various typos/cleanup

Author: TylerMSFT

learn-pr/wwl-data-ai/leverage-ai-tools/includes/2a-ai-foundations.md

@@ -1,19 +1,19 @@
-Modern AI is built on a foundation of data science and machine learning. The primary goal of AI is to use machines for capabilities that are usually associated with humans. Let's see data science concepts support the foundation of AI. 
+Modern AI is built on a foundation of data science and machine learning. The primary goal of AI is to use machines for capabilities that are usually associated with humans. Let's explore data science concepts that support the foundation of AI. 
 
 ## What is data science?
 Data science is an **interdisciplinary field** whose aim is to achieve AI. It primarily uses machine learning and statistics techniques. In most cases, data scientists are the experts in charge of solving AI problems.
 
 ## What is machine learning?
-Machine learning is a **technique** where a machine sifts through numerous amounts of data to find patterns. This technique is frequently used for AI purposes. Machine learning uses algorithms that train a machine to learn patterns based on differentiating features about the data. The more training data, the more accurate the predictions. 
+Machine learning is a **technique** where a machine sifts through numerous amounts of data to find patterns. Machine learning uses algorithms that train a machine to learn patterns based on features of the data. The more training data, the more accurate the predictions. 
 
 Here are some examples:
 * **Email spam detection** - Machine learning could look for patterns where email has words like "free" or "guarantee", the email address domain is on a blocked list, or a link displayed in text doesn't match the URL behind it.
 * **Credit card fraud detection** - Machine learning could look for patterns like the spending in a zip code the owner doesn't usually visit, buying an expensive item, or a sudden shopping spree.
 
 ## What is deep learning?
-Deep learning is a **subset of machine learning**. Deep learning is imitating how a human brain processes information, as a connected artificial neural network. Unlike machine learning, deep learning can discover complex patterns and differentiating features about the data on its own. It normally works with unstructured data like images, text, and audio. It requires enormous amounts of data for better analysis and massive computing power for speed.
+Deep learning is a **subset of machine learning**. Deep learning imitates how the human brain processes information, as a connected artificial neural network. Unlike machine learning, deep learning can discover complex patterns and differentiating features about the data on its own. It normally works with unstructured data like images, text, and audio. It requires enormous amounts of data for better analysis and massive computing power for speed.
 
-For instance, deep learning can be used to detect cancerous cells in medical images. Deep learning scans every pixel in the image as input to the neural nodes. The nodes analyze each pixel to filter out features that look cancerous. Each layer of nodes pushes findings of potential cancerous cells to the next layer of nodes to repeat the process and eventually aggregate all of the findings to classify the image. For example, the image might be classified as a healthy image or an image with cancerous features.
+For instance, deep learning can be used to detect cancerous cells in medical images. Deep learning scans the image as input to a neural network. The nodes analyze each pixel to filter out features that look cancerous. Each layer of nodes pushes findings of potential cancerous cells to the next layer of nodes to repeat the process and eventually aggregate all of the findings to classify the image. For example, the image might be classified as a healthy image or an image with cancerous features.
 
 ![Diagram showing AI methodologies (deep learning, machine learning, and data science).](../media/2-machine-learning-concepts.png)
 

learn-pr/wwl-data-ai/leverage-ai-tools/includes/3-learn-microsoft-ai-approach.md

@@ -1,21 +1,21 @@
 AI is disrupting every industry and every business. For the last decade, AI has enabled companies of all sizes to achieve better business results. There's already a **mainstream business use of AI** thanks to these three trends:
 
 * Access to massive amounts of data.
-* Access to massive computing power through cloud.
+* Access to massive computing power through the cloud.
 * Access to AI algorithms.
 
-However, AI is now experiencing major breakthroughs. A new generation of LLMs enables new use cases that weren't possible a few years ago, such as those based on high-quality generative AI. Based on these technologies, organizations will experience a **second wave of AI-powered transformation**. However, businesses need an easy way to access the latest AI if they want to take full advantage of it.
+AI is experiencing major breakthroughs. A new generation of Large Language Models (LLMs) enables new use cases that weren't possible a few years ago, such as those based on high-quality generative AI. Based on these technologies, organizations will experience a **second wave of AI-powered transformation**. However, businesses need an easy way to access the latest AI capabilities to take full advantage of them.
 
-Microsoft is working to democratize AI use. For this, it has designed a wide range of solutions and services to bring AI to everyone, irrespective of their level of AI expertise. There are four approaches, ranged from the level of AI and coding expertise required.
+Microsoft is working to democratize AI use. It has designed a wide range of solutions and services to bring AI to everyone, irrespective of their level of AI expertise. There are four approaches, varying based on the level of AI and coding expertise required.
 
