Differentiable architecture search (DARTS) is widely considered to be easy to overfit the validation set which leads to performance degradation. We first employ a series of exploratory experiments to verify that neither high-strength architecture parameters regularization nor warmup training scheme can effectively solve this problem. Based on the insights from the experiments, we conjecture that the performance of DARTS does not depend on the well-trained supernet weights and argue that the architecture parameters should be trained by the gradients which are obtained in the early stage rather than the final stage of training. This argument is then verified by exchanging the learning rate schemes of weights and parameters. Experimental results show that the simple swap of the learning rates can effectively solve the degradation and achieve competitive performance. Further empirical evidence suggests that the degradation is not a simple problem of the validation set overfitting but exhibit some links between the degradation and the operation selection bias within bilevel optimization dynamics. We demonstrate the generalization of this bias and propose to utilize this bias to achieve an operation-magnitude-based selective stop.

Algorithmic fairness has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept and that technologies for algorithmic fairness should therefore be approached through a sociotechnical lens. We advance the discourse on algorithmic fairness as a sociotechnical phenomenon. Our research objective is to embed AF in the sociotechnical view of IS. Specifically, we elaborate on why outcomes of a system that uses algorithmic means to assure fairness depends on mutual influences between technical and social structures. This perspective can generate new insights that integrate knowledge from both technical fields and social studies. Further, it spurs new directions for IS debates. We contribute as follows: First, we problematize fundamental assumptions in the current discourse on algorithmic fairness based on a systematic analysis of 310 articles. Second, we respond to these assumptions by theorizing algorithmic fairness as a sociotechnical construct. Third, we propose directions for IS researchers to enhance their impacts by pursuing a unique understanding of sociotechnical algorithmic fairness. We call for and undertake a holistic approach to AF. A sociotechnical perspective on algorithmic fairness can yield holistic solutions to systemic biases and discrimination.

In this paper we propose a lightning fast graph embedding method called graph encoder embedding. The proposed method has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC -- an unattainable feat for any existing graph embedding method. The speedup is achieved without sacrificing embedding performance: the encoder embedding performs as good as, and can be viewed as a transformation of the more costly spectral embedding. The encoder embedding is applicable to either adjacency matrix or graph Laplacian, and is theoretically sound, i.e., under stochastic block model or random dot product graph, the graph encoder embedding asymptotically converges to the block probability or latent positions, and is approximately normally distributed. We showcase three important applications: vertex classification, vertex clustering, and graph bootstrap; and the embedding performance is evaluated via a comprehensive set of synthetic and real data. In every case, the graph encoder embedding exhibits unrivalled computational advantages while delivering excellent numerical performance.

Question Answering, including Reading Comprehension, is one of the NLP research areas that has seen significant scientific breakthroughs over the past few years, thanks to the concomitant advances in Language Modeling. Most of these breakthroughs, however, are centered on the English language. In 2020, as a first strong initiative to bridge the gap to the French language, Illuin Technology introduced FQuAD1.1, a French Native Reading Comprehension dataset composed of 60,000+ questions and answers samples extracted from Wikipedia articles. Nonetheless, Question Answering models trained on this dataset have a major drawback: they are not able to predict when a given question has no answer in the paragraph of interest, therefore making unreliable predictions in various industrial use-cases. In the present work, we introduce FQuAD2.0, which extends FQuAD with 17,000+ unanswerable questions, annotated adversarially, in order to be similar to answerable ones. This new dataset, comprising a total of almost 80,000 questions, makes it possible to train French Question Answering models with the ability of distinguishing unanswerable questions from answerable ones. We benchmark several models with this dataset: our best model, a fine-tuned CamemBERT-large, achieves a F1 score of 82.3% on this classification task, and a F1 score of 83% on the Reading Comprehension task.

If you are a software engineer, or count any of them among your circle of acquaintances, then you're probably already aware at some level of Copilot. Copilot is GitHub's new deep learning code completion tool. Autocomplete tools for programmers are nothing new, and Copilot is not even the first to make use of deep learning nor even the first to use a GPT transformer. After all, TabNine sprung out of a summer project by OpenAI alum Jacob Jackson and makes use of the GPT-2 general purpose transformer. Microsoft (which owns GitHub) has packaged their own IntelliSense code completion tool with programming products since at least 1996, and autocomplete and text correction has been an active area of research since the 1950s.

Today artificial intelligence (AI) driven digital technologies are giving us new pathways to always have your loved ones with you, 7x24. Not really, despite the eeriness from Black Mirror episodes, or Carrie Fisher digitally created to carry on as Princess Leia in Star Wars, and Microsoft securing a patent for software that could reincarnate people as a chat bot, opening the door to more uses of AI contemplating how to bring the dead back to life are rapidly accelerating. Are we ready for death resurrections? Is this the right thing for us to be doing? From my research, we don't have all the answers to this complex question yet, but what we have are many innovators, academics, researchers shaping the answer to this question that will enable richer immersive digital learning experiences – and others that bringing grandma back to life – and persisting forever – may feel positively therapeutic to ease a deep grief, or feel like you are immersed in a Stephen King movie.

Colleges have had success with chatbots for a few years, but high school students can now benefit from the first nationally accessible (and free) AI college adviser chatbot, Oli. The tool is the result of a partnership between Common App and Mainstay (formerly AdmitHub). Oli stands ready to help students around the clock with a wide range of tasks, such as selecting the right school, completing college and scholarship applications and understanding financial aid forms. It responds to questions via text and also sends users deadline reminders, updates and resources several times a week. When extra help is required, Oli connects students with a trained college adviser from College Advising Corps.

Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later. The field of AI was formally founded in 1956. But it's only now--more than six decades later--that AI is expected to revolutionize the way humanity will live and work in the coming decades.

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When freak lightning ignited massive wildfires across Northern California last year, it also sparked efforts from data scientists to improve predictions for blazes. One effort came from SpaceML, an initiative of the Frontier Development Lab, which is an AI research lab for NASA in partnership with the SETI Institute. Dedicated to open-source research, the SpaceML developer community is creating image recognition models to help advance the study of natural disaster risks, including wildfires. SpaceML uses accelerated computing on petabytes of data for the study of Earth and space sciences, with the goal of advancing projects for NASA researchers. It brings together data scientists and volunteer citizen scientists on projects that tap into the NASA Earth Observing System Data and Information System data.