Hybrid Intelligence

DI in Industry (DIiI)

Andy Weeger

Neu-Ulm University of Applied Sciences

March 31, 2025

Terms & definitions

Intelligence

Intelligence is the ability to accomplish complex goals, learn, reason, and adaptively perform effective actions within an environment. Gottfredson (1997)

Or in short: think and act humanly and/or rationally.

Human intelligence

Human intelligence “covers the capacity to learn, reason, and adaptively perform effective actions within an environment, based on existing knowledge. This allows humans to adapt to changing environments and act towards achieving their goals.” Dellermann et al. (2019, 632)

Sternberg et al. (1985) proposes three distinctive dimensions:

• Componential intelligence refers to the ability to take apart problems and being able to see solutions not often seen.
• Experiential intelligence refers to the ability to learn and adapt through evolutionary experience.
• Contextual intelligence refers to the capacity to create an ideal fit between themselves and their environment by adaptation, shaping, and selection.

Thinking as a group

Collective intelligence

Collective intelligence refers to “[…] groups of individuals acting collectively in ways that seem intelligent.” Malone (2015, 3)

The concept implies that, under certain conditions, a (large) group of average, homogeneous individuals can outperform any individual of the group or even a single expert (Leimeister 2010).

Originally, research studies how groups of people act and think “as a whole”, e.g. using various coordination and decision-making mechanisms.

Today, research increasingly focuses on hybrid collective intelligence to explore how of heterogeneous agents (i.e., humans and machines) can be connected so that they combine their complementary intelligence and act more intelligently (Malone 2015).

Artificial intelligence

The term artificial intelligence is used to describe systems that perform “[…] activities that we associate with human thinking, activities such as decision-making, problem solving, learning […]” Bellman (1978, 3)

The basic idea behind this concept is that systems becomes capable of analyzing their environment and adapting to new circumstances in this environment.

AI can be defined as “[…] the art of creating machines that perform functions that require intelligence when performed by people […]” Kurzweil et al. (1990, 580:117)

Or in short: think and act humanly and/or rationally.

Hybrid intelligence

Concept

The idea is to combine the complementary capabilities of humans and computers to augment each other. Dellermann et al. (2019)

Complementary strengths

Figure 1: Complementary strengths of humans and machines (Dellermann et al. 2019, 640)

Effective human-AI collaboration leverages the complementary strengths of both parties. AI systems excel at processing large datasets, recognizing patterns, maintaining consistency, and perfect recall of information. Humans, on the other hand, provide contextual understanding, creativity, ethical judgment, and social intelligence (Seeber et al. 2020).

By designing collaborative systems that allocate tasks according to these complementary strengths, teams can achieve outcomes superior to what either humans or AI could accomplish independently. For example, in medical diagnosis, AI might identify patterns in medical images or patient data, while human doctors integrate this information with patient context, ethical considerations, and treatment planning.

The concept of complementary intelligence reframes AI not as a replacement for humans but as a partner that enhances human capabilities while being enhanced by human input. However, there is also a technology-centric perspective that assumes that true intelligence can ultimately only be found in well-developed and mature (general) AI systems. Humans are biologically limited in their information processing and reasoning abilities and exhibit many types of cognitive biases, while computers offer virtually infinite possibilities to develop rational intelligence at human levels and beyond (Peeters et al. 2021).

Model of human cognition

Kahneman (2011) proposed a two-system model of human cognition, which he called System 1 and System 2.

System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control. It’s intuitive, emotional, and unconscious. Characteristics include:

• Fast and effortless processing
• Forms impressions and feelings instantly
• Operates automatically and involuntarily
• Relies on heuristics (mental shortcuts)
• Excels at pattern recognition
• Prone to cognitive biases

System 2 allocates attention to effortful mental activities that demand it. It’s analytical, deliberate, and conscious. Characteristics include:

• Slow and effortful processing
• Requires focused attention and concentration
• Involves logical reasoning and critical analysis
• Can override System 1 impulses when needed
• Handles complex computations Limited by cognitive resources

This dual-process theory helps explain why we sometimes make irrational decisions despite our capacity for logical thinking. System 1 provides quick judgments that are often useful but can lead to systematic errors. System 2 can catch these errors, but it requires mental effort that we often conserve.

A practical application: when faced with complex problems, we can improve decision-making by consciously engaging System 2 to review and potentially override System 1’s automatic responses. This deliberate approach helps mitigate cognitive biases and leads to more rational conclusions.

