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Active inference: building a new bridge between control theory and embodied cognitive science

Sat, 08/03/2019 - 11:39

The application of Bayesian techniques to the study and computational modelling of biological systems is one of the most remarkable advances in the natural and cognitive sciences over the last 50 years. More recently, it has been proposed that Bayesian frameworks are not only useful for building descriptive models of biological functions, but that living systems themselves can be seen as Bayesian (inference) machines. On this view, the statistical tools more traditionally used to account for data in biology, neuroscience and psychology, are now used to model the mechanisms underlying functions and properties of living systems as if the systems themselves were the ones“calculating”those probabilities following Bayesian inference schemes. The free energy principle (FEP) is a framework proposed in light of this paradigm shift, advocating the minimisation of variational free energy, a proxy for sensory surprisal, as a general computational principle for biological systems. More intuitively and under some simplifying assumptions,the minimisation of variational free energy reduces,for an agent,to the minimisation of prediction errors on sensory input. Initially proposed as a candidate unifying theory of brain functioning, the FEP was later extended to encompass hypotheses on the origins of life, and is nowadays discussed in the cognitive science community for its possible implications for theories of the mind. In particular,one of the most popular process theories derived from the FEP,active inference,describes a biologically plausible algorithmic implementation of this principle with several repercussions on our understanding of cognition. In this thesis, I will focus on the role of this process theory for action and perception. In active inference, the two of them are combined in a closed sensorimotor loopasco-dependent processes of minimisation of a single loss function,variational free energy, with respect to different sets of variables. Building on this, I will suggest that some of the core ideas of active inference are best seen in terms of enactive, embodied, extended and embedded (4E) theories, in contrast to the majority of the literature emphasising its apparent connections to more traditional, computational, accounts of the mind. In particular, I will develop this argument by focusing on some proposals central to 4E approaches: (a) the non-brain-centric nature of cognitive processes,(b)the lack of explicit representations of the world,(c)the coupling of agent-environment systems and (d) the necessity of real-time feedback signals from the environment. Under the FEP formulation, I will present a series of case studies with mainly two objectives in mind: 1) to conceptually analyse and reframe these 4E ideas in the context of active inference, arguing for the advantages of their formalisation in a more general probabilistic (Bayesian) framework and, 2) to present new mathematical models and agent-based implementations of some of the conceptual connections between Bayesian inference frameworks and 4E proposals, largely missing in the literature.


Baltieri, Manuel (2019) Active inference: building a new bridge between control theory and embodied cognitive science. Doctoral thesis (PhD), University of Sussex.


Decoding the neuroscience of consciousness

Sat, 08/03/2019 - 09:37

A growing understanding of consciousness could lead to fresh treatments for brain injuries and phobias.


How to Make Change Happen

Sat, 08/03/2019 - 08:44

In this podcast, the author of Nudge, Professor Cass Sunstein, presents a guide for anyone who wishes to fuel – or block – transformative social change.

Sometimes all it takes to change society is for one person to decide they will no longer remain silent. A child announces that the emperor has no clothes. A woman tweets, #MeToo. Suddenly, a taboo collapses for the better – or for the worse. Once white nationalism was kept out of the mainstream media and politics; now it is in the White House. Social movements can begin when rage is released – or quietly, with millions of people nudged into making different decisions until, without noticing, we live with a new status quo.

Bringing together behavioural economics, psychology, politics and law, Cass Sunstein and LBC Presenter Matthew Stadlen explore Cass’s career new science of social movements. What can we as individuals do to harness the power of social movements to make change happen? What kinds of interventions make a difference, and what kind lead to bans and mandates? How can we overcome social division, cause transformative cascades, and employ political parties as a force for good?


Consistency and differences between centrality measures across distinct classes of networks

Fri, 08/02/2019 - 19:26

The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.


Oldham S, Fulcher B, Parkes L, Arnatkevic̆iūtė A, Suo C, Fornito A (2019) Consistency and differences between centrality measures across distinct classes of networks. PLoS ONE 14(7): e0220061.


