and functionality have begun to amplify. Explainable and interpretable ML features grow more crucial for building trust in emerging models aiming to guide consequential sustainability policies. Initiatives like the [Montreal Carbon Pledge](https://unfccc.int/news/montreal-carbon-pledge) brought tech leaders together to commit to publishing impact assessments before launching environmental systems, as pledged below:
 
-*"As institutional investors, we must act in the best long-term interests of our beneficiaries. In this fiduciary role, long-term investment risks are associated with greenhouse gas emissions, climate change, and carbon regulation.
-
-Measuring our carbon footprint is integral to understanding better, quantifying, and managing the carbon and climate change-related impacts, risks, and opportunities in our investments. Therefore, as a first step, we commit to measuring and disclosing the carbon footprint of our investments annually to use this information to develop an engagement strategy and identify and set carbon footprint reduction targets."*
+> _"As institutional investors, we must act in the best long-term interests of our beneficiaries. In  this fiduciary role, long-term investment risks are associated with greenhouse gas emissions, climate change, and carbon regulation. Measuring our carbon footprint is integral to understanding better, quantifying, and managing the carbon and climate change-related impacts, risks, and opportunities in our investments. Therefore, as a first step, we commit to measuring and disclosing the carbon footprint of our investments annually to use this information to develop an engagement strategy and identify and set carbon footprint reduction targets." -- Montréal Carbon Pledge_
 
 We need a similar pledge for AI sustainability and responsibility. Widespread acceptance and impact of AI sustainability solutions will partly be on deliberate communication of validation schemes, metrics, and layers of human judgment applied before live deployment. Efforts like [NIST's Principles for Explainable AI](https://oecd.ai/en/dashboards/policy-initiatives/http:%2F%2Faipo.oecd.org%2F2021-data-policyInitiatives-26746) can help foster transparency into AI systems. The National Institute of Standards and Technology (NIST) has published an influential set of guidelines dubbed the Principles for Explainable AI [@phillips2020four]. This framework articulates best practices for designing, evaluating, and deploying responsible AI systems with transparent and interpretable features that build critical user understanding and trust.
 
@@ -648,7 +646,11 @@ Another important direction is the integration of renewable energy sources into
 
 ### Challenges
 
-Despite these promising directions, several challenges need to be addressed. One of the major challenges is the need for consistent standards and methodologies for measuring and reporting the environmental impact of AI. These methods must capture the complexity of the life cycles of AI models and system hardware. Next, efficient and environmentally sustainable AI infrastructure and system hardware are needed. This consists of three components. It maximizes the utilization of accelerator and system resources, prolong the lifetime of AI infrastructure, and design systems hardware with environmental impact in mind.
+Despite these promising directions, several challenges need to be addressed. One of the major challenges is the need for consistent standards and methodologies for measuring and reporting the environmental impact of AI. These methods must capture the complexity of the life cycles of AI models and system hardware. Also, efficient and environmentally sustainable AI infrastructure and system hardware are needed. This consists of three components:
+
+1. Maximize the utilization of accelerator and system resources.
+2. Prolong the lifetime of AI infrastructure.
+3. design systems hardware with environmental impact in mind.
 
 On the software side, we should trade off experimentation and the subsequent training cost. Techniques such as neural architecture search and hyperparameter optimization can be used for design space exploration. However, these are often very resource-intensive. Efficient experimentation can significantly reduce the environmental footprint overhead. Next, methods to reduce wasted training efforts should be explored.
 
@@ -708,4 +710,3 @@ In addition to exercises, we offer hands-on labs that allow students to gain pra
 
 * _Coming soon._
 :::
-