Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy

Tuning Kinetic Magnetism of Strongly Correlated Electrons via Staggered Flux

We explore the kinetic magnetism of the infinite-$U$ repulsive Hubbard models at low hole densities on various lattices with nearest-neighbor hopping integrals modulated by a staggered magnetic flux $\pm\phi$. Tuning $\phi$ from 0 to $\pi$ makes the ground state (GS) change from a Nagaoka-type ferromagnetic state to a Haerter-Shastry-type antiferromagnetic state at a critical $\phi_c$, with both states being of kinetic origin. Intra-plaquette spin correlation, as well as the GS energy, signals such a quantum criticality. This tunable kinetic magnetism is generic, and appears in chains, ladders and two-dimensional lattices with squares or triangles as elementary constituents.Comment: 4 pages, 5 figures, 1 tabl

A geometry for optimizing nanoscale magnetic resonance force microscopy

We implement magnetic resonance force microscopy (MRFM) in an experimental geometry, where the long axis of the cantilever is normal to both the external magnetic field and the RF microwire source. Measurements are made of the statistical polarization of $^1$H in polystyrene with negligible magnetic dissipation, gradients greater than $10^5$ T/m within 100 nm of the magnetic tip, and rotating RF magnetic fields over 12 mT at 115 MHz. This geometry could facilitate the application of nanometer-scale MRFM to nuclear species with low gyro-magnetic ratios and samples with broadened resonances, such as In spins in quantum dots.Comment: 4 pages, 5 figure

Locating the Gribov horizon

We explore whether a tree-level expression for the gluon two-point function, supposed to express effects of an horizon term introduced to eliminate the Gribov ambiguity, is consistent with the propagator obtained in simulations of lattice-regularised quantum chromodynamics (QCD). In doing so, we insist that the gluon two-point function obey constraints that ensure a minimal level of consistency with parton-like behaviour at ultraviolet momenta. In consequence, we are led to a position which supports a conjecture that the gluon mass and horizon scale are equivalent emergent mass-scales, each with a value of roughly $0.5\,$GeV; and wherefrom it appears plausible that the dynamical generation of a running gluon mass may alone be sufficient to remove the Gribov ambiguity.Comment: 7 pages, 2 figures, 2 table