Finished hello world part of assignment 1 and started the explication…

projects/1-explications/Mishra.md

@@ -0,0 +1,7 @@
+    One example of an AI system is the Nest Learning Thermostat. Taking one step closer to the sought after smart home, Nest developed a termostat the sets the temperature after learning user's preferences. It adapts as users preferences change through seasons or other variables. They claim that users just need to use the device for a week before it "programs itself" to automatically set the temperature. 
+    Nest's learning thermostat was created by former Apple engineers Tony Fadell and Matt Rodgers after Fadell was inspired to bring a better thermostat to the market after building a vacation home. He created the learning thermostat to take advanced technology and apply it to everyday products to brighten the overall home experience. To do so, he made the termostat simple, well-designed, and, according to their website, "human." 
+    The thermostat's "human" label seems to be a lofty claim, but on a basic level Nest seems to achieve its goal. Nest uses primarily statistical methods since it learns from the users desires instead of hand-coded facts. Over the course of the first week, it takes data from the user throughout the day to learn their temperature preferences. From this data, it most likely fits a function to predict the temperature for time beyond the first week. Of course, there will be times where the thermostat is wrong due to fluctuations in the user's patterns. However, humans are habit-based creatures and so the thermostat will most times be correct. The system interacts with humans through an app connected to the thermostat. On the app, users can set and see the temperature for individual rooms/locations on a dial. It also displays auxilary information such as the humidity percentage. 
+    Where Nest's thermostat becomes "human" is in it's collective analysis of the data it learns from to predict human patterns without guidance. For example, it has a feature called Auto-Away that saves energy by not heating/cooling the house while it's inhabitants are away. This means that, while you're at work for example, the thermostat will turn off, but be smart enough to know that you come home, say, around 5 o'clock to have the house at a comfortable temperature by that time. Nest learns it's users patterns inside and out to provide complex heating/cooling patterns. This is an example of statistical learning, but it also has knowledge-based learning. For example, Auto-Away also has a night feature, where most people are sleeping, to keep the temperature comfortable from 8 pm up to 11 am. These times can be adjusted based on preferences, but are, ultimatley, hard-coded into the system. One flaw in the thermostat arises from the spontaneity of human nature. It cannot predict certain decisions humans may make, and so, can experience subtle flaws in its design. Nonetheless, the thermostat still manages to retain its human qualities through learning general patterns as specifically as possible. 
+    Other flaws peek through in the interdependency of Nest's thermostat. For example, they use a separate iPhone app for humans to control the device. Although security backs up this app, hostile third party could hack into the app as a whole or on an individual basis. Through this, they could change features for the entire database or provide inaccurate data to the thermostat. The latter would change the thermostat's patterns drastically, thus destroying the intrinsic value of the product as a predictive thermostat. 
+    Overall, Nest has a stagerring 1.1 million users with diverse home locations and interests. Still, they manage to serve those differences by providing a general product applicable for most of its users. Nest's target market are all house owners with heating/cooling systems. This population has a general need of control over the temperature of their house, which Nest's thermostat can fill. Still, biases reside in the design and hard-coded parts of the thermostat. These can include such things as what time a user goes to sleep/wakes up alongside other factors. 
+    Nest achives its mission of creating a simple and "human" thermostat for the general population because of their dependence on AI. Although they face errors in spontaneity, hacking, and biases, Nest's thermostat remains a widespread product that can learn general human patterns.  

projects/1-hello-world/Mishra.ipynb

@@ -0,0 +1,44 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hello! My name's Katie and I live in Menlo Park, quite close to Stanford. I started programming in freshman year (I'll be a junior next year). Since then, I have completed Stanford's CS106A (equivalent of AP CS in Java) and CS106B (C++ Algorithms + Data Structures). I've also completed MakeSchool's Swift course to make apps and Coursera's Machine Learning course from Stanford. I lead the Java programming club at my school as well as the more general CS club. I'm also the lead programmer of my FIRST Robotics team where I lead the programming of industrial size robots and develop vision systems for a global competition. I also volunteer a lot at the Boys and Girls Club for their CS classes. This summer I'm developing CS curriculum for their middle and high school programs to be used throughout the bay area. This Spring, I created an iOS app to aid teen stress and was a global semi-finalist in the all-girls Technovation competition. Aside from CS, I've published two books (and will publish the third in August!) :) From this course, I hope to learn how to implement diverse algorithms to apply in my individual machine learning work with robotics and other projects. AI has always been the most interesting topic in CS for me, so I'm very interested in everything about it - from ethics to algorithms to history and more! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "print(Katie Mishra)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [Root]",
+   "language": "python",
+   "name": "Python [Root]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}