Syntrophy emerges spontaneously in complex metabolic systems

Fri, 08/02/2019 - 13:55

By exchanging resources, the members of a microbial community can survive in environments where individual species cannot. Despite the abundance of such syntrophy, little is known about its evolutionary origin. The predominant hypothesis is that syntrophy arises when originally independent organisms in the same community become interdependent by accumulating mutations. In this view, syntrophy arises when organisms co-evolve. In sharp contrast we find that different metabolism can interact syntrophically without a shared evolutionary history. We show that syntrophy is an inherent and emergent property of the complex chemical reaction networks that constitute metabolism.


Libby E, Hébert-Dufresne L, Hosseini S-R, Wagner A (2019) Syntrophy emerges spontaneously in complex metabolic systems. PLoS Comput Biol 15(7): e1007169.


Home-work carpooling for social mixing

Fri, 08/02/2019 - 13:17

Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in home-work commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of traffic in cities. Accordingly, most of the carpooling algorithms are optimized for reducing total travel time, cost, and other transportation-related metrics. In this paper, we analyze carpooling from a new perspective, investigating the question of whether it can be used also as a tool to favor social integration, and to what extent social benefits should be traded off with transportation efficiency. By incorporating traveler’s social characteristics into a recently introduced network-based approach to model ride-sharing opportunities, we define two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, we provide corresponding optimal and computationally efficient solutions. We then demonstrate our approach on two datasets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social benefits of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.

Home-work carpooling for social mixing
Federico Librino, M. Elena Renda, Paolo Santi, Francesca Martelli, Giovanni Resta, Fabio Duarte, Carlo Ratti, Jinhua Zhao



Nonlinearity and distance of ancient routes in the Aztec Empire

Fri, 08/02/2019 - 11:29

This study explores the way in which traveling paths in ancient cultures are characterized by the relationship between nonlinear shapes and path lengths in terms of distances. In particular, we analyze the case of trade routes that connected Aztec settlements around 1521 CE in central Mexico. Based on the complex systems perspective, we used the least cost path approximation to reconstruct a hypothetical large-scale map of routes reproducing physical connections among ancient places. We compared these connections with different spatial configurations and identified the probability distribution functions of path lengths. We evaluated the nonlinearity using the mean absolute error based on the path fitness of simple linear models. We found asymmetrical distributions and positive relationships between those measures. If a path length increases, so does its nonlinearity. Thus, the simple pattern of traveling in the Aztec region is fairly unlikely to be straight and short. Complex pathways can represent most of the ancient routes in central Mexico.


Lugo I, Alatriste-Contreras MG (2019) Nonlinearity and distance of ancient routes in the Aztec Empire. PLoS ONE 14(7): e0218593.


Editorial: Statistical Relational Artificial Intelligence

Wed, 07/31/2019 - 10:55

Statistical Relational Artificial Intelligence (StarAI) aims at integrating logical (or relational) AI with probabilistic (or statistical) AI (De Raedt et al., 2016; Riguzzi, 2018). Relational AI achieved impressive results in structured machine learning and data mining, especially in bio- and chemo-informatics. Statistical AI is based on probabilistic (graphical) models that enable efficient reasoning and learning, and that have been applied to a wide variety of fields such as diagnosis, network communication, computational biology, computer vision, and robotics. Ultimately, StarAI may provide good starting points for developing Systems AI—the computational and mathematical modeling of complex AI systems—and in turn an engineering discipline for Artificial Intelligence and Machine Learning.

This Research Topic “Statistical Relational Artificial Intelligence” aims at presenting an overview of the latest approaches in StarAI. This topic was followed by a summer school1held in 2018 in Ferrara, Italy, as part of the series of Advanced Courses on AI (ACAI) promoted by the European Association for Artificial Intelligence.


Front. Robot. AI, 30 July 2019 |
Editorial: Statistical Relational Artificial Intelligence
Fabrizio Riguzzi, Kristian Kersting, Marco Lippi and Sriraam Natarajan


Bittorio revisited: structural coupling in the Game of Life

Tue, 07/30/2019 - 16:59

The notion of structural coupling plays a central role in Maturana and Varela’s biology of cognition framework and strongly influenced Varela’s subsequent enactive elaboration of this framework. Building upon previous work using a glider in the Game of Life (GoL) cellular automaton as a toy model of a minimal autopoietic system with which to concretely explore these theoretical frameworks, this article presents an analysis of structural coupling between a glider and its environment. Specifically, for sufficiently small GoL universes, we completely characterize the nonautonomous dynamics of both a glider and its environment in terms of interaction graphs, derive the set of possible glider lives determined by the mutual constraints between these interaction graphs, and show how such lives are embedded in the state transition graph of the entire GoL universe.


