Are Robots Sociopaths? A Neuroscientific Approach to the Alignment Problem

Artificial intelligence (AI) is expanding into every niche of human life, organizing our activity, expanding our agency and interacting with us to an exponentially increasing extent. At the same time, AI’s efficiency, complexity and refinement are growing at an accelerating speed. An expanding, ubiquitous intelligence that does not have a means to care about us poses a species-level risk. Justifiably, there is a growing concern with the immediate problem of how to engineer an AI that is aligned with human interests. Computational approaches to the alignment problem currently focus on engineering AI systems to (i) parameterize human values such as harm and flourishing, and (ii) avoid overly drastic solutions, even if these are seemingly optimal. In parallel, ongoing work in applied AI (caregiving, consumer care) is concerned with developing artificial empathy, teaching AI’s to decode human feelings and behavior, and evince appropriate emotional responses.We propose that in the absence of affective empathy (which allows us to share in the states of others), existing approaches to artificial empathy may fail to reliably produce the pro-social, caring component of empathy, potentially resulting in increasingly cognitively complex sociopaths. We adopt the colloquial usage of the term “sociopath” to signify an intelligence possessing cognitive empathy (i.e., the ability to decode, infer, and model the mental and affective states of others), but crucially lacking pro-social, empathic concern arising from shared affect and embodiment. It is widely acknowledged that aversion to causing harm is foundational to the formation of empathy and moral behavior. However, harm aversion is itself predicated on the experience of harm, within the context of the preservation of physical integrity. Following from this, we argue that a “top-down” rule-based approach to achieving caring AI may be inherently unable to anticipate and adapt to the inevitable novel moral/logistical dilemmas faced by an expanding AI. Crucially, it may be more effective to coax caring to emerge from the bottom up, baked into an embodied, vulnerable artificial intelligence with an incentive to preserve its physical integrity. This may be achieved via iterative optimization within a series of tailored environments with incentives and contingencies inspired by the development of empathic concern in humans. Here we attempt an outline of what these training steps might look like. We speculate that work of this kind may allow for AI that surpasses empathic fatigue and the idiosyncrasies, biases, and computational limits that restrict human empathy. While for us, “a single death is a tragedy, a million deaths are a statistic”, the scaleable complexity of AI may allow it to deal proportionately with complex, large-scale ethical dilemmas. Hopefully, by addressing this problem seriously in the early stages of AI’s integration with society, we may one day be accompanied by AI that plans and behaves with a deeply ingrained weight placed on the welfare of others, coupled with the cognitive complexity necessary to understand and solve extraordinary problems.

Enhancing energy efficiency for cellular-assisted vehicular networks by online learning-based mmWave beam selection

Millimeter Wave (mmWave) technology has been regarded as a feasible approach for future vehicular communications. Nevertheless, high path loss and penetration loss raise severe questions on mmWave communications. These problems can be mitigated by directional communication, which is not easy to achieve in highly dynamic vehicular communications. The existing works addressed the beam alignment problem by designing online learning-based mmWave beam selection schemes, which can be well adapted to high dynamic vehicular scenarios. However, this kind of work focuses on network throughput rather than network energy efficiency, which ignores the consideration of energy consumption. Therefore, we propose an Energy efficiency-based FML (EFML) scheme to compensate for this shortfall. In EFML, the energy consumption is reduced as far as possible under the premise of meeting the basic data rate requirements of vehicle users, and the users requesting the same content in close proximity can be organized into the same receiving group to share the same mmWave beam. The simulation results demonstrate that, compare with the comparison method with best energy efficiency, the proposed EFML improves energy efficiency by 17–41% in different scenarios.

Damage quantification of aluminum plates using SC-DTW method based on Lamb waves

Lamb waves were utilized to quantify micro-crack damage in aluminum plates, and the scattering and mode conversion of Lamb waves passing through cracks were analyzed. The dynamic time warping (DWT) method was used to match and compare each Lamb wave time series that represented different damage degrees. The matching difference between the damaged plate and undamaged plate was taken as a marker to measure the damage degree of the workpiece. At the same time, due to the pathological alignment of traditional DTW methods, the shape context (SC) profile recognition method was introduced to optimize the algorithm for calculating the distance between sampling points in the DTW method and solve the pathological alignment problem. Finally, the SC-DTW method based on Lamb waves was verified by the finite element simulation model and bending test of aluminum plates. The results showed that the method was feasible for quantifying the damage degree of aluminum plates and had a great advantage in the analysis and processing of time series in low-sampling frequency and high-noise scenarios.

