Artificial Intelligence (AI) systems are increasingly placed in positions where their decisions have real consequences, e.g., moderating online spaces, conducting research, and advising on policy. Ensuring they operate in a safe and ethically acceptable fashion is thus critical. However, most solutions have been a form of one-size-fits-all "alignment". We are worried that such systems, which overlook enduring moral diversity, will spark resistance, erode trust, and destabilize our institutions. This paper traces the underlying problem to an often-unstated Axiom of Rational Convergence: the idea that under ideal conditions, rational agents will converge in the limit of conversation on a single ethics. Treating that premise as both optional and doubtful, we propose what we call the appropriateness framework: an alternative approach grounded in conflict theory, cultural evolution, multi-agent systems, and institutional economics. The appropriateness framework treats persistent disagreement as the normal case and designs for it by applying four principles: (1) contextual grounding, (2) community customization, (3) continual adaptation, and (4) polycentric governance. We argue here that adopting these design principles is a good way to shift the main alignment metaphor from moral unification to a more productive metaphor of conflict management, and that taking this step is both desirable and urgent.

A well trained deep network model can help to discern This work investigates the application of skeletal detection crucial grasping regions on fabric such as edges and wrinkles and segmentation techniques, combined with a deep learning [12]. By collecting extensive deformation data of various model, to efficiently spread a quilt over an infant, addressing fabric types within simulators, neural networks can discern challenges posed by limb interference. While robots and perform tasks across different fabric colors, shapes, excel at manipulating rigid objects, handling flexible materials--crucial textures, and sizes [13]. Compared to RGB images, tactile in textiles [1], [2] and medicine [3]--remains a sensors can directly capture fabric morphology when they are challenge. The primary objective of this work is to devise an fixed to the fingertips. Training a classifier in conjunction manipulation actions to ensure infants, especially when their with these sensors can determine if a robot has grasped a limbs are laid on a quilt during sleep, remain adequately specific number of fabric layers [14].

Continuous machine learning pipelines are common in industrial settings where models are periodically trained on data streams. Unfortunately, concept drifts may occur in data streams where the joint distribution of the data X and label y, P(X, y), changes over time and possibly degrade model accuracy. Existing concept drift adaptation approaches mostly focus on updating the model to the new data possibly using ensemble techniques of previous models and tend to discard the drifted historical data. However, we contend that explicitly utilizing the drifted data together leads to much better model accuracy and propose Quilt, a data-centric framework for identifying and selecting data segments that maximize model accuracy. To address the potential downside of efficiency, Quilt extends existing data subset selection techniques, which can be used to reduce the training data without compromising model accuracy. These techniques cannot be used as is because they only assume virtual drifts where the posterior probabilities P(y|X) are assumed not to change. In contrast, a key challenge in our setup is to also discard undesirable data segments with concept drifts. Quilt thus discards drifted data segments and selects data segment subsets holistically for accurate and efficient model training. The two operations use gradient-based scores, which have little computation overhead. In our experiments, we show that Quilt outperforms state-of-the-art drift adaptation and data selection baselines on synthetic and real datasets.

Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. Quilt is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. Quilt is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise-free. Quilt demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.

The country-wide lockdown to tackle the Covid-19 pandemic in India resulted in an unprecedented drop in air pollution levels across cities. As people practise social distancing and marvel at the positive impact of the lack of human mobility on the environment, this is an opportune time to curate and run an effective air pollution campaign so that the new normal might be brighter. As many as 21 out of the 30 most polluted cities in the world are in India. Yet, public outrage and civic action towards air pollution are sporadic and scattered, peaking during Diwali but remaining low-key for the rest of the year. In light of this trend, Clean Air Fund and Quilt.AI studied the history and impact of 30 major environmental and public health campaigns in India since 2015.

CAPTURING data in today's world is easy. Be it an action in the digital world on a website or an application or in the physical world in a retail or commercial environment -- everything can be tracked. Making sense of that data, however, requires more than just employing data scientists. Founder and Chief of Product Angad Chowdhry told Tech Wire Asia that subject matter expertise is the most important piece of the puzzle when it comes to making sense of data. Chowdhry's company works with a variety of for-profit and non-for-profit businesses, tapping into data from a plethora of sources, and running artificial intelligence (AI)-powered models to answer questions that help better understand markets, invest resources, and plan for the future. Quilt.ai recently collaborated with School of Oriental and African Studies (SOAS) and the Barbican Centre in the UK on a project to help AI understand the context when it sees a photograph.

In this article, we'll train a PyTorch model to perform super-resolution imaging, a technique for gracefully upscaling images. Super-resolution imaging (right) infers pixel values from a lower-resolution image (left). Machine learning projects typically begin by acquiring data, cleaning the data, and converting the data into model-native formats. Such manual data pipelines are tedious to create and difficult to reproduce over time, across collaborators, and across machines. Moreover, trained models are often stored haphazardly, without version control.

Last week, I was in San Francisco's Mission District, betwixt testing Prisma filters on my photos, and enjoying the fine cuisine, when I noticed an actual painting of the Golden Gate Bridge on the wall. "It's like Prisma in real life," I reacted. In today's technology-fueled hyper-sensualized world, one of the many symptoms is a blurring of the line between art and design. Design is function-oriented, and though some readily fetishize consumer electronics as art objects, the bounds of design are neatly wrapped in utility. Art, on the other hand, is characterized by an effort that redefines the confines of knowledge with a particular emphasis on questioning the boundaries of emotions, politics, and society.