AI systems excel at System 2-like processes: they can perform complex calculations, maintain consistent logical reasoning, and analyze vast datasets without fatigue. However, they often lack the intuitive pattern recognition, contextual understanding, and creative leaps that characterize human System 1 thinking.

Humans, conversely, bring powerful System 1 capabilities: intuition, emotional intelligence, ethical judgment, and creative insight. But we’re limited in our System 2 capacities by cognitive biases, fatigue, and processing constraints.

In an effective human-AI collaboration, AI augments human System 2 limitations by:

• Processing large amounts of information systematically
• Maintaining logical consistency across complex analyses
• Identifying patterns too subtle for human detection
• Reducing cognitive load on repetitive analytical tasks

And Humans contribute System 1 strengths by:

• Providing contextual understanding and domain expertise
• Making intuitive leaps based on incomplete information
• Applying ethical considerations and values
• Recognizing novel patterns or anomalies that fall outside AI training

Definition

Hybrid intelligence is defined as the ability to achieve complex goals by combining human and artificial intelligence, thereby reaching superior results to those each of them could have accomplished separately, and continuously improve by learning from each other. Dellermann et al. (2019, 640)

Main characteristics of hybrid intelligence are:

• Collectively means that tasks are performed collectively and activities are conditionally dependent.
• Superior results means that neither AI nor humans could have achieved the outcome without the other.
• Continuous learning means that all components of the socio-technical system learn from each other through experience.

Visualization

Figure 2: Distribution of roles in hybrid intelligence (Dellermann et al. 2019, 640)

Implications

According to Peeters et al. (2021) following conclusions can be drawn:

• Intelligence should not be studied at the level of individual humans or AI-machines, but at the group level of humans and AI-machines working together.
• Increasing the intelligence of a system should be achieved by increasing the quality of the interaction between its constituents rather than the intelligence of the constituents themselves.
• Both human as well as artificial intelligence can be regarded as very shallow when considered in isolation.
• No AI is an island (it’s about the system of systems).

Examples

Robots in de klas

A team consisting of a remedial teacher, an educational therapist, and a Nao robot collaborate to support a child with learning difficulties. The robot provides expertise and advice while also helping the child stay focused and engaged.

Spawn

The musician Holly Herndon created “Spawn,” an AI system that generates unique music different from her usual style. By using Spawn as a tool, Holly is able to avoid creating music that repeats her previous works but to to expand the possibilities of their music.

GitHub Copilot

In collaborative coding, Copilot can engage in back-and-forth dialogue about software design decisions, propose implementations, and explain reasoning about technical approaches - moving beyond simply generating code.

What examples do come to your mind?

Q&A

Literature

Bellman, Richard. 1978. An Introduction to Artificial Intelligence: Can Computers Think? Thomson Course Technology.

Dellermann, Dominik, Philipp Ebel, Matthias Söllner, and Jan Marco Leimeister. 2019. “Hybrid Intelligence.” Business & Information Systems Engineering 61: 637–43.

Gottfredson, Linda S. 1997. “Mainstream Science on Intelligence: An Editorial with 52 Signatories, History, and Bibliography.” Intelligence. JAI.

Kahneman, Daniel. 2011. Thinking, Fast and Slow. macmillan.

Kurzweil, Ray, Robert Richter, Ray Kurzweil, and Martin L Schneider. 1990. The Age of Intelligent Machines. Vol. 580. MIT press Cambridge.

Leimeister, Jan Marco. 2010. “Collective Intelligence.” Business & Information Systems Engineering 2: 245–48.

Malone, TW. 2015. “Handbook of Collective Intelligence; Bernstein, MS, Ed.” The MIT Press: Cambridge/London, UK.

Peeters, Marieke MM, Jurriaan van Diggelen, Karel Van Den Bosch, Adelbert Bronkhorst, Mark A Neerincx, Jan Maarten Schraagen, and Stephan Raaijmakers. 2021. “Hybrid Collective Intelligence in a Human–AI Society.” AI & Society 36: 217–38.

Seeber, Isabella, Eva Bittner, Robert O Briggs, Triparna De Vreede, Gert-Jan De Vreede, Aaron Elkins, Ronald Maier, et al. 2020. “Machines as Teammates: A Research Agenda on AI in Team Collaboration.” Information & Management 57 (2): 103174.

Sternberg, Robert J et al. 1985. Beyond IQ: A Triarchic Theory of Human Intelligence. CUP Archive.