In December 2021 and January 2022, we asked recipients of our Data and AI Newsletters to participate in our annual survey on AI adoption. We were particularly interested in what, if anything, has changed since last year. Are companies farther along in AI adoption? Do they have working applications in production? Are they using tools like AutoML to generate models, and other tools to streamline AI deployment? We also wanted to get a sense of where AI is headed. The hype has clearly moved on to blockchains and NFTs. AI is in the news often enough, but the steady drumbeat of new advances and techniques has gotten a lot quieter. Compared to last year, significantly fewer people responded. This year's survey ran during the holiday season (December 8, 2021, to January 19, 2022, though we received very few responses in the new year); last year's ran from January 27, 2021, to February 12, 2021. Pandemic or not, holiday schedules no doubt limited the number of respondents.

The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards1. In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people2. Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data3,4. Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes—including exclusion errors, total social welfare and measures of fairness—under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9–35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date. Machine-learning algorithms can take advantage of survey and mobile phone data to help to identify people most in need of aid, complementing traditional methods for targeting humanitarian assistance.

Manipulation of deformable objects is a challenging task for a robot. It will be problematic to use a single sensory input to track the behaviour of such objects: vision can be subjected to occlusions, whereas tactile inputs cannot capture the global information that is useful for the task. In this paper, we study the problem of using vision and tactile inputs together to complete the task of following deformable linear objects, for the first time. We create a Reinforcement Learning agent using different sensing modalities and investigate how its behaviour can be boosted using visual-tactile fusion, compared to using a single sensing modality. To this end, we developed a benchmark in simulation for manipulating the deformable linear objects using multimodal sensing inputs. The policy of the agent uses distilled information, e.g., the pose of the object in both visual and tactile perspectives, instead of the raw sensing signals, so that it can be directly transferred to real environments. In this way, we disentangle the perception system and the learned control policy. Our extensive experiments show that the use of both vision and tactile inputs, together with proprioception, allows the agent to complete the task in up to 92% of cases, compared to 77% when only one of the signals is given. Our results can provide valuable insights for the future design of tactile sensors and for deformable objects manipulation.

Mood is a unique way for researchers to try to measure the impact of natural or unnatural disasters on people. However, it's simply impractical to ask every single person in the world how they're feeling in the aftermath of a sweeping event. But scientists from the Massachusetts Institute of Technology, the Chinese Academy of Sciences, and the Max Planck Institute for Human Development found a workaround. They used machine learning techniques to scan social media for sentiment shifts following the first wave of COVID-19 in 100 different countries and get real-time reads on how happy or sad the events related to the pandemic made people across the world. Think of the process as an AI-powered mood ring, but for millions of people.

We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an iterative pair-wise optimization to compute a deformation field that aligns two images. Recent learning-based approaches leverage large image datasets to learn a function that rapidly estimates a deformation for a given image pair. In both strategies, the accuracy of the resulting spatial correspondences is strongly influenced by the choice of certain hyperparameter values. However, an effective hyperparameter search consumes substantial time and human effort as it often involves training multiple models for different fixed hyperparameter values and may lead to suboptimal registration. We propose an amortized hyperparameter learning strategy to alleviate this burden by learning the impact of hyperparameters on deformation fields. We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values. This strategy enables fast, high-resolution hyperparameter search at test-time, reducing the inefficiency of traditional approaches while increasing flexibility. We also demonstrate additional benefits of HyperMorph, including enhanced robustness to model initialization and the ability to rapidly identify optimal hyperparameter values specific to a dataset, image contrast, task, or even anatomical region, all without the need to retrain models. We make our code publicly available at http://hypermorph.voxelmorph.net.

Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC), a novel and generic method that utilizes the information of vertices co-membership in multiple communities. CMMAC is domain-free and almost unaffected by communities' sizes and densities. Specifically, we train a classifier to predict the probability of each vertex in a community being a member of the community. We then rank the communities by the aggregated membership probabilities of each community's vertices. The lowest-ranked communities are considered to be anomalous. Furthermore, we present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities to facilitate research in the field. We utilized it to generate two datasets, composed of thousands of labeled anomaly-infused networks, and published them. We experimented extensively on thousands of simulated, and real-world networks, infused with artificial anomalies. CMMAC outperformed other existing methods in a range of settings. Additionally, we demonstrated that CMMAC can identify abnormal communities in real-world unlabeled networks in different domains, such as Reddit and Wikipedia.

When training deep learning models for least-squares regression, we cannot expect that the training error residuals of the final model, selected after a fixed training time or based on performance on a hold-out data set, sum to zero. This can introduce a systematic error that accumulates if we are interested in the total aggregated performance over many data points. We suggest to adjust the bias of the machine learning model after training as a default postprocessing step, which efficiently solves the problem. The severeness of the error accumulation and the effectiveness of the bias correction is demonstrated in exemplary experiments. Here X is some arbitrary input space and w.l.o.g.

At The Good AI, we strongly believe Artificial Intelligence (AI) should be inclusive and celebrate diversity. However, AI is also the reflector of its creators and this translates into the reproduction of certain biases into AI products related to race, gender or sexual orientation among others. The following article from the MIT Technology Review explains how. In the light of this, the tech industry has an important responsibility towards society, and the death of George Floyd at the hands of a city police officer in Minneapolis, USA on 25 May 2020, -one in a long series of racists attacks against African Americans -, should urge us to take action. We need to make sure we are not perpetuating and letting racism or any other kind of discrimination take roots in our AI systems.

Born to Eritrean parents in Ethiopia, Gebru spoke with The Associated Press recently about how poorly Big Tech's AI priorities -- and its AI-fueled social media platforms -- serve Africa and elsewhere. The new institute focuses on AI research from the perspective of the places and people most likely to experience its harms. She's also co-founder of the group Black in AI, which promotes Black employment and leadership in the field. And she's known for co-authoring a landmark 2018 study that found racial and gender bias in facial recognition software. The interview has been edited for length and clarity.