We demonstrate a library for the integration of domain knowledge in deep learning architectures. Using this library, the structure of the data is expressed symbolically via graph declarations and the logical constraints over outputs or latent variables can be seamlessly added to the deep models. The domain knowledge can be defined explicitly, which improves the models' explainability in addition to the performance and generalizability in the low-data regime. Several approaches for such an integration of symbolic and sub-symbolic models have been introduced; however, there is no library to facilitate the programming for such an integration in a generic way while various underlying algorithms can be used. Our library aims to simplify programming for such an integration in both training and inference phases while separating the knowledge representation from learning algorithms. We showcase various NLP benchmark tasks and beyond. The framework is publicly available at Github(https://github.com/HLR/DomiKnowS).

In this paper we study the personalized book recommender system in a child-robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children's fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance and it can operate on embedded consumer devices with limited computational resources.

In this paper, we study endogeneity problems in algorithmic decision-making where data and actions are interdependent. When there are endogenous covariates in a contextual multi-armed bandit model, a novel bias (self-fulfilling bias) arises because the endogeneity of the covariates spills over to the actions. We propose a class of algorithms to correct for the bias by incorporating instrumental variables into leading online learning algorithms. These algorithms also attain regret levels that match the best known lower bound for the cases without endogeneity. To establish the theoretical properties, we develop a general technique that untangles the interdependence between data and actions.

Do you wish you could perform this function using Pandas. Well, there is a good possibility you can! For data scientists who use Python as their primary programming language, the Pandas package is a must-have data analysis tool. The Pandas package has everything a data scientist needs, and every course taught us how to utilise it at first. It is so large, powerful and performs almost every tabular manipulation you can imagine. However, this breadth can be a disadvantage at times.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Machine learning is becoming an important tool in many industries and fields of science. But ML research and product development present several challenges that, if not addressed, can steer your project in the wrong direction. In a paper recently published on the arXiv preprint server, Michael Lones, Associate Professor in the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, provides a list of dos and don'ts for machine learning research. The paper, which Lones describes as "lessons that were learnt whilst doing ML research in academia, and whilst supervising students doing ML research," covers the challenges of different stages of the machine learning research lifecycle. Although aimed at academic researchers, the paper's guidelines are also useful for developers who are creating machine learning models for real-world applications.

The Tribune, now published from Chandigarh, started publication on February 2, 1881, in Lahore (now in Pakistan). It was started by Sardar Dyal Singh Majithia, a public-spirited philanthropist, and is run by a trust comprising four eminent persons as trustees. The Tribune, the largest selling English daily in North India, publishes news and views without any bias or prejudice of any kind. Restraint and moderation, rather than agitational language and partisanship, are the hallmarks of the paper. It is an independent newspaper in the real sense of the term. The Tribune has two sister publications, Punjabi Tribune (in Punjabi) and Dainik Tribune (in Hindi).

RoboCupJunior (RCJ) is designed to introduce RoboCup to school children, with the focus being on education. RCJ offers several challenges, each emphasizing cooperation and problem-solving. This initiative provides an exciting introduction to the field of AI and robotics. Following the conclusion of this year's RoboCup, we spoke to trustee Amy Eguchi about RCJ and how the events went in this virtual edition. Amy has been involved with RCJ since 2000 and, in that time, has seen the number of participating teams grow from around 30 to 200.

Maths PhD student, Alex Terenin, recently presented his group's work at the 2021 International Conference of Artificial Intelligence and Statistics. AISTATS is a prestigious event that brings together researchers from the machine learning and statistics communities. One of the group's papers, Matérn Gaussian Processes on Graphs, won the Best Student Paper award at the event – congratulations! We caught up with Alex to find out more about his experience at the conference and as a PhD student at Imperial, why he's fascinated with research into uncertainty, and to get his thoughts on why the visual aspect of machine learning is vital. My research focuses on artificial intelligence, particularly on learning-based decision-making systems.

With the high rise in demand for talent in the field of Artificial Intelligence (AI), the need for professionals who have expertise in this field has also increased immensely. Worldwide, many organizations are on the lookout for individuals who possess a great skillsets in the field of AI. This demand gave rise to the artificial intelligence engineer certification program, which is offered by several online learning institutes. If a person wants to enhance their skill set and also stay ahead in the growing populations then doing a certification program in the field of AI is the best choice. In this article, let's understand the most affordable and industry-recognized top AI certifications that one can do to jump the career ladder.

At the start of this year, I published a mind map on the Data Science learning roadmap (shown below). The roadmap was widely accepted, that article got translated into different languages, and a large number of folks thanked me for publishing it. Everything was good until a few aspirants pointed out that there are too many resources and many of them are expensive. Python programming was the only branch that had a number of really good courses, but it ends right there for beginners. Answers to a lot of these questions can be found in the book Deep Learning by Ian Goodfellow and Yoshua Bengio.But that book is a bit too technical and math-heavy for many.