Merge pull request #579 from harvard-edge/dev

Preparing major v0.3.0 release

.all-contributorsrc

@@ -41,20 +41,20 @@
             "profile": "https://github.com/Mjrovai",
             "contributions": []
         },
-        {
-            "login": "Sara-Khosravi",
-            "name": "Sara Khosravi",
-            "avatar_url": "https://avatars.githubusercontent.com/Sara-Khosravi",
-            "profile": "https://github.com/Sara-Khosravi",
-            "contributions": []
-        },
         {
             "login": "kai4avaya",
             "name": "Kai Kleinbard",
             "avatar_url": "https://avatars.githubusercontent.com/kai4avaya",
             "profile": "https://github.com/kai4avaya",
             "contributions": []
         },
+        {
+            "login": "Sara-Khosravi",
+            "name": "Sara Khosravi",
+            "avatar_url": "https://avatars.githubusercontent.com/Sara-Khosravi",
+            "profile": "https://github.com/Sara-Khosravi",
+            "contributions": []
+        },
         {
             "login": "V0XNIHILI",
             "name": "Douwe den Blanken",
@@ -69,13 +69,6 @@
             "profile": "https://github.com/shanzehbatool",
             "contributions": []
         },
-        {
-            "login": "mpstewart1",
-            "name": "Matthew Stewart",
-            "avatar_url": "https://avatars.githubusercontent.com/mpstewart1",
-            "profile": "https://github.com/mpstewart1",
-            "contributions": []
-        },
         {
             "login": "eliasab16",
             "name": "Elias",
@@ -90,6 +83,13 @@
             "profile": "https://github.com/JaredP94",
             "contributions": []
         },
+        {
+            "login": "mpstewart1",
+            "name": "Matthew Stewart",
+            "avatar_url": "https://avatars.githubusercontent.com/mpstewart1",
+            "profile": "https://github.com/mpstewart1",
+            "contributions": []
+        },
         {
             "login": "ishapira1",
             "name": "Itai Shapira",

README.md

@@ -125,15 +125,15 @@ This project follows the [all-contributors](https://allcontributors.org) specifi
       <td align="center" valign="top" width="20%"><a href="https://github.com/Mjrovai"><img src="https://avatars.githubusercontent.com/Mjrovai?s=100" width="100px;" alt="Marcelo Rovai"/><br /><sub><b>Marcelo Rovai</b></sub></a><br /></td>
     </tr>
     <tr>
-      <td align="center" valign="top" width="20%"><a href="https://github.com/Sara-Khosravi"><img src="https://avatars.githubusercontent.com/Sara-Khosravi?s=100" width="100px;" alt="Sara Khosravi"/><br /><sub><b>Sara Khosravi</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/kai4avaya"><img src="https://avatars.githubusercontent.com/kai4avaya?s=100" width="100px;" alt="Kai Kleinbard"/><br /><sub><b>Kai Kleinbard</b></sub></a><br /></td>
+      <td align="center" valign="top" width="20%"><a href="https://github.com/Sara-Khosravi"><img src="https://avatars.githubusercontent.com/Sara-Khosravi?s=100" width="100px;" alt="Sara Khosravi"/><br /><sub><b>Sara Khosravi</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/V0XNIHILI"><img src="https://avatars.githubusercontent.com/V0XNIHILI?s=100" width="100px;" alt="Douwe den Blanken"/><br /><sub><b>Douwe den Blanken</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/shanzehbatool"><img src="https://avatars.githubusercontent.com/shanzehbatool?s=100" width="100px;" alt="shanzehbatool"/><br /><sub><b>shanzehbatool</b></sub></a><br /></td>
-      <td align="center" valign="top" width="20%"><a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"/><br /><sub><b>Matthew Stewart</b></sub></a><br /></td>
+      <td align="center" valign="top" width="20%"><a href="https://github.com/eliasab16"><img src="https://avatars.githubusercontent.com/eliasab16?s=100" width="100px;" alt="Elias"/><br /><sub><b>Elias</b></sub></a><br /></td>
     </tr>
     <tr>
-      <td align="center" valign="top" width="20%"><a href="https://github.com/eliasab16"><img src="https://avatars.githubusercontent.com/eliasab16?s=100" width="100px;" alt="Elias"/><br /><sub><b>Elias</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/JaredP94"><img src="https://avatars.githubusercontent.com/JaredP94?s=100" width="100px;" alt="Jared Ping"/><br /><sub><b>Jared Ping</b></sub></a><br /></td>
+      <td align="center" valign="top" width="20%"><a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"/><br /><sub><b>Matthew Stewart</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/ishapira1"><img src="https://avatars.githubusercontent.com/ishapira1?s=100" width="100px;" alt="Itai Shapira"/><br /><sub><b>Itai Shapira</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/harvard-edge/cs249r_book/graphs/contributors"><img src="https://www.gravatar.com/avatar/8863743b4f26c1a20e730fcf7ebc3bc0?d=identicon&s=100?s=100" width="100px;" alt="Maximilian Lam"/><br /><sub><b>Maximilian Lam</b></sub></a><br /></td>
       <td align="center" valign="top" width="20%"><a href="https://github.com/jaysonzlin"><img src="https://avatars.githubusercontent.com/jaysonzlin?s=100" width="100px;" alt="Jayson Lin"/><br /><sub><b>Jayson Lin</b></sub></a><br /></td>

