Chile joins the group of countries such as Brazil, Argentina, Mexico and Colombia, which, within the region, recently presented AI strategies.

Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) is the original inference mechanism for LDA. Many variants of VB for LDA, as well as for VB in general, have been developed since LDA's inception in 2013, but standard VB is still widely applied to LDA. Variational message passing (VMP) is the message passing equivalent of VB and is a useful tool for constructing a variational inference solution for a large variety of conjugate exponential graphical models (there is also a non conjugate variant available for other models). In this article we present the VMP equations for LDA and also provide a brief discussion of the equations. We hope that this will assist others when deriving variational inference solutions to other similar graphical models.

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.