Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.

We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded in the form of natural language. We investigate their systematic generalization abilities on a logical reasoning task in natural language, which involves reasoning over relationships between entities grounded in first-order logical proofs. Specifically, we perform soft theorem-proving by leveraging TLMs to generate natural language proofs. We test the generated proofs for logical consistency, along with the accuracy of the final inference. We observe length-generalization issues when evaluated on longer-than-trained sequences. However, we observe TLMs improve their generalization performance after being exposed to longer, exhaustive proofs. In addition, we discover that TLMs are able to generalize better using backward-chaining proofs compared to their forward-chaining counterparts, while they find it easier to generate forward chaining proofs. We observe that models that are not trained to generate proofs are better at generalizing to problems based on longer proofs. This suggests that Transformers have efficient internal reasoning strategies that are harder to interpret. These results highlight the systematic generalization behavior of TLMs in the context of logical reasoning, and we believe this work motivates deeper inspection of their underlying reasoning strategies.

The New York Times reported that an AI system known as Aristo had become the first to successfully pass a standardized eighth-grade science test. The achievement arrived four years after a competition in which 700-plus scientists all failed to build a system capable of accomplishing the same task despite the incentive of the contest's $80,000 prize. Aristo has been viewed as a significant breakthrough in the evolution of AI technology, with far-reaching implications for natural language processing, business intelligence and more. The system provides a vivid illustration of the differences between human and artificial intelligence. It shows why the most effective AI systems still incorporate help from human experts -- a fact that has big implications for AI in business and other applications. The Aristo system represents a major step toward imbuing AI with what one Wired article refers to as "common sense," the expansive and unconscious background knowledge that we apply when navigating new situations or engaging in conversation.

This is an archived copy of the 2019-2020 catalog. To access the most recent version of the catalog, please visit http://bulletin.iit.edu. A maximum of three courses may be shared between the Artificial Intelligence minor and the Computational Structures minor.

We also especially encourage students from underrepresented minorities to participate. Hands-on programming labs are a core part of our curriculum, so having some programming knowledge (specifically Python) will help participants get more out of the workshop. However, programming knowledge is not required; the workshop will include a track for participants who are completely new to programming. Experience with typical undergraduate math (calculus, linear algebra) and statistics (intro probability) is also helpful, but not required. The workshop will be run on Eastern Time, though students from outside this timezone are welcome to apply.

With its unique ability to identify and'learn' from data patterns and to develop predictive mappings between variables – machine and deep learning – artificial intelligence (AI) has proved to be an indispensable tool in the fight against the coronavirus pandemic. AI has enabled the deployment of predictive models of potential disease contagion and containment, and has been used for screening and tracking patients. AI has been deployed across the globe to improve understanding of the potential consequences of the viral infection for different economy sectors. Companies have increasingly relied on machine-learning-enabled systems to reengineer production delivery in the face of a massive disruption in supply chains. Policy-makers have also turned to AI technologies due to their great promise in strengthening the quality of remote education delivery, at times where schools and education systems struggle to remain accessible to learners.

I'm underground, back where it all started. Sitting at the hidden cafe where I first met Mike. I'd been studying in my bedroom for the past 9-months and decided to step out of the cave. Half of me was concerned about having to pay $19 for breakfast (unless it's Christmas, driving Uber on the weekends isn't very lucrative), the other half about whether any of this study I'd been doing online meant anything. In 2017, I left Apple, tried to build a web startup, failed, discovered machine learning, fell in love, signed up to a deep learning course with zero coding experience, emailed the support team asking what the refund policy was, didn't get a refund, spent the next 3-months handing in the assignments four to six days late, somehow passed, decided to keep going and created my own AI Masters Degree.

Robots trained with reinforcement learning (RL) have the potential to be used across a huge variety of challenging real world problems. To apply RL to a new problem, you typically set up the environment, define a reward function, and train the robot to solve the task by allowing it to explore the new environment from scratch. While this may eventually work, these "online" RL methods are data hungry and repeating this data inefficient process for every new problem makes it difficult to apply online RL to real world robotics problems. What if instead of repeating the data collection and learning process from scratch every time, we were able to reuse data across multiple problems or experiments? By doing so, we could greatly reduce the burden of data collection with every new problem that is encountered.

Researchers at Riiid are pleased to announce the Call for Papers for a virtual Workshop on AI Education to be held at AAAI 2021 in early February 2021. COVID-19 has brought upon us the inevitable transformation towards virtual education. The ensuing need for scalable, personalized learning systems has led to an unprecedented demand for understanding large-scale educational data. In this workshop, we will call for papers related to important Artificial Intelligence in Education (AIEd) topics that can help us imagine what new education will look like post COVID-19. Submissions of papers including Kaggle competition technical papers, shared task technical papers and general submissions should follow the AAAI format and can be up to 8 pages excluding references and appendices.

The growing demand for professionals in the areas of Artificial Intelligence and Data Science has reflected in the new-generation interdisciplinary programmes proposed by government and aided colleges under Mahatma Gandhi University (MGU) for the academic year 2020-21. The university had invited applications from higher educational institutions based on the government directive that such courses could be launched from November 1. The Higher Education Department had asked universities to initiate steps to launch new undergraduate and postgraduate programmes in innovative areas. They included four- and five-year programmes recommended by an expert committee set up by the government. The integrated M.Sc programme in Computer Science (Artificial Intelligence and Machine Learning) figured top among the innovative programmes proposed by the affiliated colleges.