Behavioral personal digital assistants: The seventh generation of computing

Area of Science:

• Artificial Intelligence
• Behavioral Science
• Computer Science

Background:

• Two main approaches to artificial intelligence exist: rule-based systems and adaptive networks.
• Adaptive networks model behavior as stimulus-operant associations, learning through reinforcement.
• This approach aligns with advancements in neural network technology.

Purpose of the Study:

• To extend existing adaptive network software for acquiring verbal behavior.
• To bridge the gap between behavior analysis and artificial intelligence.
• To foster collaboration between academic and commercial sectors.

Main Methods:

• Utilizing an existing adaptive network software system developed since 1983.
• Extending the software based on principles of behavior analysis.
• Focusing on associative networks and reinforcement learning.

Main Results:

• The proposed extension enables adaptive networks to acquire verbal behavior.
• Demonstrates a viable alternative to traditional rule-based AI systems.
• Highlights the potential for significant advancements in AI.

Conclusions:

• Adaptive networks offer a promising avenue for developing intelligent systems.
• Extending these systems can lead to a new generation of AI.
• Collaboration is key to realizing the potential of behavior analytic AI.