critical resource
Preparing for AI cybercrime before it's too late
Artificial Intelligence (AI) is currently used by IT professionals to manage cybersecurity threats, protecting organisations from ongoing cybercrimes. With its ability to take large volumes of information and deduce clusters of similarity, it won't be long until AI will turn on us. Another quality that AI is exhibiting is its ability to mimic humans to a worryingly accurate degree. It can draw pictures, age photographs of people, and just recently, it has been found to impersonate human voices. This means that AI could potentially replicate human hacking tactics, which are currently the most damaging and the most time-consuming form of attack for hackers.
Cybercrime, meet AI
Now, however, with artificial intelligence (AI) – essentially advanced analytical models – coming onto the market, cybersecurity actually has the edge. At present, vendors are doing far more than hackers with AI. Not that we can expect it to stay that way forever, but right now the good guys have the upper hand – and that gives the industry some time to prepare itself for the eventual rise of AI-enabled cybercriminals. The value of AI in this model is that it lets companies take large volumes of information and find clusters of similarity. This is always the focus of cybersecurity to a degree, but organisations are often unequipped to do so in sufficient depth because of time and resourcing constraints.
Temporal Planning for Interacting Durative Actions with Continuous Effects
Kecici, Serdar (Istanbul Technical University) | Talay, Sanem Sariel (Istanbul Technical University)
We consider planning domains with both discrete and continuous changes. Continuous change occurs especially when agents share time-dependent critical resources. In these cases, besides discrete and continuous changes, their interactions should also be taken into consideration. However concurrency of durative actions with interacting continuous effects cannot be exploited by existing temporal planners. To overcome this problem, we propose an action lifting approach and we analyze path sharing problem to illustrate interaction of continuous linear effects in the planning domain.