Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.

Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any subset) plays a key role in areas of the human brain linked with complex cognition. However, two questions remain unanswered: (a) how and why a cognitive system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as networks learn multiple diverse tasks, and that in tasks requiring integration of multiple sources, performance critically relies on synergistic neurons. Overall, our results suggest that synergy is used to combine information from multiple modalities-and more generally for flexible and efficient learning. These findings reveal new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies on the system's information dynamics.

Direct Human-AI Comparison in the Animal-AI Environment.

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing (such as Chess and Go) towards ability-oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6-10 (n = 52). We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, "ironbar" and "Trrrrr," from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6-10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines.

An unusual skin infection with Achromobacter xylosoxidans.

Achromobacter xylosoxidans is an emerging, multidrug-resistant pathogen capable of forming biofilms on medical products that primarily infects immunocompromised patients. We present the case of a 50-year-old immunocompetent woman who developed an A xylosoxidans granulomatous abscess at the attachment site of her insulin pump. She was successfully treated with surgical excision and oral trimethoprim-sulfamethoxazole (TMP-SMX). This case demonstrates further emergence of A xylosoxidans as a potential pathogen, not only in immunocompromised individuals but in any patient with an indwelling catheter.

Artificial Intelligence and the Common Sense of Animals.

The problem of common sense remains a major obstacle to progress in artificial intelligence. Here, we argue that common sense in humans is founded on a set of basic capacities that are possessed by many other animals, capacities pertaining to the understanding of objects, space, and causality. The field of animal cognition has developed numerous experimental protocols for studying these capacities and, thanks to progress in deep reinforcement learning (RL), it is now possible to apply these methods directly to evaluate RL agents in 3D environments. Besides evaluation, the animal cognition literature offers a rich source of behavioural data, which can serve as inspiration for RL tasks and curricula.

The limits of machine intelligence: Despite progress in machine intelligence, artificial general intelligence is still a major challenge.

Despite recent breakthroughs in machine learning, current artificial systems lack key features of biological intelligence. Whether the current limitations can be overcome is an open question, but critical to answer, given the implications for society.

Aplasia cutis congenita type V: a case report and review of the literature.

Aplasia cutis congenita (ACC) is a relatively rare congenital anomaly that most commonly occurs as a solitary cutaneous defect on the scalp. Depth of involvement varies, and involvement of deeper calvarium and dural structures can be seen in more severe cases. Multiple classification systems have been devised with the Frieden Classification System being the most widely adopted. Using this system, we describe a patient that developed Type V ACC with associated fetal papyraceous. The child healed remarkably well with the application of petrolatum impregnated gauze and topical silver sulfadiazine twice daily for approximately 4 weeks. The child was noted to have no significant contractures or complications at 6-months and 1-year follow-up exams. Herein, we review the current literature on type V ACC including a discussion of treatment options.