_quarto.yml

@@ -244,7 +244,7 @@ format:
 
     reference-location: margin
     citation-location: margin
-    sidenote: true  Enable sidenotes for Tufte style
+    sidenote: true  #Enable sidenotes for Tufte style
     linkcolor: "#A51C30"
     urlcolor: "#A51C30"
     highlight-style: github

contents/core/conclusion/conclusion.qmd

@@ -44,7 +44,7 @@ In addition to distributed training, we discussed techniques for optimizing the
 
 Deploying trained ML models is more complex than simply running the networks; efficiency is critical (@sec-efficient_ai). In this chapter on AI efficiency, we emphasized that efficiency is not merely a luxury but a necessity in artificial intelligence systems. We dug into the key concepts underpinning AI systems' efficiency, recognizing that the computational demands on neural networks can be daunting, even for minimal systems. For AI to be seamlessly integrated into everyday devices and essential systems, it must perform optimally within the constraints of limited resources while maintaining its efficacy.
 
-Throughout the book, we have highlighted the importance of pursuing efficiency to ensure that AI models are streamlined, rapid, and sustainable. By optimizing models for efficiency, we can widen their applicability across various platforms and scenarios, enabling AI to be deployed in resource-constrained environments such as embedded systems and edge devices. This pursuit of efficiency is crucial for the widespread adoption and practical implementation of AI technologies in real-world applications.
+Throughout the book, we have highlighted the importance of pursuing efficiency to ensure that AI models are streamlined, rapid, and sustainable. By optimizing models for efficiency, we can widen their applicability across various platforms and scenarios, enabling AI to be deployed in resource-constrained environments such as embedded systems and edge devices. This pursuit of efficiency is necessary for the widespread adoption and practical implementation of AI technologies in real-world applications.
 
 ## Optimizing ML Model Architectures
 
@@ -90,19 +90,19 @@ In addition to security, we addressed the critical issue of data privacy. Techni
 
 ## Upholding Ethical Considerations
 
-As we embrace ML advancements in all facets of our lives, it is crucial to remain mindful of the ethical considerations that will shape the future of AI (@sec-responsible_ai). Fairness, transparency, accountability, and privacy in AI systems will be paramount as they become more integrated into our lives and decision-making processes.
+As we embrace ML advancements in all facets of our lives, it is essential to remain mindful of the ethical considerations that will shape the future of AI (@sec-responsible_ai). Fairness, transparency, accountability, and privacy in AI systems will be paramount as they become more integrated into our lives and decision-making processes.
 
