Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role for the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions towards which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness.

Simulated environments with procedurally generated content have become popular benchmarks for testing systematic generalization of reinforcement learning agents. Every level in such an environment is algorithmically created, thereby exhibiting a unique configuration of underlying factors of variation, such as layout, positions of entities, asset appearances, or even the rules governing environment transitions. Fixed sets of training levels can be determined to aid comparison and reproducibility, and test levels can be held out to evaluate the generalization and robustness of agents. We introduce Prioritized Level Replay, a general framework for estimating the future learning potential of a level given the current state of the agent's policy. We find that temporal-difference (TD) errors, while previously used to selectively sample past transitions, also prove effective for scoring a level's future learning potential in generating entire episodes that an agent would experience when replaying it. We report significantly improved sample-efficiency and generalization on the majority of Procgen Benchmark environments as well as two challenging MiniGrid environments. Lastly, we present a qualitative analysis showing that Prioritized Level Replay induces an implicit curriculum, taking the agent gradually from easier to harder levels. Environments generated using procedural content generation (PCG) have garnered increasing interest in RL research, leading to a surge of PCG environments such as MiniGrid (Chevalier-Boisvert et al., 2018), the Obstacle Tower Challenge (Juliani et al., 2019), the Procgen Benchmark (Cobbe et al., 2019), and the NetHack Learning Environment (Küttler et al., 2020).

Reliable bipedal walking over complex terrain is a challenging problem, using a curriculum can help learning. Curriculum learning is the idea of starting with an achievable version of a task and increasing the difficulty as a success criteria is met. We propose a 3-stage curriculum to train Deep Reinforcement Learning policies for bipedal walking over various challenging terrains. In the first stage, the agent starts on an easy terrain and the terrain difficulty is gradually increased, while forces derived from a target policy are applied to the robot joints and the base. In the second stage, the guiding forces are gradually reduced to zero. Finally, in the third stage, random perturbations with increasing magnitude are applied to the robot base, so the robustness of the policies are improved. In simulation experiments, we show that our approach is effective in learning walking policies, separate from each other, for five terrain types: flat, hurdles, gaps, stairs, and steps. Moreover, we demonstrate that in the absence of human demonstrations, a simple hand designed walking trajectory is a sufficient prior to learn to traverse complex terrain types. In ablation studies, we show that taking out any one of the three stages of the curriculum degrades the learning performance.

Given a simple request (e.g., Put a washed apple in the kitchen fridge), humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we see the kitchen in question, we can update our abstract plans to fit the scene. Embodied agents require the same abilities, but existing work does not yet provide the infrastructure necessary for both reasoning abstractly and executing concretely. We address this limitation by introducing ALFWorld, a simulator that enables agents to learn abstract, text-based policies in TextWorld (C\^ot\'e et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment. ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions. In turn, as we demonstrate empirically, this fosters better agent generalization than training only in the visually grounded environment. BUTLER's simple, modular design factors the problem to allow researchers to focus on models for improving every piece of the pipeline (language understanding, planning, navigation, visual scene understanding, and so forth).

The Indian Institute of Technology Jodhpur (IIT Jodhpur) has announced the launch of a new BTech programme in Artificial Intelligence (AI) and Data Science from the 2020-21 academic session. The new undergraduate programme seeks to provide students with opportunities to explore areas including visual computing, socio-digital realities, language technologies, robotics and AI of things. The curriculum of BTech in AI and Data Science will include courses in Computer Science, Mathematics, Artificial Intelligence, Machine Learning, Data Science, and their applications. IIT Jodhpur will also allow BTech in AI and Data Science students to enroll for an MBA in Technology in the fifth-year as a dual degree option at the School of Management and Entrepreneurship. "An interesting feature of the B Tech programme is the opportunity for the interested students to choose a minor area which would prepare them for an entrepreneurial career in the field of AI and Data Science," read an IIT Jodhpur statement.

SINGAPORE - Businesses in Singapore are set to benefit from free health screenings to spot weaknesses in their Web domain, e-mail system and connectivity. This freely provided diagnostic is part of the newly unveiled Safer Cyberspace Masterplan 2020 that aims to protect Singapore's digital sphere. The national plan also outlines the use of artificial intelligence (AI) to sniff out security threats in key infrastructure, including broadband and 5G networks, and consumer devices such as webcams and Wi-Fi routers. Coordinated by the Cyber Security Agency (CSA) of Singapore, the masterplan is central to Singapore's plans to lead in AI and smart nation deployments globally, and comes amid rapid digitalisation in recent months. "The pandemic accelerated the pace of change... Telecommuting, video calls, e-learning, online shopping, and digital payment surged," said Deputy Prime Minister Heng Swee Keat in unveiling the masterplan on Tuesday (Oct 6).

Since we, human being got the cognitive ability, we keep making tool which automate a different task. In 18th century, industrial revolution has completely changed the faces of industry and shows us the real power of automation. In a daily routine, many big IT companies like Google Facebook Microsoft Apple keep automating there software job for data analysis, reporting purpose, image processing text mining. So if you want to do Data science and Machine learning and if you don't know about the coding. There is a great tool available in the market (Alteryx) with which by just drag and drop you can build complete data science or machine learning project workflow.

The University of Maryland Center for Machine Learning will host four female researchers this fall as part of a program that encourages and supports underrepresented doctoral candidates whose scientific work is focused on machine learning. Diana Cai, Irene Chen, Mahsa Ghasemi and Nan Rosemary Ke (pictured clockwise from top left) were recently selected as this year's Rising Stars in Machine Learning based on their novel research, academic accomplishments and exceptional work experience. The Rising Stars program, launched by the center last year, is focused on supporting upper-level graduate students from disadvantaged or underrepresented groups as they pursue new scientific discoveries and academic opportunities in machine learning. This year's cohort--who hail from Princeton University, the Massachusetts Institute of Technology, the University of Texas at Austin and the University of Montreal--were chosen from a competitive pool of 17 applicants. "After extensive review, we chose these four candidates based on their record of excellence in research and scholarship," said Soheil Feizi, assistant professor of computer science and a core faculty member in the Center for Machine Learning.

This summer, my peers marched and spoke out against blatant acts of racial injustice. Meanwhile, as a 17-year-old student who dabbles in computer programming, I've been stewing about a newfangled, less-overt threat that also relates to systemic racism. What I did not realize until this summer was that my generation is already experiencing bias from our most trusted ally: the computer. If you are a student, you may have already been the target of some sort of algorithmic bias, even if you don't know it. Consider one telling fact: for a good number of high schoolers like myself who take state standardized tests, written essays might not be graded not by an English teacher, but by a robot! My first reaction to learning this was simple surprise; I had never thought that my essays might be graded by inanimate objects.

The architecture behind Michelangelo uses a modern but complex stack based on technologies such as HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.