Welcome to our September 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. In this edition we cover the release of the latest AI100 report, an award winning paper from IJCAI, some useful AI resources, and more. In this interesting article, IJCAI 2021 invited speaker Edith Elkind writes about the continuing quest to bring the theory of fair land division closer to practice. This is work that won Edith, and co-authors Erel Segal-Halevi and Warut Suksompong, a distinguished paper award at IJCAI 2021. Their article "Keep your distance: land division with separation" investigates fair land allocation under separation constraints.

South Korean scientists have developed a flexible battery that bends and stretches like a snake, an innovation that could find application in advanced wearable devices and soft robots used in disaster management. Engineers from the Korea Institute of Machinery and Materials (KIMM) said the battery's structure draws inspiration from snake scales, which while rigid, can fold together to protect against external impact, and also possess traits that allow them to be highly stretchable and move flexibly. The stretchable device, described in the journal Soft Robotics, enables flexible movement by connecting several small, hard batteries in a scale-like structure. It consists of small, hexagonal battery cells resembling a snake scale which are connected together using a hinge mechanism made of a polymer and copper material to fold and unfold. "This study proposes a novel structure with individual, overlapping units, similar to snake scales that can be used to construct shape-morphing batteries for untethered soft robots," the scientists wrote in the study.

The problem of phase retrieval, or the algorithmic recovery of lost phase information from measured intensity alone, underlies various imaging methods from astronomy to nanoscale imaging. Traditional methods of phase retrieval are iterative in nature, and are therefore computationally expensive and time consuming. More recently, deep learning (DL) models have been developed to either provide learned priors to iterative phase retrieval or in some cases completely replace phase retrieval with networks that learn to recover the lost phase information from measured intensity alone. However, such models require vast amounts of labeled data, which can only be obtained through simulation or performing computationally prohibitive phase retrieval on hundreds of or even thousands of experimental datasets. Using a 3D nanoscale X-ray imaging modality (Bragg Coherent Diffraction Imaging or BCDI) as a representative technique, we demonstrate AutoPhaseNN, a DL-based approach which learns to solve the phase problem without labeled data. By incorporating the physics of the imaging technique into the DL model during training, AutoPhaseNN learns to invert 3D BCDI data from reciprocal space to real space in a single shot without ever being shown real space images. Once trained, AutoPhaseNN is about one hundred times faster than traditional iterative phase retrieval methods while providing comparable image quality.

Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technologies, safety for ML should be a leading research priority. In response to emerging safety challenges in ML, such as those introduced by recent large-scale models, we provide a new roadmap for ML Safety and refine the technical problems that the field needs to address. We present four problems ready for research, namely withstanding hazards ("Robustness"), identifying hazards ("Monitoring"), steering ML systems ("Alignment"), and reducing risks to how ML systems are handled ("External Safety"). Throughout, we clarify each problem's motivation and provide concrete research directions.

Motivated by the problem of learning when the number of training samples is small, this paper shows how to incorporate into support-vector machines (SVMs) those properties that have made convolutional neural networks (CNNs) successful. Particularly important is the ability to incorporate domain knowledge of invariances, e.g., translational invariance of images. Kernels based on the \textit{minimum} distance over a group of transformations, which corresponds to defining similarity as the \textit{best} over the possible transformations, are not generally positive definite. Perhaps it is for this reason that they have neither previously been experimentally tested for their performance nor studied theoretically. Instead, previous attempts have employed kernels based on the \textit{average} distance over a group of transformations, which are trivially positive definite, but which generally yield both poor margins as well as poor performance, as we show. We address this lacuna and show that positive definiteness indeed holds \textit{with high probability} for kernels based on the minimum distance in the small training sample set regime of interest, and that they do yield the best results in that regime. Another important property of CNNs is their ability to incorporate local features at multiple spatial scales, e.g., through max pooling. A third important property is their ability to provide the benefits of composition through the architecture of multiple layers. We show how these additional properties can also be embedded into SVMs. We verify through experiments on widely available image sets that the resulting SVMs do provide superior accuracy in comparison to well-established deep neural network (DNN) benchmarks for small sample sizes.