 As AI systems become more pervasive and influential, it is important to ensure that they are designed and deployed in a manner that upholds ethical principles. This means actively mitigating biases, promoting fairness, and preventing discriminatory outcomes. Additionally, ethical AI design ensures transparency in how AI systems make decisions, enabling users to understand and trust their outputs.
 
 Accountability is another critical ethical consideration. As AI systems take on more responsibilities and make decisions that impact individuals and society, there must be clear mechanisms for holding these systems and their creators accountable. This includes establishing frameworks for auditing and monitoring AI systems and defining liability and redress mechanisms in case of harm or unintended consequences.
 
 Ethical frameworks, regulations, and standards will be essential to address these ethical challenges. These frameworks should guide the responsible development and deployment of AI technologies, ensuring that they align with societal values and promote the well-being of individuals and communities.
 
-Moreover, ongoing discussions and collaborations among researchers, practitioners, policymakers, and society will be crucial in navigating the ethical landscape of AI. These conversations should be inclusive and diverse, bringing together different perspectives and expertise to develop comprehensive and equitable solutions. As we move forward, it is the collective responsibility of all stakeholders to prioritize ethical considerations in the development and deployment of AI systems.
+Moreover, ongoing discussions and collaborations among researchers, practitioners, policymakers, and society will be important in navigating the ethical landscape of AI. These conversations should be inclusive and diverse, bringing together different perspectives and expertise to develop comprehensive and equitable solutions. As we move forward, it is the collective responsibility of all stakeholders to prioritize ethical considerations in the development and deployment of AI systems.
 
 ## Promoting Sustainability
 
-The increasing computational demands of machine learning, particularly for training large models, have raised concerns about their environmental impact due to high energy consumption and carbon emissions (@sec-sustainable_ai). As the scale and complexity of models continue to grow, addressing the sustainability challenges associated with AI development becomes imperative. To mitigate the environmental footprint of AI, the development of energy-efficient algorithms is crucial. This involves optimizing models and training procedures to minimize computational requirements while maintaining performance. Techniques such as model compression, quantization, and efficient neural architecture search can help reduce the energy consumption of AI systems.
+The increasing computational demands of machine learning, particularly for training large models, have raised concerns about their environmental impact due to high energy consumption and carbon emissions (@sec-sustainable_ai). As the scale and complexity of models continue to grow, addressing the sustainability challenges associated with AI development becomes imperative. To mitigate the environmental footprint of AI, the development of energy-efficient algorithms is necessary. This involves optimizing models and training procedures to minimize computational requirements while maintaining performance. Techniques such as model compression, quantization, and efficient neural architecture search can help reduce the energy consumption of AI systems.
 
 Using renewable energy sources to power AI infrastructure is another important step towards sustainability. By transitioning to clean energy sources such as solar, wind, and hydropower, the carbon emissions associated with AI development can be significantly reduced. This requires a concerted effort from the AI community and support from policymakers and industry leaders to invest in and adopt renewable energy solutions. In addition, exploring alternative computing paradigms, such as neuromorphic and photonic computing, holds promise for developing more energy-efficient AI systems. By developing hardware and algorithms that emulate the brain's processing mechanisms, we can potentially create AI systems that are both powerful and sustainable.
 
@@ -124,17 +124,17 @@ As we look to the future, the trajectory of ML systems points towards a paradigm
 
 We anticipate a growing emphasis on data curation, labeling, and augmentation techniques in the coming years. These practices aim to ensure that models are trained on high-quality, representative data that accurately reflects the complexities and nuances of real-world scenarios. By focusing on data quality and diversity, we can mitigate the risks of biased or skewed models that may perpetuate unfair or discriminatory outcomes.
 
