Laber Labs – Vikram K, Maddie L, Brooke W – CA Work Experience Program

Laber Labs | Day 6

Today we spent half of our time working on the video game, Zombies on Treadmills for Laber Labs! We collaborated with some of the game designers previously and today we tested out their levels. It felt especially special because us three were the first people to get to test the game!

The rest of the day we spent finalizing the quiz questions for our Alternative Stats quiz. We came up with some fitting responses to questions like Which is known for having a body of steel, a slim physique, and a charming personality? After determining whether the answer to this is David Blackwell, or a flirtatious spatula, you will get responses such as, David Blackwell was known for his modest and enthusiastic personality and his infectious smile, but was not made out of metal, and the flirtatious spatula is firm and unyielding in their body AND their attraction to you. ( ͡° ͜ʖ ͡°)

Laber Labs: Day 6

Today we spent our work day testing the game that we contributed some level designs for, Laber Lab’s Zombies on Treadmills. You can play the demo on your web browser here! (Just a reminder that this is a demo and not reflective of the final product)

Shown in the picture above is a level that I designed, it is a stage in the shape of a dinosaur 😊

The premise of the game is that zombies have taken over, and you want to use the treadmills (the platforms with the arrows) to direct them on a path through walking in the directions of the arrows towards volcanoes (which kill zombies) and away from houses (which contain people, who you want to minimize the deaths of). You can use the WASD keys to move the camera, as well as zoom in and out using the trackpad. You can change the direction of the treadmills by clicking on them to create a path for the zombies to walk. The “Zombie Noncompliance” variable is the chance that zombies may not follow the direction of the treadmills, which you will need to account for. Once you have created a path you believe should work, you can click the “Go!” button to spawn the zombies and see everything play out.

We were the first people to be able to test the game, and gave feedback to the team at Laber Labs on various aspects of the demo, such as the artwork, gameplay mechanics, sound design, and controls. It was certainly a one of a kind experience that I am grateful for Laber Labs for giving me the opportunity to have a glimpse at the behind the scenes of developing a video game!

Day 6: Laber Labs

On our final day, Danny and Sasha came back to show us the levels we created on our first day in the game. We got the chance to play through them and give them feedback on what we liked and what we thought could be improved.

Afterwards, we finished the alternative stats quiz. We changed the spatula to be the “flirtatious spatula”, and we came up with around six questions that had David Blackwell as the correct answer and two with the spatula as correct.

I am extraordinarily thankful to Dr. Laber and everyone who came to teach us during the Work Experience Program for giving their time to help us grow and develop our interest in data science. Working at Laber Labs was incredibly rewarding and I was able to learn a lot throughout the program, and it furthered my decision to pursue a career in computer science in my post secondary education.

Laber Labs | Day 5

We were over zoom today as well, and met with Dr. Laber to discuss statistical models and do some R code. Specifically, we learned how to make a regression model using R. We brainstormed ideas on how to model this data, and then put it into R.

In the afternoon, we met with Jesse Clifton a statistics student at NC State and he discussed AI and the moral and ethics of using such technology. Firstly, he explained some of the big breakthroughs in AI tech lately, then venturing on to potential risks in using AI, such as the AI prioritizing different things and putting us (humans) at risk.

Laber Labs: Day 5

Today’s workday was also over a Zoom meeting. We learned how to make a regression model using R, a programming language that specializes in statistical computing! We brainstormed ideas for making a model of data, using patients as an example. One way would be to plot the x and y points and find the line of best fit and create a mathematical formula/function y = f(x), and another way would be to find similar patients and utilize their already-existing data and formulas.

In the afternoon, we listened to Jesse Clifton, a Ph.D. statistics student at NC State University, talk about the morals and ethics of using artificial intelligence. First we were introduced to recent significant breakthroughs in AI, such as AlphaZero (an AI that plays chess, shogi, and go) and GPT3 (an AI that translates a text input into code). We learned about the risks of using artificial intelligence, such as fairness and reducing bias, catastrophic outcomes due to caring/prioritizing about the wrong thing, disempowering humans, and getting into conflicts.

Laber Labs | Day 4

Over a zoom meeting, Justin Weltz from Duke university called us to discuss social networks and sampling methods. We discussed the pros and cons of biased and random sampling and applied sampling probabilities with math. We also further discussed reinforcement learning today and how it connects to AI and machine learning.

Day 5: Laber Labs

Since he had contracted COVID, Dr. Laber connected with us over Zoom today. We started the day off by learning how to plot a linear regression model in R. A linear regression model’s goal is to find the line of best fit on a scatter plot, and it does this by minimizing the loss (the distance between the points and the line of best fit on the y-axis). We also talked about K Nearest Neighbors, which is an algorithm that makes predictions about where a specific point may be. He also gave us the opportunity to write our own K Nearest Neighbors program where we write the same program he gave us but with multiple neighbors. This was my attempt to do so.

In the afternoon, we talked to Jesse Clifton, who’s a PhD student at NC State, about the ethics of Artificial Intelligence. Since machine learning is utilized by giving it a task, it’ll accomplish that task to the best of its ability by whatever means necessary. This means that if you give it a goal that isn’t perfect, it’ll find loopholes in order to maximize its rewards. The reason why it’s so important to have diversity in the field of artificial intelligence and machine learning is so that the goals of these algorithms aren’t determined by a group made up by people with the same viewpoint or perspective.

Laber Labs | Day 3

Alex Cloud, from Riot Games, came in to talk and teach us about some new statistical concepts. Including, luck/skill in games, estimands, estimators, and more. I got a lot of new takes on statistics and how it works its way into lots of things from this talk. We also got to play around with the neural network called DALL-E. Its function is to take captions produced by people and create AI-generated photos from the caption given. He let us play around with the AI and make different captions for the AI to try and recreate, some turned out really great, some didn’t fit the concept we were looking for, and some looked like it was pulled off of DeviantArt (an art sharing platform) haha

Here are some examples taken from the DALL-E website of what the AI can recreate with those given captions.

Laber Labs: Day 4

Today’s workday was over a Zoom meeting, in which Justin Weltz of Duke University taught us about various sampling methods and social networks. We learned about the pros and cons between random sampling and biased sampling, as well as how to apply sampling probabilities to specific mathematic formulas. Additionally, we learned more about reinforcement learning (which we had touched upon previously) and its relationship to artificial intelligence and machine learning. We also got to ask Mr. Weltz about his experience as a current graduate student doing research projects at Duke, such as what it’s like to work on research projects and how to set goals for these projects, advice for graduate school, and what topics in math and programming he would advise to learn if one is interested in pursuing a career in data science.

Day 4: Laber Labs

Justin Weltz, a PhD student at Duke, talked to us over a Zoom call about the pros and cons of random sampling as well as reinforcement learning, which is machine learning (which we touched upon with Alex and Dr. Laber) that concerns how agents should act in order to maximize the “rewards” they get (rewards are basically like treats for a program if it does what it’s told). He spoke about what fields he utilizes these in, such as precision medicine. Afterwards, we got the opportunity to speak with him about what being a PhD student is like and what doing research is like for him.