An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Nicole E. Kogan, Leonardo Clemente, Parker Liautaud, Justin Kaashoek, Nicholas B. Link, Andre T. Nguyen, Fred S. Lu, Peter Huybers, Bernd Resch, Clemens Havas, Andreas Petutschnig, Jessica Davis, Matteo Chinazzi, Backtosch Mustafa, William P. Hanage, Alessandro Vespignani, Mauricio Santillana

 

Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.

Source: arxiv.org

A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions

Hector Zenil

Entropy 2020, 22(6), 612

 

Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and may exhibit their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented and despite their many challenges, some of these methods can be better motivated by and better grounded in the principles of algorithmic information theory. It will be explained how different approaches to algorithmic complexity can explore the relaxation of different necessary and sufficient conditions in their pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance. We conclude with a discussion of possible directions that may or should be taken into consideration to advance the field and encourage methodological innovation, but more importantly, to contribute to scientific discovery. This paper also serves as a rebuttal of claims made in a previously published minireview by another author, and offers an alternative account.

Source: www.mdpi.com

ALIFE 2020: The 2020 Conference on Artificial Life (proceedings)

Editors: Josh Bongard, Juniper Lovato, Laurent Hebert-Dufrésne, Radhakrishna Dasari and Lisa Soros

 

This volume presents the proceedings of the 2020 Conference on Artificial Life (ALIFE 2020) which took place online July 13-18. Originally scheduled to be held in Montreal, Canada, this was the first time our conference had been conducted in this manner. Of course, our community was not alone: just about every human community has had to adapt to the covid-19 pandemic and its repercussions. It is difficult to avoid seeing the irony in this: Artificial Life researchers have declared, since the field’s inception at a small workshop at Los Alamos in 1987, that we wish to understand how life adapts to unforeseen circumstances. Further, we wish to incorporate learned mechanisms of adaptation into our technologies and, possibly, our societies. Put simply, Artificial Life invites us to think and learn about adaptation; SARS-CoV-2 forces us to adapt. More simple yet: ALife is theory; COVID is practice. There is a long tradition in our field of peering at our computer screens or into our petri dishes, waiting with bated breath to see what new forms emerge. Likewise for the post-pandemic world. Whatever does emerge from the conference, and from the pandemic — and whether we learn from it, and whether we use that knowledge to benefit each other — it is our honor to be part of the adventure with you.

Source: www.mitpressjournals.org

Multilayer Networks in a Nutshell

Alberto Aleta and Yamir Moreno

Annual Review of Condensed Matter Physics
Vol. 10:45-62

 

Complex systems are characterized by many interacting units that give rise to emergent behavior. A particularly advantageous way to study these systems is through the analysis of the networks that encode the interactions among the system constituents. During the past two decades, network science has provided many insights in natural, social, biological, and technological systems. However, real systems are often interconnected, with many interdependencies that are not properly captured by single-layer networks. To account for this source of complexity, a more general framework, in which different networks evolve or interact with each other, is needed. These are known as multilayer networks. Here, we provide an overview of the basic methodology used to describe multilayer systems as well as of some representative dynamical processes that take place on top of them. We round off the review with a summary of several applications in diverse fields of science.

Source: www.annualreviews.org

On Crashing the Barrier of Meaning in Artificial Intelligence

Melanie Mitchell

AI Magazine

 

In 1986, the mathematician and philosopher Gian-Carlo Rota wrote, “I wonder whether or when artificial intelligence will ever crash the barrier of meaning” (Rota 1986). Here, the phrase “barrier of meaning” refers to a belief about humans versus machines: Humans are able to actually understand the situations they encounter, whereas even the most advanced of today’s artificial intelligence systems do not yet have a humanlike understanding of the concepts that we are trying to teach them. This lack of understanding may underlie current limitations on the generality and reliability of modern artificial intelligence systems. In October 2018, the Santa Fe Institute held a three-day workshop, organized by Barbara Grosz, Dawn Song, and myself, called Artificial Intelligence and the Barrier of Meaning. Thirty participants from a diverse set of disciplines — artificial intelligence, robotics, cognitive and developmental psychology, animal behavior, information theory, and philosophy, among others — met to discuss questions related to the notion of understanding in living systems and the prospect for such understanding in machines. In the hope that the results of the workshop will be useful to the broader community, this article summarizes the main themes of discussion and highlights some of the ideas developed at the workshop.

Source: www.aaai.org