The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work-a possibility that has sparked ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students' perspectives regarding the use of such tools in school work, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a global survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use in school work. We find that ChatGPT's performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks.
Artificial Intelligence , Communication , Humans , Universities , Schools , Perception
The scope and scale of biological data are increasing at an exponential rate, as technologies like next-generation sequencing are becoming radically cheaper and more prevalent. Over the last two decades, the cost of sequencing a genome has dropped from $100 million to nearly $100-a factor of over 106-and the amount of data to be analyzed has increased proportionally. Yet, as Moore's Law continues to slow, computational biologists can no longer rely on computing hardware to compensate for the ever-increasing size of biological datasets. In a field where many researchers are primarily focused on biological analysis over computational optimization, the unfortunate solution to this problem is often to simply buy larger and faster machines. Here, we introduce Seq, the first language tailored specifically to bioinformatics, which marries the ease and productivity of Python with C-like performance. Seq starts with a subset of Python-and is in many cases a drop-in replacement-yet also incorporates novel bioinformatics- and computational genomics-oriented data types, language constructs and optimizations. Seq enables users to write high-level, Pythonic code without having to worry about low-level or domain-specific optimizations, and allows for the seamless expression of the algorithms, idioms and patterns found in many genomics or bioinformatics applications. We evaluated Seq on several standard computational genomics tasks like reverse complementation, k-mer manipulation, sequence pattern matching and large genomic index queries. On equivalent CPython code, Seq attains a performance improvement of up to two orders of magnitude, and a 160× improvement once domain-specific language features and optimizations are used. With parallelism, we demonstrate up to a 650× improvement. Compared to optimized C++ code, which is already difficult for most biologists to produce, Seq frequently attains up to a 2× improvement, and with shorter, cleaner code. Thus, Seq opens the door to an age of democratization of highly-optimized bioinformatics software.
