We can struggle to maintain working relationships when our social group grows too large, but it seems that artificial intelligence models may not face the same limitation, suggesting thousands of AIs could work together to solve problems that humans can't. The idea that there is a fundamental limit on how many people we can interact with dates back to the 1990s, when anthropologist Robin Dunbar noticed a link between the size of a primate's brain and the typical size of its social group.

In software development there is a constant pressure to produce code faster and faster without compromising on quality. New tools supporting developers are created in response to this demand. Currently a new generation of such solutions is about to be launched - Artificial Intelligence driven tools. On 29 June 2021 Github Copilot was announced. It uses trained model to generate code based on human understandable language. The focus of this research was to investigate software developers' approach to this tool. For this purpose a survey containing 18 questions was prepared and shared with programmers. A total of 42 answers were gathered. The results of the research indicate that developers' opinions are divided. Most of them met Github Copilot before attending the survey. The attitude to the tool was mostly positive but not many participants were willing to use it. Concerns are caused by security issues associated with using of Github Copilot.

Automated discovery of candidate communities in networks finds a variety of applications in physical and social sciences (biological and biochemical networks, physical and virtual human networks) [1, 2]. Given a graph representing binary relations among nodes, informally and intuitively, a community corresponds to a subgraph, i.e. a subset of nodes, with relatively high edge density among the community members (nodes of the subgraph), and comparatively lower density of edges going outside the community. Defining communities more precisely and what overall community structure may be in various domains, and design of efficient robust algorithms for uncovering such in networks has been the subject of much research [1, 3]. In our use-case, we are interested in the automated discovery and effective presentation of candidate communities comprised of computers (hosts) in an enterprise network. In particular this effort is a component of a tool that provides a user, such as a security administrator of an organization, visibility into their complex network, and importantly helps the user partition the network into groups corresponding to geographic partitions, different departments, and hosts running different applications in the organization. This partitioning and naming of the groups is a necessary step in defining and maintaining network security policies, aka network segmentation: hosts in different groups (segments) can only communicate on a few well-defined and restricted channels. Such policy enforcement severely limits penetration and spread of malware and hackers. This step of grouping hosts and assigning meaningful names/labels to the groups, with the human in the loop, is also highly useful in generating insights, for example in uncovering broad patterns of communications with applications not just for security but also for network optimization.

An eye-tracking mask that also measures the wearer's pulse could be used to study people's reactions to things they are viewing. Trisha Andrew at the University of Massachusetts Amherst and her colleagues developed two fabric-based electrodes, which enable continuous monitoring of the wearer's eye movements and pulse for up to 8 hours. Because the electrodes are made of fabric, the mask is also washable and reusable. This could make it useful for health monitoring, particularly of sleep. Eye movement changes are important indicators of sleep phase, such as rapid eye movement (REM) and non REM sleep, says Andrew.

You may think of psychopathy as an antisocial behaviour, but a new study suggests that people with these traits may actually be good for society. Researchers have found that while most people struggle to make moral decisions, psychopaths are more cut-throat about making pragmatic choices for the greater good. The findings show that, in certain circumstances, psychopathic traits could be considered beneficial. The researchers compared a questionnaire with actions in immersive moral dilemmas created using a robotic device that measures force, resistance, and speed, whilst simulating the action of harming a human. In several dilemmas, participants had to decide whether to sacrifice a person by performing a harmful action against them, in order to save a larger group of people.

The way groups of auditory neurons interact to code acoustic information is investigated using an information theoretic approach. We develop measures of redundancy among groups of neurons, and apply them to the study of collaborative coding efficiency in two processing stations in the auditory pathway: the inferior colliculus (IC) and the primary auditory cortex (AI). Under two schemes for the coding of the acoustic content, acoustic segments coding and stimulus identity coding, we show differences both in information content and group redundancies between IC and AI neurons. These results provide for the first time a direct evidence for redundancy reduction along the ascending auditory pathway, as has been hypothesized for theoretical considerations [Barlow 1959,2001]. The redundancy effects under the single-spikes coding scheme are significant only for groups larger than ten cells, and cannot be revealed with the redundancy measures that use only pairs of cells. The results suggest that the auditory system transforms low level representations that contain redundancies due to the statistical structure of natural stimuli, into a representation in which cortical neurons extract rare and independent component of complex acoustic signals, that are useful for auditory scene analysis.

The way groups of auditory neurons interact to code acoustic information is investigated using an information theoretic approach. We develop measures of redundancy among groups of neurons, and apply them to the study of collaborative coding efficiency in two processing stations in the auditory pathway: the inferior colliculus (IC) and the primary auditory cortex (AI). Under two schemes for the coding of the acoustic content, acoustic segments coding and stimulus identity coding, we show differences both in information content and group redundancies between IC and AI neurons. These results provide for the first time a direct evidence for redundancy reduction along the ascending auditory pathway, as has been hypothesized for theoretical considerations [Barlow 1959,2001]. The redundancy effects under the single-spikes coding scheme are significant only for groups larger than ten cells, and cannot be revealed with the redundancy measures that use only pairs of cells. The results suggest that the auditory system transforms low level representations that contain redundancies due to the statistical structure of natural stimuli, into a representation in which cortical neurons extract rare and independent component of complex acoustic signals, that are useful for auditory scene analysis.

The way groups of auditory neurons interact to code acoustic information isinvestigated using an information theoretic approach. We develop measures of redundancy among groups of neurons, and apply them to the study of collaborative coding efficiency in two processing stations in the auditory pathway: the inferior colliculus (IC) and the primary auditory cortex (AI). Under two schemes for the coding of the acoustic content, acoustic segments coding and stimulus identity coding, we show differences both in information content and group redundancies between IC and AI neurons. These results provide for the first time a direct evidence for redundancy reduction along the ascending auditory pathway, as has been hypothesized fortheoretical considerations [Barlow 1959,2001]. The redundancy effects under the single-spikes coding scheme are significant onlyfor groups larger than ten cells, and cannot be revealed with the redundancy measures that use only pairs of cells. The results suggest that the auditory system transforms low level representations thatcontain redundancies due to the statistical structure of natural stimuli, into a representation in which cortical neurons extractrare and independent component of complex acoustic signals, that are useful for auditory scene analysis.