Search Results for author:
Found 20 papers, 6 papers with code
Learning Alternative Ways of Performing a Task
We evaluate the inferred models with respect to two metrics that measure how well the models represent the examples and capture the different forms of executing a task showed by the examples.
Animal-AI 3: What's New & Why You Should Care
The Animal-AI Environment is a unique game-based research platform designed to serve both the artificial intelligence and cognitive science research communities.
An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI
Given rapid progress toward advanced AI and risks from frontier AI systems (advanced AI systems pushing the boundaries of the AI capabilities frontier), the creation and implementation of AI governance and regulatory schemes deserves prioritization and substantial investment.
Predictable Artificial Intelligence
We introduce the fundamental ideas and challenges of Predictable AI, a nascent research area that explores the ways in which we can anticipate key indicators of present and future AI ecosystems.
Inferring Capabilities from Task Performance with Bayesian Triangulation
As machine learning models become more general, we need to characterise them in richer, more meaningful ways.
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?
Large language models have recently shown a remarkable ability for few-shot learning, including patterns of algorithmic nature.
Compute and Energy Consumption Trends in Deep Learning Inference
The progress of some AI paradigms such as deep learning is said to be linked to an exponential growth in the number of parameters.
Conditional Teaching Size
Recent research in machine teaching has explored the instruction of any concept expressed in a universal language.
Automating Data Science: Prospects and Challenges
Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.
The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition
Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents.
Fairness and Missing Values
To support this claim, (1) we analyse the sources of missing data and bias, and we map the common causes, (2) we find that rows containing missing values are usually fairer than the rest, which should not be treated as the uncomfortable ugly data that different techniques and libraries get rid of at the first occasion, and (3) we study the trade-off between performance and fairness when the rows with missing values are used (either because the technique deals with them directly or by imputation methods).
Analysing Results from AI Benchmarks: Key Indicators and How to Obtain Them
The two-parameter IRT model provides two indicators (difficulty and discrimination) on the side of the item (or AI problem) while only one indicator (ability) on the side of the respondent (or AI agent).
General-purpose Declarative Inductive Programming with Domain-Specific Background Knowledge for Data Wrangling Automation
In this paper we propose to use IP as a means for automating repetitive data manipulation tasks, frequently presented during the process of {\em data wrangling} in many data manipulation problems.
A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work
This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work.
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.
Between Progress and Potential Impact of AI: the Neglected Dimensions
We reframe the analysis of progress in AI by incorporating into an overall framework both the task performance of a system, and the time and resource costs incurred in the development and deployment of the system.
Forgetting and consolidation for incremental and cumulative knowledge acquisition systems
The application of cognitive mechanisms to support knowledge acquisition is, from our point of view, crucial for making the resulting models coherent, efficient, credible, easy to use and understandable.
Definition and properties to assess multi-agent environments as social intelligence tests
Instead, in this paper we start from a parametrised definition of social intelligence as the expected performance in a set of environments with several agents, and we assess and derive tests from it.
On the definition of a general learning system with user-defined operators
As a result, the architecture can be seen as a 'system for writing machine learning systems' or to explore new operators where the policy reuse (as a kind of transfer learning) is allowed.
Test cost and misclassification cost trade-off using reframing
Many solutions to cost-sensitive classification (and regression) rely on some or all of the following assumptions: we have complete knowledge about the cost context at training time, we can easily re-train whenever the cost context changes, and we have technique-specific methods (such as cost-sensitive decision trees) that can take advantage of that information.
