Desiderius (given name)
Desiderius is a Latin given name, related to desiderium - which can be translated as "ardent desire" or "the longed-for". Various other forms include Desiderio in Italian, Desiderio or Desi in Spanish, Desidério in Portuguese, Didier in French and Dezső in Hungarian.
Desiderius may refer to:
- Desiderius (died c. 786), the last king of the Lombard Kingdom of northern Italy
- Desiderius, Abbot of Monte Cassino (c. 1026-1087), successor of Pope Gregory VII
- Desiderius Erasmus (c. 1466-1536), Dutch humanist and theologian
- Desiderius Hampel (1895-1981), Waffen-SS general
- Desiderius of Aquitaine (died 587), Gallo-Roman dux in the Kingdom of the Franks
- Desiderius Wein (1873-1944), Hungarian doctor and gymnast
Saints
- Desiderius (lector), (died c. 303)
- Desiderius of Auxerre, (died 621), bishop of Auxerre
- Desiderius of Cahors (c. 580–655), Merovingian royal official
- Desiderius of Fontenelle (died c. 700), Frankish saint
- Desiderius of Vienne (died 607), archbishop of Vienne and chronicler
- Desiderius of Pistoia, (died 725); See Barontius and Desiderius
gollark: > There is burgeoning interest in designing AI-basedsystems to assist humans in designing computing systems,including tools that automatically generate computer code.The most notable of these comes in the form of the first self-described ‘AI pair programmer’, GitHub Copilot, a languagemodel trained over open-source GitHub code. However, codeoften contains bugs—and so, given the vast quantity of unvettedcode that Copilot has processed, it is certain that the languagemodel will have learned from exploitable, buggy code. Thisraises concerns on the security of Copilot’s code contributions.In this work, we systematically investigate the prevalence andconditions that can cause GitHub Copilot to recommend insecurecode. To perform this analysis we prompt Copilot to generatecode in scenarios relevant to high-risk CWEs (e.g. those fromMITRE’s “Top 25” list). We explore Copilot’s performance onthree distinct code generation axes—examining how it performsgiven diversity of weaknesses, diversity of prompts, and diversityof domains. In total, we produce 89 different scenarios forCopilot to complete, producing 1,692 programs. Of these, wefound approximately 40 % to be vulnerable.Index Terms—Cybersecurity, AI, code generation, CWE
gollark: https://arxiv.org/pdf/2108.09293.pdf
gollark: This is probably below basically everywhere's minimum wage.
gollark: (in general)
gollark: <@!319753218592866315> Your thoughts?
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