We've all heard of Kaggle, but that also means there's more competition -- recently, Kaggle reached 5 million users. Further, not all competitions are open to everyone in the world. "Members of the Kaggle community who are not United States Citizens or legal permanent residents at the time of entry are allowed to participate in the Competition but are not eligible to win prizes. If a team has one or more members who are not prize eligible, then the entire team is not prize eligible." By trying out other competition platforms, you can be a "big fish in a small pond," as there are a lot fewer competitors.

As healthcare professionals are facing massive pressure not only to ensure the quality of care, but also to come up with new solutions, cures and treatments, they are becoming increasingly dependent on advanced technologies like artificial intelligence (AI) and machine learning (ML). But it is hardly a smooth partnership. The issues of skills shortages at the entry-level and of "messy data" in leveraging patient records at the high end are merely book-ends for a range of challenges that span these fields. Last week's annual Amazon Web Services Re:Invent conference, one of the largest cloud-focused events in the world, saw the launch or demonstration of a range of new cloud-based tools that are ideal for health research and treatment. ML, defined as computer algorithms that improve automatically through experience, was at the heart of these.

It's been a busy few days at NeurIPS 2020 so far with all manner of talks, workshops, tutorials and socials on offer. This selection of tweets gives a flavour of the various events and discussions taking place. Go watch it right now, you won't regret it! Interesting talk by Chris Bishop at #NeurIPS2020 Basic or Applied research is not a 1D space. Next up at #NeurIPS2020: Shafi Goldwasser presenting on three works about privacy, verifiability, and robustness in machine learning.

Don't miss our final Global Cougs Speaker Series of the semester! Darlington Akogo, Founder of KaraAgro AI and minoHealth, will be speaking about the application of AI in African healthcare and agriculture. It will happen this Thursday, December 10th, at 10 a.m.

Just as the name implies, data science is a branch of science that applies the scientific method to data with the goal of studying the relationships between different features and drawing out meaningful conclusions based on these relationships. Data is, therefore, the key component in data science. A dataset is a particular instance of data that is used for analysis or model building at any given time. A dataset comes in different flavors such as numerical data, categorical data, text data, image data, voice data, and video data. A dataset could be static (not changing) or dynamic (changes with time, for example, stock prices).

Growing research demonstrates that synthetic failure modes imply poor generalization. We compare commonly used audio-to-audio losses on a synthetic benchmark, measuring the pitch distance between two stationary sinusoids. The results are surprising: many have poor sense of pitch direction. These shortcomings are exposed using simple rank assumptions. Our task is trivial for humans but difficult for these audio distances, suggesting significant progress can be made in self-supervised audio learning by improving current losses.

Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications. They are widely used because they are simple, flexible and provide very accurate estimates for nonlinear problems, especially in parameter retrieval. An addition to a predictive mean function, GPs come equipped with a useful property: the predictive variance function which provides confidence intervals for the predictions. The GP formulation usually assumes that there is no input noise in the training and testing points, only in the observations. However, this is often not the case in Earth observation problems where an accurate assessment of the instrument error is usually available. In this paper, we showcase how the derivative of a GP model can be used to provide an analytical error propagation formulation and we analyze the predictive variance and the propagated error terms in a temperature prediction problem from infrared sounding data.

I've had the good fortune to become friends with Timnit over the last several weeks as we've spent hours discussing the spread of mis/disinformation and hate speech on social media in Ethiopia. Our collaboration began with a frank conversation around the limitations of the AI ethics community. I felt she sincerely engaged with the critiques I raised about the representation politics in predominantly white institutions interpolating a handful of African elites as ambassadors of the Black American experience. Out of the love I got for her and this community of computer scientists, data/tech policy analysts, academics, I feel the need to be harsh and keep it real about the moral collapse of AI Ethics. If demands for corporate transparency crystalized in the Standing with Dr. Timnit Gebru Petition defines the horizon for tech worker resistance, we are doomed.

Forecasting the number of Olympic medals for each nation is highly relevant for different stakeholders: Ex ante, sports betting companies can determine the odds while sponsors and media companies can allocate their resources to promising teams. Ex post, sports politicians and managers can benchmark the performance of their teams and evaluate the drivers of success. To significantly increase the Olympic medal forecasting accuracy, we apply machine learning, more specifically a two-staged Random Forest, thus outperforming more traditional na\"ive forecast for three previous Olympics held between 2008 and 2016 for the first time. Regarding the Tokyo 2020 Games in 2021, our model suggests that the United States will lead the Olympic medal table, winning 120 medals, followed by China (87) and Great Britain (74). Intriguingly, we predict that the current COVID-19 pandemic will not significantly alter the medal count as all countries suffer from the pandemic to some extent (data inherent) and limited historical data points on comparable diseases (model inherent).

Determining the readability of a text is the first step to its simplification. In this paper, we present a readability analysis tool capable of analyzing text written in the Bengali language to provide in-depth information on its readability and complexity. Despite being the 7th most spoken language in the world with 230 million native speakers, Bengali suffers from a lack of fundamental resources for natural language processing. Readability related research of the Bengali language so far can be considered to be narrow and sometimes faulty due to the lack of resources. Therefore, we correctly adopt document-level readability formulas traditionally used for U.S. based education system to the Bengali language with a proper age-to-age comparison. Due to the unavailability of large-scale human-annotated corpora, we further divide the document-level task into sentence-level and experiment with neural architectures, which will serve as a baseline for the future works of Bengali readability prediction. During the process, we present several human-annotated corpora and dictionaries such as a document-level dataset comprising 618 documents with 12 different grade levels, a large-scale sentence-level dataset comprising more than 96K sentences with simple and complex labels, a consonant conjunct count algorithm and a corpus of 341 words to validate the effectiveness of the algorithm, a list of 3,396 easy words, and an updated pronunciation dictionary with more than 67K words. These resources can be useful for several other tasks of this low-resource language. We make our Code & Dataset publicly available at https://github.com/tafseer-nayeem/BengaliReadability} for reproduciblity.