In 2022 the covid-19 pandemic will continue to impact our lives in many ways. This means that we will continue to see an accelerated rate of digitization and virtualization of business and society. However, as we move into a new year, the need for sustainability, ever-increasing data volumes, and increasing compute and network speeds will begin to regain their status as the most important drivers of digital transformation. For many individuals and organizations, the most important lesson of the last two years or so has been that truly transformative change isn't as difficult to implement as might have once been thought, if the motivation is there! As a society, we will undoubtedly continue to harness this newfound openness to flexibility, agility, and innovative thinking, as the focus shifts from merely attempting to survive in a changing world to thriving in it. With that in mind, here are my predictions for the specific trends that are likely to have the biggest impact in 2022.

Accurate forecasts of photovoltaic power generation (PVPG) are essential to optimize operations between energy supply and demand. Recently, the propagation of sensors and smart meters has produced an enormous volume of data, which supports the development of data based PVPG forecasting. Although emerging deep learning (DL) models, such as the long short-term memory (LSTM) model, based on historical data, have provided effective solutions for PVPG forecasting with great successes, these models utilize offline learning. As a result, DL models cannot take advantage of the opportunity to learn from newly-arrived data, and are unable to handle concept drift caused by installing extra PV units and unforeseen PV unit failures. Consequently, to improve day-ahead PVPG forecasting accuracy, as well as eliminate the impacts of concept drift, this paper proposes an adaptive LSTM (AD-LSTM) model, which is a DL framework that can not only acquire general knowledge from historical data, but also dynamically learn specific knowledge from newly-arrived data. A two-phase adaptive learning strategy (TP-ALS) is integrated into AD-LSTM, and a sliding window (SDWIN) algorithm is proposed, to detect concept drift in PV systems. Multiple datasets from PV systems are utilized to assess the feasibility and effectiveness of the proposed approaches. The developed AD-LSTM model demonstrates greater forecasting capability than the offline LSTM model, particularly in the presence of concept drift. Additionally, the proposed AD-LSTM model also achieves superior performance in terms of day-ahead PVPG forecasting compared to other traditional machine learning models and statistical models in the literature.

Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the R package onlineforecast that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of linear regression-based models. Furthermore, dynamical and non-linear effects can be easily included in the models. The setup is tailored to enable effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular system applications and run models in an operational online setting. The package also allows users to easily replace parts of the setup, e.g. use kernel or neural network methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied in all fields where online forecasting is used.

Although our computational techniques and hardware resources have advanced greatly these past few decades, given the rise of large language models which have applications in multiple sectors, the environmental impact of training and developing NLP models, particularly on a large scale, could have detrimental consequences on the environment. This is due to the energy usage (whether carbon neutral or not) [1, 2] possibly contributing directly or indirectly to the effects of climate change. With experiments on total time expected for models such as Transformer, BERT, and GPT-2 to train and the subsequent cost of training, Strubell et al. [2] provides substantial evidence that researchers need to increasingly prioritize computationally efficient hardware and algorithms. There has been research to suggest that large language models could be outperformed by their less computationally intensive counterparts on multiple tasks with the help of fine-tuning [3] and techniques such as using random search for hyperparameter search [1, 4-6] or pruning [7, 8]. Additionally, as performance across different tasks tends to vary based on the languages used, data availability, model architectures among other factors, it is likely that training models to a certain performance level for some languages are less carbon-intensive than others. This is speculation is substantiated by the correlation found between morphological ambiguity of languages and the performance of language models on European languages [9]. The primary objective of our work is to measure the differences in carbon emissions released between multiple language pairs and assess the contributions of various components, within the two architectures we've used, to the carbon We are grateful to the Research Society MIT, Manipal for supporting this work, and we attribute equal contribution to all the authors of this paper.

An artificial intelligence program currently in development could very well be the saving grace of the human race. The results on the research of the deep-learning algorithm are being reported in a research paper, which is headed by applied mathematics professor Chris Bauch of the University of Waterloo. According to Phys.org, the paper is trying to look for specific climate crisis events called "tipping points," which are situations when humanity can no longer change the course of devastating climate change. Bauch stated that he and his team has found that their new artificial intelligence algorithm is able to predict these tipping points more accurately than before, but also offer new information at what the state of the world will be past the tipping point. A few of these "tipping points'' that Bauch's team talks about include Arctic permafrost.