Update Q1 2023 Funding Announcement.md

Author: Almalibro

content/news/2022/Q1 2023 Funding Announcement.md

@@ -87,7 +87,9 @@ We're also lacking a higher foundation for SPA development. For example, we don'
 
 My goal with this round is to implement Sparse Matrix support in Neanderthal. Neanderthal is a Clojure library for fast matrix computations based on the highly optimized native libraries and computation routines for both CPU and GPU.
 
-It is a lean, high performance, infrastructure for working with vectors and matrices in Clojure, which is the foundation for implementing high performance computing related tasks, including, but not limited to, machine learning and artificial intelligence. It currently supports operations on vectors, general matrices, and structured sparse matrices. A major missing part is support for arbitrary, unstructured, sparse matrices, and functions operating on them. Sparse matrices are matrices with mostly *zero* values. Although in theory the same data can be represented by general matrices, sparse matrices enable efficient support for gigantic matrices, since we do not need space for storing all these zeroes, and we can optimize operations to only compute the affected values (which are small percentage of the total).
+It is a lean, high performance, infrastructure for working with vectors and matrices in Clojure, which is the foundation for implementing high performance computing related tasks, including, but not limited to, machine learning and artificial intelligence. It currently supports operations on vectors, general matrices, and structured sparse matrices. 
+
+A major missing part is support for arbitrary, unstructured, sparse matrices, and functions operating on them. Sparse matrices are matrices with mostly *zero* values. Although in theory the same data can be represented by general matrices, sparse matrices enable efficient support for gigantic matrices, since we do not need space for storing all these zeroes, and we can optimize operations to only compute the affected values (which are small percentage of the total).
 
 Sparse matrices are useful wherever general matrices are useful (which is in most places where we work with many numbers) and the ratio of non-zero to zero values is relatively small (which is often the case). Besides machine learning, this includes common problems where we represent sparse conectedness between items (say, which out of many millions of items each of hopefully millions of users bought) and subsequent operations of such data. This could potentially be very useful for developers of graph-based software (lots of Clojure Code works with graphs, implicitly, or explicitly).