By Anne Li
Last month, I had the opportunity to attend Apple’s WorldWide Developer’s Conference as one of 350 scholarship winners from around the world. In this post, I’ll go over my entire experience – from applying back in March to attending in June.
I’ve known about WWDC for several years now, and this year was my second time applying for the scholarship – so I have a general idea of what both a winning submission and a not-winning submission look like. Before getting into that, though, let’s cover the basics:
- I’m honestly not sure about this, but if I remember correctly, the application usually opens the second week of March
- The submission window is small – I think around 10 days from when the application portal opens to when it closes
- The application requires applicants to upload a Swift or Xcode Playground along with responses to several essay questions
I was rejected the first time I applied, which was last year. I submitted an Xcode Playground that displayed a graph of a Taylor polynomial for the sine function (link to Github repo). I thought it was really cool at the time, but in retrospect I think it was pretty lame (probably because I don’t remember how to find Taylor polynomials anymore). The playground also gave users the option of changing the center and degree of the polynomial in order to see how those factor into its overall shape.
This year, I wanted to do something involving the algorithms I’d encountered in competitive programming, so I submitted an Xcode Playground that introduced users to breadth-first search and depth-first search. I tried to make it a lot more creative this year – incorporating mazes as a way of teaching the graph-traversal algorithms, and making it more of a game. I also drew some cute illustrations (in MS Paint lol). You can check out my playground here.
Apple paid for my ticket to the conference, as well as a week of lodging, but I was responsible for transportation to and from the conference.
Here’s a day-by-day summary of the conference:
- Orientations, check-in, scholarship winners’ kickoff, etc.
- I got to meet my fantastic counselor, roommate, and some other scholarship winners!
- We also got our badges and a bunch of random stuff from Apple (pins, jacket, etc.)
- Keynote, Platform State of the Union, Apple Design Awards
- One of the most important events of WWDC; Tim Cook (Apple CEO) and other Apple engineers and developers spoke during the Keynote
- A lot of announcements, including Dark Mode for iOS, SwiftUI, Mac Pro, etc.
- Women@WWDC Breakfast, NCWIT Roundtable Discussion
- The breakfast included a panel of women who’d won scholarships this year, as well as alumni of Apple’s Entrepreneurship Camp. One of our friends was selected for the panel, so we went to watch and support her and the others
- The roundtable discussion was later in the day – we got to talk to Apple Senior Director of WorldWide Developer Marketing Esther Hare and four Entrepreneurship Camp alumni (link to post on NCWIT blog about the experience)
- Mostly just technology labs
- I think almost any WWDC-related blog you’ll find on the web will urge you to attend labs rather than sessions, since sessions are available online after the conference – and I have to agree. Getting one-on-one advice from Apple engineers on any projects you might be working on is infinitely more helpful
- More labs
- One of the best ones I attended was the UI Design Lab; you’re paired with an Apple designer who looks at your stuff and provides feedback on the design
- The scholarship lounge is really nice
- The food is okay
- San Jose has a lot of boba shops, including two within a couple minutes’ walking distance from the convention center (I think Gongcha and Breaktime)
- A lot of walking, especially if you decide to explore the city and/or get food outside of the conference and/or get boba
- If you end up going, be sure to check out some of the events taking place at AltConf! AltConf is free and takes place in the Marriott directly adjacent to the convention center. I went with a couple friends to a really great talk by Mayuko Inoue
- I tried to ride the VTA once and got on the wrong one. Apparently I still haven’t learned how to read numbers. I also didn’t pay attention, so didn’t realize I was on the wrong one until ~20 minutes into the trip. In conclusion, don’t ride the VTA unless you’re capable of reading numbers.
How to win a WWDC scholarship
I don’t think there’s a formulaic or clear-cut method to win a scholarship. Sorry if you just read the last ~1100 words just to read this :( . But I do have one piece of advice – start early! Like I mentioned earlier, the submission period is very short, so it helps to have an idea of what you’re going to do before the submission portal opens.
This post started off formally enough and slowly descended into anarchy. I am so sorry. But thanks for reading, and best of luck if you’re applying for a WWDC scholarship in the future!
Image Credit: Jason Blackeye
By Sashrika Pandey
Climate change is an issue we hear about every day - rising sea levels, melting ice caps, and increasing temperatures are just a few of the effects of global warming that directly affect us. According to NASA, the five highest annual temperatures have occurred since 2010, giving us some insight into the drastic effects of climate change in the past decade. While it may seem like hopes for a sustainable future are lost, several undergoing projects aim to mitigate the effects of climate change through the use of artificial intelligence (AI).
IBM’s Green Horizon Project, for instance, uses extensive modeling strategies to predict the effects of pollution in great detail; by taking in data from numerous sources, which is a process that is also powered by the Internet of Things, and accounting for seasonal changes and physical locations, the project is able to provide insightful results that can then be utilized by people in the impacted areas. The Green Horizon Project also takes into account the use of renewable energy resources and how alterations in the climate can affect the living conditions of those in densely populated areas. The project’s underlying methods rely on the constant adaptation of models that can take into account the conditions of a certain region. The use of a robust model in combating the detrimental effects of climate change provides a look into what the future has in store for using AI on a larger scale.
We are excited to announce that Allgirlithm's two co-founders, Taylor Fang and Joanna Liu, were featured in the webinar Closing the Gender Gap in AI as part of ISTE's AI Explorations Program (Allgirlithm's third co-founder, Anne Li, was unable to participate due to a schedule conflict). The course was offered to over 300 computer science educators across the nation, and each of the participants who enrolled in the program received insight as to how Allgirlithm helped bring artificial intelligence education into the classroom.
