Precisely defining consciousness and identifying the mechanisms that effect it is a long-standing question, particularly relevant with advances in artificial intelligence. The scientific community is divided between physicalism and natural dualism. Physicalism posits consciousness is a physical process that can be modeled computationally; natural dualism rejects this hypothesis. Finding a computational model has proven elusive, particularly because of conflation of consciousness with other cognitive capabilities exhibited by humans, such as intelligence and physiological sensations. Here we show such a computational model that precisely models consciousness, natural or artificial, identifying the structural and functional mechanisms that effect it, confirming the physicalism hypothesis. We found such a model is obtainable when including the underlying (biological or digital) substrate and accounting for reactive behavior in substrate sub-systems (e.g., autonomous physiological responses). Results show that, unlike all other computational processes, consciousness is not independent of its substrate and possessing it is an evolutionary advantage for intelligent entities. Our result shows there is no impediment to the realization of fully artificial consciousness but, surprisingly, that it is also possible to realize artificial intelligence of arbitrary level without consciousness whatsoever, and that there is no advantage in imbuing artificial systems with consciousness.

Today, cybersecurity is in a state of continuous growth and improvement. In this on-demand webinar, learn how two organizations use a continuous AI feedback loop to identify vulnerabilities, harden defenses and improve the outcomes of their cybersecurity programs. The security risk landscape is in tremendous flux, and the traditional on-premises approach to cybersecurity is no longer enough. Remote work has become the norm, and outside the office walls, employees are letting down their personal security defenses. Cyber risks introduced by the supply chain via third parties are still a major vulnerability, so organizations need to think about not only their defenses but those of their suppliers to protect their priority assets and information from infiltration and exploitation.

Darktrace, the world's leading cyber AI company, announced today that travel company, Global Travel, a Singapore Top 500 Enterprise, has deployed artificial intelligence to protect confidential traveler information, including passport data. With more than 40 years of experience in corporate and leisure travel, Global Travel's reputation in Singapore is well-established. The company takes cybersecurity seriously in light of the numerous cyber-attacks wielded on organisations all over the world, where cyber-criminals look to steal or compromise personal information. While the company complies with Singaporean data privacy regulations under the Personal Data Protection Act (PDPA), Global Travel selected Darktrace to dramatically strengthen its security posture. It relies on Darktrace's world-leading cyber AI to not only monitor its digital systems 24/7, but also to act on its behalf when the AI spots malicious activity occurring. This'machine fights back' capability is known in the industry as'Autonomous Response', and enables computer-speed attacks to be quelled in a matter of seconds.

Discussions around AI cyber defence have traditionally focused on the ability of advanced machine learning to detect the earliest signs of an unfolding attack, including sophisticated, never-seen-before threats. This real-time threat detection overcomes the shortcomings of legacy tools and cuts through the noise in live, complex networks to accurately identify threatening anomalies, including'unknown unknowns'. But while the capability to identify the entire spectrum of threats in their nascent stages before a problem becomes a crisis is incredibly powerful in its own right, it also serves as a fundamental enabler for autonomous response measures, which truly deliver on the promise of artificial intelligence in cyber defense. Before the advent of AI cyber defense, the principal obstacle to achieving autonomous response was determining the exact action that is needed to stop an infection from spreading, while keeping the business operational. By their very nature and definition, traditional approaches to cyber security cannot make the jump from detection to response.