“Image recognition” is the training of computers to recognize objects in images. When you think of image recognition, do you think of hard-to-use software running complicated algorithms with thousands of lines of code? I know I do. But in reality, open-source datasets and free apps have made it easier than ever to dive right into image recognition. Here are a few ideas for image recognition-related projects and activities you can try!
1. Build an image recognition iOS app.
Xcode (Apple’s free development environment for creating iOS, macOS, watchOS, and tvOS apps) introduced CreateML in 2018. The framework was designed to allow Apple developers to easily build machine learning models for use in their apps. Although Xcode and its CoreML framework are capable of integrating ML models built using more specialized software, CreateML’s greatest asset is its native, easy-to-use, drag-and-drop interface which trains, tests, and selects appropriate ML classifiers automatically.
The basic steps for building an image recognition iOS app are:
2. Play an image recognition game.
There are a few fantastic image recognition-related web apps. One of them is Google’s Quick, Draw! game, which gives you something to draw, then guesses what you’re drawing. Another is Google’s Autodraw experiment, in which the computer attempts to complete your drawing.
Another of Google’s AI experiments, Handwriting with a Neural Net, generates strokes matching the style of your handwriting samples, while Cartoonify creates cartoons out of your drawings.
One thing to keep in mind when playing these games is you’re providing your data (your doodles) to Google to improve their software. This may or may not be something you’d want to do; in any similar situation, it’s always a good idea to consider both privacy issues and possible issues with the research and future software you may be enabling (e.g. facial recognition technology).
3. Conduct image recognition research.
Another idea for image recognition-related projects you can try is research, in which you identify a problem, develop a hypothesis, follow a procedure (collect and analyze data), and come to a conclusion regarding the problem you posed previously. Research projects typically last several months.
This may seem a little more daunting than the other two, but one thing you can try is brainstorming ideas for research you might be interested in. There are lots of fantastic datasets out there; I’ve listed a few below that you might find helpful for developing your ideas!
Skin (e.g. moles and lesions): https://isic-archive.com
Handwritten digits: http://yann.lecun.com/exdb/mnist/
Remember: these are just a few examples of well-constructed datasets you can use for your image recognition research. If you’re interested in any topics that aren’t listed above, you can check out a number of sites dedicated to machine learning research and datasets--Kaggle, for example, has a huge inventory of information, datasets, and even competitions dedicated to machine learning.
Which of these image recognition activities do you plan to try? Let us know in the comments! And in the meantime, happy coding!
Photo credit: Artem Kniaz
When you think of technology as an abstract concept, it’s common to associate the term with extraterrestrial spaceships or sentient machines. Technology, however, actually involves the progression of humanity through the development of certain skills, products, et cetera. We tend to restrict our perception of technology to utopian societies or Orwellian dystopias because that is the path that we assume that human development will take us. Sometimes, however, technology can be used to look into the past, which can reveal much more about the human race than we previously predicted.
Archaeological sites, buried under sediment and time, are one of the pieces of history that researchers are looking into. Through her platform GlobalXplorer, Sarah Parcak has revolutionized the way that we look at studying the past. GlobalXplorer accumulates millions of satellite images online so that viewers can peruse the images and identify archaeological sites that may have previously gone unnoticed. According to National Geographic, the project has contributed to the identification of key archaeological structures in Peru and will soon be moved to India, where its past successes are sure to provide key insights. A core concept of Parcak’s model is that it involves volunteers to a great extent. Rather than limiting the ability to make contributions to researchers, GlobalXplorer has provided a platform for people across the globe to piece together the scattered remains of human history.
In late June, headlines in the technology industry stated the conclusions of a recently published study: this century will not see women publishing the equivalent amount of computer science research as men, based on the most optimistic analysis of past trends. With an apparent increase in the representation of female scientists and coding organizations that aid underrepresented groups, it was disheartening to hear that there are still decades before parity is projected to be achieved. It appears as if this trend has been occurring for decades - women continue to face barriers to entry in technology fields, even with the advent of new products and job opportunities, but any hope for a clear solution is shrouded in years of stereotypes and deeply rooted obstacles in industry and academia alike. With both a gender gap and wage gap in STEM fields, how can incoming computer scientists counter a system that seems to be built against them?
