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001			978-3-031-61050-9
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008			260131s2026 sz | s |||| 0|eng d
020			_a9783031610509 _9978-3-031-61050-9
024	7		_a10.1007/978-3-031-61050-9 _2doi
050		4	_aQ295
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072		7	_aSCI064000 _2bisacsh
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082	0	4	_a530.1 _223
245	1	0	_aHandbook of Human-AI Collaboration _h[electronic resource] / _cedited by Mohamed Chetouani, Andrzej Nowak, Paul Lukowicz.
264		1	_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2026.
300			_aXX, 980 p. 40 illus., 20 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aSection 1: Foundations of Foundation Models -- Section 2: Foundations of Human AI Collaboration -- Section 3: Multimodal Foundation Models -- Section 4: Learning and reasoning with Foundation Models -- Section 5: Interaction with Foundation Models -- Section 6: Society-Large Whatever Models Interaction -- Section 7: Ethical and legal aspects of Foundation Models -- Section 8: Critical roadmap on collaborative foundation models.
506	0		_aOpen Access
520			_aThis Open Access book presents the historical evolution of artificial neural networks and the principles that underpin deep learning. It introduces the main concepts of Foundation Models employed in Large Language Models (LLMs) and more generally in Large Whatever Models (LWMs). It addresses the crucial need for explainability in both language and hybrid models, projecting future directions in the field. The work extends beyond technical dimensions to explore the intricate dynamics of Human-AI Collaboration, from the foundations of human-centered AI methodologies to generalized AI-human intelligence. The book explores challenges of multimodal foundation models in particular when it comes to multimodal perception, generation and embodiment. Contributors delve into topics such as complex reasoning, planning, argumentation, and applications in education and personal growth. Human-Large Whatever Models Interaction is examined in the context of co-adaptation, co-evolution, and the reciprocal influence between AI and human cognition, emotions, and behaviours. Benchmarking criteria and datasets for evaluation are discussed, providing insights into the evolving landscape of human-AI interaction. The societal impact of foundation models is explored in-depth, considering the dynamics of AI-driven techno-social systems, role distribution in AI-human collaborations, and the long-term implications on society. Ethical and legal aspects encompass conceptual backgrounds, metrics, and regulatory frameworks. The critical roadmap on foundation models addresses diverse stakeholders, including policy and decision-makers, the public sector, researchers, and developers. As the book unfolds, it illuminates the intricate interplay between society and foundation models, providing a comprehensive overview of the past, present, and potential future trajectories of foundation models in the ever-evolving landscape of artificial intelligence.
650		0	_aSystem theory. _9260
650		0	_aArtificial intelligence.
650		0	_aNeural networks (Computer science) . _911310
650	1	4	_aComplex Systems. _9267
650	2	4	_aArtificial Intelligence.
650	2	4	_aMathematical Models of Cognitive Processes and Neural Networks. _911312
700	1		_aChetouani, Mohamed. _eeditor. _0(orcid)0000-0002-2920-4539 _1https://orcid.org/0000-0002-2920-4539 _4edt _4http://id.loc.gov/vocabulary/relators/edt _923841
700	1		_aNowak, Andrzej. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _96957
700	1		_aLukowicz, Paul. _eeditor. _0(orcid)0000-0003-0320-6656 _1https://orcid.org/0000-0003-0320-6656 _4edt _4http://id.loc.gov/vocabulary/relators/edt _923842
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature Living Reference
856	4	0	_uhttps://doi.org/10.1007/978-3-031-61050-9
912			_aZDB-2-146
912			_aZDB-2-SXRC
912			_aZDB-2-SOB
912			_aZDB-2-SLR
999			_c579893 _d579893