Determining how much of the sensory information carried by a neural code contributes to behavioral performance is key to understand sensory function and neural information flow. However, there are as yet no analytical tools to compute this information that lies at the intersection between sensory coding and behavioral readout.

Why do collectives outperform individuals when solving some problems? Fundamentally, collectives have greater computational resources with more sensory information, more memory, more processing capacity, and more ways to act. While greater resources present opportunities, there are also challenges in coordination and cooperation inherent in collectives with distributed, modular structures. Despite these challenges, we show how collective resource advantages lead directly to well-known forms of collective intelligence including the wisdom of the crowd, collective sensing, division of labour, and cultural learning. Our framework also generates testable predictions about collective capabilities in distributed reasoning and context-dependent behavioural switching. Through case studies of animal navigation and decision-making, we demonstrate how collectives leverage their computational resources to solve problems not only more effectively than individuals, but by using qualitatively different problem-solving strategies.

In a world where deepfakes and cloned voices are emerging as sophisticated attack vectors, organizations require a new security mindset: Sensorial Zero Trust [9]. This article presents a scientific analysis of the need to systematically doubt information perceived through the senses, establishing rigorous verification protocols to mitigate the risks of fraud based on generative artificial intelligence. Key concepts, such as Out-of-Band verification, Vision-Language Models (VLMs) as forensic collaborators, cryptographic provenance, and human training, are integrated into a framework that extends Zero Trust principles to human sensory information. The approach is grounded in empirical findings and academic research, emphasizing that in an era of AI-generated realities, even our eyes and ears can no longer be implicitly trusted without verification. Leaders are called to foster a culture of methodological skepticism to protect organizational integrity in this new threat landscape.

Technical quality: This paper proposes an interesting model and derives three main theorems. The paper demonstrates when the decision maker acquires new information (continuation) and when makes a final prediction (stop) in a scenario where the deadline depends on the sensory information. It also shows that the subject's belief state is a supermartingle, and that the optimal policy has a "rendezvous" structure. The continuation and stopping region in this model depend both on the subject's belief state and "context" of the sensory observations. The paper is interesting and technically sound.

Determining how much of the sensory information carried by a neural code contributes to behavioral performance is key to understand sensory function and neural information flow. However, there are as yet no analytical tools to compute this information that lies at the intersection between sensory coding and behavioral readout.

In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is undeniable. The natural language processing (NLP) community has often missed the opportunity to merge sensory knowledge with emotion classification. To address this gap, we propose SensoryT5, a neuro-cognitive approach that integrates sensory information into the T5 (Text-to-Text Transfer Transformer) model, designed specifically for fine-grained emotion classification. This methodology incorporates sensory cues into the T5's attention mechanism, enabling a harmonious balance between contextual understanding and sensory awareness. The resulting model amplifies the richness of emotional representations. In rigorous tests across various detailed emotion classification datasets, SensoryT5 showcases improved performance, surpassing both the foundational T5 model and current state-of-the-art works. Notably, SensoryT5's success signifies a pivotal change in the NLP domain, highlighting the potential influence of neuro-cognitive data in refining machine learning models' emotional sensitivity.

Generative AI--think Dall.E, ChatGPT-4, and many more--is all the rage. It's remarkable successes, and occasional catastrophic failures, have kick-started important debates about both the scope and dangers of advanced forms of artificial intelligence. But what, if anything, does this work reveal about natural intelligences such as our own? I'm a philosopher and cognitive scientist who has spent their entire career trying to understand how the human mind works. Drawing on research spanning psychology, neuroscience, and artificial intelligence, my search has drawn me towards a picture of how natural minds work that is both interestingly similar to, yet also deeply different from, the core operating principles of the generative AIs.

Published in 1909, Korbinian Brodmann's groundbreaking analysis of the brain can still be found in neurology textbooks and on classroom posters to this day. Using a specialized microscope, Brodmann painstakingly analyzed the entire surface of the Cerebral Cortex on cellular structure alone. After a decade of effort, Brodmann produced the most detailed map of the Cerebral Cortex yet produced, assigning each region a different number. Over time these areas have been widely used to link brain regions with specific functions, such as area four: the primary motor cortex. This region of the Cerebral Cortex is believed to control motor movements such as moving the hands and face as well as breathing and voluntary blinking. Brodmann's areas have also been mapped to functions such as processing numbers, planning, and processing emotions. Of course, the complexity doesn't stop there as scientists now believe the Cortex has at least 180 distinct regions important for language, perception, consciousness, and attention.

The rapid advancements in artificial intelligence (AI) have led to the development of powerful tools and platforms, such as ChatGPT, which have shown great promise in a wide range of applications, from language understanding to decision-making support. As AI systems continue to evolve and become more sophisticated, there is a growing interest in harnessing their potential for personalized support, assistance, and enhancement through human-AI interactions. One promising approach is developing AI systems that can share sensory experiences with humans, thereby fostering a stronger bond between the two entities. In this paper, we propose a novel concept called Symbiotic Artificial Intelligence with Shared Sensory Experiences (SAISSE), which aims to establish a mutually beneficial relationship between AI systems and human users through shared sensory experiences. By integrating multiple sensory input channels and processing human experiences, SAISSE enables AI systems, such as multimodal ChatGPT, to learn from and adapt to individual users, providing personalized support, assistance, and enhancement. The SAISSE concept represents a paradigm shift in AI-human interaction, where AI systems not only process and analyze data but also actively participate in shared sensory experiences, leading to more empathetic, responsive, and adaptive AI solutions. By fostering a stronger bond between humans and AI systems, SAISSE has the potential to revolutionize the way we interact with technology, paving the way for seamless integration of AI in our daily lives and enabling us to unlock new opportunities for human growth, development, and well-being.

Peter van der Made is the founder and CTO of BrainChip Ltd. BrainChip produces advanced AI processors in digital neuromorphic technologies. The artificial intelligence (AI) revolution is upon us, and companies must prepare to adapt to this change. It is important to make an inventory of the current skills within the company to identify which additional skills the employees need to learn. The company does well in developing an AI strategy to outline the areas where AI is most effective, whether in a product or a service.