Merge pull request #532 from harvard-edge/531-a-warning-and-a-few-typos

531 A warning and a few typos

contents/ai/socratiq.qmd

@@ -125,7 +125,7 @@ Before diving into your studies, take a moment to configure SocratiQ for your ac
 You can augment any AI SocratiQ response using the dropdown menu at the top of each message.
 
 ::: {.content-visible when-format="html"}
-![Redo an AI message by choosing a new experience level..](images/gif/adjust_message_difficulty.gif){#fig-adjust_message_difficulty}
+![Redo an AI message by choosing a new experience level.](images/gif/adjust_message_difficulty.gif){#fig-adjust_message_difficulty}
 :::
 
 ::: {.content-visible when-format="pdf"}
@@ -254,7 +254,7 @@ For persistent issues, please contact us at vj[@]eecs.harvard.edu.
 
 Your feedback helps us improve SocratiQ. You can report technical issues, suggest improvements to quiz questions, or share thoughts about AI responses using the feedback buttons located throughout the interface.
 
-You can file a [GitHub issue](https://github.com/harvard-edge/cs249r_book/issues), or if you prefer leaving feedback via Google Form, you are welcome to do so via this link:
+You can submit a [GitHub issue](https://github.com/harvard-edge/cs249r_book/issues), or if you prefer leaving feedback via Google Form, you are welcome to do so via this link:
 
 [Share Your Feedback]{.btn .btn-primary onclick="window.open('https://docs.google.com/forms/d/e/1FAIpQLSeK8RXgc6kbT1IbWVLjyUhwowp3x1ySbAjUQQqztdDs5ccmmQ/viewform?embedded=true', '_blank')"}
 

contents/core/introduction/introduction.qmd

@@ -56,7 +56,7 @@ STUDENT would:
 
 Early AI like STUDENT suffered from a fundamental limitation: they could only handle inputs that exactly matched their pre-programmed patterns and rules. Imagine a language translator that only works when sentences follow perfect grammatical structure---even slight variations like changing word order, using synonyms, or natural speech patterns would cause the STUDENT to fail. This "brittleness" meant that while these solutions could appear intelligent when handling very specific cases they were designed for, they would break down completely when faced with even minor variations or real-world complexity. This limitation wasn't just a technical inconvenience—it revealed a deeper problem with rule-based approaches to AI: they couldn't genuinely understand or generalize from their programming, they could only match and manipulate patterns exactly as specified.
 
-### Expert Systems(1970s-1980s)
+### Expert Systems (1970s-1980s)
 
 By the mid-1970s, researchers realized that general AI was too ambitious. Instead, they focused on capturing human expert knowledge in specific domains. MYCIN, developed at Stanford, was one of the first large-scale expert systems designed to diagnose blood infections:
 
@@ -92,6 +92,7 @@ IF contains("viagra") OR contains("winner") THEN spam
 
 Statistical (1990s):
 P(spam|word) = (frequency in spam emails) / (total frequency)
+
 Combined using Naive Bayes:
 P(spam|email) ∝ P(spam) × ∏ P(word|spam)
 ```
@@ -126,8 +127,8 @@ The table serves as a bridge between the early approaches we've discussed and th
 
 The 2000s marked a fascinating period in machine learning history that we now call the "shallow learning" era. To understand why it's "shallow," imagine building a house: deep learning (which came later) is like having multiple construction crews working at different levels simultaneously, each crew learning from the work of crews below them. In contrast, shallow learning typically had just one or two levels of processing - like having just a foundation crew and a framing crew.
 
-During this time, several powerful algorithms dominated the machine learning landscape. Each brought unique strengths to different problems: Decision trees provided interpretable results by making choices much like a flowchart. K-nearest neighbors made predictions by finding similar examples in past data, like asking your most experienced neighbors for advice. Linear and logistic regression offered straightforward, interpretable models that worked well for many real-world problems. Support Vector Machines (SVMs) excelled at finding complex boundaries between categories using the "kernel trick" - imagine being able to untangle a bowl of spaghetti into straight lines by lifting it into a higher dimension.
-These algorithms formed the foundation of practical machine learning because:
+During this time, several powerful algorithms dominated the machine learning landscape. Each brought unique strengths to different problems: Decision trees provided interpretable results by making choices much like a flowchart. K-nearest neighbors made predictions by finding similar examples in past data, like asking your most experienced neighbors for advice. Linear and logistic regression offered straightforward, interpretable models that worked well for many real-world problems. Support Vector Machines (SVMs) excelled at finding complex boundaries between categories using the "kernel trick" - imagine being able to untangle a bowl of spaghetti into straight lines by lifting it into a higher dimension. These algorithms formed the foundation of practical machine.
+
 Consider a typical computer vision solution from 2005:
 
 ::: {.callout-note}
@@ -143,7 +144,7 @@ Consider a typical computer vision solution from 2005:
 ```
 :::
 
-What made this era distinct was its hybrid approach: human-engineered features combined with statistical learning. They had strong mathematical foundations (researchers could prove why they worked). They performed well even with limited data. They were computationally efficient. They produced reliable, reproducible results. 
+What made this era distinct was its hybrid approach: human-engineered features combined with statistical learning. They had strong mathematical foundations (researchers could prove why they worked). They performed well even with limited data. They were computationally efficient. They produced reliable, reproducible results.
 
