This study investigates the task of knowledge-based question generation (KBQG). Conventional KBQG works generated questions from fact triples in the knowledge graph, which could not express complex operations like aggregation and comparison in SPARQL. Moreover, due to the costly annotation of large-scale SPARQL-question pairs, KBQG from SPARQL under low-resource scenarios urgently needs to be explored. Recently, since the generative pre-trained language models (PLMs) typically trained in natural language (NL)-to-NL paradigm have been proven effective for low-resource generation, e.g., T5 and BART, how to effectively utilize them to generate NL-question from non-NL SPARQL is challenging. To address these challenges, AutoQGS, an auto-prompt approach for low-resource KBQG from SPARQL, is proposed. Firstly, we put forward to generate questions directly from SPARQL for the KBQG task to handle complex operations. Secondly, we propose an auto-prompter trained on large-scale unsupervised data to rephrase SPARQL into NL description, smoothing the low-resource transformation from non-NL SPARQL to NL question with PLMs. Experimental results on the WebQuestionsSP, ComlexWebQuestions 1.1, and PathQuestions show that our model achieves state-of-the-art performance, especially in low-resource settings. Furthermore, a corpus of 330k factoid complex question-SPARQL pairs is generated for further KBQG research.

Artificial Intelligence (AI) has had the power of ruling the technologically dominated world for quite some time now. Today, we have reached a stage wherein advanced artificial intelligence has become one of the most sought after techniques to bring about innovation and solve complex business problems. Over the last few years, the technology has matured to the extent that it has become highly scalable. In the midst of all this, what has grabbed eyeballs from everywhere across is reinforcement learning – training the machine learning models to be able to make the best possible decisions. Reinforcement learning makes use of algorithms that do not rely only on historical data sets, to learn to make a prediction or perform a task.

Just what computer scientists want - dumb jocks getting all of the credit for artificial intelligence. Or maybe computer scientists are simply letting football players think they matter, and they are really just data. For artificial intelligence to get out of its 20-year rut, a computer has to be able to observe a complex operation, learn how to do it, and then optimize those operations or accomplish other related tasks. What if a computer could watch video of football plays, learn from them, and then design plays and control players in a football simulation or video game? As it turns out, football is very complex, and computers struggle to see and understand plays a coach or even an average fan would find routine, just like a 4-year-old could see a cartoon drawing of a chicken and say "that's a chicken" while a computer could not.

Controlling robots using the human mind might seem like something taken from science fiction. But the technology is already available, and it could soon be used by the US army. A team of researchers has developed technology that lets a human control multiple drones using their brain waves, and the group is now working on squadrons of drones that could perform complex operations. Controlling robots using the human mind might seem like something taken straight out of science fiction. The Arizona researchers are not the first to investigate using brain waves to control drones.