Prior work in standardized science exams requires support from large text corpus, such as targeted science corpus from Wikipedia or SimpleWikipedia. However, retrieving knowledge from the large corpus is time-consuming and questions embedded in complex semantic representation may interfere with retrieval. Inspired by the dual process theory in cognitive science, we propose a MetaQA framework, where system 1 is an intuitive meta-classifier and system 2 is a reasoning module. Specifically, our method based on meta-learning method and large language model BERT, which can efficiently solve science problems by learning from related example questions without relying on external knowledge bases. We evaluate our method on AI2 Reasoning Challenge (ARC), and the experimental results show that meta-classifier yields considerable classification performance on emerging question types. The information provided by meta-classifier significantly improves the accuracy of reasoning module from 46.6% to 64.2%, which has a competitive advantage over retrieval-based QA methods.

Last week, researchers at the Allen Institute for Artificial Intelligence demonstrated in a new paper that an AI they'd designed could ace an eighth-grade multiple-choice science test with more than 90 percent correct answers -- and do quite well on a 12th-grade science test, too, with more than 80 percent correct answers. The system, called Aristo, took the New York Regents Science Exam (a standardized test for students across New York State), with a few limitations: it didn't have to solve the problems that involved looking at diagrams. Nonetheless, the researchers tested the program on different versions of the test as well as on tests from different years and found that its performance was pretty consistent: It's an A student. Aristo demonstrates how quickly AI is advancing. As recently as 2016, the paper's authors note, no one in the field could manage to score as well as 60 percent on a similar eighth-grade science exam.

Recent surveys, studies, forecasts and other quantitative assessments of the impact and progress of AI highlighted the need to retrain many workers, improving AI's score from F to A on 8th-grade science exam, and the $97.9 billion the AI market will reach in 2023. In the next three years, as many as 120 million workers in the world's 12 largest economies may need to be retrained or reskilled as a result of AI and intelligent automation; only 41% of CEOs surveyed say that they have the people, skills and resources required to execute their business strategies; the time it takes to close a skills gap through training has increased from 3 days on average in 2014 to 36 days in 2018 [IBM] Top drivers for investing in robotics and automation: Reduced cost (80%), improved quality (55%), increased productivity (54%), improved capabilities of robots (54%). "I was at MIT for another fifteen years after I graduated…twenty years after I went and asked to do my bachelor's thesis [with Victor Zue on speech recognition], Siri comes out… twenty years ago, we [wanted to] have a device where you can talk to it and it gives you answers and twenty years later there it was. So, that, for me, that was a cue that maybe it's time to go where the action is, which was in companies that were building these things. Once you have a large company like Microsoft or Google throwing their resources behind these hard problems, then you can't compete when you're in academia for that space. You know, you have to move on to something harder and more far out… So, I joined Microsoft to work on Cortana…"--T.J. Hazen The worldwide market for AI systems will reach $97.9 billion in 2023, up from $37.5 billion in 2019.

Aristo has passed an American eighth grade science test. If you are told Aristo is an earnest kid who loves to read all he can about Faraday and plays the drums you will say so what, big deal. Aristo, though, is an artificial intelligence program and scientists would like the world to know this is a big deal, as "a benchmark in AI development," as Melissa Locker called it in Fast Company. We mean, just think about it. Cade Metz, in The New York Times, has thought about it.

The field of artificial intelligence has made great strides recently, as in AlphaGo's victories in the game of Go over world champion South Korean Lee Sedol in March 2016 and top-ranked Chinese Go player Ke Jie in May 2017, leading to great optimism for the field. But are we really moving toward smarter machines, or are these successes restricted to certain classes of problems, leaving others untouched? In 2015, the Allen Institute for Artificial Intelligence (AI2) ran its first Allen AI Science Challenge, a competition to test machines on an ostensibly difficult task--answering eighth-grade science questions. Our motivations were to encourage the field to set its sights more broadly by exploring a problem that appears to require modeling, reasoning, language understanding, and commonsense knowledge in order to probe the state of the art while sowing the seeds for possible future breakthroughs. Challenge problems have historically played an important role in motivating and driving progress in research.

The field of Artificial Intelligence has made great strides forward recently, for example AlphaGo's recent victory against the world champion Lee Sedol in the game of Go, leading to great optimism about the field. But are we really moving towards smarter machines, or are these successes restricted to certain classes of problems, leaving other challenges untouched? In 2016, the Allen Institute for Artificial Intelligence (AI2) ran the Allen AI Science Challenge, a competition to test machines on an ostensibly difficult task, namely answering 8th Grade science questions. Our motivations were to encourage the field to set its sights broader and higher by exploring a problem that appears to require modeling, reasoning, language understanding, and commonsense knowledge, to probe the state of the art on this task, and sow the seeds for possible future breakthroughs. The challenge received a strong response, with 780 teams from all over the world participating.