In a Venn diagram between types of CompSci and AI research, there’s obviously a lot of overlap. The element that makes AI so specific, however, is that all AI professionals have the specific goal of harnessing intelligence beyond what humans are capable of. Whether it be in healthcare, finance, education, agriculture, or any other field, you can find ways of automating tasks, making predictions, processing lots of data, and improving decision-making using AI technology to make life easier or better. The AI jobs that are most in demand right now include:
Machine learning engineers who write algorithms that help machines spot patterns and take action without human intervention
Data scientists who work with large amounts of information and identify patterns, make predictions, and/or generate recommendations
Business intelligence developers who specifically spot trends in sales or the stock market.
Software engineers who design, program, and test software that increasingly incorporates AI components
Robotics engineers who design, build, and test mechanical prototypes
NLP (Natural Language Processing) engineers who train machines models to “understand” human language
Today just about every industry uses AI in some capacity. AI is used to help make medical diagnoses, drug discoveries, and administrative tasks in healthcare. It’s used to detect fraud, make financial forecasts, and calculate credit scores in finance. It powers which books Amazon and what shows Netflix will recommend to you. It is even starting to help us educate ourselves, drive, and monitor our crops. You can take just about anything you’re interested in, and there will be an AI component that either already exists or is in development.
AI research was officially initiated in a conference at Dartmouth College in the summer of 1956, often referred to as the "Dartmouth Workshop." This event marked the birth of AI as a distinct field of study. Therefore, as of 2023, AI is over 65 years old and has seen significant advancements and breakthroughs. Particular progress has been made over the past decade with the development of deep learning and neural networks, which led to advancements in various AI applications, including natural language processing, computer vision, and reinforcement learning.
Because AI is such a relatively “new” science and new innovations are still being made, there is no standard curriculum to follow. But there are some basic recommendations you can follow. A focus on math—particularly algebra, calculus, and statistics—is foundational for understanding AI concepts. You can also start programming with a language like Python, which is widely used. We’ll also talk about some classes, books, resources, competitions, and clubs you might want to check out in the next sections.
1. Take a Class in High School
The availability of psychology classes varies greatly from school to school, but most high schools offer at least a few of the types of courses listed below. You can also look for courses at your local community college or seek out online versions of these courses. The versatility of AI means that it intersects with many fields of study. Exploring unusual or less conventional courses can also lead to unique insights and opportunities in the field of AI.
Math: Strong math skills are crucial. Take courses in algebra, calculus, and statistics. These are fundamental for understanding AI algorithms and concepts.
Computer Science: Build your programming skills, particularly in Python.
Physics: Physics courses can provide valuable problem-solving skills and a deeper understanding of algorithms and models used in AI.
Biology and Neuroscience: Understanding biological and neural systems can be beneficial for areas like neural networks and biologically inspired AI.
Ethics and Philosophy: Understanding the ethical and societal implications of AI is increasingly important in implementations such as self-driving cars. Consider courses in ethics and philosophy.
Linguistics: Linguistics classes can help you understand the structure of language, which is crucial for natural language processing in AI.
Music Theory: Understanding the principles of music theory can be valuable for developing AI systems for music composition and analysis.
Economics: Knowledge in economics can be helpful for AI applications in financial forecasting and market analysis
Machine Learning and AI (if available): Some high schools have started offering specialized courses in machine learning or AI. If such courses are available, definitely jump in.
Independent Study: If your school doesn't offer AI-related courses, consider self-study through online resources, books, and tutorials.
2. Read a Book
Books are a great start, but since AI is such a relatively young field, its “foundational” texts are generally under 20 years old. You should definitely augment your reading with reputable online news sources (such as Wired and MIT Technology Review) and check out research papers and journals (using platforms like arXiv, ResearchGate, and Google Scholar and from conferences like NeurIPS, CVPR, and ACL). Reddit’s r/MachineLearning forum has some good posts, and there are many new podcasts (“Data Skeptic) and YouTube videos (AI-related TED talks, Two Minute Papers, and Siraj Raval) on the subject. Whenever information gathering online, be sure to doublecheck your sources for reliability.
Foundational books:
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig (1995): This is a widely-used textbook that covers a broad range of AI topics, providing a solid introduction to the field.
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy (2012): This book delves into the probabilistic foundations of machine learning, a key component of AI.
