In an entailment relation, if p is true is a difficult task that requires an accurate understanding then h cannot be false, otherwise there is a contradiction. of natural language meaning. The ambiguity and variability NLI is a broader task than conflict identification, and thus, of linguistic expression in natural language complicates the good models to classify logical relations will naturally be recognition of these relations such as entailment and contradiction applicable to detect contract conflicts. Importantly, since contained in texts. The ability to classify these logical NLI has seen a surge in research, including new machine inferences among different text is a significant feature learning models and dataset curation (Bowman et al. 2015; of an intelligent system (Bos and Markert 2005). Detecting Williams, Nangia, and Bowman 2018), it offers substantial these logical relations can help humans to interpret a more labelled training data in much larger quantities than contract complex text, where entailment and contradiction are crucial conflict datasets (Aires, Pinheiro, and Meneguzzi 2017).

Ethics statements have been proposed as a mechanism to increase transparency and promote reflection on the societal impacts of published research. In 2020, the machine learning (ML) conference NeurIPS broke new ground by requiring that all papers include a broader impact statement. This requirement was removed in 2021, in favour of a checklist approach. The 2020 statements therefore provide a unique opportunity to learn from the broader impact experiment: to investigate the benefits and challenges of this and similar governance mechanisms, as well as providing an insight into how ML researchers think about the societal impacts of their own work. Such learning is needed as NeurIPS and other venues continue to question and adapt their policies. To enable this, we have created a dataset containing the impact statements from all NeurIPS 2020 papers, along with additional information such as affiliation type, location and subject area, and a simple visualisation tool for exploration. We also provide an initial quantitative analysis of the dataset, covering representation, engagement, common themes, and willingness to discuss potential harms alongside benefits. We investigate how these vary by geography, affiliation type and subject area. Drawing on these findings, we discuss the potential benefits and negative outcomes of ethics statement requirements, and their possible causes and associated challenges. These lead us to several lessons to be learnt from the 2020 requirement: (i) the importance of creating the right incentives, (ii) the need for clear expectations and guidance, and (iii) the importance of transparency and constructive deliberation. We encourage other researchers to use our dataset to provide additional analysis, to further our understanding of how researchers responded to this requirement, and to investigate the benefits and challenges of this and related mechanisms.

When dealing with supervised learning within deep learning, we might say that there are some classical approaches to follow. The first solution is the so-called "heroic" strategy where one creates a completely new deep neural network (DNN) from scratch and train/evaluate it. In practical terms, this solution may not be very interesting since there are countless DNNs available nowadays, like many deep convolutional neural networks (CNNs), that can be reused. The second path is simply to consider a deployable DNN, trained for a certain context, and see its operation in another context. Despite all the advances in deep learning, models can present bad performances if the contexts are too diverse.

If there is life beyond Earth, it's going to be found on an exoplanet--planets orbiting stars other than our own sun. The number of exoplanets is thought to number in the trillions [1], but despite an abundance of data from exoplanet-finding missions like Kepler, K2, and TESS, just a few thousands of potential exoplanets have been confirmed. The biggest problem is a glut of information; About 30 GB is being collected every day from NASA's Transiting Exoplanet Survey Satellite (TESS) mission [2], which launched in 2018. This gives astronomers an overwhelming amount of data to analyze. In addition, the sheer number of candidate stars, which stretches into the millions, confounds the problem and results in too many candidate exoplanets to manage without the assistance of ML.

José V. Fernández first got interested in using technology to ramp up financial inclusion when he first arrived in New York City from Spain around 10 years ago. His job working as a trade officer for Spain didn't impress numerous prospective landlords, none of whom would rent him an apartment because he lacked a U.S. credit score. Finally, one company agreed to sign a one-year lease, but only if Fernández paid six months of his pricey Manhattan rent ahead of time. A few years after that, Fernández co-founded a fintech firm to open up microloans to unbanked people in West Africa. Then, last year, he founded Bankuish, which aims to give gig workers and freelancers a way to access banking services they normally wouldn't be able to tap.

You probably know that October is the Breast Cancer Awareness Month and following my learning path I decided to write a simple-yet-powerful classifier that predicts whether a test result indicates a benign or malignant tumor using the K-Nearest Neighbors algorithm. I think the beauty of the KNN is its simplicity. In Brazil we have a popular saying that says: "tell me who you're with and I'll tell you who you are". The same happens in KNN: given a set of points in a n-dimensional space and a point X, we predict that the class of X will be the most predominant class among X's K-Nearest Neighbors. Let's use the poorly illustrated image bellow to exemplify.

Artificial intelligence (AI) continues to find more numerous and more critical applications in the financial services industry, giving rise to fair and ethical AI as an industry-wide objective. While many ethical principles and guidelines have been published in recent years, they fall short of addressing the serious challenges that model developers face when building ethical AI solutions. We survey the practical and overarching issues surrounding model development, from design and implementation complexities, to the shortage of tools, and the lack of organizational constructs. We show how practical considerations reveal the gaps between high-level principles and concrete, deployed AI applications, with the aim of starting industry-wide conversations toward solution approaches.

If there is life beyond Earth, it's going to be found on an exoplanet--planets orbiting stars other than our own sun. The number of exoplanets is thought to number in the trillions [1], but despite an abundance of data from exoplanet-finding missions like Kepler, K2, and TESS, just a few thousand potential exoplanets have been confirmed. The biggest problem is a glut of information; About 30 GB is being collected every day from NASA's Transiting Exoplanet Survey Satellite (TESS) mission [2], which launched in 2018. This gives astronomers an overwhelming amount of data to analyze. The sheer number of candidate stars, which stretches into the millions, confounds the problem and results in too many candidate exoplanets to manage without the assistance of ML.

The graph represents a network of 3,441 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 29 October 2021 at 11:01 UTC. The requested start date was Friday, 29 October 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 15-hour, 36-minute period from Tuesday, 26 October 2021 at 08:24 UTC to Friday, 29 October 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Artificial Intelligence has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. Deep neural networks have shown same or better performance than clinicians in many tasks owing to the rapid increase in the available data and computational power. In order to conform to the principles of trustworthy AI, it is essential that the AI system be transparent, robust, fair and ensure accountability. Current deep neural solutions are referred to as black-boxes due to a lack of understanding of the specifics concerning the decision making process. Therefore, there is a need to ensure interpretability of deep neural networks before they can be incorporated in the routine clinical workflow. In this narrative review, we utilized systematic keyword searches and domain expertise to identify nine different types of interpretability methods that have been used for understanding deep learning models for medical image analysis applications based on the type of generated explanations and technical similarities. Furthermore, we report the progress made towards evaluating the explanations produced by various interpretability methods. Finally we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis.