Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

686
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
686
Introduction to Learning01:18

Introduction to Learning

575
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
575
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.9K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.9K
Associative Learning01:27

Associative Learning

630
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
630
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

180
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
180
Purposive Learning01:22

Purposive Learning

214
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
214

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Automated epilepsy and seizure type phenotyping with pre-trained language models.

medRxiv : the preprint server for health sciences·2026
Same author

Zero-Shot Extraction of Seizure Outcomes from Clinical Notes Using Generative Pretrained Transformers.

Journal of healthcare informatics research·2025
Same author

Comparative effectiveness of anti-seizure medications in emulated trials using medical informatics.

Brain : a journal of neurology·2025
Same author

Elucidating linear programs by neural encodings.

Frontiers in artificial intelligence·2025
Same author

Disparities in seizure outcomes revealed by large language models.

Journal of the American Medical Informatics Association : JAMIA·2024
Same author

Modelling dataset bias in machine-learned theories of economic decision-making.

Nature human behaviour·2024
Same journal

Logic, inference, understanding: cross-domain generalization for generative language models.

Frontiers in artificial intelligence·2026
Same journal

Label tree semantic losses for rich multi-class medical image segmentation.

Frontiers in artificial intelligence·2026
Same journal

Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.

Frontiers in artificial intelligence·2026
Same journal

Resource-efficient retrieval-augmented question answering for the Indian Lok Sabha dataset.

Frontiers in artificial intelligence·2026
Same journal

Violation detection in power operation sites based on multi-scale detection and few-shot learning.

Frontiers in artificial intelligence·2026
Same journal

Deep reinforcement learning-based reversible medical image encryption framework for secure IoMT environments.

Frontiers in artificial intelligence·2026
See all related articles

Related Experiment Video

Updated: Sep 28, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.7K

Declarative Learning-Based Programming as an Interface to AI Systems.

Parisa Kordjamshidi1, Dan Roth2, Kristian Kersting3

  • 1Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, United States.

Frontiers in Artificial Intelligence
|April 4, 2022
PubMed
Summary
This summary is machine-generated.

This review examines programming languages for designing complex artificial intelligence (AI) systems. It classifies and compares current AI frameworks for machine learning and reasoning, highlighting future directions.

Keywords:
artificial intelligencedeclarative programmingintegration paradigmsmachine learningprobabilistic programmingprogramming languages for machine learning

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K

Related Experiment Videos

Last Updated: Sep 28, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.7K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K

Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Data-driven approaches and machine learning models are integral to modern scientific problem-solving.
  • Developing complex AI systems often requires integrating multiple learning models and reasoning capabilities.
  • Current tools for designing AI systems are cumbersome for both domain experts and machine learning specialists.

Approach:

  • This study reviews and classifies existing AI frameworks based on their learning and reasoning techniques.
  • The classification considers data and knowledge representations within these frameworks.
  • The comparison focuses on how current tools address challenges in programming real-world AI applications.

Key Points:

  • Existing AI frameworks offer high-level abstractions for designing complex AI systems.
  • Frameworks are categorized by technique type and data/knowledge representation.
  • The review highlights shortcomings and suggests future research directions in AI system design.

Conclusions:

  • There is a need for more accessible and efficient tools for developing sophisticated AI systems.
  • Further research should focus on improving the usability and capabilities of AI programming frameworks.
  • Qualitative comparison reveals areas for advancement in AI development methodologies.