Reflection – Learning Launch 

 Challenge 

 I am a member of my organization ’s engineering team and our challenge is to develop a compelling and   robust software engineering solution for our customer . Our customer is an automobile company and the   challenge is to develop a software tool / service that tests the Artificial Intelligence ( AI ) Software that   makes the car more autonomous . For example , there would be a software to test Tesla ’s Autopilot feature   and tell how good / bad it is doing . We are trying to do something similar that is in line with our customer ’s   expectations . We are a team of 6 and the allocated time for the project is 6 months . With the learning   launches that take place alongside the incremental software development activity during the 6 month   time period , we will focus on trying to uncover what our customer appreciates and what needs more work   in order to improve a certain functionality offered by the software tool . 

 Selection 

 We are going to go ahead with the learning launch tool in order to quickly engage with the customer and   test our key customer assumptions . Learning launch is a fast and inexpensive experiment that allows us   to test assumptions , ideas and prototypes . 

 Application 

 Learning Launch 0.1 : Prior to engaging with the customer , we developed a prototype of our software tool   and had our ‘ Learning Launch 0.1 ’ event one month into the project . Although all the internal   functionalities of the prototype was n’t yet ready , the User Interface ( UI ) of the prototype in fact was quite   advanced , and with this UI we were able to showcase the various offerings of the software tool . After a   comprehensive demonstration of the prototype , we let the customer experiment with the prototype by   navigating through the UI of the prototype on the computer screen . Through this hands - on exercise , the   customer got a look and feel of the prototype ( and in a sense what the eventual end product would be   like ) and this tested our assumption about how easy it is to use the tool and how the tool provides various   metrics ( example : The accuracy of the Artificial Intelligence Software is 81 % ) that the customer expects .   Broadly speaking , this tested the assumption that our understanding of what the tool should offer is in   line with customer expectations . 

 Learning Launch 0.2 : About 4 months into the project , the first version of our end product ( software tool )   was ready . In order to perform more iterations on our end product based on customer feedback , we had   our ‘ Learning Launch 0.2 ’ event . Here , similar to ‘ Learning Launch 0.1 ’ the customer was able to use the   software tool , get a good look and feel of it and also got to play around with some sample data we had   made available . This sample data consisted of various images that one would see while driving on the   road . The software tool would rate the performance of the Artificial Intelligence ( AI ) Software ( example :   Tesla ’s autopilot ) and provide feedback on how good the AI software is in detecting objects such as   pedestrians , other cars , trucks , traffic signs etc . , that were present in the sample image data . This tested   our assumption that the software tool is robust and in fact made use of data varied in both quality and 

 quantity in order to test the AI software system . We also asked our customer to participate in a survey   along with us . This survey consisted of questions such as which functionalities of the software tool needed   more work ? , which functionalities were highly mature ? , did the team need any additional resources to   complete the project within the 6 - month time period ? And any additional comments if any . 

 Insight 

 By applying the learning launch tool , our team identified our assumptions and designed launches to test   these assumptions . From the first learning launch , we got feedback on the user experience ( UX ) of the   software tool and it was pointed out to us that this experience could in fact be improved by not throwing   in a lot of information at the computer screen , but rather allowing the user the option to see advanced   information . From the second learning launch , we learned that speed of the tool ( how fast it responds to   the user ) could be improved . We also learned that the customer was happy with the variety of data that   was used to test the AI system . Going further , our team will review the mentioned insight . 

 Approach 

 The reviewed insight will be incorporated in our next learning launch : ‘ Learning Launch 0.3 ’ . This learning   launch is expected to happen before the 6 - month time period ends . The incorporation of insight is in   progress and is quite significant as the next launch comes before a full rollout of our end product . ‘ Learning   Launch 0.3 ’ will reflect insight gained , progress made , and come closer to a robust and compelling   software engineering solution for our customer . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Reflection – Learning Launch 

 Challenge 

 I am a member of my organization ’s engineering team and our challenge is to develop a compelling and   robust software engineering solution for our customer . Our customer is an automobile company and the   challenge is to develop a software tool / service that tests the Artificial Intelligence ( AI ) Software that   makes the car more autonomous . For example , there would be a software to test Tesla ’s Autopilot feature   and tell how good / bad it is doing . We are trying to do something similar that is in line with our customer ’s   expectations . We are a team of 6 and the allocated time for the project is 6 months . With the learning   launches that take place alongside the incremental software development activity during the 6 month   time period , we will focus on trying to uncover what our customer appreciates and what needs more work   in order to improve a certain functionality offered by the software tool . 

 Selection 

 We are going to go ahead with the learning launch tool in order to quickly engage with the customer and   test our key customer assumptions . Learning launch is a fast and inexpensive experiment that allows us   to test assumptions , ideas and prototypes . 

 Application 

 Learning Launch 0.1 : Prior to engaging with the customer , we developed a prototype of our software tool   and had our ‘ Learning Launch 0.1 ’ event one month into the project . Although all the internal   functionalities of the prototype was n’t yet ready , the User Interface ( UI ) of the prototype in fact was quite   advanced , and with this UI we were able to showcase the various offerings of the software tool . After a   comprehensive demonstration of the prototype , we let the customer experiment with the prototype by   navigating through the UI of the prototype on the computer screen . Through this hands - on exercise , the   customer got a look and feel of the prototype ( and in a sense what the eventual end product would be   like ) and this tested our assumption about how easy it is to use the tool and how the tool provides various   metrics ( example : The accuracy of the Artificial Intelligence Software is 81 % ) that the customer expects .   Broadly speaking , this tested the assumption that our understanding of what the tool should offer is in   line with customer expectations . 

 Learning Launch 0.2 : About 4 months into the project , the first version of our end product ( software tool )   was ready . In order to perform more iterations on our end product based on customer feedback , we had   our ‘ Learning Launch 0.2 ’ event . Here , similar to ‘ Learning Launch 0.1 ’ the customer was able to use the   software tool , get a good look and feel of it and also got to play around with some sample data we had   made available . This sample data consisted of various images that one would see while driving on the   road . The software tool would rate the performance of the Artificial Intelligence ( AI ) Software ( example :   Tesla ’s autopilot ) and provide feedback on how good the AI software is in detecting objects such as   pedestrians , other cars , trucks , traffic signs etc . , that were present in the sample image data . This tested   our assumption that the software tool is robust and in fact made use of data varied in both quality and 

 quantity in order to test the AI software system . We also asked our customer to participate in a survey   along with us . This survey consisted of questions such as which functionalities of the software tool needed   more work ? , which functionalities were highly mature ? , did the team need any additional resources to   complete the project within the 6 - month time period ? And any additional comments if any . 

 Insight 

 By applying the learning launch tool , our team identified our assumptions and designed launches to test   these assumptions . From the first learning launch , we got feedback on the user experience ( UX ) of the   software tool and it was pointed out to us that this experience could in fact be improved by not throwing   in a lot of information at the computer screen , but rather allowing the user the option to see advanced   information . From the second learning launch , we learned that speed of the tool ( how fast it responds to   the user ) could be improved . We also learned that the customer was happy with the variety of data that   was used to test the AI system . Going further , our team will review the mentioned insight . 

 Approach 

 The reviewed insight will be incorporated in our next learning launch : ‘ Learning Launch 0.3 ’ . This learning   launch is expected to happen before the 6 - month time period ends . The incorporation of insight is in   progress and is quite significant as the next launch comes before a full rollout of our end product . ‘ Learning   Launch 0.3 ’ will reflect insight gained , progress made , and come closer to a robust and compelling   software engineering solution for our customer .
### Output: 