• Database: Applied Science & Technology Abstracts 
  • Search 1 parameters: artificial intelligence AND analog. Linked full text
    • Results: 23
    • Analysis
      • I chose these parameters because I wanted to learn more analog artificial intelligence. About half of the results were relevant, but they often dealt with very specific subjects or applications that I would need extra experience with to understand.
  • Search 2 parameters: artificial intelligence AND quantum. Linked full text
    • Results: 33
    • Analysis
      • I chose these parameters because I wanted to learn more quantum artificial intelligence. Overall, these results don’t appear to be very useful. Many of them discussed hypothetical future applications for quantum computing and artificial intelligence. This is not a surprise because quantum computing is still a fledgling technology.
• Database: Computer Science Collection
  • Search 3 parameters: artificial intelligence AND quantum. Linked full text. Published since 2019. Academic Journals
    • Results: 102
    • Analysis
      • These results look like they could be very helpful
  • Search 4 parameters: artificial intelligence AND analog. Linked full text. Published since 2019. Academic Journals
    • Results: 63
    • Analysis
      • These results are also helpful.
  • search 5 parameters: artificial intelligence AND physics AND discovery. Published since 2019. Academic Journals. Linked full text.
    • Results: 145
    • Analysis
      • These results include papers that will be hard to understand because they deal with applications in fields I am not experienced in, but I will still be able to find some useful papers.
  • Search 6 parameters: artificial intelligence AND explainability. Linked full text.
    • Results: 5
    • Analysis
      • I only found one result that is useful, but explainable artificial intelligence is the topic I want to target most.

Annotations

From Search 5

Bayzidi, H., Talatahari, S., Saraee, M., & Lamarche, C.-P. (2021). Social Network Search for Solving Engineering Optimization Problems. Computational Intelligence and Neuroscience, 2021. https://link.gale.com/apps/doc/A696850154/CDB?u=klnb_fhsuniv&sid=bookmark-CDB&xid=ec357bd6

This paper provides and explains the results of experiments carried out that analyzed the effectiveness of an algorithm called social network search in solving engineering optimization problems. The paper can be trusted to be reliable because the authors are affiliated with several different major universities in different countries. This paper is extremely valuable to my research because it addresses one of the specific topics I am targeting. It has changed my outlook on the topic because it provides an example of a useful real-world application of the technologies that are relevant to the topic of AI’s applications in science and engineering.

From Search 6

Amparore, Elvio, et al. “To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods.” PeerJ Computer Science, vol. 7, 16 Apr. 2021, p. e479. Gale OneFile: Computer Science, link.gale.com/apps/doc/A658663601/CDB?u=klnb_fhsuniv&sid=bookmark-CDB&xid=57a7ab8b. Accessed 13 Apr. 2022.

This paper analyzes the effectiveness of different explanation models used to explain machine learning models. The paper can be trusted to be reliable because the authors are affiliated with the University of Turin in Italy. This paper is extremely valuable to my research because it also addresses the topic of explainable AI, which I am interested in. I could learn a lot from this paper in particular because the topic of explainable AI has not been researched extensively.