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docs/00-intro.md

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 How can understanding AI help you be a better leader? 
 
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 We think understanding AI is essential for executives. It helps today's leaders make strategic decisions, drive innovation, enhance efficiency, and foster a culture that embraces the transformative power of these technologies. Specifically, AI proficiency can help leaders in the following ways:
 
@@ -40,7 +40,7 @@ This specialization is intended for executives, decision-makers, and business le
 
 We also believe that learning is a life-long process. This specialization is targeted toward those who value continuous learning and want to stay ahead in today's fast-paced technology landscape.
 
-<img src="resources/images/00-intro_files/figure-html//13QC5DTLefV5AIUd_3QPW42D-Zf2Yf6tgrJivIf7jbaQ_g2a3e68cb5a3_0_16.png" title="CAPTION HERE" alt="CAPTION HERE" width="100%" style="display: block; margin: auto;" />
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docs/01b-AI_Possibilities-what_is_ai.md

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 The term "artificial intelligence", or AI, often makes people envision humanoid robots. For some of us, this prompts concerns about their ability to outsmart us. The notion of robots passing tests that blur the line between human and machine, often depicted in science fiction, adds to these worries, particularly when considering the potential for AI systems to act in self-interest and make decisions independently.
 
-<img src="resources/images/01b-AI_Possibilities-what_is_ai_files/figure-html//1-Mm-Vym3xdtB8xLRHR24jNCLSbNgFHw6N62iJrYe63c_g2a68fa11a90_0_0.png" title="CAPTION HERE" alt="CAPTION HERE" width="100%" style="display: block; margin: auto;" />
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 ## Specific and General Intelligence
 
 Currently, no AI system can perform all the intellectual tasks that a human can. This is an active area of research, specifically into what's called **artificial general intelligence**. We aren't there yet. Currently, artificial intelligence systems are optimized to perform a specific task well, but not for general, multi-purpose tasks. For example, the AI application for recognizing voices can not be directly applied to drive cars, and vice versa. Similarly, a language translation app could not recognize images, and vice versa.
 
-<img src="resources/images/01b-AI_Possibilities-what_is_ai_files/figure-html//1-Mm-Vym3xdtB8xLRHR24jNCLSbNgFHw6N62iJrYe63c_gcf1264c749_0_130.png" title="CAPTION HERE" alt="CAPTION HERE" width="100%" style="display: block; margin: auto;" />
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 ## Shifting Goalposts
 
 Defining what AI is can be tricky because what experts consider to be AI changes frequently. John McCarthy, one of the leading early figures in AI once said, "As soon as it works, no one calls it artificial intelligence anymore".
 
 For instance, 20 years ago, the idea of an email spam checker was new. People were surprised that an algorithm could identify junk email accurately, and called it “artificial intelligence”. Since this type of algorithm has become so common, it is no longer called "artificial intelligence". This transition happened because we no longer think it is surprising that computers can filter spam messages. Because it is not learning something new and surprising, it is no longer considered intelligent.
 
-<img src="resources/images/01b-AI_Possibilities-what_is_ai_files/figure-html//1-Mm-Vym3xdtB8xLRHR24jNCLSbNgFHw6N62iJrYe63c_g2a162964683_1_176.png" title="CAPTION HERE" alt="CAPTION HERE" width="100%" style="display: block; margin: auto;" />
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 We often look at human intelligence the same way. For example, many years ago, only a few people knew how to use the internet. These people might have been considered extremely talented and intelligent. Now, the massive growth of online resources and social media mean that fluent internet use is almost required!
 
@@ -41,7 +41,7 @@ At its core, AI is about problem solving [@fogel2022]. But how does it do this?
 
 1. **Interface**: AI needs a physical interface or software for the trained algorithm to receive a data input and execute the human-like task in the real world. For example, you might interface with a chatbot in your web browser.
 
-<img src="resources/images/01b-AI_Possibilities-what_is_ai_files/figure-html//10PZ8gQQIIxIjC8ELCMZdshoOUxrIMo1iKjbKspM2J4E_g2a972176106_0_178.png" title="CAPTION HERE" alt="CAPTION HERE" width="100%" style="display: block; margin: auto;" />
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 As an example, consider Amazon Echo’s voice control device [@wikiECHO]. The data set consists of customer voices talking to Amazon Echo or other devices. The algorithm predicts what a new customer voice is asking it to do. Given human voice request, it may set a kitchen timer. Lastly, the interface, is a physical device with a microphone, speaker, and computer software running the algorithm and accessing the data. It is the part that will interact with humans.
 
@@ -51,7 +51,7 @@ Let's look at a few of examples. We can then decide whether or not the examples
 
 ### Smartphones
 
-<img src="resources/images/01b-AI_Possibilities-what_is_ai_files/figure-html//10PZ8gQQIIxIjC8ELCMZdshoOUxrIMo1iKjbKspM2J4E_g2a972176106_0_185.png" title="CAPTION HERE" alt="CAPTION HERE" width="70%" style="display: block; margin: auto;" />
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 The name "smartphone" implies these devices are making decisions and are powered by AI. Let's consider our three criteria:
 
@@ -65,7 +65,7 @@ While there are some features on smartphones that are powered by AI models, like
 
 ### Calculators
 
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 Many of us use basic calculators, as you might find in Microsoft Excel, every day. AI also makes many calculations. Is it just a scaled-up calculator?
 