Bittorio revisited: structural coupling in the Game of Life
Randall D Beer

Adaptive Behavior


Opening for Principal Investigator (Professor or Associate Professor) Earth-Life Science Institute, Tokyo Institute of Technology

Mon, 07/29/2019 - 16:46

ELSI aims to answer the fundamental questions of how the Earth was formed, how life originated in the environment of early Earth, and how this life evolved into complexity. ELSI pursues these questions by studying the "origin and evolution of life" and the "origin and evolution of the Earth" through an interdisciplinary collaboration between the fields of Earth, Life, and Planetary Sciences. By understanding the early Earth context that allowed for the rise of initial life, we also work to establish a greater understanding of the likelihood of extraterrestrial life elsewhere in the universe.
We are now seeking exceptional candidates for the role of Principal Investigator (Professor or Associate Professor) to lead world-class interdisciplinary research relevant to the origin and evolution of life. ELSI works positively to eliminate biases against gender or national origin. We welcome all qualified candidates, regardless of nationality or gender. We encourage and support our candidates’ close collaborations with overseas research institutes. Our institutional language is English; Japanese language skills are not required. An unprecedented level of support for researchers to live and thrive in Japan is provided by our talented staff.


ALIFE 2019: The 2019 Conference on Artificial Life

Sun, 07/28/2019 - 17:04

This volume presents the proceedings of ALife 2019, the 2019 Conference on Artificial Life. Open Access


Who Is the Most Important Character in Frozen? What Networks Can Tell Us About the World

Sat, 07/27/2019 - 17:08

How do we determine the important characters in a movie like Frozen? We can watch it, of course, but there are also other ways—using mathematics and computers—to see who is important in the social network of a story. The idea is to compute numbers called centralities, which give ways of measuring who is important in networks. In this paper, we illustrate how different types of centralities measure importance in different ways. We also discuss how centralities are used to study many kinds of networks, not just social ones. In ongoing work, scientists are now developing centrality measures that also consider changes over time and different types of relationships.


Holme P, Porter M and Sayama H (2019) Who Is the Most Important Character in Frozen? What Networks Can Tell Us About the World. Front. Young Minds. 7:99. doi: 10.3389/frym.2019.00099


Predicting neighborhoods’ socioeconomic attributes using restaurant data

Fri, 07/26/2019 - 17:01

High-resolution socioeconomic data are crucial for place-based policy design and implementation, but it remains scarce for many developing cities and countries. We show that an easily accessible and timely updated neighborhood attribute, restaurant, when combined with machine-learning models, can be used to effectively predict a range of socioeconomic attributes. This approach allows us to collect training samples from representative neighborhoods and then use our trained model to infer unsampled neighborhoods in the city in a granular, timely, and low-cost manner. The good cross-city transferability performance of our model can also help bridge the “data gap” between cities, by training the model in cities with rich survey data and then applying it to cities where such data are unavailable.


Predicting neighborhoods’ socioeconomic attributes using restaurant data

Lei Dong, Carlo Ratti, and Siqi Zheng


2019 Fall Program for Executives @NECSI

Thu, 07/25/2019 - 16:33

Organizations are operating in an increasingly complex global context.

Business and society are transforming and becoming increasingly complex. Artificial Intelligence, machine learning, big data analytics and hybrid human-machine systems are playing an increasing role in business products, strategy, and in the organization itself.

NECSI is hosting its two day Executive 2019 Fall Program in Washington, DC.


Curious About Consciousness? Ask the Self-Aware Machines

Thu, 07/25/2019 - 16:25

Consciousness is a famously hard problem, so Hod Lipson is starting from the basics: with self-aware robots that can help us understand how we think.


