Hybrid Intelligence Intimate Computing
The following article was written by Dr. Cornelia C. Walther, a visiting scholar at Wharton and director of global alliance POZE. A humanitarian practitioner who spent over 20 years at the United Nations, Walther’s current research focuses on leveraging AI for social good. Imagine a neurosurgeon who faces a complex, high-risk brain surgery. Despite years of experience, the case presents unpredictable variables.
Instead of relying solely on intuition, she turns to an AI-powered surgical assistant, which analyzes millions of similar cases in seconds, predicting complications and suggesting the most precise approach. As she operates, her expertise guides the procedure while the AI continuously adjusts recommendations in real time based on the patient’s vitals. When an unexpected complication arises, the AI flags an anomaly milliseconds before human detection, allowing the surgeon to act instantly and save the patient’s life. The AI extended the human’s capabilities without replacing her judgment. This is hybrid intelligence (HI) in action — natural and artificial intelligence working together, amplifying strengths, compensating for weaknesses, and achieving what neither could alone. By understanding and harnessing HI, organizations can move beyond incremental efficiency gains to unlock strategic, sustainable outcomes that future-proof the enterprise while improving the well-being of the people involved.
In the following sections, I explain how the multidimensional set-up of natural intelligence intertwines with AI to create HI, and provide a practical framework to help organizations leverage these ideas systematically and cost-effectively. Let’s start with a quick overview of the primary forms of intelligence referenced in this article: A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. I am associate professor Intimate Computing in the Human-Media Interaction group at University of Twente, The Netherlands. My research mission is to develop design methods and computational representation and reasoning techniques for creating intimate technologies that take into account our human vulnerability in supporting us in our daily lives: intimate computing...
I have a background in computer science & AI, in particular formal methods for multiagent systems. Over the past fifteen years we have been working at the intersection of AI and human-computer interaction, inspired by research in philosophy and ethics. Roles in Hybrid Intelligence: supervision of PhD candidates, co-chair HI-ESDiT committee, coordinator Special Interest Group on Ethicsformer coordinator Responsible HI research line. Hybrid Intelligence Research Objectives: Responsible HI Research interests: human-machine alignment, interactive human-machine meaning making, semantic user models, norm and value-aware agents, responsible AI, responsible design, human vulnerability in relation to intimate technologies, wearables, behaviour support agents, AI & disability, inclusion... Liu, Y.; Fu, Z.
Hybrid Intelligence: Design for Sustainable Multiverse via Integrative Cognitive Creation Model through Human–Computer Collaboration. Appl. Sci. 2024, 14, 4662. https://doi.org/10.3390/app14114662 Liu Y, Fu Z.
Hybrid Intelligence: Design for Sustainable Multiverse via Integrative Cognitive Creation Model through Human–Computer Collaboration. Applied Sciences. 2024; 14(11):4662. https://doi.org/10.3390/app14114662 Liu, Yuqi, and Zhiyong Fu. 2024.
"Hybrid Intelligence: Design for Sustainable Multiverse via Integrative Cognitive Creation Model through Human–Computer Collaboration" Applied Sciences 14, no. 11: 4662. https://doi.org/10.3390/app14114662 Liu, Y., & Fu, Z. (2024). Hybrid Intelligence: Design for Sustainable Multiverse via Integrative Cognitive Creation Model through Human–Computer Collaboration.
