diff --git a/body.tex b/body.tex
index f471eec..d10e7d1 100644
--- a/body.tex
+++ b/body.tex
@@ -19,7 +19,7 @@ figure out by themselves what connections are necessary for that.
 This feature is also their Achilles heel: it makes them effectively
 black boxes and prevents any answers to questions of causality.
 
-However, these questions of causility are of enormous consequence when
+However, these questions of causality are of enormous consequence when
 results of neural networks are used to make life changing decisions:
 Is a correlation enough to bring forth negative consequences
 for a particular person? And if so, what is the possible defence
@@ -32,7 +32,7 @@ Such an explanation must come from the network or an attached piece
 of technology to allow adoption in mass. Obviously this setting
 poses the question, how such an endeavour can be achieved.
 
-For neural networks there are fundamentally two type of tasks:
+For neural networks there are fundamentally two types of tasks:
 regression and classification. Regression deals with any case
 where the goal for the network is to come close to an ideal
 function that connects all data points. Classification, however,
@@ -72,7 +72,7 @@ that the network was never trained on a particular type of object.
 Therefore, it would be impossible for them to identify the output
 of the network as false positive.
 
-This goes back to the need for automatic explanation. Such a system
+This reaffirms the need for automatic explanation. Such a system
 should by itself recognise that the given object is unknown and
 hence mark any classification result of the network as meaningless.
 Technically there are two slightly different approaches that deal
@@ -114,9 +114,11 @@ novelty score.
 
 Auto-encoders work well for data sets like MNIST~\cite{Deng2012}
 but perform poorly on challenging real world data sets
-like MS COCO~\cite{Lin2014}. Therefore, a comparison between
-model uncertainty and novelty detection is considered out of
-scope for this thesis.
+like MS COCO~\cite{Lin2014}, complicating any potential comparison between
+them and object detection networks like SSD.
+Therefore, a comparison between model uncertainty with a network like
+SSD and novelty detection with auto-encoders is considered out of scope
+for this thesis.
 
 Miller et al.~\cite{Miller2018} used an SSD pre-trained on COCO
 without further fine-tuning on the SceneNet RGB-D data