AI Paper Summaries #44 - AI Consciousness Insights!
8/21/2023
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
"Consciousness in Artificial Intelligence: Insights from the Science of Consciousness" by Butlin et al. discusses the topic of consciousness in artificial intelligence (AI) systems. The authors argue for a rigorous and empirically grounded approach to AI consciousness, which involves assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. They survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories, they derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties.
The authors use these indicator properties to assess several recent AI systems and discuss how future systems might implement them. Their analysis suggests that no current AI systems are conscious, but also shows that there are no obvious barriers to building conscious AI systems. This is an important finding, as it suggests that it may be possible to create AI systems that are capable of experiencing consciousness.
In terms of results, the paper provides a detailed analysis of several recent AI systems and their potential for consciousness. The authors use indicator properties derived from prominent scientific theories of consciousness to assess these systems and provide insights into their potential for achieving consciousness. The paper also discusses how future AI systems might implement these indicator properties in order to achieve consciousness.
Overall, this paper provides valuable insights into the potential for consciousness in AI systems. By taking a rigorous and empirically grounded approach, the authors are able to provide a detailed analysis of existing AI systems and their potential for achieving consciousness. Their findings suggest that while no current AI systems are conscious, there is potential for future systems to achieve this state.
Check out the full paper here: https://arxiv.org/pdf/2308.08708.pdf
Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents
"Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents" by Rahman et al. discusses the challenges of robustly cooperating with unseen agents and human partners due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Teamwork (AHT) methods address this challenge by training an agent with a population of diverse teammate policies obtained through maximizing specific diversity metrics. However, these heuristic diversity metrics do not always maximize the agent's robustness in all cooperative problems.
In this work, the authors propose that maximizing an AHT agent's robustness requires it to emulate policies in the minimum coverage set (MCS), the set of best-response policies to any partner policies in the environment. They introduce the L-BRDiv algorithm that generates a set of teammate policies that, when used for AHT training, encourage agents to emulate policies from the MCS. L-BRDiv works by solving a constrained optimization problem to jointly train teammate policies for AHT training and approximating AHT agent policies that are members of the MCS.
The authors empirically demonstrate that L-BRDiv produces more robust AHT agents than state-of-the-art methods in a broader range of two-player cooperative problems without the need for extensive hyperparameter tuning for its objectives. Their study shows that L-BRDiv outperforms the baseline methods by prioritizing discovering distinct members of the MCS instead of repeatedly finding redundant policies.
Overall, this paper introduces a new approach to training robust AHT agents by using minimum coverage sets and the L-BRDiv algorithm. The results show that this approach is effective in producing more robust AHT agents than state-of-the-art methods. This work provides valuable insights into the potential for improving cooperation between agents and human partners.
Check out the full paper here: https://arxiv.org/pdf/2308.09595.pdf
The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation
"The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation" by Fernandes et al. discusses the use of large language models (LLMs) for fine-grained machine translation evaluation. The authors propose a prompting technique called AutoMQM, which leverages the reasoning and in-context learning capabilities of LLMs to identify and categorize errors in translations.
The paper starts by evaluating recent LLMs, such as PaLM and PaLM-2, through simple score prediction prompting, and studying the impact of labeled data through in-context learning and finetuning. The authors then evaluate AutoMQM with PaLM-2 models, and find that it improves performance compared to just prompting for scores, while providing interpretability through error spans that align with human annotations.
The results of this study show that AutoMQM is effective in leveraging LLMs for fine-grained machine translation evaluation. The use of LLMs allows for more detailed analysis of translation errors, providing valuable insights into the quality of machine translation systems.
Overall, this paper introduces a new approach to machine translation evaluation using large language models. The proposed AutoMQM technique is shown to be effective in identifying and categorizing errors in translations, providing valuable insights into the quality of machine translation systems.
Check out the full paper here: https://arxiv.org/pdf/2308.07286.pdf