We have linked half of the webinar down below, but feel free to check out the full video, featuring one of our partners, creAIte, here.
Photo Credit: Tobin Rogers
By Sashrika Pandey
When artificial intelligence (AI) is used to combat large scale problems, models are first exposed to small sets of training data before being fed more substantial sets of data to analyze. After years of considering social and ecological problems from multiple angles, researchers have compiled a plethora of information that can be evaluated, yielding results that can then be used by human researchers to identify areas where immediate aid is effective. Wildlife conservation, for instance, is an issue that continues to plague our planet, but once researchers turn their attention to the overlap between AI and ecology, new opportunities emerge.
In an interview with Forbes, Shahrzad Gholami discusses her background in wildlife conservation as part of Teamcore, a research group at the University of South California. When asked about her perspective on applying AI to other fields, Gholami states, “We need more interdisciplinary research by joining forces with other domain experts… Lots of AI researchers want to do impactful work, but they don’t know how to find it… And people with real-world problems don’t realize that AI can help them.” Data analysis and ecology are often separated into distinct fields, but there are numerous convergences where using machines can help ecological researchers work efficiently. A partnership between human researchers and computational power can lead to a much faster response to constantly changing wildlife populations.
Take, for instance, the work of research coordinator Jenna Stacy-Dawes at the San Diego Zoo’s Institute for Conservation Research. By using the software Wildbook, researchers have been able to determine the populations of giraffes by training a model to examine photos. This process has rapidly decreased the time necessary to comprehend the changes in giraffe populations for researchers, which is essential when fluctuations in the population size can have drastic effects later down the line. National Geographic explains that aerial surveillance of giraffe populations isn’t feasible due to the high cost and extensive amount of time spent. The use of AI algorithms, therefore, could serve as a potential solution for researchers that work on time-sensitive and data-heavy projects.
While AI may seem like an infallible solution, data analysis with AI presents its own challenges. In an article discussing the use of AI in a study where researchers were confronted with ample data but limited funds, Nature adds that using AI doesn’t mean that the analysis will be error-free; rather, training a model with several sample sets and then testing it by generalizing it to other populations can give insight into its accuracy. Additionally, software developer Peter Ersts at the American Museum of Natural History’s Center for Biodiversity advises against wholly relying on AI for research practices and emphasizes cooperation between humans and machines.
The loss of wildlife is a serious problem that is associated with numerous ecological issues on our planet. Mitigating the extent of this problem is a task that researchers and machines alike can work towards. The use of data analysis in this sector does raise questions about how AI can be used in other fields to combat areas where progress appears stagnant. Consequently, the widespread use of data analytics in supplementing the work of researchers is sure to spur interdisciplinary cooperation.
Forbes - “How AI Can Stop Wildlife Poaching”
Nature - “AI empowers conservation biology”
National Geographic - “How artificial intelligence is changing wildlife research”
Image Credit: KD Nuggets
By Sashrika Pandey
When the majority of people think of artificial intelligence (AI), the images that come to mind are the autonomous beings in Star Wars or the virtual assistants from superhero movies. While we may not yet live in a future where AI has evolved to include semi-sentient beings, there are numerous advancements being made in AI that can greatly influence your daily life. Machine learning, for instance, involves the training of a program based on past data so that it can produce a model that can be used for later analysis.
There are a plethora of subsets of machine learning, but one that stands out from the others is reinforcement learning. Reinforcement learning algorithms are unique in their training of a model since they use an underlying process known as a reward system; as a model exhibits a series of actions in response to a certain task, it is either positively or negatively affected for its actions. This reward system ensures that, through successive iterations, the model is trained to maximize the number of rewards it receives.
Take AlphaGo, which is notable for its successes in the complex game of Go. By implementing reinforcement learning, the program instead aims to maximize the rewards it receives. However, the unit of this reward may vary per scenario; as stated by Martin Heller of InfoWorld, “AlphaGo maximizes the estimated probability of an eventual win to determine its next move. It doesn’t care whether it wins by one stone or 50 stones.” While the “reward” may sometimes be numerical, it can also be the likelihood of a favorable outcome, as illustrated here.
The underlying theory of reinforcement learning, however, emphasizes that the model is attempting to maximize the reward. Through the manipulation of several parameters and designing a model that responds to certain behaviors by increasing or decreasing a reward by a factor, researchers can ensure that the model mimics the characteristics that they are aiming for.
On a larger scale, one may take a look at Amazon, which is revolutionizing its delivery system through the use of drones that utilize reinforcement learning. According to Ron Schmelzer, Amazon “used machine learning to iterate and simulate over 50,000 configurations of drone design before choosing the optimal approach.” This particular use of reinforcement learning illustrates the efficiency through which designs and models can be adapted to a situation. Rather than a manual or basic algorithmic approach, Amazon’s use of a reward system in place of traditional methods emphasizes the possible future of reinforcement learning.
One of the most fascinating aspects of reinforcement learning is that there is a multitude of possibilities to explore and queries to test. While there are a plethora of uses for reinforcement learning currently, there are sure to be more in the future. So while we may not yet live in a society where communicating with robots is as simple as talking to other humans, we are definitely on our way to making significant advances in machine learning and artificial intelligence as a whole.