starting the broader outlook section

readme.md

@@ -98,7 +98,7 @@ we use the MRR.
 | MRR@5           | **0.5039**   | 0.3957|
 | MRR@10          | **0.5204**   | 0.4129|
 
-Is it true that we used MSCOCO-IT in training, and this might give us an advantage. However the original CLIP model was trained
+It is true that we used MSCOCO-IT in training, and this might give us an advantage. However the original CLIP model was trained
 on 400million images (and some of them probably were from MSCOCO).
 
 [Colab: Image Retrieval Evaluation](https://colab.research.google.com/drive/1bLVwVKpAndpEDHqjzxVPr_9nGrSbuOQd?usp=sharing)
@@ -130,6 +130,10 @@ the translated image labels might have had an impact on the final scores.
 
 # Broader Outlook
 
+We believe that this model can be useful for many different applications, not only in research settings. Italy has many different collections
+of photos in digital format. For example, the [Istituto Luce Cinecittà](https://it.wikipedia.org/wiki/Istituto_Luce_Cinecitt%C3%A0) is an Italian governative entity that collects photos of Italy since the
+early 1900 and it is part of the largest movie studios in Europe (Cinecittà). 
+
 # References
 
 Scaiella, A., Croce, D., & Basili, R. (2019). [Large scale datasets for Image and Video Captioning in Italian.](http://www.ai-lc.it/IJCoL/v5n2/IJCOL_5_2_3___scaiella_et_al.pdf) IJCoL. Italian Journal of Computational Linguistics, 5(5-2), 49-60.