 
 |**Microsoft Approach**|**Description**|
 |-|-|
-|**Microsoft Copilot** ![Photograph showing people at computers and the Microsoft Copilot logo.](../media/3-copilots.png)|Microsoft has embedded AI in everyday applications, so business users can benefit from it, even if they don't have coding or data science expertise. In this approach, AI is delivered as a Software as a Service (SaaS) and becomes transparent, that is, it's fully integrated within the provided service without users having to worry about it. For example, Microsoft Copilot for Microsoft 365 incorporates the latest generative AI in the shape of a virtual assistant that performs tasks for you in Microsoft 365 apps.|
+|**Microsoft Copilot** ![Photograph showing people at computers and the Microsoft Copilot logo.](../media/3-copilots.png)|Microsoft has embedded AI in everyday applications, so business users can benefit from it even if they don't have coding or data science expertise. In this approach, AI is delivered as Software as a Service (SaaS) and becomes transparent, that is, it's fully integrated within the provided service without users having to worry about it. For example, Microsoft Copilot for Microsoft 365 incorporates the latest generative AI in the shape of a virtual assistant that performs tasks for you in Microsoft 365 apps.|
 |**Microsoft Power Platform** ![Photograph showing person at computer and the Power Platform logo.](../media/3-power-platform.png)|A suite of low-code products that help you build different pieces of applications. These products have a layer of AI, but it's transparent as well and you can benefit from it without handling it directly.|
 |**Azure AI Services** ![Photograph showing person at computer and the Azure AI services logo.](../media/3-azure-ai-services.png)|These are the solutions for users who want to deliver an AI project but have little data science expertise. They offer pretrained AI models for you to reuse or customize.|
 |**Azure Machine Learning** ![Photograph showing person at computer and the Azure Machine Learning logo.](../media/3-azure-machine-learning.png)|All machine learning tasks can be handled from this service. It helps data science teams in setting, automating, and enabling machine learning best practices.|
 
-Keep in mind that Microsoft has designed all these products and services following [strict responsible AI principles](https://www.microsoft.com/ai/responsible-ai?activetab=pivot1:primaryr6). Any AI implementation should be equally respectful.
+Microsoft has designed all these products and services following [strict responsible AI principles](https://www.microsoft.com/ai/responsible-ai?activetab=pivot1:primaryr6). Any AI implementation should be equally respectful.
 
-The rest of this module covers each of these options. Next, we'll discuss the one with the lowest entry barrier, AI as copilot, embedded in everyday applications.
+The rest of this module covers each of these options. Next, we'll explore the simplest approach—AI as a copilot, seamlessly integrated into everyday applications.

learn-pr/wwl-data-ai/leverage-ai-tools/includes/4-use-ai-embedded-everyday-applications.md

@@ -1,4 +1,4 @@
-To truly realize the potential of AI, it’s essential to bring AI to every employee in ways that are relevant and meaningful to their work. Microsoft makes this possible by embedding AI in the applications people use in their everyday routine. No code or data science expertise is required because AI is delivered as just another feature of a SaaS product. The result is a wide range of intelligent applications for business users. 
+To truly realize the potential of AI, it’s essential to bring AI to every employee in ways that are relevant and meaningful to their work. Microsoft makes this possible by embedding AI in the applications people use everyday. No code or data science expertise is required because AI is delivered as just another feature of a SaaS product. The result is a wide range of intelligent applications for business users. 
 
 *Copilot* refers to AI embedded into applications. Microsoft Copilot provides a transformed experience across business functions and everyday routines. 
 
@@ -12,7 +12,7 @@ These solutions are often delivered as SaaS AI solutions, which deliver fast and
 
 |**Business function**|**Example scenario**|
 |-|-|
-|**Commerce**|Commerce users can use AI insights to help them more effectively manage cashflows using payment recommendations, intelligent budget proposals, and cashflow forecasting. They can even use AI to better protect their e-commerce business—and their customers—against fraud.|
+|**Commerce**| Users can use AI insights to help them more effectively manage cashflows using payment recommendations, intelligent budget proposals, and cashflow forecasting. They can even use AI to better protect their e-commerce business—and their customers—against fraud.|
 |**Customer service**|Customer service users can gain insights to address increasing volumes and manage efficient agent distribution. They can also create virtual agents that identify and resolve customer issues quickly—all without having to write code.|
 |**Finance**|Analysts are provided a range of AI-powered tools for real-time reporting, embedded analytics, and insights. For example, AI can predict when or whether their customers will pay their invoices.|
 |**Human Resources**|Workforce data can be transformed into actionable insights and next-best-action guidance. AI can also be used to automate HR tasks for employees, making procedures more agile.|
@@ -46,7 +46,7 @@ When you're not speaking in person, some nuances are missing and misunderstandin
 Nowadays, workers' routines are too often interrupted by distractions, calls, and multitasking. AI can also help cope with this problem and enable employees to focus their time and attention on what matters most.
 
 > [!NOTE] 
-> 68 percent of workers complain of their lack of uninterrupted focus time during their working routines.<sup>1</sup>
+> 68 percent of workers complain of their lack of uninterrupted focus time during their working routines.
 
 For instance, Microsoft 365 Copilot includes features for focus to make sure users don't forget any important issues. In OneNote, for example, it identifies unanswered questions all across existing notes and grouping them in one centralized location. In Teams, Copilot can extract action points from the conversation in real time.
 
@@ -65,7 +65,7 @@ Harnessing information has become the key to almost everything—from improving
 AI-powered search experiences like Microsoft Search can help business users wade through this data to uncover more effective insights and make better data-driven decisions. Microsoft Search enables users to search for people, files, sites, and more across their organizational data and public web data—all from within the Microsoft 365 products they’re already working in. Results are even personalized to each user to ensure relevance. This feature is improved with Copilot.
 
 > [!NOTE] 
-> 62 percent of employees consider they spend too much time struggling to find the information they need.<sup>1</sup>
+> 62 percent of employees consider they spend too much time struggling to find the information they need.
 
 > [!TIP]
 > ![Photograph showing manager in a car using Copilot on a tablet.](../media/copilot-customer-story.jpg) \