ORLANDO, Fla., April 24, 2022 (GLOBE NEWSWIRE) -- Retail Asset Protection Conference, Orlando, Booth #RIC-11 – Pathr.ai, the industry's first and only Artificial Intelligence (AI) powered Spatial Intelligence platform, announced today it is working with Hanwha Techwin America, a global supplier of video surveillance solutions, to jointly deliver spatial intelligence solutions to current and prospective customers. This integration allows Pathr.ai to engage with Hanwha on new and cross-sell opportunities, including supporting camera installations with Pathr.ai "Working with Hanwha is a great step towards scaling our spatial intelligence solution to new and existing customers and delivering strong ROI to them," said Alan Flohr, Chief Revenue Officer of Pathr.ai. "Companies can use their existing non-PTZ Hanwha security cameras, such as the Hanwha Wisenet P series, X series, L series, and Q series, to collect meaningful and real-time insights into how people move and interact inside their physical spaces and tie those analytics to business decisions that improve profitability." AI-powered spatial intelligence technology integrates with existing camera infrastructure and delivers real-time behavioral insights to companies, delivering a 10x or greater return on investment.

It's a great feeling to see our retailers reopen nationwide. As customers, we have waited to walk into our favorite stores, eagerly finding new merchandise and admiring new floor layouts. At the same time, retailers are keen to understand how customers interact inside their physical stores. Tech innovations, especially powered by AI, can reveal insights about customers that retailers may not see in plain sight. Today, I'm excited to focus on spatial intelligence -- technology that measures how people and objects move and interact in a given space.

Penetration Testing is a methodology for assessing network security, by generating and executing possible hacking attacks. Doing so automatically allows for regular and systematic testing. A key question is how to generate the attacks. This is naturally formulated as planning under uncertainty, i.e., under incomplete knowledge about the network configuration. Previous work uses classical planning, and requires costly pre-processes reducing this uncertainty by extensive application of scanning methods. By contrast, we herein model the attack planning problem in terms of partially observable Markov decision processes (POMDP). This allows to reason about the knowledge available, and to intelligently employ scanning actions as part of the attack. As one would expect, this accurate solution does not scale. We devise a method that relies on POMDPs to find good attacks on individual machines, which are then composed into an attack on the network as a whole. This decomposition exploits network structure to the extent possible, making targeted approximations (only) where needed. Evaluating this method on a suitably adapted industrial test suite, we demonstrate its effectiveness in both runtime and solution quality.