Tryng to beat acrolinx

Author: GraemeMalcolm

learn-pr/wwl-data-ai/get-started-ai-fundamentals/includes/5-natural-language-processing.md

@@ -2,12 +2,12 @@
 
 Key points to understand about natural language processing (NLP) include:
 
-- NLP capabilities are based on models that are trained to perform particular types of text analysis.
+- NLP capabilities are based on models that are trained to do particular types of text analysis.
 - While many natural language processing scenarios are handled by generative AI models today, there are many common text analytics use cases where simpler NLP language models can be more cost-effective.
 - Common NLP tasks include *entity extraction* (identifying mentions of entities like people, places, organizations in a document), *text classification* (assigning document to a specific category) - including *sentiment analysis* (determining whether a body of text is positive, negative, or neutral), and language detection (identifying the language in which text is written).
 
 > [!NOTE]
-> In this module, we've used the term *natural language processing* (NLP) to describe AI capabilities that involve deriving meaning from "ordinary" human language" (usually in text format). You might also see this area of AI referred to as *natural language understanding* (NLU).
+> In this module, we've used the term *natural language processing* (NLP) to describe AI capabilities derive meaning from "ordinary" human language. You might also see this area of AI referred to as *natural language understanding* (NLU).
 
 ## Natural language processing scenarios
 

learn-pr/wwl-data-ai/get-started-ai-fundamentals/includes/7-responsible-ai.md

@@ -4,17 +4,17 @@ Key points to understand about responsible AI include:
 
 - **Fairness**: AI models are trained using data, which is generally sourced and selected by humans. There's substantial risk that the data selection criteria, or the data itself reflects unconscious *bias* that may cause a model to produce discriminatory outputs. AI developers need to take care to minimize bias in training data and test AI systems for fairness.
 - **Reliability and safety**: AI is based on probabilistic models, it is not infallible. AI-powered applications need to take this into account and mitigate risks accordingly.
-- **Privacy and security**: Models are trained using data, which may include personal information. AI developers have a responsibility to ensure that the training data is kept secure, and that the trained models themselves cannot be used to reveal private personal or organizational details.
-- **Inclusiveness**: The potential of AI to improve lives and drive success should be open to everyone. AI developers should strive to ensure that their solutions do not exclude some users.
+- **Privacy and security**: Models are trained using data, which may include personal information. AI developers have a responsibility to ensure that the training data is kept secure, and that the trained models themselves can't be used to reveal private personal or organizational details.
+- **Inclusiveness**: The potential of AI to improve lives and drive success should be open to everyone. AI developers should strive to ensure that their solutions don't exclude some users.
 - **Transparency**: AI can sometimes seem like "magic", but it's important to make users aware of how the system works and any potential limitations it may have.
-- **Accountability**: Ultimately, the people and organizations that develop and distribute Ai solutions are accountable for their actions. It's important for organizations developing AI models and applications to define and apply a framework of governance to help ensure that they apply responsible AI principles to their work.
+- **Accountability**: Ultimately, the people and organizations that develop and distribute AI solutions are accountable for their actions. It's important for organizations developing AI models and applications to define and apply a framework of governance to help ensure that they apply responsible AI principles to their work.
 
 ## Responsible AI examples
 
 Some example of scenarios where responsible AI practices should be applied include:
 
 - An AI-powered college admissions system should be tested to ensure it evaluates all applications fairly, taking into account relevant academic criteria but avoiding unfounded discrimination based on irrelevant demographic factors.
 - An AI-powered robotic solution that uses computer vision to detect objects should avoid unintentional harm or damage. One way to accomplish this goal is to use probability values to determine "confidence" in object identification before interacting with physical objects, and avoid any action if the confidence level is below a specific threshold.
-- A facial identification system used in an airport or other secure area should delete personal images that are used for temporary access as soon as they are no loner required. Additionally, safeguards should be in place to prevent the images being made accessible to operators or users who have no need to view them.
+- A facial identification system used in an airport or other secure area should delete personal images that are used for temporary access as soon as they're no loner required. Additionally, safeguards should prevent the images being made accessible to operators or users who have no need to view them.
 - A web-based chatbot that offers speech-based interaction should also generate text captions to avoid making the system unusable for users with a hearing impairment.
 - A bank that uses an AI-based loan-approval application should disclose the use of AI, and describe features of the data on which it was trained (without revealing confidential information).