As Notre Dame Cathedral's majestic spire tumbled into the inferno on Monday night, live newsreaders around the world decried the tragic loss of this 12th-century marvel. The great timber roof – nicknamed "the forest" for the thousands of trees used in its beams – was gone, the rose windows feared melted, the heart of Paris destroyed forever. What few realised in the heat of the shocking footage was that much of what was ablaze was a 19th-century fantasy. Like most buildings of this age, Notre Dame is the sum of centuries of restorations and reinventions, a muddled patchwork of myth and speculation. Standing as a sturdy hulk on the banks of the Seine, the great stone pile has never been the most elegant or commanding of the ancient cathedrals, but it became the most famous. Begun in 1163, it was larger than any gothic church before it, employing some of the first flying buttresses to allow taller, thinner walls and larger expanses of glazing – including the spectacular rose windows that projected great cosmic wheels of colour into the luminous interior. "Where would [one] find … such magnificence and perfection, so high, so large, so strong, clothed round about with such a multiple variety of ornaments?"

Halfway through my week without Google, my wife mentions that she would like to go out to see a film that evening, and I agree to deal with the logistics. In what I initially think is an inspired move, I drop by the local cinema on the way home and scribble down all the film times in my notebook. Then my wife insists on going to a different cinema. "Can I do this by phone?" "Is 118 still a thing?" Turns out it is, and an expensive one: £2.50 a call, plus 75p a minute, plus a 55p access charge from my mobile provider. But more than a million people a year still use the service, and it even offers a text facility that answers questions – although you're essentially just asking someone to Google something for you and text you back, for £3.50 a go. Before I started this experiment, when I tried to imagine what it would be like to take a break from Google, what I was really trying to remember was how my life worked all those years before it started.

The long-delayed live-action Minecraft movie has a new release date, so fans might want to make a note in their calendars for March 4th, 2022. With so many delays, it was clear it'd still be a while yet before the film hits theatres, but the 2022 news might come as a disappointment to those who were at one point expecting to see Minecraft next month. Warner Bros. and Microsoft have also revealed some story details. The movie will focus on "a teenage girl and her unlikely group of adventurers. After the malevolent Ender Dragon sets out on a path of destruction, they must save their beautiful, blocky Overworld."

In my latest weekend-project I have been using a Variational Autoencoder to build a feature-based face editor. The model is explained in my youtube video. The feature editing is based on modifying the latent distribution of the VAE. After training of the VAE is completed, the latent space is mapped by encoding the training data once more. Latent space vectors of each feature are determined based on the labels of the training data.

While following the guide on How to Develop a Neural Machine Translation System from Scratch, I'm trying to create a translation system for Portuguese-English.

Abstract: Recent advancements in machine learning research, i.e., deep learning, introduced methods that excel conventional algorithms as well as humans in several complex tasks, ranging from detection of objects in images and speech recognition to playing difficult strategic games. However, the current methodology of machine learning research and consequently, implementations of the real-world applications of such algorithms, seems to have a recurring HARKing (Hypothesizing After the Results are Known) issue. In this work, we elaborate on the algorithmic, economic and social reasons and consequences of this phenomenon. Furthermore, a potential future trajectory of machine learning research and development from the perspective of accountable, unbiased, ethical and privacy-aware algorithmic decision making is discussed. We would like to emphasize that with this discussion we neither claim to provide an exhaustive argumentation nor blame any specific institution or individual on the raised issues. This is simply a discussion put forth by us, insiders of the machine learning field, reflecting on us.

Abstract: We show how to teach machines to paint like human painters, who can use a few strokes to create fantastic paintings. By combining the neural renderer and model-based Deep Reinforcement Learning (DRL), our agent can decompose texture-rich images into strokes and make long-term plans. For each stroke, the agent directly determines the position and color of the stroke. Excellent visual effect can be achieved using hundreds of strokes. The training process does not require experience of human painting or stroke tracking data.

Limited interpretability - It is important because of new insights into relationships between numerous variables and expected outcomes, it increases the trust and understandability.

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that if there is a large group of users who share the same taste and none of them have consumed an item before, then it is likely that the item is not of interest to the group. We perform multidimensional clustering on implicit feedback data using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups and make recommendations for a user based on her memberships in the groups and on the past behavior of the groups. Experiments on two real-world datasets from different domains show that CoF has superior performance compared to several common baselines.

In this paper, we address the question answering challenge with the SQuAD 2.0 dataset. We design a model architecture which leverages BERT's capability of context-aware word embeddings and BiDAF's context interactive exploration mechanism. By integrating these two state-of-the-art architectures, our system tries to extract the contextual word representation at word and character levels, for better comprehension of both question and context and their correlations. We also propose our original joint posterior probability predictor module and its associated loss functions. Our best model so far obtains F1 score of 75.842% and EM score of 72.24% on the test PCE leaderboad.