"The problem of giving rules for producing true scientific statements has been replaced by the problem of finding efficient heuristic rules for culling the reasonable candidates for an explanation from an appropriate set of possible candidates [and finding methods for constructing the candidates]."
– B. Buchanan, quoted in Lindley Darden. Recent Work in Computational Scientific Discovery.
Have you ever read the history of recruitment? No? Let me give you a gist on the history of recruitment which started way back in 1940's. During the World War II, all soldiers were recruited to increase the battalions. The Recruitment process through the years have completely/drastically changed. In today's time technology has taken a big leap towards automating the entire recruitment lifecycle starting from Sourcing a Candidate till Onboarding a candidate.
The threat landscape has moved far beyond programmers trying to show off their exploitative coding skills to their peers. Modern cybercriminals choose efficacy over spectacle and employ a variety of attack methodologies to breach network security. They leverage the most cutting-edge tech to launch swifter, more powerful, and highly sophisticated attacks. With advanced technologies such as machine learning and artificial intelligence now being integrated into cyber attack methodologies, security experts believe that 2018 could be the year that witnesses the first wave of attacks with true AI capabilities. This spells trouble for global businesses already struggling to deal with high attack volumes and multidimensional attack vectors.
Helping students develop skills in both critical thinking and scientific reasoning is fundamental to science education. However, the relationship between these two constructs remains largely unknown. Dowd et al. examined this issue by investigating how students' critical thinking skills related to scientific reasoning in the context of undergraduate thesis writing. The authors used the BioTAP rubric to assess scientific reasoning and the California Critical Thinking Skills Test to assess critical thinking. Results support the role of inference in scientific reasoning in writing, while also revealing other aspects of scientific reasoning (epistemological considerations and writing conventions) not related to critical thinking.
Although not all researchers agree on the exact bounds of scientific discovery, theory formation is clearly at the core of the domain. Relevant AI research done in scientific discovery includes Kocabas (1992); Karp (1989); Prager, Belanger, and De Mori (1989); Kulkarni and Simon (1988); and Langley et al. (1987). I consider model-based discovery to be a diagnosis and design problem. More precisely, modelbased-theory refinement can be seen as a four-step process: (1) gather data, (2) compare the data to model-based predictions, (3) identify the sources of discrepancies between the predictions and the field data, and (4) fix these discrepancies by modifying the model. The first three steps are traditionally addressed by diagnosis systems, but the fourth step requires design techniques.