Davinci Goes to Bebras: A Study on the Problem Solving Ability of GPT-3

In this paper we study the problem-solving ability of the Large Language Model known as GPT-3 (codename DaVinci), by considering its performance in solving tasks proposed in the “Bebras International Challenge on Informatics and Computational Thinking”. In our experiment, GPT-3 was able to answer with a majority of correct answers about one third of the Bebras tasks we submitted to it. The linguistic fluency of GPT-3 is impressive and, at a first reading, its explanations sound coherent, on-topic and authoritative; however the answers it produced are in fact erratic and the explanations often questionable or plainly wrong. The tasks in which the system performs better are those that describe a procedure, asking to execute it on a specific instance of the problem. Tasks solvable with simple, one-step deductive reasoning are more likely to obtain better answers and explanations. Synthesis tasks, or tasks that require a more complex logical consistency get the most incorrect answers

Mr. Nobody's eyes

Historia sobre las dificultades en la escuela y en casa y para aprender sobre la confianza, el amor, y las propias obligaciones con los demás. Las cosas han sido difíciles para Harry ya que su madre se ha vuelto a casar,hay un nuevo bebé en casa, y en la escuela Miss Hardcastle lo ha catalogado como un alborotador: la vida es insoportable. El mundo de Harry es solitario hasta que conoce a dos artistas de circo: Blondini y Ocky, un chimpancé. Cuando Ocky se escapa del circo, Harry lo oculta en su guarida secreta, pero una complicada cadena de acontecimientos fuerza a los dos a huir de sus hogares. Al final gana el respeto de su padrastro y regresa a su casa. Para niños entre diez y catorce años.SCBiblioteca de Educación del Ministerio de Educación, Cultura y Deporte; Calle San Agustín, 5 - 3 planta; 28014 Madrid; Tel. +34917748000; [email protected]

Half a Man

https://stars.library.ucf.edu/diversefamilies/2855/thumbnail.jp

War Horse

Young Albert enlists to serve in World War I after his beloved horse is sold to the cavalry. Albert\u27s hopeful journey takes him out of England and to the front lines as the war rages on

Learning to program in a constructionist way

Although programming is often seen as a key element of constructionist approaches, the research on learning to program through a constructionist strategy is somewhat limited, mostly focusing on how to bring the abstract and formal nature of programming languages into \u201cconcrete\u201d or even tangible objects, graspable even by children with limited abstraction power. However, in order to enable constructionism in programming several challenges must be addressed. One of the crucial difficulties for novice programmers is to understand the complex relationship between the program itself (the text of the code) and the actions that take place when the program is run by the interpreter. A good command of the notional machine is a necessary condition to build programming skills, as is recognizing how a relatively low number of abstract patterns can be applied to a potentially infinite spectrum of specific conditions. Programming languages and environments can either help or distract novices, thus the choice is not neutral and their characteristics should be analyzed carefully to foster a good learning context. The mastery of the notional machine, however, is just the beginning of the game: to develop a real competence one must be able to think about problems in a way suitable to automatic elaboration; to devise, analyse, and compare solutions, being able to adapt them to unexpected hurdles and needs. Moreover, it is important to learn to work productively in a team, in an \u201corganized\u201d way: agile methods seem based on common philosophical grounds with constructionism

Learning to program in a constructionist way

International audienceAlthough programming is often seen as a key element of constructionist approaches, the research on learning to program through a constructionist strategy is somewhat limited, mostly focusing on how to bring the abstract and formal nature of programming languages into "concrete" or even tangible objects, graspable even by children with limited abstraction power. However, in order to enable constructionism in programming several challenges must be addressed. One of the crucial difficulties for novice programmers is to understand the complex relationship between the program itself (the text of the code) and the actions that take place when the program is run by the interpreter. A good command of the notional machine is a necessary condition to build programming skills, as is recognizing how a relatively low number of abstract patterns can be applied to a potentially infinite spectrum of specific situations. Programming languages and environments can either help or distract novices, thus the choice is not neutral and their characteristics should be analyzed carefully to foster a good learning context. The mastery of the notional machine, however, is just the beginning of the game: to develop a real competence one must be able to think about problems in a way suitable to automatic elaboration; to devise, analyse, and compare solutions, being able to adapt them to unexpected hurdles and needs. Moreover, it is important to learn to work productively in a team, in an "organized" way: agile methods seem based on common philosophical grounds with constructionism

Davinci Goes to Bebras: A Study on the Problem Solving Ability of GPT-3

International audienceIn this paper we study the problem-solving ability of the Large Language Model known as GPT-3 (codename DaVinci), by considering its performance in solving tasks proposed in the "Bebras International Challenge on Informatics and Computational Thinking". In our experiment, GPT-3 was able to answer with a majority of correct answers about one third of the Bebras tasks we submitted to it. The linguistic fluency of GPT-3 is impressive and, at a first reading, its explanations sound coherent, on-topic and authoritative; however the answers it produced are in fact erratic and the explanations often questionable or plainly wrong. The tasks in which the system performs better are those that describe a procedure, asking to execute it on a specific instance of the problem. Tasks solvable with simple, one-step deductive reasoning are more likely to obtain better answers and explanations. Synthesis tasks, or tasks that require a more complex logical consistency get the most incorrect answers

Identifying atomically thin crystals with diffusively reflected light

AbstractData set for: 2D Materials 8, 045016 (2021) https://doi.org/10.1088/2053-1583/ac171