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016): Offers an in-depth understanding of deep learning techniques, which are at the forefront of AI research.
Pattern Recognition and Machine Learning by Christopher M. Bishop (2006): Focuses on pattern recognition and statistical methods in machine learning.
Python Machine Learning by Sebastian Raschka and Vahid Mirjalili (2015): A practical book that helps you implement machine learning algorithms using Python.
Controversial and challenging AI books:
The Age of Em: Work, Love, and Life when Robots Rule the Earth by Robin Hanson (2016): Explores a future where brain emulation technology creates a new era of AI and its societal implications.
Rebooting AI: Building Artificial Intelligence We Can Trust by Gary Marcus and Ernest Davis (2019): Questions the current direction of AI research and its limitations, offering a more cautious perspective.
The Book of Why: The New Science of Cause and Effect by Judea Pearl (2018): Focuses on causality in AI and challenges conventional statistical methods.
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil (2016): Discusses the ethical and societal challenges posed by AI and algorithms.
AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee (2018): Explores the AI race between China and the United States, providing insights into AI's global impact.
AI Ethics edited by Mark Coeckelbergh (2020): A collection of essays addressing the ethical issues and dilemmas surrounding AI.
3. Extracurricular Study
Coding hackathons and robotics competitions: AI-specific hackathons, codeathons, and other competitions provide a platform for students to showcase their skills and collaborate on AI-related projects. Here are a few to check out. To ensure eligibility, it's important to visit their websites and review their rules and age restrictions, as they may vary from one event to another. If any event doesn't explicitly mention eligibility requirements, you can often contact the organizers for clarification. And while the emphasis for some of these competitions is mechanical design and engineering, teams may use AI techniques to program their robots to perform tasks independently, make decisions based on sensor data, and navigate complex environments.
Open Source Contributions: Read about and contribute to open-source AI projects or libraries on platforms like GitHub. (This useful “Essential Cheat Sheets for Machine Learning and Deep Learning,” for example, is from a GitHub user.) It's a great way to learn, collaborate, and give back to the community.
AI Reading Groups: Form or join AI reading groups where you discuss and analyze research papers and books in the field.
Math and Computer Science Olympiads: Compete in regional, national, or international math and computer science competitions to challenge yourself and build skills.
Internships or Part-Time Jobs: Seek internships or part-time jobs at local tech companies, startups, or IT departments. Gaining real-world work experience can be invaluable plus you could make some money.
Online Courses and Certifications: Enroll in online courses in areas like Python, engineering, and even specifically AI. You can find free online offerings on Coursera such as Mathematics for Machine Learning and Data Science Specialization.
For more great ideas, check out our 10 Ways to Dive into Chatbot Development as a High School Student: Your Ultimate Guide.
Once you have a better grasp of what kind of project you want to do, you could pursue your vision at a local community college, a competition, an internship, or a virtual program. If you want to be free to conduct your own project, we still advise that you give yourself a deadline and have a qualified adult advisor or mentor who you can consult with. While GitHub is great, there’s nothing like a human being to clear up misunderstandings and give you quick personalized guidance.
1. Find research programs close to home
We’ll go into summer programs in more depth in the next section, but if you want to find all types of research opportunities close to home, our High School Student Research Opportunities Database is an excellent resource. Click on your state, then search based on your location, institution, event type (in-person or virtual), and tuition (paid or free). AI is a very specific, so you may need to widen your search to something broader such as computer science or engineering.
2. Work with a professor
If you have a clear idea of your passions, you can reach out to professors in your field to see if they are open to collaborating with you. Refer to our Guide to Cold-Emailing Professors (written by Polygence literature research mentor Daniel Hazard, a Ph.D. candidate at Princeton University).
3. Engage in your own research project
Students with initiative and focus can opt to tackle research independently. Carly Taylor, a Stanford University senior who has completed several research projects this way, outlined a guide about how to write a self-guided research paper. By reading it, you’ll get a better understanding of what to expect when taking on this type of project.
Here are some picks for summer AI research programs. We chose them based on a combination of their affordability, name recognition, social opportunities, and academic rigor.