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 ### Computer Programs
 
-<img src="resources/images/01b-AI_Possibilities-what_is_ai_files/figure-html//10PZ8gQQIIxIjC8ELCMZdshoOUxrIMo1iKjbKspM2J4E_g2a972176106_0_200.png" title="CAPTION HERE" alt="CAPTION HERE" width="70%" style="display: block; margin: auto;" />
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 Like calculators, computers follow set procedures for problem solving and computation. Everyday computers use these procedures to help automate repetitive tasks and save time. However, this isn't generally considered AI, because the computer's algorithms aren't being trained with new data you supply. AI systems exhibit the ability to adapt and handle new inputs for tasks that might be more complicated.
 

docs/01c-AI_Possibilities-how_ai_works.md

@@ -28,7 +28,7 @@ Dr. D could make future diagnoses based on this single data point, but these dia
 
 Like Dr. D, AI will get better at finding the right patterns with more data. In order to train an AI algorithm to detect possible skin cancer, we'll first want to gather as many pictures of normal and cancerous skin as we can. This is the **raw data** [@Leek2017].
 
-<img src="resources/images/01c-AI_Possibilities-how_ai_works_files/figure-html//1OydUyEv1uEzn8ONPznxH1mGd4VHC9n88_aUGqkHJX4I_g263e06ef889_36_153.png" title="CAPTION HERE" alt="CAPTION HERE" width="100%" style="display: block; margin: auto;" />
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 ### What Is Data
 
@@ -47,7 +47,7 @@ The tech industry's lack of diversity contributes to these issues, often leading
 Large Language Models (LLMs), which we will cover later, are great examples of using high quantity and quality of data. Think about how much text information is freely available on the internet! Throughout the internet, we're much more likely to see the phrase "cancer is a disease" than "cancer is a computer program". Many LLMs are trained on sources like [Wikipedia](https://www.wikipedia.org/), which are typically grammatically sound and informative, leading to higher quality output.
 
 
-<img src="resources/images/01c-AI_Possibilities-how_ai_works_files/figure-html//10PZ8gQQIIxIjC8ELCMZdshoOUxrIMo1iKjbKspM2J4E_g2a972176106_1_0.png" title="CAPTION HERE" alt="CAPTION HERE" width="100%" style="display: block; margin: auto;" />
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 <div class = "ethics"> 
 It is **essential** that you and your team think critically about data sources. Many companies releasing generative AI systems have come under fire for training these systems on data that doesn't belong to them [@Walsh2023]. Individual people also have a right to data privacy. No personal data should be used without permission, even if that data could be interesting or useful.  
@@ -60,7 +60,7 @@ It's important to remember that AI systems need specific instructions to start d
 
 Once data is labeled, either "cancer" or "not cancer", we can use it to train the algorithm in the next step. This data is aptly called **training data**.
 
-<img src="resources/images/01c-AI_Possibilities-how_ai_works_files/figure-html//1OydUyEv1uEzn8ONPznxH1mGd4VHC9n88_aUGqkHJX4I_g263e06ef889_36_318.png" title="CAPTION HERE" alt="CAPTION HERE" width="100%" style="display: block; margin: auto;" />
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 ## Understanding the Algorithm
 
@@ -78,7 +78,7 @@ As an example, here is a very simple algorithm with one feature (spot perimeter)
 
 1. If the perimeter of the spot is not circular, label the image "cancer".
 
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 ### Testing the Algorithm
 
@@ -92,7 +92,7 @@ After setting up and quantifying the features, we want to make sure the AI is ac
 
 Humans play a big part in what kind of scores are acceptable when producing outputs. With cancer screening, we might be very worried about missing a real instance of cancer. Therefore, we might tell the AI to score false negatives more harshly than false positives.
 
-<img src="resources/images/01c-AI_Possibilities-how_ai_works_files/figure-html//1OydUyEv1uEzn8ONPznxH1mGd4VHC9n88_aUGqkHJX4I_g263e06ef889_36_360.png" title="CAPTION HERE" alt="CAPTION HERE" width="100%" style="display: block; margin: auto;" />
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 ## Interfacing with AI
 
@@ -102,7 +102,7 @@ Finally, AI would not work without an interface. This is where we can get creati
 
 - Telling people they might have cancer could be very upsetting for them. Our interface should provide supporting resources and clear disclaimers about its abilities.
 
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 ## Understanding the AI Spring
 
@@ -129,7 +129,7 @@ However, let's imagine we wanted AI to generate examples of skin cancer. If the
 **Discriminative AI**: Tells things apart or makes decisions based on what it has learned.
 </div>
 
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 We'll talk next about some generative AI models which have made recent breakthroughs possible.
 
@@ -141,13 +141,13 @@ Take for example this paragraph from the Wikipedia entry for skin cancer. A tran
 
 > More than 90% of cases are caused by exposure to ultraviolet radiation from the Sun.[4] This exposure increases the risk of all three main types of skin cancer.[4] Exposure has increased, partly due to a thinner ozone layer. Tanning beds are another common source of ultraviolet radiation. For melanomas and basal-cell cancers, exposure during childhood is particularly harmful. For squamous-cell skin cancers, total exposure, irrespective of when it occurs, is more important. Between 20% and 30% of melanomas develop from moles.[6] People with lighter skin are at higher risk as are those with poor immune function such as from medications or HIV/AIDS. Diagnosis is by biopsy.
 
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 ### Diffusion Models
 
 Like transformers, diffusion models are useful for generative AI, particularly image generation. The key to diffusion models is that they have a lot of training in how to fill in the blanks. The model starts with many "noisy" images (imagine a photo with lots of holes or black spots) and tries to reproduce the original image. This process is called "denoising score matching". It then uses this training to generate entirely new content.
 
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 ## Summary