Our manifesto: This is what YEN.com.gh Ghanaian-American, Joy Buolamwini, is a computer scientist who is known for her work which highlights the social implications of Artificial Intelligence. The young female tech genius just landed a deal with Random House for her book, Justice Decoded. According to a Face2Face Africa report, her book will research into the harms and biases of AI and other technologies. Her book will also investigate the racial bias in facial surveillance to gender bias in voice recognition and more.

At the time of developing the AI models through machine learning (ML) first and most important thing you need, relevant training data sets, which can only help the algorithms understand the scenario through new data or seeing the objects and predict when used in real-life making various tasks autonomous. In the visual perception based AI model, you need images, containing the objects that we see in our real life. And to make the object of interest recognizable to such models the images need to be annotated with the right techniques. And image annotation is the process, used to create such annotated images. The applications of image annotation in machine learning and AI is substantial in terms of model success.

In the unsupervised open set domain adaptation (UOSDA), the target domain contains unknown classes that are not observed in the source domain. Researchers in this area aim to train a classifier to accurately: 1) recognize unknown target data (data with unknown classes) and, 2) classify other target data. To achieve this aim, a previous study has proven an upper bound of the target-domain risk, and the open set difference, as an important term in the upper bound, is used to measure the risk on unknown target data. By minimizing the upper bound, a shallow classifier can be trained to achieve the aim. However, if the classifier is very flexible (e.g., deep neural networks (DNNs)), the open set difference will converge to a negative value when minimizing the upper bound, which causes an issue where most target data are recognized as unknown data. To address this issue, we propose a new upper bound of target-domain risk for UOSDA, which includes four terms: source-domain risk, $\epsilon$-open set difference ($\Delta_\epsilon$), a distributional discrepancy between domains, and a constant. Compared to the open set difference, $\Delta_\epsilon$ is more robust against the issue when it is being minimized, and thus we are able to use very flexible classifiers (i.e., DNNs). Then, we propose a new principle-guided deep UOSDA method that trains DNNs via minimizing the new upper bound. Specifically, source-domain risk and $\Delta_\epsilon$ are minimized by gradient descent, and the distributional discrepancy is minimized via a novel open-set conditional adversarial training strategy. Finally, compared to existing shallow and deep UOSDA methods, our method shows the state-of-the-art performance on several benchmark datasets, including digit recognition (MNIST, SVHN, USPS), object recognition (Office-31, Office-Home), and face recognition (PIE).

Covid-19 patients in the UK are being treated with dexamethasone today after a UK trial of the drug found it could save lives. "The treatment is immediately available and already in use on the NHS," said health minister Matt Hancock. "It is not by any means a cure but it is the best news we have had," Hancock told parliament today. The UK's chief medical officers say it should be used immediately, according to the BBC. A preliminary study found that the steroid, which is already widely prescribed for treating allergies and asthma, reduces the risk of dying from covid-19 by a third for patients on ventilators, and by a fifth for those receiving oxygen. Dexamethasone should only be taken if prescribed by a doctor. Officials in Beijing, China confirmed 31 new coronavirus cases today, bringing the total to 137 in the last six days. The city is again restricting all non-essential travel. Schools, swimming pools and gyms are all closed from today.

Allen School professor Adriana Schulz and adjunct professor Nadya Peek are among the 35 "Innovators Under 35" recognized by MIT Technology Review as part of its 2020 TR35 Awards. Each year, the TR35 Awards highlight early-career innovators who are already transforming the future of science and technology through their work. Schulz, a member of the Allen School's Graphics & Imaging Laboratory (GRAIL) and Fabrication research group, was honored for her visionary work on computer-based design tools that enable engineers and average users alike to create functional, complex objects. Peek, a professor in the Department of Human-Centered Design & Engineering, was honored in the "Inventors" category for her work on modular machines for supporting individual creativity. Schulz and Peek are also among the leaders of the new cross-campus Center for Digital Fabrication (DFab), a collaboration among researchers, educators, industry partners, and the maker community focused on advancing the field of digital fabrication.

We solve active target tracking, one of the essential tasks in autonomous systems, using a deep reinforcement learning (RL) approach. In this problem, an autonomous agent is tasked with acquiring information about targets of interests using its onboard sensors. The classical challenges in this problem are system model dependence and the difficulty of computing information-theoretic cost functions for a long planning horizon. RL provides solutions for these challenges as the length of its effective planning horizon does not affect the computational complexity, and it drops the strong dependency of an algorithm on system models. In particular, we introduce Active Tracking Target Network (ATTN), a unified RL policy that is capable of solving major sub-tasks of active target tracking -- in-sight tracking, navigation, and exploration. The policy shows robust behavior for tracking agile and anomalous targets with a partially known target model. Additionally, the same policy is able to navigate in obstacle environments to reach distant targets as well as explore the environment when targets are positioned in unexpected locations.

Slate is now asking those who read the most to support our journalism more directly by subscribing to Slate Plus. Dear Prudence is online weekly to chat live with readers. Here's an edited transcript of this week's chat. I know it's a petty, marriage-killing thing to dwell on … but I'm smarter than my husband. Because he insisted we both get IQ tests. It turns out I qualify for MENSA and he just does not. Except now he's telling our friends his fairly impressive IQ and when they ask about me, he says: "Oh well, it doesn't really matter. What's important is how you use what God gave you."

This month we discuss the role of arXiv and publishing in the field of artificial intelligence research. Joining the discussion this week are: Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Carles Sierra (CSIC) and Oskar von Stryk (Technische Universität Darmstadt). Sabine Hauert: Tom, you are one of the moderators for arXiv. Could you tell us a bit about how the process works? Tom Dietterich: Generally, authors have to submit a LaTeX source and it gets automatically regenerated and watermarked and then it goes to a panel of moderators.

This work studies the robust subspace tracking (ST) problem. Robust ST can be simply understood as a (slow) time-varying subspace extension of robust PCA. It assumes that the true data lies in a low-dimensional subspace that is either fixed or changes slowly with time. The goal is to track the changing subspaces over time in the presence of additive sparse outliers and to do this quickly (with a short delay). We introduce a ``fast'' mini-batch robust ST solution that is provably correct under mild assumptions. Here ``fast'' means two things: (i) the subspace changes can be detected and the subspaces can be tracked with near-optimal delay, and (ii) the time complexity of doing this is the same as that of simple (non-robust) PCA. Our main result assumes piecewise constant subspaces (needed for identifiability), but we also provide a corollary for the case when there is a little change at each time. A second contribution is a novel non-asymptotic guarantee for PCA in linearly data-dependent noise. An important setting where this result is useful is for linearly data-dependent noise that is sparse with enough support changes over time. The subspace update step of our proposed robust ST solution uses this result.

Falaah Arif Khan is the creator of "Meet AI" – a scientific comic strip about the human-AI story. She currently works as a Research Engineer at Dell EMC, Bangalore, but will shortly will be heading to New York University's Center for Data Science to pursue a Master's in Data Science. We talked about some of the machine learning projects she's worked on, her comic book creations, and the need for clear and accurate communication in the field of AI. I like to describe my research area as meta-security. When customers come to us it is to enhance the security of their product through access management, service authorization, session management and/or authentication. My role within the team is to use data-driven insights to build features that will bolster the security of our Identity and Access Management (IAM) product.