Machine Learning and Artificial Intelligence aims at providing a platform to discuss the significant impact that ML and AI has on other fields in science, society and industry. The section welcomes foundational and applied papers from a wide range of topics underpinning both ML and AI, and explores emerging cross-disciplinary themes.
Big Data is no fad. In more ways than one, the world is growing at an exponential rate, and so is the size of data collected across the globe. Data is becoming more meaningful and contextually relevant, breaks new ground for machine learning (ML) and artificial intelligence (AI), and even moves both of them from research labs to production. The focus has shifted from collecting massive amounts of data to making sense of it, i.e. to turn data into knowledge, conclusions, and actions.
But what does this mean for ML and AI? Is ML converging with AI, as suggested by some news, blogs and other media? Are big data and machine learning really the answer to open questions in AI. To understand this, the section welcomes foundational and applied papers from a wide range of topics underpinning both ML and AI. Specifically, we welcome papers on:
· Classification, regression, recognition, and prediction
· Supervised and unsupervised learning methods
· Problem solving and planning
· Data Exploration
· Reasoning and inference
· Deep inference, learning, and architectures
· Combinatorial optimization
· Constraint processing and learning
· AutoML and AI
· Explainable ML and AI
· Probabilistic (logic) programming
· Statistical Relational AI
· Tractable Inference and Learning
· Learning to infer and to learn
· Learning to understand non-standard data
· Robot Learning
· Multi-Agent inference and learning
· Computational cognitive science
· Visualization for and of ML
· Industrial, financial, and scientific applications of all kinds
The journal will also explore and discuss emerging cross-disciplinary themes, such as learning-based programming, machine reasoning, and ML engineering for computationally and mathematically understanding and modeling complex AI systems. It also aims at providing a platform to discuss the significant impact that ML and AI has on other fields in science, society and industry.
The journal publishes original research as Articles. We also publish a range of other content types including Brief Research Reports, Case Reports, Empirical Studies, Evaluations, Mini Reviews, Perspectives, Codes, Data Reports, Comments, Reviews, among others.
Indexed in: Google Scholar, CrossRef, CLOCKSS, OpenAIRE
Machine Learning and Artificial Intelligence welcomes submissions of the following article types: Code, Correction, Data Report, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Specialty Grand Challenge and Technology Report.
All manuscripts must be submitted directly to the section Machine Learning and Artificial Intelligence, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Machine Learning and Artificial Intelligence will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Big Data.
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