Merge pull request #446 from harvard-edge/445-notes-in-ai_for_goodqmd

445 Notes in "ai_for_good.qmd"

contents/ai_for_good/ai_for_good.qmd

@@ -89,9 +89,9 @@ This exercise teaches you how to predict crop yields in Nepal by combining satel
 
 ### Expanding Access
 
-Universal health coverage and quality care remain out of reach for millions worldwide. In many regions, more medical professionals are required to Access basic diagnosis and treatment. Additionally, healthcare infrastructure like clinics, hospitals, and utilities to power complex equipment needs to be improved. These gaps disproportionately impact marginalized communities, exacerbating health disparities.
+Universal health coverage and quality care remain out of reach for millions worldwide. In many regions, more medical professionals are required to access basic diagnosis and treatment. Additionally, healthcare infrastructure like clinics, hospitals, and utilities to power complex equipment needs to be improved. These gaps disproportionately impact marginalized communities, exacerbating health disparities.
 
-TinyML offers a promising technological solution to help expand Access to quality healthcare globally. TinyML refers to the ability to deploy machine learning algorithms on microcontrollers, tiny chips with processing power, memory, and connectivity. TinyML enables real-time data analysis and intelligence in low-powered, compact devices.
+TinyML offers a promising technological solution to help expand Access to quality healthcare globally. TinyML refers to the ability to deploy machine learning algorithms on microcontrollers, tiny chips with limited processing power, memory, and connectivity. TinyML enables real-time data analysis and intelligence in low-powered, compact devices.
 
 This creates opportunities for transformative medical tools that are portable, affordable, and accessible. TinyML software and hardware can be optimized to run even in resource-constrained environments. For example, a TinyML system could analyze symptoms or make diagnostic predictions using minimal computing power, no continuous internet connectivity, and a battery or solar power source. These capabilities can bring medical-grade screening and monitoring directly to underserved patients.
 
@@ -158,9 +158,11 @@ Researchers from Moulay Ismail University of Meknes in Morocco [@bamoumen2022tin
 
 ## Disaster Response
 
-In disaster response, speed and safety are paramount. But rubble and wreckage create hazardous, confined environments that impede human search efforts. TinyML enables nimble drones to assist rescue teams in these dangerous scenarios.
+In disaster response, speed and safety are paramount. But rubble and wreckage create hazardous, confined environments that impede human search efforts. TinyML enables nimble drones to assist rescue teams in these dangerous scenarios. Processing data locally using TinyML allows for quick interpretation to guide rescue efforts. 
 
-When buildings collapse after earthquakes, small drones can prove invaluable. Equipped with TinyML navigation algorithms, micro-sized drones like the [CrazyFlie](https://www.bitcraze.io/) can traverse cramped voids and map pathways beyond human reach [@duisterhof2019learning]. Obstacle avoidance allows the drones to weave through unstable debris. This autonomous mobility lets them rapidly sweep areas humans cannot access. @vid-l2seek presents the [@duisterhof2019learning] paper on deep reinforcement learning using drones for source-seeking.
+When buildings collapse after earthquakes, small drones can prove invaluable. Equipped with TinyML navigation algorithms, micro-sized drones like the CrazyFlie with less than 200 KB of RAM and only 168 MHz CPU clock frequency can safely traverse cramped voids and map pathways beyond human reach [@duisterhof2019learning]. Obstacle avoidance allows these drones to weave through unstable debris. This autonomous mobility lets them rapidly sweep areas humans cannot access. Onboard sensors and TinyML processors analyze real-time data to identify signs of survivors. Thermal cameras can detect body heat, microphones can pick up calls for help, and gas sensors can warn of leaks [@duisterhof2021sniffy]. 
+
+@vid-l2seek demonstrates how deep reinforcement learning can be used to enable drones to autonomously seek light sources.
 
 :::{#vid-l2seek .callout-important}
 
@@ -170,7 +172,7 @@ When buildings collapse after earthquakes, small drones can prove invaluable. Eq
 
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-Crucially, onboard sensors and TinyML processors analyze real-time data to identify signs of survivors. Thermal cameras detect body heat, microphones pick up calls for help, and gas sensors warn of leaks [@duisterhof2021sniffy]. Processing data locally using TinyML allows for quick interpretation to guide rescue efforts. As conditions evolve, the drones can adapt by adjusting their search patterns and priorities. @vid-sniffybug is an overview of autonomous drones for gas leak detection.
+@vid-sniffybug is an overview of autonomous drones for gas leak detection.
 
 :::{#vid-sniffybug .callout-important}
 
@@ -228,7 +230,7 @@ These examples demonstrate TinyML's huge potential for efficient, sustainable ci
 
 While TinyML presents immense opportunities, thoughtful consideration of challenges and ethical implications will be critical as adoption spreads globally. Researchers have highlighted key factors to address, especially when deploying TinyML in developing regions.
 
-A foremost challenge is limited Access to training and hardware [@ooko2021tinyml]. Only educational programs exist tailored to TinyML, and emerging economies often need a robust electronics supply chain. Thorough training and partnerships will be needed to nurture expertise and make devices available to underserved communities. Initiatives like the TinyML4D network help provide structured learning pathways.
+A foremost challenge is limited access to training and hardware [@ooko2021tinyml]. Only educational programs exist tailored to TinyML, and emerging economies often need a robust electronics supply chain. Thorough training and partnerships will be needed to nurture expertise and make devices available to underserved communities. Initiatives like the TinyML4D network help provide structured learning pathways.
 
 Data limitations also pose hurdles. TinyML models require quality localized datasets, which are scarce in under-resourced environments. Creating frameworks to crowdsource data ethically could address this. However, data collection should benefit local communities directly, not just extract value.