To inspire the creation of innovative solutions, workshops such as "Machine Learning in Medicine," "Will Biotech Save the World?

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An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in "echo chambers" and may fall prey to fake news and disinformation, lacking easy access to dissenting views. We suggest a novel task aiming to alleviate some of these concerns -- that of detecting articles that most effectively counter the arguments -- and not just the stance -- made in a given text. We study this problem in the context of debate speeches. Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We provide a large dataset of 3,685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research. All data collected during this work is freely available for research.

Analyzing fashion trends is essential in the fashion industry. Current fashion forecasting firms, such as WGSN, utilize the visual information from around the world to analyze and predict fashion trends. However, analyzing fashion trends is time-consuming and extremely labor intensive, requiring individual employees' manual editing and classification. To improve the efficiency of data analysis of such image-based information and lower the cost of analyzing fashion images, this study proposes a data-driven quantitative abstracting approach using an artificial intelligence (A.I.) algorithm. Specifically, an A.I. model was trained on fashion images from a large-scale dataset under different scenarios, for example in online stores and street snapshots. This model was used to detect garments and classify clothing attributes such as textures, garment style, and details for runway photos and videos. It was found that the A.I. model can generate rich attribute descriptions of detected regions and accurately bind the garments in the images. Adoption of A.I. algorithm demonstrated promising results and the potential to classify garment types and details automatically, which can make the process of trend forecasting more cost-effective and faster.

WEST LAFAYETTE, Ind. -- If any factory worker could program low-cost robots, then more factories could actually use robotics to increase worker productivity. This is because workers would be able to shift to taking on more varied and higher-level tasks, and factories could produce a greater variety of products. That's the idea behind a prototype smartphone app Purdue University researchers have developed that allows a user to easily program any robot to perform a mundane activity, such as picking up parts from one area and delivering them to another. The setup could also take care of household chores – no more plants dying because you forgot to water them. Purdue researchers present their research on the embedded app, called VRa, on June 23 at DIS 2019 in San Diego.

The Royal Society yesterday announced that Turing Award winner and director of MILA Yoshua Bengio has been elected as one of its new Fellows and Foreign Members. "As one of the founding fathers of deep learning, Yoshua Bengio is honoured this year. His work on neural networks and machine translation helped bring about the AI revolution transforming the 21st century," reads the Royal Society announcement.

This week, Facebook responded with its new state-of-the-art, open-source chatbot, Blender. "This is the first chatbot to blend a diverse set of conversational skills -- including empathy, knowledge, and personality -- together in one system," Facebook researchers boasted in a blog post. Read more: Facebook Says Its'Blender' Chatbot Is the Most Humanlike Ever The paper Recipes for Building an Open-domain Chatbot is on arXiv, and the code can be found here.

Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks. Yet, due to its black-box nature, it is inherently difficult to understand which aspects of the input data drive the decisions of the network. There are various real-world scenarios in which humans need to make actionable decisions based on the output DNNs. Such decision support systems can be found in critical domains, such as legislation, law enforcement, etc. It is important that the humans making high-level decisions can be sure that the DNN decisions are driven by combinations of data features that are appropriate in the context of the deployment of the decision support system and that the decisions made are legally or ethically defensible.

Abstract: Deep learning's great success motivates many practitioners and students to learn about this exciting technology. However, it is often challenging for beginners to take their first step due to the complexity of understanding and applying deep learning. We present CNN Explainer, an interactive visualization tool designed for non-experts to learn and examine convolutional neural networks (CNNs), a foundational deep learning model architecture. Our tool addresses key challenges that novices face while learning about CNNs, which we identify from interviews with instructors and a survey with past students. Users can interactively visualize and inspect the data transformation and flow of intermediate results in a CNN.