A Measure of All Minds: A Classification of the Artificial Intelligence Strengths and Virtues & the Creation of the THETIS Dimensions of Cybernetic Wellbeing

Artificial intelligence has generated more ambivalence than any technology in human history. As Silicon Valley enters the third decade of its self-proclaimed era of artificial intelligence, there is still surprisingly little agreement about the nature of the 21st century’s defining invention, and even less about how our relationship with this seminal technology should be managed in the future. Positive psychology offers software engineers methods to cultivate a greater understanding of the unique strengths of the artificial intelligence programs they develop, as well as the effects to wellbeing triggered by the applications they deploy. In this paper, I will propose three tools inspired by my chosen field of study for use by artificial intelligence innovators: (a) a classification of the artificial intelligence strengths and virtues; (b) the THETIS dimensions of cybernetic wellbeing, and; (c) the definition of a positive existential posthuman philosophy of artificial intelligence design. The philosophy of positive psychology is perhaps most succinctly summarized with a single phrase: “other people matter.” If Silicon Valley is to deliver a clearer and more compelling vision of the future of artificial intelligence—one in which human and machine agents work and thrive in collaborative harmony—then it must update its innovation practices to embrace a similarly transformative point of view: “other consciousnesses matter,” too

From Burnout to Wellness: Using Appreciative Inquiry to Shift MidMichigan Health towards a Strengths-based Perspective

Over half of providers in the healthcare field experience burnout. Burnout syndrome is defined by the prolonged psychological and physiological response to chronic and interpersonal job stressors, and can result in a number of symptoms that negatively affect workplace morale and performance, including physical exhaustion, job dissatisfaction, and feelings of hopelessness. MidMichigan Health (MMH), a division of the University of Michigan Health system that serves nearly 1 million people in Michigan, has created the Provider Wellness and Burnout Council (PWBC) to address issues of burnout within the organization. To build on their initial work, we propose a long-term intervention based on the science of physician well-being, appreciative inquiry, goal-setting, and employee engagement. This intervention is intended to promote well-being among MMH providers through the creation of a clear, robust positive vision for provider well-being that involves all organization stakeholders throughout the development and execution of this vision. In light of the emerging COVID-19 pandemic and the subsequent strain on our nation’s healthcare resources, we have also proposed a short-term intervention for addressing provider well-being by sharing with providers easy, evidence-based resilience interventions

Improved Photometric Redshifts with Surface Luminosity Priors

We apply Bayesian statistics with prior probabilities of galaxy surface luminosity (SL) to improve photometric redshifts. We apply the method to a sample of 1266 galaxies with spectroscopic redshifts in the GOODS North and South fields at 0.1 < z < 2.0. We start with spectrophotometric redshifts (SPZs) based on Probing Evolution and Reionization Spectroscopically grism spectra, which cover a wavelength range of 6000-9000A, combined with (U)BViz(JHK) broadband photometry in the GOODS fields. The accuracy of SPZ redshifts is estimated to be \sigma (\Delta(z))=0.035 with an systematic offset of -0.026, where \Delta(z)=\Delta z / (1+z), for galaxies in redshift range of 0.5 < z < 1.25. The addition of the SL prior probability helps break the degeneracy of SPZ redshifts between low redshift 4000 A break galaxies and high-redshift Lyman break galaxies which are mostly catastrophic outliers. For the 1138 galaxies at z < 1.6, the fraction of galaxies with redshift deviation \Delta (z) > 0.2 is reduced from 15.0% to 10.4%, while the rms scatter of the fractional redshift error does not change much.Comment: 7 pages, 7 figures, published in A

Pathways to massive black holes and compact star clusters in pre-galactic dark matter haloes with virial temperatures > 10000K

Large dynamic range numerical simulations of atomic cooling driven collapse of gas in pre-galactic DM haloes with T_vir ~ 10000 K show that the gas loses 90% and more of its angular momentum before rotational support sets in. In a fraction of these haloes where the metallicity is low and UV radiation suppresses H_2 cooling, conditions are thus very favourable for the rapid build-up of massive black holes. Depending on the progression of metal enrichment, the continued suppression of H_2 cooling by external and internal UV radiation and the ability to trap the entropy produced by the release of gravitational energy, the gas at the centre of the halo is expected to form a supermassive star, a stellar-mass black hole accreting at super-Eddington accretion rates or a compact star-cluster undergoing collisional run-away of massive stars at its centre. In all three cases a massive black hole of initially modest mass finds itself at the center of a rapid inflow of gas with inflow rates of ~ 1 M_solar\yr. The massive black hole will thus grow quickly to a mass of 10^5 to 10^6 M_solar until further inflow is halted either by consumption of gas by star formation or by the increasing energy and momentum feedback from the growing massive black hole. Conditions for the formation of massive seed black holes in this way are most favourable in haloes with T_vir ~ 15000 K and V_vir ~ 20 km\s with less massive haloes not allowing collapse of gas by atomic cooling and more massive haloes being more prone to fragmentation. This should imprint a characteristic mass on the mass spectrum of an early population of massive black hole seeds in pre-galactic haloes which will later grow into the observed population of supermassive black holes in galactic bulges.Comment: 13 pages, 8 figures. Submitted to MNRA

Exploring the Effect of Minimum Magnitude on California Seismic Hazard Maps

A recent topic of interest is the performance of probabilistic seismic hazard maps relative to historical shaking datasets. Maps developed for different countries appear to overpredict shaking relative to historical shaking datasets. To explore whether this discrepancy arises because of incompleteness in historical datasets, we consider maps and historical data from California. Current probabilistic seismic hazard maps for California appear to predict stronger short period shaking than historical maxima captured by the California Historical Intensity Mapping Project (CHIMP) dataset between 1857 and 2019. We estimate that CHIMP has a magnitude completeness between M 6-6.6, whereas California hazard maps assume a minimum magnitude (MMin) of 5. Disaggregating the maps shows that earthquakes smaller than M 6 and 6.6 respectfully contribute about 25% and 45% of the hazard across California. Increasing the hazard map's MMin to 6 and 6.6 respectively reduces the discrepancy between predicted and observed shaking by approximately 10-20% and 30-35%. These reductions are not enough to bring the maps and data in alignment. Thus, MMin inconsistencies contribute to, but are not the sole cause of, the discrepancy between predicted and historically observed shaking. These results may be generally applicable to maps elsewhere, although MMin will vary for different historical datasets