Toward understanding the impact of artificial intelligence on labor
Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.
Toward understanding the impact of artificial intelligence on labor
Morgan R. Frank, David Autor, James E. Bessen, Erik Brynjolfsson, Manuel Cebrian, David J. Deming, Maryann Feldman, Matthew Groh, José Lobo, Esteban Moro, Dashun Wang, Hyejin Youn, and Iyad Rahwan
PNAS
Source: www.pnas.org
Science looks worse because it’s getting better
It is easy to assume that science is more flawed than in the past, given widespread coverage of the reproducibility crisis, perverse incentives and P-value hacking, alongside a proliferation of corrective measures (…). But it could be that we are now seeing more problems simply because we are more alert to them.
Source: www.nature.com
Hash Chemistry: An Open-Ended Evolutionary System with Cardinality Leap and Universal Fitness Evaluation
Center for Collective Dynamics of Complex Systems (CoCo) Seminar Series March 27, 2019 Hiroki Sayama (Systems Science and Industrial Engineering, Binghamton University) "Hash…
Source: vimeo.com
Why People Harm the Environment Although They Try to Treat It Well: An Evolutionary-Cognitive Perspective on Climate Compensation
Anthropogenic climate changes stress the importance of understanding why people harm the environment despite their attempts to behave in climate friendly ways. This paper argues that one reason behind why people do this is that people apply heuristics, originally shaped to handle social exchange, on the issues of environmental impact. Reciprocity and balance in social relations have been fundamental to social cooperation, and thus to survival, and therefore the human brain has become specialized by natural selection to compute and seek this balance. When the same reasoning is applied to environment-related behaviors, people tend to think in terms of a balance between “environmentally friendly” and “harmful” behaviors, and to morally account for the average of these components rather than the sum. This balancing heuristic leads to compensatory green beliefs and negative footprint illusions—the misconceptions that “green” choices can compensate for unsustainable ones. “Eco-guilt” from imbalance in the moral environmental account may promote pro-environmental acts, but also acts that are seemingly pro-environmental but in reality more harmful than doing nothing at all. Strategies for handling problems caused by this cognitive insufficiency are discussed.
Why People Harm the Environment Although They Try to Treat It Well: An Evolutionary-Cognitive Perspective on Climate Compensation
Patrik Sörqvist and Linda Langeborg
Front. Psychol., 04 March 2019 | https://doi.org/10.3389/fpsyg.2019.00348
Source: www.frontiersin.org
On the Complex Behaviour of Natural and Artificial Machines and Systems
One of the most important aims of the fields of robotics, artificial intelligence and artificial life is the design and construction of systems and machines as versatile and as reliable as living organisms at performing high level human-like tasks. But how are we to evaluate artificial systems if we are not certain how to measure these capacities in living systems, let alone how to define life or intelligence? Here I survey a concrete metric towards measuring abstract properties of natural and artificial systems, such as the ability to react to the environment and to control one’s own behaviour.
On the Complex Behaviour of Natural and Artificial Machines and Systems
H. Zenil
Metrics of Sensory Motor Coordination and Integration in Robots and Animals pp 111-125
Source: link.springer.com
Postdoctoral Fellowships at the Centro de Ciencias de la Complejidad (C3), UNAM
The C3-UNAM announces that each year there will be 2 periods, April-May and December-January, that applications will be received for 2 postdoctoral grants from the UNAM to realize research at the C3-UNAM, starting in September and March, respectively (4 postdoc grants yearly). The purpose of the grants is to realize research in complexity science in one of the following areas: computational intelligence and mathematical modeling, complexity and health, neurosciences, ecological complexity and environment (postdoctoral grants for research in humanistic sciences such as social complexity, and arts, science and complexity will be announced separately), please find the academic programs that are developed at the C3-UNAM in the page:
https://www.c3.unam.mx/progacademicos.html
Technical details for the application are explained in the page:
http://dgapa.unam.mx/images/posdoc/2019_posdoc_convocatoria.pdf
The grants are for 1 year and renewable for a 2nd year in function of the results obtained.
Source: www.c3.unam.mx
Math Proof Finds All Change Is Mix of Order and Randomness
All descriptions of change are a unique blend of chance and determinism, according to the sweeping mathematical proof of the “weak Pinsker conjecture.”
Source: www.quantamagazine.org
An Exact No Free Lunch Theorem for Community Detection
A precondition for a No Free Lunch theorem is evaluation with a loss function which does not assume a priori superiority of some outputs over others. A previous result for community detection by Peel et al. (2017) relies on a mismatch between the loss function and the problem domain. The loss function computes an expectation over only a subset of the universe of possible outputs; thus, it is only asymptotically appropriate with respect to the problem size. By using the correct random model for the problem domain, we provide a stronger, exact No Free Lunch theorem for community detection. The claim generalizes to other set-partitioning tasks including core/periphery separation, k-clustering, and graph partitioning. Finally, we review the literature of proposed evaluation functions and identify functions which (perhaps with slight modifications) are compatible with an exact No Free Lunch theorem.
An Exact No Free Lunch Theorem for Community Detection
Arya D. McCarthy, Tongfei Chen, Seth Ebner
Source: arxiv.org
How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles
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
Source: arxiv.org
