We propose an approach of open-ended evolution via the simulation of swarm dynamics. In nature, swarms possess remarkable properties, which allow many organisms, from swarming bacteria to ants and flocking birds, to form higher-order structures that enhance their behavior as a group. Swarm simulations highlight three important factors to create novelty and diversity: (a) communication generates combinatorial cooperative dynamics, (b) concurrency allows for separation of timescales, and (c) complexity and size increases push the system towards transitions in innovation. We illustrate these three components in a model computing the continuous evolution of a swarm of agents. The results, divided in three distinct applications, show how emergent structures are capable of filtering information through the bottleneck of their memory, to produce meaningful novelty and diversity within their simulated environment.
How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles
Olaf Witkowski, Takashi Ikegami
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias–variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton–proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute.
A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta, et al.
Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein–protein interactome, we show the existence of six distinct classes of drug–drug–disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.
Network-based prediction of drug combinations
Feixiong Cheng, István A. Kovács & Albert-László Barabási
Nature Communications volume 10, Article number: 1197 (2019)
In this guest blog Nele van Hooste from Board of Innovation speaks about the 6 mistakes they made while introducting a network of sel-managing teams.
The insertion of robotic and artificial intelligent (AI) systems in therapeutic settings is accelerating. In this paper, we investigate the legal and ethical challenges of the growing inclusion of social robots in therapy. Typical examples of such systems are Kaspar, Hookie, Pleo, Tito, Robota,Nao, Leka or Keepon. Although recent studies support the adoption of robotic technologies for therapy and education, these technological developments interact socially with children, elderly or disabled, and may raise concerns that range from physical to cognitive safety, including data protection. Research in other fields also suggests that technology has a profound and alerting impact on us and our human nature. This article brings all these findings into the debate on whether the adoption of therapeutic AI and robot technologies are adequate, not only to raise awareness of the possible impacts of this technology but also to help steer the development and use of AI and robot technologies in therapeutic settings in the appropriate direction. Our contribution seeks to provide a thoughtful analysis of some issues concerning the use and development of social robots in therapy, in the hope that this can inform the policy debate and set the scene for further research.
“I’ll take care of you,” said the robot
Reflecting upon the legal and ethical aspects of the use and development of social robots for therapy
Eduard Fosch-Villaronga Jordi Albo-Canals
Paladyn, Journal of Behavioral Robotics