1. AI4ALL / UW
Hosting Institution: University of Washington
Cost: Free
Format: In-person (Seattle, WA)
Application deadline: Mid-April
Students from traditionally underrepresented groups are invited for a free, 2-week data science and artificial intelligence introductory workshop. With a focus on non-ableist AI, participants learn to interrogate bias and fairness in AI. Working toward the ultimate goal of understanding impactful technology decisions, students learn to understand, analyze, interpret and discuss real-world applications of data science and machine learning. Check the site for the most current application information.
2. Python Data Science & Machine Learning Program NYC
Hosting Institution: NextGen Bootcamp
Cost: $2,495 USD
Format: Online and In-person (commuter only in New York, NY)
Application deadline: N.A.
Over the course of two weeks, students learn to input, analyze, and graph data. First, participants learn the fundamentals of Python code before transitioning into more complicated programming tasks. The second half of the course focuses on data science using Pandas, Matplotlib, and Sci-Kit Learn. Check the site for the most current application information.
Check out the full list of Data Science Research Opportunities for High School Students for more options.
While data science and AI are related and often work in conjunction, they have distinct focuses. Data science is broader and encompasses tasks like data collection, cleaning, and exploratory data analysis, while AI specifically deals with creating intelligent systems that can make decisions or predictions based on data. Both fields are vital in today's data-driven world and contribute to various applications and industries.
A few of the summer programs we found were either paid or unpaid internships.
1. DSI Summer Lab
Hosting Institution: The University of Chicago
Cost: Paid internship
Format: In-person (Chicago, IL)
Application deadline: Mid-February
In this immersive 10-week paid summer data science research program, high school and undergraduate students are paired with a mentor in various domains, including: computer science, data science, social science, climate and energy policy, public policy, materials science, and biomedical research. Interns learn to hone their skills in research methodologies, practices, and teamwork. Participation is encouraged from a range of students and requires no prior research experience.
2. Data Science Summer Program
Hosting Institution: Harvard University
Cost: Tuition for the program is free; students are responsible for purchase of materials
Format: Online
Application deadline: Mid-May 15
This two-week online day camp introduces students to machine learning and programming through a project in which they program various machine learning algorithms to recognize images and make a self-driving toy car. The course consists of lectures covering conceptual level statistical, machine learning, and programming components. Once they achieve high-quality performance, they will install their program into a toy car equipped with a camera which will self drive using their programmed neural network.
For more internship opportunities be sure to read our post on "AI Internships for High School Students."
The best way to start brainstorming your AI project is to explore your interests. Consider topics you're passionate about, like healthcare, climate, or gaming, and envision how AI can address related challenges. Another good thing to do is review existing AI research papers (check out Google Scholar, JSTOR, and dedicated AI journals like the "Journal of Artificial Intelligence Research" and the "International Journal of Artificial Intelligence”) to understand current trends and identify gaps or areas where you can contribute. As you get more familiar with the topic by reading books, participating in extracurricular activities, checking out competitions, and participating in forums and tutorials on GitHub, you will start to generate ideas for projects.
Polygence Scholars Are Also Passionate About
Here are a few ideas from our Polygence mentors.
AI Playing GeoGuessr
GeoGuessr is an online game where users are presented with Google street view images and are asked to predict the location as close as possible. I am interested to work on a project where we develop an AI that can achieve (at least) human-level performance on GeoGuessr. This would be a comprehensive ML project where we would start by creating a dataset of Google street view images labeled with longitude/latitude. Then we can train a CNN or RNN based model that can predict a geolocation given the Google street view images. Later, we can also try to improve the performance of this model using a reinforcement learning approach. Coding skills: Python, C/C++, Java, JavaScript, MATLAB
Idea by mentor Ali Ekin G
Predicting Crop Yields from Weather Data
Given data about an area's temperature, humidity, sunshine, and rainfall, how well can you predict how well a given crop will grow? In this machine learning project, you will leverage freely available datasets to create a machine learning model to predict crop yield from weather data. Such a machine learning model could be used to predict crop shortages, and increase food security in the wake of climate change. The results of this research can be written up as a research paper and/or blog post.
Coding skills: Python, MatLab, JavaScript, Java, HTML, CSS
Idea by mentor Carter S.
Investigating the Relationship between Air Pollution and Health Outcomes in Rural and Metropolitan Areas
This project would involve obtaining publicly available data on air pollution levels and health outcomes (ie hospital admissions for respiratory illnesses, mortality rates, lung cancer prevelance/incidence, etc). The student could then analyze the data to determine if there is a correlation between air pollution levels and negative health outcomes. The student could also explore the potential impact of factors such as socioeconomic status, age, or sex/gender on the relationship between air pollution and health outcomes. The student could then summarize this on a poster, write up the results, and present their findings at a conference or virtually.
