[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128649-en":3,"doc-seo-128649-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128649,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","The Challenges of Machine Learning: A Critical Review","Machine learning faces fundamental methodological challenges despite strong empirical performance. The work contrasts common views of learning with the way ML systems acquire and generalize from data, emphasizing that statistical optimization does not guarantee causal explanation. Neural-network “black-box” models may predict outcomes such as creditworthiness while remaining opaque about which factors drove a decision, raising transparency and interpretability concerns. The text also argues that learning in supervised or unsupervised settings is not equivalent to true comprehension of skills, while reinforcement and imitation learning offer closer parallels to human cognition.","electronics   \nReview  \nThe Challenges of Machine Learning: A Critical Review  \nEnrico Barbierato *,† and Alice Gatti †  \nCitation: Barbierato, E.; Gatti, A. The Challenges of Machine Learning:  \nA Critical Review. Electronics 2024, 13, 416. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)electronics13020416  \nAcademic Editor: Aryya Gangopadhyay  \nReceived: 4 December 2023  \nRevised: 5 January 2024  \nAccepted: 17 January 2024  \nPublished: 19 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Mathematics and Physics, Catholic University of the Sacred Heart, 25133 Brescia, Italy; [alice.gatti@unicatt.it](alice.gatti@unicatt.it)  \n* Correspondence: [enrico.barbierato@unicatt.it](enrico.barbierato@unicatt.it)[ ](enrico.barbierato@unicatt.it)† These authors contributed equally to this work.  \nAbstract: The concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch of Artificial Intelligence (AI) and develops algorithms that can learn from data and generalize their judgment to new observations by exploiting primarily statistical methods. The new millennium has seen the proliferation of Artificial Neural Networks (ANNs), a formalism able to reach extraordinary achievements in complex problems such as computer vision and natural language recognition. In particular, designers claim that this formalism has a strong resemblance to the way the biological neurons operate. This work argues that although ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science, at least from a methodological perspective. The main reason is that ML algorithms have notable prediction power although they cannot necessarily provide a causal explanation about the achieved predictions. For example, an ANN could be trained on a large dataset of consumer financial information to predict creditworthiness. The model takes into account various factors like income, credit history, debt, spending patterns, and more. It then outputsa credit score or a decision on credit approval. However, the complex and multi-layered nature of the neural network makes it almost impossible to understand which specific factors or combinations of factors the model is using to arrive at its decision. This lack of transparency can be problematic, especially if the model denies credit and the applicant wants to know the specific reasons for the denial. The model’s “black box” nature means it cannot provide a clear explanation or breakdown of how it weighed the various factors in its decision-making process. Secondly, this work rejects the belief that a machine can simply learn from data, either in supervised or unsupervised mode, just by applying statistical methods. The process of learning is much more complex, as it requires the full comprehension of a learned ability or skill. In this sense, further ML advancements, such as reinforcement learning and imitation learning denote encouraging similarities to similar cognitive skills used in human learning.  \nKeywords: machine learning; scientific method; imitation learning; mirror neurons  \n1. Introduction  \nThe notion of learning is far from sharing a unique interpretation as the scientific literature presents different perspectives, ranging from pedagogic to philosophic approaches, even involving sociology or hard sciences. For example, according to Bloom [1], learning is the acquisition of knowledge or skills (“Learning is a relatively permanent change in behavior potentiality that occurs as a result of reinforced practic","cbCaitfhVFJzrcah","https://ap.wps.com/l/cbCaitfhVFJzrcah","pdf",2041450,3,1,30,"English","en",105,"# Introduction\n## Learning as a Multi-Interpretation Concept\n## Supervised and Unsupervised Learning Pipelines\n## Training, Testing, and Generalization","[{\"question\":\"Why does the paper question whether machine learning is a science from a methodological perspective?\",\"answer\":\"ML can be highly predictive, but it cannot necessarily provide causal explanations for those predictions, which limits its methodological status as a science.\"},{\"question\":\"What transparency problem does the paper illustrate using neural networks in credit scoring?\",\"answer\":\"A neural network may output a credit score or approval decision, yet its multi-layer structure makes it hard to determine which specific factors or combinations produced the result.\"},{\"question\":\"How does the paper describe supervised versus unsupervised learning?\",\"answer\":\"Supervised learning uses labeled data with a target variable to classify or cluster tasks, while unsupervised learning discovers structure such as clusters using methods like SOM and PCA.\"}]","The Challenges of Machine Learning: A Critical Review | 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does the paper question whether machine learning is a science from a methodological perspective?","Question",{"text":76,"@type":77},"ML can be highly predictive, but it cannot necessarily provide causal explanations for those predictions, which limits its methodological status as a science.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What transparency problem does the paper illustrate using neural networks in credit scoring?",{"text":81,"@type":77},"A neural network may output a credit score or approval decision, yet its multi-layer structure makes it hard to determine which specific factors or combinations produced the result.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the paper describe supervised versus unsupervised learning?",{"text":85,"@type":77},"Supervised learning uses labeled data with a target variable to classify or cluster tasks, while unsupervised learning discovers structure such as clusters using methods like SOM and 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