The next generation of educational applications need to significantly improve the way feedback is offered to both teachers and students. Simply determining coarse-grained entailment relations between the teacher's reference answer as a whole and a student response will not be sufficient. A finer-grained analysis is needed to determine which aspects of the reference answer have been understood and which have not. To this end, we propose an approach that splits the reference answer into its constituent propositions and two methods for detecting entailment relations between each reference answer proposition and a student response. Both methods, one using hand-crafted features and an SVM and the other using word embeddings and deep neural networks, achieve significant improvements over a state-of-the-art system and two alternative approaches.

We investigate the task of automatic dietary assessment: given meal images and descriptions uploaded by real users, our task is to automatically rate the meals and deliver advisory comments for improving users' diets. To address this practical yet challenging problem, which is multi-modal and multi-task in nature, an end-to-end neural model is proposed. In particular, comprehensive meal representations are obtained from images, descriptions and user information. We further introduce a novel memory network architecture to store meal representations and reason over the meal representations to support predictions. Results on a real-world dataset show that our method outperforms two strong image captioning baselines significantly.

Lifelong machine learning (LML) is a paradigm to design adaptive agents that can learn in dynamic environments. Current LML algorithms consider a single agent that has centralized access to all data. However, given privacy and security constraints, data might be distributed among multiple agents that can collaborate and learn from collective experience. Our goal is to extend LML from a single agent to a network of multiple agents that collectively learn a series of tasks.

Why is our contribution important to the community? The recent boom in deep neural networks has resulted in Learning without any explicit supervision for a task ipso their being used for a wide variety of applications, many of facto provides interesting properties to our approach. An example which find significance when run on memory-constrained is that the learning method is domain and task independent, environments. Popular methods for neural network compression since instead of learning a given task, we learn aim to achieve a reduction in the number of parameters a way to learn that from the teacher. Hence, it should be while retaining state-of-the-art results. A seminal work well suited to classification, retrieval, clustering or any other on model compression was by Hinton et al [2] who introduced method across domains. Another interesting fact about this a technique in which a small student network learns approach is that humans learn in a similar way too - they from a large teacher network that is trained to saturation.

The purpose of this study is to design a machine learning approach to predict the student response in mixed-format tests. Particularly, a novel contextual collaborative filtering model is proposed to extract latent factors for students and test items, by exploiting the item information. Empirical results from a simulation study validate the effectiveness of the proposed method.

Machine comprehension of text is the problem to answer a query based on a given context. Many existing systems use RNN-based units for contextual modeling linked with some attention mechanisms. In this paper, however, we propose StackReader, an end-to-end neural network model, to solve this problem, without recurrent neural network (RNN) units and its variants. This simple model is based solely on attention mechanism and gated convolutional neural network. Experiments on SQuAD have shown to have relatively high accuracy with a significant decrease in training time.

We propose two distinct levels of learning for general autonomous intelligent agents. Level 1 consists of fixed architectural learning mechanisms that are innate and automatic. Level 2 consists of deliberate learning strategies that are controlled by the agent's knowledge. We describe these levels and provide an example of their use in a task-learning agent. We also explore other potential levels and discuss the implications of this view of learning for the design of autonomous agents.

Computer vision, as a subfield in the general artificial intelligence (AI), is a technology can be visualized and easily found in a large number of state-of-art applications. In this project, undergraduate students performed research on a landmark recognition task using computer vision techniques. The project focused on analyzing, designing, configuring, and testing the two core components in landmark recognition: feature detection and description. The project modeled the landmark recognition system as a tour guide for visitors to the campus and evaluated the performance in the real world circumstances. By analyzing real-world data and solving problems, student's cognitive skills and critical thinking skills were sharpened. Their knowledge and understanding in mathematical modeling and data processing were also enhanced.

Artificial Intelligence (AI) is becoming increasingly important. Thus, sound knowledge about the principles of AI will be a crucial factor for future careers of young people as well as for the development of novel, innovative products. Addressing this challenge, we present an ambitious 3-year project focusing on developing and implementing a professional, internationally accepted, standardized training and certification system for AI which will also be recognized by the industry and educational institutions. The approach is based on already implemented and evaluated pilot projects in the area of AI education. The project’s main goal is to train and certify teachers and mentors as well as students and young people in basic and advanced AI topics, fostering AI literacy among this target audience.

Thymio is a small, inexpensive, mass-produced mobile robot with widespread use in primary and secondary education. In order to make it more versatile and effectively use it in later educational stages, including university levels, we have expanded Thymio's capabilities by adding off-the-shelf hardware and open software components. The resulting robot, that we call Mighty Thymio, provides additional sensing functionalities, increased computing power, networking, and full ROS integration. We present the architecture of Mighty Thymio and show its application in advanced educational activities.