Coding skills: Python, MATLAB, R, pytorch
Idea by mentor Adrian W
You can also brainstorm your own project ideas based on what ways you would harness artificial intelligence to solve problems. If you want support, the Pathfinders program gives you the chance to meet with three different mentors who specialize in your field of interest. You can discuss your project ideas with them, and they can help you grow your idea, discover new research techniques, and point the way to great resources and alternative options.
For more ideas check out 12 Artificial Intelligence Research Project Ideas.
For a sense of how varied the subjects and methods for AI projects can be, take a look at topics covered by some of our Polygence Scholars.
The Semi-Autonomous Systems’ Dangers to Humans
Shalini reviewed the limitations and risks posed by current semi-autonomous systems and addressed the need for enhanced safety measures and potential adjustments to semi-autonomous systems, arguing that semi-autonomous systems amplify risks on the road rather than enhancing driving efficiency. She gave a presentation of her project at the 9th Symposium of Rising Scholars and you can read her research paper here.
How can AI be utilized to standardize ballet teaching?
Ballet teaching is and has been historically passed down from teacher to student, with little standardization across different schools. Priyanka evaluated the ways that Artificial Intelligence can be utilized to standardize ballet teaching, improve accessibility, and minimize the differences in the techniques learned by different dancers. You can read her full research paper here.
Using Computational Linguistics to Analyze the Framing of 2020 Presidential Candidates in the News
Audrey learned about Natural Language Processing (NLP) and how it can be used to understand how people are described in text. She focused on the 2020 presidential elections and compared different media sources through investigating how they discuss the Democratic candidates. Her results show significant differences in the framing of candidates across the news sources along several dimensions, such as sentiment and agency, paving the way for a deeper investigation. She submitted this research paper to the most prestigious conference in NLP, the Annual Conference of the Association for Computational Linguistics.
See more AI projects done by Polygence Scholars.
Since computer science provides a lot of the foundational principles and tools of AI, it is useful to take a look at this great CompSci research primer written by Polygence mentor Ross Greer, a PhD Candidate in Electrical & Computer Engineering studying Intelligent Systems, Robotics, and Control at the University of California, San Diego. He helpfully breaks down the states of your research into: 1.) scoping out your topic 2.) working on the project and 3.) completing the project. Dividing what can seem like an overwhelming beast into these three chunks definitely makes the endeavor more manageable. Ross is big on the idea of finding the best project for you—one that takes your skillset, your interests, and your goals into account.
Again AI research is a dynamic and rapidly evolving field, so staying current with the latest developments is crucial when writing your AI research paper. Additionally, be aware of ethical considerations and the potential societal impact of your work, as these aspects are increasingly important in AI research.
Another useful resource is this post about outlining your research paper. Your research will generally include sections such as Materials, Methods, Data, Discussion, and Conclusion. You’ll also need to write an Introduction that opens with the problem you’re trying to solve, any existing research, and an overview of your research—all of which is best written about after you’ve finished your research and programming. Another important piece to your paper is your thesis statement. You can always come up with a preliminary or working thesis and then refine it or completely revise it as you learn more. You also may need to write an abstract. At its core, an abstract is a standalone piece of writing that offers a snapshot of the problem, methodology, findings, and conclusions. If you need more general guidance overall, here’s a great article on how to write a good research paper.
Finally, if you have some ideas and want to conduct computer science research with the guidance of a mentor, apply to be a part of our flagship mentorship program.
Quick links:
As our Head of Engineering Ádám Gyulavári noted, when it comes to coding, “a research paper or a blog post might not be enough to demonstrate the work you’ve done and the features of the application or program you’ve created.” That’s why he created the very useful post Showcasing on GitHub: The Complete Guide. Other ways to showcase your AI research include entering your project into a robotics or AI competition, attending a conference such as Polygence’s very own Symposium of Rising Scholars, or publishing in science journals such as IJHSR, SFJ, NHSJS, the Curieux Academic Journal, or The Young Scientists Journal. For more showcasing ideas, check out 20 Journals and Conferences to Consider.
