Frequently Asked Questions

Artificial Intelligence (AI) is the broad discipline centered on developing methods to emulate human intelligence. Machine Learning (ML) is a subfield of AI utilizing statistical techniques to learn from data. Natural Language Processing (NLP) is the subfield of AI that focuses on the understanding and generation of human language, and has substantial intersection with ML. Large Language Models (LLMs) are advanced models designed to produce human-like language responses and understanding, such as answering questions.

Since the release of ChatGPT in late 2022, OpenAI's prominent LLM with over 100 million users, LLMs have become focal point in NLP research and AI/ML research more broadly.

Although our program will be in Python, you do not need Python experience as long as you have programmed in another language (e.g Java). Learning Python as a second language is straightforward; we will send you some resources to pre-study Python before the program if necessary.

Experience with AI will help your application, but is not necessary. We will teach the high-level AI fundamentals necessary for our research program and dive deep into language models and NLP more broadly.

While our main objective is educational, the skills, accomplishments, and publications you achieve here are highly regarded in academic circles and can indeed contribute to your college application. This is especially true if your research is admitted to a top conference, which is a highly prestigious feat even among professional researchers.

We are proud to share that our students have leveraged their experiences with us into tangible opportunities.

Some examples:

- We offered a few top-performing students from our Fall/Spring cohorts a research position in collaboration with University of Michigan's SLED Lab, researching web-based language model agents.

- A student from the Fall was admitted into Stanford University, citing us as a highlight in their application and letter of continued interest.

- A student from the Fall secured a research internship on LLMs at the Lawrence Livermore National Laboratory. Another secured a research internship on LLMs at UCLA. They cited their involvement in our research program as the pivotal factor in their application and interview success.

Algoverse is unlike any other high school research program; we are the only program in the space that is committed to authentic, impactful research in the AI research community. While a few other high school research programs also have mentors from top institutions, they are known to simply do literature reviews or introductory projects without any novelty.

Additionally, our students have reported that the hands-on guidance they received from our experienced mentors greatly surpasses what is available in other programs. We are heavily invested in our students' projects. This mentorship is crucial in helping students navigate complex AI research concepts and methodologies.

Despite providing unparalleled opportunities in advanced AI research and mentorship, we charge a fraction of the price of other programs. We are not in it for the money - we are driven by a passion for educating the next generation of AI researchers.

Conferences are gatherings where admitted researchers present and discuss their recent research findings.

In AI research, unlike many other engineering disciplines where journals are the primary publication venues, researchers submit their findings to conferences. This is because the field evolves rapidly, and conferences provide a timely platform for presenting and discussing cutting-edge research.

However, it's crucial to recognize that not all conferences hold the same prestige or rigor. Among the thousands of conferences held globally, only a select few are renowned for their high standards, rigorous peer review processes, and influential impact in the AI community.

In ML research, the top-tier conferences are: NeurIPS (Neural Information Processing Systems), ICLR (International Conference on Learning Representations), ICML (International Conference on Machine Learning), and AAAI (AAAI Conference on Artificial Intelligence). (Reference: ML Conference Rankings)

For NLP research, the top-tier conferences are ACL (Association for Computational Linguistics), NAACL (North American Chapter of the ACL), and EMNLP (Empirical Methods in Natural Language Processing). (Reference: NLP Conference Rankings)

Researchers also frequently publish their papers on arXiv, an open-access repository that hosts preprints of research papers before they undergo peer review. This practice allows for rapid dissemination of research findings within the scientific community.

Understanding the difference between these top-tier conferences and other lesser-known meetings is crucial. Respected AI researchers submit their papers exclusively to reputable conferences, which are recognized for their contribution to advancing the field. In contrast, there are a large multitude of conferences with less stringent review processes and lower impact that do not provide the same level of scientific rigor or recognition.

Note: If your research is accepted to a conference and you would like to attend the conference in-person, we will try to sponsor a portion of the travel costs if the travel cost is prohibitive to you.

Yes, as long as you follow all the rules and regulations of the science fair. Please contact us if you intend on submitting the research project to a science fair.

Yes, visit our website's research page to view some publication highlights from previous students.

Yes, we will record all of the lectures, so you can follow along without attending the lecture synchronously. In the Fall, we have had students from every continent except Antarctica. Our students from East Asia and South Asia followed along with the lecture recordings.

It is critical that you keep up to pace and update your research mentor regularly. The mid-week check-in is required to attend synchronously, but you'll be able to schedule it at a reasonable time based on your timezone.

The program is expected to be 5-10 hours per week, with flexibility for further exploration.