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Mostrando ítems 1-15 de 15

AI Generality and Spearman's Law of Diminishing Returns

Hernández-Orallo, José (AI Access Foundation, 2019)

[EN] Many areas of AI today use benchmarks and competitions with larger and wider sets of tasks. This tries to deter AI systems (and research effort) from specialising to a single task, and encourage them to be prepared ...
AUTOMAT[R]IX: learning simple matrix pipelines

Contreras-Ochando, Lidia; Ferri Ramírez, César; Hernández-Orallo, José (Springer-Verlag, 2021-04)

[EN] Matrices are a very common way of representing and working with data in data science and artificial intelligence. Writing a small snippet of code to make a simple matrix transformation is frequently frustrating, ...
Automating data science

De Bie, Tijl; De Raedt, Luc; Hernández-Orallo, José; Hoos, Holger H.; Smyth, Padhraic; Williams, Christopher K. I. (Association for Computing Machinery, 2022-03)

[EN] Given the complexity of data science projects and related demand for human expertise, automation has the potential to transform the data science process.
Can language models automate data wrangling?

Jaimovitch-López, Gonzalo; Ferri Ramírez, César; Hernández-Orallo, José; Martínez-Plumed, Fernando; Ramírez Quintana, María José (Springer-Verlag, 2023-06)

[EN] The automation of data science and other data manipulation processes depend on the integration and formatting of 'messy' data. Data wrangling is an umbrella term for these tedious and time-consuming tasks. Tasks such ...
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

Martínez-Plumed, Fernando; Contreras-Ochando, Lidia; Ferri Ramírez, César; Hernández-Orallo, José; Kull, Meelis; Lachiche, Nicolas; Ramírez Quintana, María José; Flach, Peter (Institute of Electrical and Electronics Engineers, 2021-08-01)

[EN] CRISP-DM (CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still thede factostandard ...
Does AI Qualify for the Job?: A Bidirectional Model Mapping Labour and AI Intensities

Martínez-Plumed, Fernando; Tolan, Songül; Pesole, Annarosa; Hernández-Orallo, José; Fernández-Macías, Enrique; Gómez, Emilia (Association for Computing Machinery (ACM), 2020-02-08)

[EN] In this paper we present a setting for examining the relation between the distribution of research intensity in AI research and the relevance for a range of work tasks (and occupations) in current and simulated ...
Dual Indicators to Analyse AI Benchmarks: Difficulty, Discrimination, Ability and Generality

Martínez-Plumed, Fernando; Hernández-Orallo, José (Institute of Electrical and Electronics Engineers (IEEE), 2020-06)

[EN] With the purpose of better analyzing the result of artificial intelligence (AI) benchmarks, we present two indicators on the side of the AI problems, difficulty and discrimination, and two indicators on the side of ...
Exploring AI Safety in Degrees: Generality, Capability and Control

Burden, John; Hernández-Orallo, José (ceur-ws.org, 2020-02-07)

[EN] The landscape of AI safety is frequently explored differently by contrasting specialised AI versus general AI (or AGI), by analysing the short-term hazards of systems with limited capabilities against those more ...
Futures of artificial intelligence through technology readiness levels

Martínez-Plumed, Fernando; Gómez, Emilia; Hernández-Orallo, José (Elsevier, 2021-05)

[EN] Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However, the main unanswered questions about this foreseen transformation are its depth, breadth and timelines. To answer them, ...
General intelligence disentangled via a generality metric for natural and artificial intelligence

Hernández-Orallo, José; Loe, Bao Sheng; Cheke, Lucy; Martínez-Plumed, Fernando; Heigeartaigh, Sean O. (Nature Publishing Group, 2021-11-24)

[EN] Success in all sorts of situations is the most classical interpretation of general intelligence. Under limited resources, however, the capability of an agent must necessarily be limited too, and generality needs to ...
Missing the missing values: The ugly duckling of fairness in machine learning

Martínez-Plumed, Fernando; Ferri Ramírez, César; Nieves, David; Hernández-Orallo, José (John Wiley & Sons, 2021-07)

[EN] Nowadays, there is an increasing concern in machine learning about the causes underlying unfair decision making, that is, algorithmic decisions discriminating some groups over others, especially with groups that are ...
Setting decision thresholds when operating conditions are uncertain

Ferri Ramírez, César; Hernández-Orallo, José; Flach, Peter (Springer-Verlag, 2019-07)

[EN] The quality of the decisions made by a machine learning model depends on the data and the operating conditions during deployment. Often, operating conditions such as class distribution and misclassification costs have ...
Surveying Safety-relevant AI Characteristics

Hernández-Orallo, José; Martínez-Plumed, Fernando; Avin, Shahar; Heigeartaigh, Sean O. (CEUR Workshop Proceedings, 2019-01-27)

[EN] The current analysis in the AI safety literature usually combines a risk or safety issue (e.g., interruptibility) with a particular paradigm for an AI agent (e.g., reinforcement learning). However, there is currently ...
The teaching size: computable teachers and learners for universal languages

Telle, Jan Arne; Hernández-Orallo, José; Ferri Ramírez, César (Springer-Verlag, 2019-09)

[EN] The theoretical hardness of machine teaching has usually been analyzed for a range of concept languages under several variants of the teaching dimension: the minimum number of examples that a teacher needs to figure ...
Twenty Years Beyond the Turing Test: Moving Beyond the Human Judges Too

Hernández-Orallo, José (Springer-Verlag, 2020-12)

[EN] In the last 20 years the Turing test has been left further behind by new developments in artificial intelligence. At the same time, however, these developments have revived some key elements of the Turing test: imitation ...