 Take the example of face detection, where the Viola-Jones algorithm (2001) achieved real-time performance using simple rectangular features and a cascade of classifiers. This algorithm powered digital camera face detection for nearly a decade.
 

contents/core/privacy_security/privacy_security.qmd

@@ -289,7 +289,7 @@ Hardware is not immune to the pervasive issue of design flaws or bugs. Attackers
 
 Meltdown [@Lipp2018meltdown] and Spectre [@Kocher2018spectre] work by taking advantage of optimizations in modern CPUs that allow them to speculatively execute instructions out of order before validity checks have been completed. This reveals data that should be inaccessible, which the attack captures through side channels like caches. The technical complexity demonstrates the difficulty of eliminating vulnerabilities even with extensive validation.
 
-If an ML system is processing sensitive data, such as personal user information or proprietary business analytics, Meltdown and Spectre represent a real and present danger to data security. Consider the case of an ML accelerator card designed to speed up machine learning processes, such as the ones we discussed in the [AI Hardware](../hw_acceleration/hw_acceleration.qmd) chapter. These accelerators work with the CPU to handle complex calculations, often related to data analytics, image recognition, and natural language processing. If such an accelerator card has a vulnerability akin to Meltdown or Spectre, it could leak the data it processes. An attacker could exploit this flaw not just to siphon off data but also to gain insights into the ML model's workings, including potentially reverse-engineering the model itself (thus, going back to the issue of [model theft](@sec-model_theft).
+If an ML system is processing sensitive data, such as personal user information or proprietary business analytics, Meltdown and Spectre represent a real and present danger to data security. Consider the case of an ML accelerator card designed to speed up machine learning processes, such as the ones we discussed in the [AI Acceleration](../hw_acceleration/hw_acceleration.qmd) chapter. These accelerators work with the CPU to handle complex calculations, often related to data analytics, image recognition, and natural language processing. If such an accelerator card has a vulnerability akin to Meltdown or Spectre, it could leak the data it processes. An attacker could exploit this flaw not just to siphon off data but also to gain insights into the ML model's workings, including potentially reverse-engineering the model itself (thus, going back to the issue of [model theft](@sec-model_theft).
 
 A real-world scenario where this could be devastating would be in the healthcare industry. ML systems routinely process highly sensitive patient data to help diagnose, plan treatment, and forecast outcomes. A bug in the system's hardware could lead to the unauthorized disclosure of personal health information, violating patient privacy and contravening strict regulatory standards like the [Health Insurance Portability and Accountability Act (HIPAA)](https://www.cdc.gov/phlp/publications/topic/hipaa.html)
 

contents/core/sustainable_ai/sustainable_ai.qmd

@@ -644,11 +644,11 @@ Altogether, these NIST principles offer AI practitioners and adopters guidance o
 
 This applies equally to the domain of environmental ability. Responsible and democratically guided AI innovation targeting shared ecological priorities depends on maintaining public vigilance, understanding, and oversight over otherwise opaque systems taking prominent roles in societal decisions. Prioritizing explainable algorithm designs and radical transparency practices per global standards can help sustain collective confidence that these tools improve rather than imperil hopes for a driven future.
 
-## Future Directions and Challenges {#future-directions-and-challenges}
+## Future Directions and Challenges
 
 As we look towards the future, the role of AI in environmental sustainability is poised to grow even more significant. AI's potential to drive advancements in renewable energy, climate modeling, conservation efforts, and more is immense. However, it is a two-sided coin, as we need to overcome several challenges and direct our efforts towards sustainable and responsible AI development.
 
-### Future Directions {#future-directions}
+### Future Directions
 
 One key future direction is the development of more energy-efficient AI models and algorithms. This involves ongoing research and innovation in areas like model pruning, quantization, and the use of low-precision numerics, as well as developing the hardware to enable full profitability of these innovations. Even further, we look at alternative computing paradigms that do not rely on von-Neumann architectures. More on this topic can be found in the hardware acceleration chapter. The goal is to create AI systems that deliver high performance while minimizing energy consumption and carbon emissions.