We address the problem of efficient exploration for learning lifted operators in sequential decision-making problems without extrinsic goals or rewards. Inspired by human curiosity, we propose goal-literal babbling (GLIB), a simple and general method for exploration in such problems. GLIB samples goals that are conjunctions of literals, which can be understood as specific, targeted effects that the agent would like to achieve in the world, and plans to achieve these goals using the operators being learned. We conduct a case study to elucidate two key benefits of GLIB: robustness to overly general preconditions and efficient exploration in domains with effects at long horizons. We also provide theoretical guarantees and further empirical results, finding GLIB to be effective on a range of benchmark planning tasks.

We present a new multimodal question answering challenge, ManyModalQA, in which an agent must answer a question by considering three distinct modalities: text, images, and tables. We collect our data by scraping Wikipedia and then utilize crowdsourcing to collect question-answer pairs. Our questions are ambiguous, in that the modality that contains the answer is not easily determined based solely upon the question. To demonstrate this ambiguity, we construct a modality selector (or disambiguator) network, and this model gets substantially lower accuracy on our challenge set, compared to existing datasets, indicating that our questions are more ambiguous. By analyzing this model, we investigate which words in the question are indicative of the modality. Next, we construct a simple baseline ManyModalQA model, which, based on the prediction from the modality selector, fires a corresponding pre-trained state-of-the-art unimodal QA model. We focus on providing the community with a new manymodal evaluation set and only provide a fine-tuning set, with the expectation that existing datasets and approaches will be transferred for most of the training, to encourage low-resource generalization without large, monolithic training sets for each new task. There is a significant gap between our baseline models and human performance; therefore, we hope that this challenge encourages research in end-to-end modality disambiguation and multimodal QA models, as well as transfer learning. Code and data available at: https://github.com/hannandarryl/ManyModalQA

The field of machine learning continues to boast incredible job growth, salaries, and skill sets that can be used in many different industries. Google utilizes this technology in their Cloud product to allow startups to build machine learning models that work on data of any size, while GE utilizes IoT to help detect and prevent anomalies and crashes in their products. These are just a snapshot of the numerous applications of machine learning in the market today that display the potential for an exponential amount of professional expansion. Currently, just in the US alone, there are over 50,000 open roles for machine learning professionals, so now is the time to develop machine learning expertise! In LinkedIn's 2020 Emerging Jobs report, AI Specialist, a role that includes machine learning, deep learning, TensorFlow, and Python as key skills, boasts 74% annual growth.

The main objective of exams consists in performing an assessment of students' expertise on a specific subject. Such expertise, also referred to as skill or knowledge level, can then be leveraged in different ways (e.g., to assign a grade to the students, to understand whether a student might need some support, etc.). Similarly, the questions appearing in the exams have to be assessed in some way before being used to evaluate students. Standard approaches to questions' assessment are either subjective (e.g., assessment by human experts) or introduce a long delay in the process of question generation (e.g., pretesting with real students). In this work we introduce R2DE (which is a Regressor for Difficulty and Discrimination Estimation), a model capable of assessing newly generated multiple-choice questions by looking at the text of the question and the text of the possible choices. In particular, it can estimate the difficulty and the discrimination of each question, as they are defined in Item Response Theory. We also present the results of extensive experiments we carried out on a real world large scale dataset coming from an e-learning platform, showing that our model can be used to perform an initial assessment of newly created questions and ease some of the problems that arise in question generation.

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.

Machine learning (ML), an application of computer programs, makes algorithms and is capable of making decisions and generating outputs without any human involvement. Hailed as one of the most impactful and significant technological developments that we have seen in recent times, machine learning has already helped us perform key real-world calculations and analytics that conventional computing would take years to solve. When it comes to the budding IT engineers and software developers, ML has been quite popular as a career choice. A lot of students have been suggested to take up a Machine Learning course and get industry-ready for the upcoming technological trend. As a matter of fact, jobs related to machine learning has seen incredible growth over the last couple of years.

The Company announced the formation of new online social group to bring together programmers and A.I. enthusiasts from the US, India, and across the globe. Data Elf online social groups geared towards the data science and programming community from individuals interested in Python programming, advanced Machine learning, Robotics, or Enterprise Solutions. One of biggest issues for new data programmers is finding ways to turn their newly formed skills into new job opportunities. Data Elf Programmer Profiles allow developers to list their programming and development skills to global audience in regards for employment or contract work. To view programmer profiles please visit https://dataelf.com/list/

The opportunities created by enhanced computing power, availability of data, and progress in algorithms, have made Artificial Intelligence (AI) the main technological revolution of our times. Artificial Intelligence represents an area of strategic importance and a key driver of economic development. With the help of Elements of AI, the groundbreaking online course made by Reaktor and the University of Helsinki, all EU citizens can acquire basic understanding of Artificial Intelligence. The ambitious goal is to educate 1% of European citizens on Artificial Intelligence by 2021. The course will be made available in all the official EU languages.

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Chinese tech giant Baidu has created a new way of teaching language to AIs, according to a report in TechnologyReview. The new method offers better results than the ones used by Google and Microsoft, beating out both companies in the General Language and Understanding Evaluation (GLUE) competition. Baidu's new model is called Enhanced Representation through kNowledge IntEgration, or ERNIE. It was named after the Sesame Street character because Google named the former champion model the Bidirectional Encoder Representations from Transformers or BERT. To take the crown from Google, ERNIE had to outperform its rival in the nine different language tests of GLUE.