On a social scale, it appears that young women exposed to computer science may shy away from the field based on the stereotypes around it. A study conducted through Microsoft found that 91% of girls and 80% of young women would describe themselves as creative, which conflicts with the traditional description of programming as a purely logical occupation. Placing all of the different aspects of STEM under a specific label inhibits people from making connections with the other parts of the field that may not all fall in that category. Many students may not explore STEM fields because of the traditional descriptions of tech, and without having introductory courses or direct experiences with the field, they may never be able to ignite their interest in STEM. According to BBC, a report from the Council for the Curriculum, Examinations and Assessment found that girls were often uncomfortable with studying computer-related disciplines and felt a pressure to perform better if they did end up pursuing computing.
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 I get 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. Unrelated, but my dad decided to come along as well, though I don’t remember why. Anyways, he mostly hung out in Cupertino and visited random places. I think he also ate at every Chinese restaurant in the area he was staying.
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.
- I did notice that women were fairly well-represented in the Keynote – several of the speakers and presenters were women. I’m not sure how the numbers compare to previous years, but just a casual observation
- I really enjoyed the Apple Design Awards! One of the winners was a ultrasound app (ButterflyIQ) which I found really cool
- 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
- I don’t have much to say on this, because I had to go to my dad’s Airbnb to get a phone charger, and then he offered to take me to a random Chinese restaurant™ with really good noodles he’d tried earlier in the week and I will exchange half of my soul for good noodles. But I heard Craig Federighi was in the crowd, so I’m still slightly jealous. Also I didn’t get to say goodbye to some friends who were leaving early :’(
- I didn’t go to any sessions or labs on Friday, hehe
- 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.
If I could go again, I would:
- Have more questions prepared for the labs
- For a couple of the labs, I kind of just wandered by and thought, “Oh, this might be helpful!” And then I couldn’t think of anything to ask.
- Attend more of the breakfasts
- In addition to Women@WWDC, there were two other breakfasts: Black@WWDC and Latinx@WWDC. The breakfast events are chances to hear from lots of different perspectives – everyone’s journey in tech is different, and recognizing that is crucial for anyone trying to promote inclusivity in tech
- Talk to more people
- I got to meet a lot of brilliant people doing brilliant things, but I do wish I’d been a bit more outgoing – though this is something I’m still working on. Sometimes I worry about being the least competent person at tech events, but the people I met at WWDC were extremely friendly and supportive regardless of accomplishment, experience, etc.
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!
Credit: Jason Blackeye
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
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
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.
Hey Allgirlithm Readers! Hope you're as excited for summer as we are. (And for those still in school, keep pushing!) We're here with some cool opportunities and resources to help you keep up with your computer science studies. P.S., if you haven't checked out our AI Club program, you're missing out! Summer is a great time to start planning for projects during the school year, and we'd love to have you as part of the team. We're also rolling out a similar program for tech workshops and other outreach events, using open-sourced curriculum free and open to everyone, so stay tuned.
For a refresher on all things tech, check out this great resource again: https://code.likeagirl.io/a-high-school-students-guide-to-cs-programs-internships-487586031e07. You'll see a bunch of summer programs; although some deadlines have passed, the internship advice is also really great.
This is also a great time to look for scholarships. Check out this spreadsheet for a comprehensive list:
Need other opportunities? Look at NCWIT: https://www.aspirations.org/participate/opportunities
Or just want to get mentorship? Join #BuiltByGirls WAVE program:
For those in the Bay Area: Check out Bay Area Teen Science at http://bayareateenscience.org/, or give them a follow on social. They have some great opportunities specific to California.
The Congressional App Challenge is coming up in November. You will need submit an app, a demo video, and written responses to the competition, so it's great to start in the summer. Free resources include MIT AppInventor (http://appinventor.mit.edu/explore/) and Xcode (free download on Mac).
The NCWIT Aspirations in Computing Award application is due in the fall of 2019. The application includes several questions and essays. The award is based off of aspirations, so don’t worry if you are relatively new to coding!
That's all for now! As always, look at Allgirlithm's Resources page if you're stuck, or reach out to us at email@example.com
Photo credit: brilliant.org
A classic problem in probability, and one that has prompted numerous discussions and the use of simulations, the Monty Hall problem encourages the use of critical thinking in determining whether new evidence can alter one’s course of judgement.