Potential malicious misuse of civilian artificial intelligence (AI) poses serious threats to security on a national and international level. Besides defining autonomous systems from a technological viewpoint and explaining how AI development is characterized, we show how already existing and openly available AI technology could be misused. To underline this, we developed three exemplary use cases of potentially misused AI that threaten political, digital and physical security. The use cases can be built from existing AI technologies and components from academia, the private sector and the developer-community. This shows how freely available AI can be combined into autonomous weapon systems. Based on the use cases, we deduce points of control and further measures to prevent the potential threat through misused AI. Further, we promote the consideration of malicious misuse of civilian AI systems in the discussion on autonomous weapon systems (AWS).

Physical security is the protection of people, property, and physical assets from actions and events that could cause damage or loss. Though often overlooked in favor of cybersecurity, physical security is equally important. And, indeed, it has grown into a $30 billion industry. All the firewalls in the world can't help you if an attacker removes your storage media from the storage room. The growing sophistication of physical security through technologies such as artificial intelligence (AI) and the internet of things (IoT) means IT and physical security are becoming more closely connected, and as a result security teams need to be working together to secure both the physical and digital assets.

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The ability to automate human sight is opening up massive opportunities for value creation across ... [ ] every sector of the economy. Computer vision is the most technologically mature field in modern artificial intelligence. This is about to translate into enormous commercial value creation. The deep learning revolution has its roots in computer vision. At the now-historic 2012 ImageNet competition, Geoff Hinton and team debuted a neural network--a novel architecture at the time--whose performance eclipsed all previous efforts at computer-based image recognition. The era of deep learning was born, with computer vision as its original use case.

The ability to automate human sight is opening up massive opportunities for value creation across ... [ ] every sector of the economy. Computer vision is the most technologically mature field in modern artificial intelligence. This is about to translate into enormous commercial value creation. The deep learning revolution has its roots in computer vision. At the now-historic 2012 ImageNet competition, Geoff Hinton and team debuted a neural network--a novel architecture at the time--whose performance eclipsed all previous efforts at computer-based image recognition. The era of deep learning was born, with computer vision as its original use case.

Machine learning and artificial intelligence are touted as the cure-all for everything that ails a data center. While much of it is hype and baseless optimism, AI-powered tools are already useful and practical in some areas. Those areas include data center physical security, where AI is making a difference on three fronts: image and sound recognition, anomaly detection, and predictive analytics. Image recognition is one of the big success stories in AI, and the technology is quickly being embedded everywhere. And so is its close cousin, sound recognition.

In our recent post on security for 2020, one of the upcoming trends we explored was artificial intelligence (AI). It's a crucial area that will drive a great deal of change as we enter the next decade, so let's take a closer look at how artificial intelligence is set to transform the security landscape. Artificial intelligence is a powerful technology that can defend property and prevent crime. AI is sophisticated enough to assist major security operations such as the military, gathering huge amounts of data from smart sensors, and analysing events in real time so as to expose potential risks. But it can also come into its own on a smaller scale.

Bottom Line: Real-time analysis of remote video feeds is rapidly improving thanks to AI, increasing the accuracy of remote equipment and facility monitoring. Agriculture, construction, oil & gas, utilities, and critical infrastructure all need to merge cybersecurity and physical security to adapt to an increasingly complex threatscape. What needs to be the top priority is improving the accuracy, insight, and speed of response to remote threats that AI-based video recognition systems provide. Machine learning techniques as part of a broader AI strategy are proving effective in identifying anomalies and threats in real-time using video, often correlating them back to cyber threats, which are often part of an orchestrated attack on remote facilities. The future of remote security monitoring is being defined by the rapid advances in supervised, unsupervised, and reinforcement machine learning algorithms and their contributions to AI-based visual recognition systems.

There Is a Range of Tasks Your Face Recognition App Can Be Designed to Perform If You Use the Right Face Recognition Methods. The Facial Recognition technology has been one of those, gaining ground fastest over recent years and one that is still, obviously, pretty far from its heyday. Invented to, virtually, enhance, or rather, extend one of the 6 human senses, it is finding new, often, critically important (for example, public security-related) uses and becoming more wide-spread globally by the day. According to Researchandmarkets.com, the total worth of the global Face Recognition software market is estimated to have constituted some USD 3.85 billion in 2017 and it is predicted to reach USD 9.78 billion in 2023, thus showing a nearly threefold growth. This can only mean that while giving those better equipped with Face Recognition apps an edge and an additional means of control, the rapidly developing Facial Recognition technology is also becoming a competitive factor for businesses in various industry sectors.

In today's dynamic environments, risk must be proactively managed. However, lack of sufficient intelligence can make it impossible to get ahead of threats and breaches. Fortunately, artificial intelligence (AI) has emerged as a valuable tool in augmenting physical security with a more proactive approach. While AI in physical security is still in the early stages, many critical capabilities exist today. Let's examine four ways that AI is making it easier and more efficient to manage physical security risk in the real world.