Democratising AI: Multiple Meanings, Goals, and Methods

Numerous parties are calling for the democratisation of AI, but the phrase is used to refer to a variety of goals, the pursuit of which sometimes conflict. This paper identifies four kinds of AI democratisation that are commonly discussed: (1) the democratisation of AI use, (2) the democratisation of AI development, (3) the democratisation of AI profits, and (4) the democratisation of AI governance. Numerous goals and methods of achieving each form of democratisation are discussed. The main takeaway from this paper is that AI democratisation is a multifarious and sometimes conflicting concept that should not be conflated with improving AI accessibility. If we want to move beyond ambiguous commitments to democratising AI, to productive discussions of concrete policies and trade-offs, then we need to recognise the principal role of the democratisation of AI governance in navigating tradeoffs and risks across decisions around use, development, and profits.Comment: Changed second author affiliation; added citation to section 5.2; edit to author contribution statemen

Interoceptive Ability Predicts Survival on a London Trading Floor.

Interoception is the sensing of physiological signals originating inside the body, such as hunger, pain and heart rate. People with greater sensitivity to interoceptive signals, as measured by, for example, tests of heart beat detection, perform better in laboratory studies of risky decision-making. However, there has been little field work to determine if interoceptive sensitivity contributes to success in real-world, high-stakes risk taking. Here, we report on a study in which we quantified heartbeat detection skills in a group of financial traders working on a London trading floor. We found that traders are better able to perceive their own heartbeats than matched controls from the non-trading population. Moreover, the interoceptive ability of traders predicted their relative profitability, and strikingly, how long they survived in the financial markets. Our results suggest that signals from the body - the gut feelings of financial lore - contribute to success in the markets.UK Economic and Social Research Council (Programme Grant), European Research Council (Grant ID: ERC AdG324150:CCFIB), Dr. Mortimer and Theresa Sackler Foundation, National Institute for Health Research Cambridge Biomedical Research Centre, ARC DECRA Fellowship, Queensland Smart Future FundThis is the final version of the article. It first appeared from Nature Publishing Group via https://doi.org/10.1038/srep3298

Neural mediators of subjective and autonomic responding during threat learning and regulation

Threat learning elicits robust changes across multiple affective domains, including changes in autonomic indices and subjective reports of fear and anxiety. It has been argued that the underlying causes of such changes may be dissociable at a neural level, but there is currently limited evidence to support this notion. To address this, we examined the neural mediators of trial-by-trial skin conductance responses (SCR), and subjective reports of anxious arousal and valence in participants (n = 27; 17 females) performing a threat reversal task during ultra-high field functional magnetic resonance imaging. This allowed us to identify brain mediators during initial threat learning and subsequent threat reversal. Significant neural mediators of anxious arousal during threat learning included the dorsal anterior cingulate, anterior insula cortex (AIC), and ventromedial prefrontal cortex (vmPFC), subcortical regions including the amygdala, ventral striatum, caudate and putamen, and brain-stem regions including the pons and midbrain. By comparison, autonomic changes (SCR) were mediated by a subset of regions embedded within this broader circuitry that included the caudate, putamen and thalamus, and two distinct clusters within the vmPFC. The neural mediators of subjective negative valence showed prominent effects in posterior cortical regions and, with the exception of the AIC, did not overlap with threat learning task effects. During threat reversal, positive mediators of both subjective anxious arousal and valence mapped to the default mode network; this included the vmPFC, posterior cingulate, temporoparietal junction, and angular gyrus. Decreased SCR during threat reversal was positively mediated by regions including the mid cingulate, AIC, two sub-regions of vmPFC, the thalamus, and the hippocampus. Our findings add novel evidence to support distinct underlying neural processes facilitating autonomic and subjective responding during threat learning and threat reversal. The results suggest that the brain systems engaged in threat learning mostly capture the subjective (anxious arousal) nature of the learning process, and that appropriate responding during threat reversal is facilitated by participants engaging self- and valence-based processes. Autonomic changes (SCR) appear to involve distinct facilitatory and regulatory contributions of vmPFC sub-regions

Model evaluation for extreme risks

Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through "dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through "alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security

Reconstructing biblical military campaigns using geomagnetic field data.

The Hebrew Bible and other ancient Near Eastern texts describe Egyptian, Aramean, Assyrian, and Babylonian military campaigns to the Southern Levant during the 10th to sixth centuries BCE. Indeed, many destruction layers dated to this period have been unearthed in archaeological excavations. Several of these layers are securely linked to specific campaigns and are widely accepted as chronological anchors. However, the dating of many other destruction layers is often debated, challenging the ability to accurately reconstruct the different military campaigns and raising questions regarding the historicity of the biblical narrative. Here, we present a synchronization of the historically dated chronological anchors and other destruction layers and artifacts using the direction and/or intensity of the ancient geomagnetic field recorded in mud bricks from 20 burnt destruction layers and in two ceramic assemblages. During the period in question, the geomagnetic field in this region was extremely anomalous with rapid changes and high-intensity values, including spikes of more than twice the intensity of today's field. The data are useful in the effort to pinpoint these short-term variations on the timescale, and they resolve chronological debates regarding the campaigns against the kingdoms of Israel and Judah, the relationship between the two kingdoms, and their administrations

Measurement of the Dipion Mass Spectrum in X(3872) -> J/Psi Pi+ Pi- Decays

We measure the dipion mass spectrum in X(3872)--> J/Psi Pi+ Pi- decays using 360 pb-1 of pbar-p collisions at 1.96 TeV collected with the CDF II detector. The spectrum is fit with predictions for odd C-parity (3S1, 1P1, and 3DJ) charmonia decaying to J/Psi Pi+ Pi-, as well as even C-parity states in which the pions are from Rho0 decay. The latter case also encompasses exotic interpretations, such as a D0-D*0Bar molecule. Only the 3S1 and J/Psi Rho hypotheses are compatible with our data. Since 3S1 is untenable on other grounds, decay via J/Psi Rho is favored, which implies C=+1 for the X(3872). Models for different J/Psi-Rho angular momenta L are considered. Flexibility in the models, especially the introduction of Rho-Omega interference, enable good descriptions of our data for both L=0 and 1.Comment: 7 pages, 4 figures -- Submitted to Phys. Rev. Let