Vectorial parameterizations of pose

Robotics and computer vision problems commonly require handling rigid-body motions comprising translation and rotation – together referred to as pose. In some situations, a vectorial parameterization of pose can be useful, where elements of a vector space are surjectively mapped to a matrix Lie group. For example, these vectorial representations can be employed for optimization as well as uncertainty representation on groups. The most common mapping is the matrix exponential, which maps elements of a Lie algebra onto the associated Lie group. However, this choice is not unique. It has been previously shown how to characterize all such vectorial parameterizations for SO(3), the group of rotations. Some results are also known for the group of poses, where it is possible to build a family of vectorial mappings that includes the matrix exponential as well as the Cayley transformation. We extend what is known for these pose mappings to the $4 \times 4$ representation common in robotics and also demonstrate three different examples of the proposed pose mappings: (i) pose interpolation, (ii) pose servoing control, and (iii) pose estimation in a pointcloud alignment problem. In the pointcloud alignment problem, our results lead to a new algorithm based on the Cayley transformation, which we call CayPer.

Russian-English dataset and comparative analysis of algorithms for cross-language embedding-based entity alignment

The problem of data fusion from data bases and knowledge graphs in different languages is becoming increasingly important. The main step of such a fusion is the identification of equivalent entities in different knowledge graphs and merging their descriptions. This problem is known as the identity resolution, or entity alignment problem. Recently, a large group of new entity alignment methods has emerged. They look for the so called “embeddings” of entities and establish the equivalence of entities by comparing their embeddings. This paper presents experiments with embedding-based entity alignment algorithms on a Russian-English dataset. The purpose of this work is to identify language-specific features of the entity alignment algorithms. Also, future directions of research are outlined.

Enhancing Energy Efficiency for Cellular-Assisted Vehicular Networks by Online Learning-Based mmWave Beam Selection

Millimeter-Wave (mmWave) technology is deemed as a feasible approach for future vehicular communications. However, mmWave signals are characterized by high path loss and penetration loss, which can be alleviated by directional communication. Directional transmission performance depends on beam alignment between transmitter and receiver, which is not easy to achieve in highly dynamic vehicular communications. The existing works addressed beam alignment problem by designing online learning-based mmWave beam selection schemes, which can be well adapted to high dynamic vehicular scenarios. However, this type of works does not take energy efficiency into account. Therefore, we propose an Energy efficiency-based FML (EFML) scheme to compensate for this shortfall, where the power consumption can be reduced as far as possible under the premise of meeting the basic data rate requirements of vehicle users and the users requesting the same content in close proximity can be organized into the same receiving group to share the same mmWave beam. The simulation results show that the EFML scheme improves both the network energy efficiency and the amount data of cellular-assisted vehicular networks at the cost of more beam performance update overhead. However, there is no difference in the cost of updating beam performance after adequate online learning.

Parallel Subtrajectory Alignment over Massive-Scale Trajectory Data

We study the problem of subtrajectory alignment over massive-scale trajectory data. Given a collection of trajectories, a subtrajectory alignment query returns new targeted trajectories by splitting and aligning existing trajectories. The resulting functionality targets a range of applications, including trajectory data analysis, route planning and recommendation, ridesharing, and general location-based services. To enable efficient and effective subtrajectory alignment computation, we propose a novel search algorithm and filtering techniques that enable the use of the parallel processing capabilities of modern processors. Experiments with large trajectory datasets are conducted for evaluating the performance of our proposal. The results show that our solution to the subtrajectory alignment problem can generate high-quality results and are capable of achieving high efficiency and scalability.

ELRUNA

Networks model a variety of complex phenomena across different domains. In many applications, one of the most essential tasks is to align two or more networks to infer the similarities between cross-network vertices and to discover potential node-level correspondence. In this article, we propose ELRUNA ( el imination ru le-based n etwork a lignment), a novel network alignment algorithm that relies exclusively on the underlying graph structure. Under the guidance of the elimination rules that we defined, ELRUNA computes the similarity between a pair of cross-network vertices iteratively by accumulating the similarities between their selected neighbors. The resulting cross-network similarity matrix is then used to infer a permutation matrix that encodes the final alignment of cross-network vertices. In addition to the novel alignment algorithm, we improve the performance of local search , a commonly used postprocessing step for solving the network alignment problem, by introducing a novel selection method RAWSEM ( ra ndom- w alk-based se lection m ethod) based on the propagation of vertices’ mismatching across the networks. The key idea is to pass on the initial levels of mismatching of vertices throughout the entire network in a random-walk fashion. Through extensive numerical experiments on real networks, we demonstrate that ELRUNA significantly outperforms the state-of-the-art alignment methods in terms of alignment accuracy under lower or comparable running time. Moreover, ELRUNA is robust to network perturbations such that it can maintain a close-to-optimal objective value under a high level of noise added to the original networks. Finally, the proposed RAWSEM can further improve the alignment quality with a smaller number of iterations compared with the naive local search method. Reproducibility : The source code and data are available at https://tinyurl.com/uwn35an.