This article focuses on elucidating the concept of consciousness from a relational and post-phenomenological theory of non-human communication agents (ANHC). Specifically, we explore the contributions of Thomas Metzinger s Self Model Theory, Katherine Hayles conceptualizations of non-conscious cognitive processes centered on knowledge processing phenomena shared between biological and technical systems and Lenore and Manuel Blum s theoretical perspective on computation, which defines consciousness as an emergent phenomenon of complex computational systems, arising from the appropriate organization of their inorganic materiality. Building on interactions with non-human cognitive agents, among other factors, the explainability of sociotechnical systems challenges the humanistic common sense of modern philosophy and science. This critical integration of various approaches ultimately questions other concepts associated with consciousness, such as autonomy, freedom, and mutual responsibility. The aim is to contribute to a necessary discussion for designing new frameworks of understanding that pave the way toward an ethical and pragmatic approach to addressing contemporary challenges in the design, regulation, and interaction with ANHC. Such frameworks, in turn, enable a more inclusive and relational understanding of agency in an interconnected world.

We have defined the Conscious Turing Machine (CTM) for the purpose of investigating a Theoretical Computer Science (TCS) approach to consciousness. For this, we have hewn to the TCS demand for simplicity and understandability. The CTM is consequently and intentionally a simple machine. It is not a model of the brain, though its design has greatly benefited - and continues to benefit - from neuroscience and psychology. The CTM is a model of and for consciousness. Although it is developed to understand consciousness, the CTM offers a thoughtful and novel guide to the creation of an Artificial General Intelligence (AGI). For example, the CTM has an enormous number of powerful processors, some with specialized expertise, others unspecialized but poised to develop an expertise. For whatever problem must be dealt with, the CTM has an excellent way to utilize those processors that have the required knowledge, ability, and time to work on the problem, even if it is not aware of which ones these may be.

Towards the end of 2022, there was a big boom in AI-generated art on social media and artist-friendly sites such as ArtStation. Though human artists have been quite vocal about how art generators are copying art that already exists from real creators, AI art is gradually becoming a part of that community and other parts of various entertainment industries such as books or music. And now a similar problem is arising in the voice acting space. Earlier in the week, Motherboard reported a story on how video game voice actors are now being asked to sign their voice rights away to company-run AI voice generators when signing on for a new project. Some actors have been made to sign contracts with these clauses built in, others don't even know the clause exists until after they've signed.

In this paper, we thoroughly analyze the scaling behavior of several state-of-the-art route planning techniques for road networks, all of which rely on preprocessing. One goal is to determine which technique is most suitable to be used on huge networks. To be able to conduct scalability studies in a clean way, we first describe a new kind of road network generator that allows to produce road networks even larger than that of our planet with similar properties as real networks. We then carefully implement several preprocessing-based route planning techniques, as contraction hierarchies, hub labels and transit nodes, to study their space consumption as well as their search spaces in different sized networks. This allows to derive functions that describe their empirical scaling behavior for the first time. We also compare our functions to existing theoretical bounds. We show that several of our results can not be sufficiently explained by the theoretical investigations conducted so far. Hence our results encourage a further look for road network models that allow for better predictions.

Renowned researchers Manuel Blum and Lenore Blum have devoted their entire lives to the study of computer science with a particular focus on consciousness. They've authored dozens of papers and taught for decades at prestigious Carnegie Mellon University. And, just recently, they published new research that could serve as a blueprint for developing and demonstrating machine consciousness. That paper, titled "A Theoretical Computer Science Perspective on Consciousness," may only a be a pre-print paper, but even if it crashes and burns at peer-review (it almost surely won't) it'll still hold an incredible distinction in the world of theoretical computer science. The Blum's are joined by a third collaborator, one Avrim Blum, their son.

The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. This paper studies consciousness from the perspective of theoretical computer science. It formalizes the Global Workspace Theory (GWT) originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, and others. Our major contribution lies in the precise formal definition of a Conscious Turing Machine (CTM), also called a Conscious AI. We define the CTM in the spirit of Alan Turing's simple yet powerful definition of a computer, the Turing Machine (TM). We are not looking for a complex model of the brain nor of cognition but for a simple model of (the admittedly complex concept of) consciousness. After formally defining CTM, we give a formal definition of consciousness in CTM. We then suggest why the CTM has the feeling of consciousness. The reasonableness of the definitions and explanations can be judged by how well they agree with commonly accepted intuitive concepts of human consciousness, the breadth of related concepts that the model explains easily and naturally, and the extent of its agreement with scientific evidence.

The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. This paper studies consciousness from the perspective of theoretical computer science. It formalizes the Global Workspace Theory (GWT) originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, and others. Our major contribution lies in the precise formal definition of a Conscious Turing Machine (CTM), also called a Conscious AI. We define the CTM in the spirit of Alan Turing's simple yet powerful definition of a computer, the Turing Machine (TM). We are not looking for a complex model of the brain nor of cognition but for a simple model of (the admittedly complex concept of) consciousness. After formally defining CTM, we give a formal definition of consciousness in CTM. We then suggest why the CTM has the feeling of consciousness. The reasonableness of the definitions and explanations can be judged by how well they agree with commonly accepted intuitive concepts of human consciousness, the breadth of related concepts that the model explains easily and naturally, and the extent of its agreement with scientific evidence.

For decades, artificial intelligence (AI) lived in data centers, where there was sufficient compute power to perform processor-demanding cognitive tasks. In time, AI made its way into software, where predictive algorithms changed the nature of how these systems support the business. Now AI has moved to the outer edges of networks. "Edge AI happens when AI techniques are embedded in Internet of Things ( IoT) endpoints, gateways, and other devices at the point of use," explains Jason Mann, vice president of IoT at SAS. "Put another way, edge computing brings the data and the compute closest to the point of interaction," says Red Hat chief technology strategist E.G. Nadhan. Edge AI is a very real (and rapidly expanding) phenomenon, powering everything from smartphones and smart speakers to automotive sensors and security cameras.

The cold, steely arm of Fernando the Barista swirled the foam of my matcha latte, set it down gently and waved goodbye from inside a glass case. Where you can get robot pizza and robot salad, and now, a robot matcha. There were humans inside the small coffee shop on Market Street, but only some of them ordered drinks. Some of them came in just to gawk at Fernando: The machine was sleek and white, like an Apple product, and its glass enclosure made it seem like a small animal on display. "They all have'it' pronouns," said Sam Blum, Cafe X's community manager.

Don't doubt it: Machine learning is hot--and getting hotter. For the past two years, public interest in building complex algorithms that automatically "learn" and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it "artificial intelligence," or (better) "machine learning." Such work has, in fact, been going on for decades. More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries.