Collaborating Authors

Puzzle Solving without Search or Human Knowledge: An Unnatural Language Approach Artificial Intelligence

For natural language generation (NLG), the transformer architecture provides a scalable mechanism to encode long-range dependencies needed to output plausible text narratives. Transformers (Vaswani, et al., 2017) have rapidly advanced to rival or overtake other deep learning architectures such as convolutional neural networks (CNN). Initially developed to handle long-term language dependencies, this approach over-weights important relations via the "attention" method rather than attempting to localize dependencies (CNN) or grow dense networks for all weights. While the resulting sparse network extends available long-term connections needed to relate distant parts-of-speech or sentence context, the net effect has grown to massive models now in the trillions of connection weights (Child, et al., 2019). This approach has since found application in other fields unrelated to the original language modeling, such as non-local effects needed for visual context problems.

Learning Chess Blindfolded: Evaluating Language Models on State Tracking Artificial Intelligence

Transformer language models have made tremendous strides in natural language understanding tasks. However, the complexity of natural language makes it challenging to ascertain how accurately these models are tracking the world state underlying the text. Motivated by this issue, we consider the task of language modeling for the game of chess. Unlike natural language, chess notations describe a simple, constrained, and deterministic domain. Moreover, we observe that the appropriate choice of chess notation allows for directly probing the world state, without requiring any additional probing-related machinery. We find that: (a) With enough training data, transformer language models can learn to track pieces and predict legal moves with high accuracy when trained solely on move sequences. (b) For small training sets providing access to board state information during training can yield significant improvements. (c) The success of transformer language models is dependent on access to the entire game history i.e. "full attention". Approximating this full attention results in a significant performance drop. We propose this testbed as a benchmark for future work on the development and analysis of transformer language models.

Navigating Language Models with Synthetic Agents Artificial Intelligence

Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently interrogatable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then compare the learned relationships of words in the model to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.

CubeTR: Learning to Solve The Rubiks Cube Using Transformers Artificial Intelligence

Since its first appearance, transformers have been successfully used in wide ranging domains from computer vision to natural language processing. Application of transformers in Reinforcement Learning by reformulating it as a sequence modelling problem was proposed only recently. Compared to other commonly explored reinforcement learning problems, the Rubiks cube poses a unique set of challenges. The Rubiks cube has a single solved state for quintillions of possible configurations which leads to extremely sparse rewards. The proposed model CubeTR attends to longer sequences of actions and addresses the problem of sparse rewards. CubeTR learns how to solve the Rubiks cube from arbitrary starting states without any human prior, and after move regularisation, the lengths of solutions generated by it are expected to be very close to those given by algorithms used by expert human solvers. CubeTR provides insights to the generalisability of learning algorithms to higher dimensional cubes and the applicability of transformers in other relevant sparse reward scenarios.

Building a Chess Engine: Part 2


Hi everyone, this will be the second instalment in my tutorial series for building a chess engine. This lesson will focus on building an AI agent that we can play. This lesson is going to be more technical than part 1, so please bear with me. I try to supply both equations and diagrams to help make things a little easier. Now that we have finished building our chess game, we can begin designing an AI that plays it.