Computing and technology are neutral for the most part. They can be used to enhance the best aspects of human nature (such as empathy, creativity, and generosity), or they can allow the worst aspects of it (such as prejudice, selfishness, and irrational thinking) to flourish and cause harm. Incorporating ethics into design and coding involves trying to anticipate all the ways – positive and negative – humans may use and interact with tech. While it’s difficult to exert complete control over human users, tech can be designed to promote particular behavior and outcomes.
Based off the CSS Zen Garden, this track aims to show you the power of HTML and CSS.
CSS stands for Cascading Style Sheets with an emphasis placed on “Style.” While HTML is used to structure a web document (defining things like headlines and paragraphs, and allowing you to embed images, video, and other media), CSS comes through and specifies your document’s style—page layouts, colors, and fonts are all determined with CSS. (source)
"Although there are new tools and technologies to help with frontend design, we want to focus our attention back on the basics. CSS is a powerful and often overlooked tool, and with this design track, we hope you will learn something new about CSS.")
This track will allow you to explore interesting data sets using machine learning and other data science techniques. The goal is simple – find something interesting in your data and present it in a compelling manner. Projects with a social impact are especially encouraged.
At Bill.com, we are constantly looking for new and exciting ways to make it simple for business owners to connect and do business. To this year's hackers we ask: "What are some outside of the box ways to leverage technology to accomplish this?"
We want to challenge you to reimagine our user experience by embedding Bill.com everywhere, and will be providing home devices, smartwatches and tablets for anyone interested in developing on these systems.
The Bill.com team will work closely with hackers to leverage the Bill.com API and client libraries, and host a Q&A workshop to help get teams up and running with their projects. We look forward to meeting all of the this year's participants and seeing what you all come up with!
From classical statistical methods to real-time constraint optimization, Bayesian neural networks, and reinforcement learning, Schlumberger uses Machine Learning to gain maximum insight from huge volumes of data. Our challenge for you: use Machine Learning to solve a problem of your choice. We will grade you on your idea, your implementation, and the amount of fun you had hacking!
Background: Chevron has large scale operations and complex process facilities (refineries and liquified natural gas production facilities). Scheduling work orders on these facilities with differing repair requirements, specialized technicians, and potentially hours of drive time between locations can be difficult.
Challenge: Build a work order tracking system that tracks (1) the work orders that are submitted and (2) the technicians that are completing them to optimize how technicians are assigned and work orders are completed. Knowing where technicians are, what they are certified/qualified to repair, how long they are planning to being there, other work orders in the same or nearby location, etc will be invaluable in being able to dynamically schedule and dispatch existing and new work orders to technicians at the beginning of the day and while onsite. Updating each technician with their schedule of work orders can be done through any means of mobile technology, SMS, call, mobile app, etc.
Data set here!
Best Algorithmically Complex App:
We challenge you to put the skills Luay has taught you to good use.
Oculus Gos will be awarded to the team that utilizes a non-trivial algorithm or AI as part of their application to create value for users.
While external libraries and services may be used to aid your app, we will only evaluate the components of the app created by your team.
A pilot decides she wants to go flying this weekend because the weather is really nice, but she can’t decide where she wants to go. We know what airports she’s flown to in the past; what’s an airport she’s never been to but would enjoy flying to? Use our historical data to build a machine learning model that can recommend a new flying destination. Then, if you’re up for the challenge, package the model into an iOS app for on-the-go flight planning!
At Two Sigma, we use machine learning, distributed computing and other technologies to find connections in the world’s data. We work with vast quantities of information from over a thousand diverse sources. We love learning from others, and we want to see what you can do in the data science space. We’ll award a prize for the best use of data analysis, data science, or big data technologies.
Our challenge will be the JPMorgan Chase “Best Hack for Social Good
Each team member will receive one of the mentioned prizes.