Merge pull request #411 from SJaffa/410-unit-1-grammar

Unit 1 grammar & spelling fix

Author: pcuenca

chapters/en/unit1/chapter1/motivation.mdx

@@ -34,11 +34,11 @@ All of this is to show you that our ability to distinguish objects extends beyon
 
 This underscores the necessity for more robust systems--ones capable of adapting to a variety of scenarios. This is why the field is so closely related to artificial intelligence. Vision is context-rich, and we need models that are capable of leveraging these clues similarly to what we do.
 
-Let's take the example of Indiana Jones running from a boulder. There is a ball and there is running, but no one would rarely call that a sport! We know this because we rely on some context clues. The ball Indiana Jones is running away from looks heavy and twice his size. His face reflects his distress. The space is very narrow and it looks like a cave which is unusual for sports. Plus, we recognize his attire and that is not usually how players dress themselves. 
+Let's take the example of Indiana Jones running from a boulder. There is a ball and there is running, but no one would really call that a sport! We know this because we rely on some context clues. The ball Indiana Jones is running away from looks heavy and twice his size. His face reflects his distress. The space is very narrow and it looks like a cave which is unusual for sports. Plus, we recognize his attire and that is not usually how players dress themselves. 
 
 ## The Motivation Behind Creating Artificial Systems Capable of Simulating Human Vision and Cognition
 
-Albeit they have similar input and output, human vision and computer vision are different processes. Sometimes they overlap. However, computer vision is primarily concerned with developing and understanding algorithms and models in vision systems and their decisions. It is not constrained to the creation of systems that replicate human vision. It can be used for problems that would be too tedious, time-consuming, expensive, or error-prone for humans to do.
+Although they have similar input and output, human vision and computer vision are different processes. Sometimes they overlap. However, computer vision is primarily concerned with developing and understanding algorithms and models in vision systems and their decisions. It is not constrained to the creation of systems that replicate human vision. It can be used for problems that would be too tedious, time-consuming, expensive, or error-prone for humans to do.
 Our ball example is still a simple one, and you might not think that is super useful. However, a model capable of tracking a ball can be used in sports events to provide faster and more fair decisions during gameplay. With the popularization of image-to-text and text-to-speech models, we could also make live sports events more accessible for people who have vision disabilities by automatically tracking the ball and its players and describing it in real time. Thus, even simple use cases can have a positive impact on society. We will discuss more about this in Section 3.
 
 We are now on the cusp of an AI renaissance.  A moment in time when we can train, deploy, and share our model freely. A moment when our models can detect things in images that we would not be able to see ourselves.