The opacity of deep neural networks remains a challenge in deploying solutions where explanation is as important as precision. We present ConceptX, a human-in-the-loop framework for interpreting and annotating latent representational space in pre-trained Language Models (pLMs). We use an unsupervised method to discover concepts learned in these models and enable a graphical interface for humans to generate explanations for the concepts. To facilitate the process, we provide auto-annotations of the concepts (based on traditional linguistic ontologies). Such annotations enable development of a linguistic resource that directly represents latent concepts learned within deep NLP models. These include not just traditional linguistic concepts, but also task-specific or sensitive concepts (words grouped based on gender or religious connotation) that helps the annotators to mark bias in the model. The framework consists of two parts (i) concept discovery and (ii) annotation platform.

Imagine being able to apply the power of artificial intelligence (AI) to invent novel materials that can potentially revolutionize many industries such as pharmaceuticals, biotech, electronics, plastics, semiconductors, glass, energy, nanotech, metal alloys, composite materials, ceramics, optics, and many more. In 2018, pioneering physicists at Stanford University in Palo Alto, California, announced in PNAS (Proceedings of the National Academy of Sciences of the United States of America) the creation of a new AI program (Atom2Vec) that was able to recreate the periodic table of elements -- a milestone first step towards creating an AI that can discover new laws of nature, and invent novel materials and compounds [1]. Atom2Vec was able to achieve this within just a "few hours," versus the many centuries it took for humans [2]. The way this was achieved was a cross-disciplinary AI approach -- applying linguistic concepts to materials science. Stanford physicists applied Zellig S. Harris' hypothesis on the distributional structure of language to atoms instead of words.

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.

Learning linguistic and visual concepts from videos and textual the word blue is represented by a subset of the colour feature descriptions without having a predefined set of representations space). We will refer to the words that have visual representations is a challenging yet important task. For example, as concrete linguistic concepts (e.g. the word humans are born without the knowledge of how many representations blue has a representation in the colour space, therefore, blue for directions there are in the world, or how they is a concrete linguistic concept). We will refer to these visual are described in natural language. In some situations, it is representations as visual concepts (e.g. the blue colour better to use the 4 directions representation (front, right, left, in the colour feature space is a visual concept). Finally, we back), in others, one can use the 8 directions (front, front will use the term groundings to refer to the connections between right, right, etc.). Humans are capable of learning these different the different linguistic concepts and visual concepts.

Metaphors being at the heart of our language and thought process, computationally modelling them is imperative for reproducing human cognitive abilities. In this work, we propose a plausible grounded cognitive model for artificial metaphor acquisition. We put forward a rule-based metaphor acquisition system, which doesn't make use of any prior 'seed metaphor set'. Through correlation between a video and co-occurring commentaries, we show that these rules can be automatically acquired by an early learner capable of manipulating multi-modal sensory input. From these grounded linguistic concepts, we derive classes based on lexico-syntactical language properties. Based on the selectional preferences of these linguistic elements, metaphorical mappings between source and target domains are acquired.