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>2�y�������&6*y��$A*� ���+3�LR\�ː����Gk��W1�$��v��a���G�ÃW����J�IM9"�j< e|-����% b �����PRğU����<�W�ks��S4�p�]���=@ �3�;K �h�����^! x�а�N��S�s�{\�r3�"�$7`�e�0p��^%�P���:B��g�) ��^�� b�V�@*���"P��� �������ZV�2��KS��\�ӫ�046U�k �fԿ�?�����3= ���b��]�������GH�������(��^�N�ި�y� �H+��fu@��'�99����ʲ������<ի� ��@��D;\�F5M��� ?�e�F���x-do���mtQ��6�|�YJ���������l�;��z<)�����bUm�Ib�(�> U�3o�*LF�<��J��9Qo�h"8C����V�v�h0sa*�@��ZG���2k�x�o���]������/b����m4�=I:���"Φ����0��;{15p�x�9�o�z$��L�R�ܔ]98�2�B���Ґ����\�O���V��Ϯ�ڨ�e�pX�m1�ŗ{���./GX�UI')q�rg�V��% d��Q�cюV���c����tp�.g��a1:����lJ� m���0$���7��D����:+ e ojcց����@�j^E�\;����VRs.�����&���BN}ɲk�B���1M�?��]#�g DWq�[��_z[㐋�S�<� _�UtoRC��N��J}X,�>���f��� ^Or�#>(��~#��� V��j+B!�&�t%3�#�R�w�#S�!� � `̆�e�4�-o�y]�u=�O���|r�Y� 4P�П N��x�%>��Ϝz�<���3 lX]h �?緆u��}�8�1Dx�>��A���r��� &Ґ����!G68����G ����S�>�D7���H�e7'���ߡ��NQ=b�X�p�tN}�e������M���[��T�\ABD~u-��8E)�����z�s(Z8������8 J82��P������L|�����9.��T���͊��h��# 0ȌڔK�tSx��6<ʯ��پ�R�(c���=w>X�*��p������o#��:"5���e\%�<�Y��}�3�h�:��<2V�&�&�7��nj�r�8�<Ւ���XK��y�X[�<Ej_d��,� 4�_��U��kշ�=Z�:�6�kw�O#� �ؚ��3>���5�jߛyS�K����� ��=,��[?J{�� ����_�q����h>��i�Z���:��79B��g;����X��b�/�AW�Q��䮺g�[ �w��R�TS��/f��h2!� K�iĶ�uC�P��r(�XF�bH���ʊ�Z䖩�r/k���IG%C��!��[�\���Q�A�... K�P�TCM��o��0�% ц8B�݀�3�-=�?y�B�]�%�O"�J&�w� ��](��Lj������dW��`Dn���jwݽ�Uw��.{ܝ>�=t���{�a8������ŵؽL��u��j�]F�&��[��{� ���b��Uﯷ��lr�wxwYX��rf����_Z�lLqd���`so endstream endobj 270 0 obj << /Type /XRef /Filter /FlateDecode /ID [ <496d096001b46794693aea95bd19a033> <496d096001b46794693aea95bd19a033> ] /Index [ 0 271 ] /Info 269 0 R /Length 650 /Root 268 0 R... In recent weeks, the AI landscape reached a new milestone with the release of several reasoning models, including OpenAI’s ChatGPT o3-mini, Deep Seek-R1 and Gemini 2.0 Flash. Building on the foundations laid by models like GPT-3 and GPT-4, these latest systems demonstrate notable gains in logical consistency and context awareness. Their rapid evolution does not unfold in isolation but coincide with technological strides on multiple fronts.
A pragmatic response to these converging trends would be an investment in double literacy, an approach that curates and aligns both natural and artificial intelligence as a way of curating hybrid intelligence. Why? As AI systems grow more capable, jailbreaking — manipulating AI to bypass its built-in constraints — has become an increasingly pressing concern. More powerful models often prove more vulnerable to exploitation, challenging developers and policymakers to keep safeguards ahead of potential misuse. At the same time, agentic AI and robotics are making striking advances. Robots researched by NASA robotics and pioneered by Boston Dynamics, among others, illustrate how physical machines equipped with sophisticated sensors and actuators can now navigate, manipulate objects, and execute tasks with unprecedented agility.
Meanwhile, purely software-based AI agents — think advanced virtual assistants or complex problem-solving programs — leverage machine learning, natural language processing, and large-scale data analysis to make autonomous decisions. They already tackle intricate tasks such as medical diagnostics or financial modeling without needing a physical form. Increasingly, these two fields converge: software agents orchestrate fleets of robots, while robots generate invaluable real-world data to feed AI’s learning processes. Yet, with the rise of powerful AI comes a heightened risk of jailbreaking and related vulnerabilities. The smarter these systems become, the harder they can be to control, underscoring the need for a balanced, human-guided approach that ensures such technologies serve constructive ends.
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