-This data-centric approach will be crucial in addressing the challenges of bias, fairness, and generalizability in ML systems. By actively seeking out and incorporating diverse and inclusive datasets, we can develop more robust, equitable, and applicable models for various contexts and populations. Moreover, the emphasis on data will drive advancements in techniques such as data augmentation, where existing datasets are expanded and diversified through data synthesis, translation, and generation. These techniques can help overcome the limitations of small or imbalanced datasets, enabling the development of more accurate and generalizable models.
+This data-centric approach will be vital in addressing the challenges of bias, fairness, and generalizability in ML systems. By actively seeking out and incorporating diverse and inclusive datasets, we can develop more robust, equitable, and applicable models for various contexts and populations. Moreover, the emphasis on data will drive advancements in techniques such as data augmentation, where existing datasets are expanded and diversified through data synthesis, translation, and generation. These techniques can help overcome the limitations of small or imbalanced datasets, enabling the development of more accurate and generalizable models.
 
-In recent years, generative AI has taken the field by storm, demonstrating remarkable capabilities in creating realistic images, videos, and text. However, the rise of generative AI also brings new challenges for ML systems (@sec-generative_ai). Unlike traditional ML systems, generative models often demand more computational resources and pose challenges in terms of scalability and efficiency. Furthermore, evaluating and benchmarking generative models presents difficulties, as traditional metrics used for classification tasks may not be directly applicable. Developing robust evaluation frameworks for generative models is an active area of research.
+In recent years, generative AI has taken the field by storm, demonstrating remarkable capabilities in creating realistic images, videos, and text. However, the rise of generative AI also brings new challenges for ML sysatem. Unlike traditional ML systems, generative models often demand more computational resources and pose challenges in terms of scalability and efficiency. Furthermore, evaluating and benchmarking generative models presents difficulties, as traditional metrics used for classification tasks may not be directly applicable. Developing robust evaluation frameworks for generative models is an active area of research, and something we hope to write about soon!
 
-Understanding and addressing these system challenges and ethical considerations will be crucial in shaping the future of generative AI and its impact on society. As ML practitioners and researchers, we are responsible for advancing the technical capabilities of generative models and developing robust systems and frameworks that can mitigate potential risks and ensure the beneficial application of this powerful technology.
+Understanding and addressing these system challenges and ethical considerations will be important in shaping the future of generative AI and its impact on society. As ML practitioners and researchers, we are responsible for advancing the technical capabilities of generative models and developing robust systems and frameworks that can mitigate potential risks and ensure the beneficial application of this powerful technology.
 
 ## Applying AI for Good
 
 The potential for AI to be used for social good is vast, provided that responsible ML systems are developed and deployed at scale across various use cases (@sec-ai_for_good). To realize this potential, it is essential for researchers and practitioners to actively engage in the process of learning, experimentation, and pushing the boundaries of what is possible.
 
-Throughout the development of ML systems, it is crucial to remember the key themes and lessons explored in this book. These include the importance of data quality and diversity, the pursuit of efficiency and robustness, the potential of TinyML and neuromorphic computing, and the imperative of security and privacy. These insights inform the work and guide the decisions of those involved in developing AI systems.
+Throughout the development of ML systems, it is important to remember the key themes and lessons explored in this book. These include the importance of data quality and diversity, the pursuit of efficiency and robustness, the potential of TinyML and neuromorphic computing, and the imperative of security and privacy. These insights inform the work and guide the decisions of those involved in developing AI systems.
 
 It is important to recognize that the development of AI is not solely a technical endeavor but also a deeply human one. It requires collaboration, empathy, and a commitment to understanding the societal implications of the systems being created. Engaging with experts from diverse fields, such as ethics, social sciences, and policy, is essential to ensure that the AI systems developed are technically sound, socially responsible, and beneficial. Embracing the opportunity to be part of this transformative field and shaping its future is a privilege and a responsibility. By working together, we can create a world where ML systems serve as tools for positive change and improving the human condition.