Estimating the success of re-identifications in incomplete datasets using generative models

Thu, 07/25/2019 - 07:41

While rich medical, behavioral, and socio-demographic data are key to modern data-driven research, their collection and use raise legitimate privacy concerns. Anonymizing datasets through de-identification and sampling before sharing them has been the main tool used to address those concerns. We here propose a generative copula-based method that can accurately estimate the likelihood of a specific person to be correctly re-identified, even in a heavily incomplete dataset. On 210 populations, our method obtains AUC scores for predicting individual uniqueness ranging from 0.84 to 0.97, with low false-discovery rate. Using our model, we find that 99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes. Our results suggest that even heavily sampled anonymized datasets are unlikely to satisfy the modern standards for anonymization set forth by GDPR and seriously challenge the technical and legal adequacy of the de-identification release-and-forget model.


Estimating the success of re-identifications in incomplete datasets using generative models
Luc Rocher, Julien M. Hendrickx & Yves-Alexandre de Montjoye
Nature Communicationsvolume 10, Article number: 3069 (2019)


Automatic Off-Line Design of Robot Swarms: A Manifesto

Tue, 07/23/2019 - 09:13

Designing collective behaviors for robot swarms is a difficult endeavor due to their fully distributed, highly redundant, and ever-changing nature. To overcome the challenge, a few approaches have been proposed, which can be classified as manual, semi-automatic, or automatic design. This paper is intended to be the manifesto of the automatic off-line design for robot swarms. We define the off-line design problem and illustrate it via a possible practical realization, highlight the core research questions, raise a number of issues regarding the existing literature that is relevant to the automatic off-line design, and provide guidelines that we deem necessary for a healthy development of the domain and for ensuring its relevance to potential real-world applications.


Automatic Off-Line Design of Robot Swarms: A Manifesto

Mauro Birattari, et al.

Front. Robot. AI, 19 July 2019


Entropy | Special Issue : Thermodynamics and Information Theory of Living Systems

Tue, 07/23/2019 - 06:37

One of the defining features of living systems is their ability to process, exchange and store large amounts of information at multiple levels of organization, ranging from the biochemical to the ecological. At the same time, living entities are non-equilibrium—possibly at criticality—physical systems that continuously exchange matter and energy with structured environments, all while obeying the laws of thermodynamics. These properties not only lead to the emergence of biological information, but also impose constraints and trade-offs on the costs of such information processing. Some of these costs arise due to the particular properties of the material substrate of living matter in which information processing takes place, while others are universal and apply to all physical systems that process information.

In the past decade, the relationship between thermodynamics and information has received renewed scientific attention, attracting an increasing number of researchers and achieving significant progress. Despite this, the field is full of open problems and challenges at all levels, especially when dealing with biological systems. In spite of these difficulties, continued progress has the potential to fundamentally shape our future understanding of biology.

In this Special Issue we encourage researchers from theoretical biology, statistical physics, neuroscience, information theory, and complex systems to present their research on the connection between thermodynamics and information, with special emphasis on their implications for biological phenomena. We welcome contributions that focus on a particular biological system, as well as contributions that propose general theoretical approaches. We also welcome contributions that use mathematical techniques from statistical physics (variational methods, fluctuation theorems, uncertainty relations, etc.) to investigate biological questions.


Information Pollution by Social Bots

Mon, 07/22/2019 - 15:48

Social media are vulnerable to deceptive social bots, which can impersonate humans to amplify misinformation and manipulate opinions. Little is known about the large-scale consequences of such pollution operations. Here we introduce an agent-based model of information spreading with quality preference and limited individual attention to evaluate the impact of different strategies that bots can exploit to pollute the network and degrade the overall quality of the information ecosystem. We find that penetrating a critical fraction of the network is more important than generating attention-grabbing content and that targeting random users is more damaging than targeting hub nodes. The model is able to reproduce empirical patterns about exposure amplification and virality of low-quality information. We discuss insights provided by our analysis, with a focus on the development of countermeasures to increase the resilience of social media users to manipulation.


Information Pollution by Social Bots

Xiaodan Lou, Alessandro Flammini, Filippo Menczer


Complexity: Science, Engineering or a State of Mind? Towards a Scientific Renaissance

Sun, 07/14/2019 - 09:31

Is complexity a Science? Is it a possibly useful new way of engineering? In this video narrated by Maxi San Miguel it will be argued that Complexity is a new way of thinking necessary for a scientific renaissance that can transform society.