AIMC Topic: Learning

Clear Filters Showing 1441 to 1450 of 1476 articles

Thinking like animals or thinking like colleagues?

The Behavioral and brain sciences
We comment on ways in which Lake et al. advance our understanding of the machinery of intelligence and offer suggestions. The first set concerns animal-level versus human-level intelligence. The second concerns the urgent need to address ethical issu...

What can the brain teach us about building artificial intelligence?

The Behavioral and brain sciences
Lake et al. offer a timely critique on the recent accomplishments in artificial intelligence from the vantage point of human intelligence and provide insightful suggestions about research directions for building more human-like intelligence. Because ...

Children begin with the same start-up software, but their software updates are cultural.

The Behavioral and brain sciences
We propose that early in ontogeny, children's core cognitive abilities are shaped by culturally dependent "software updates." The role of sociocultural inputs in the development of children's learning is largely missing from Lake et al.'s discussion ...

Back to the future: The return of cognitive functionalism.

The Behavioral and brain sciences
The claims that learning systems must build causal models and provide explanations of their inferences are not new, and advocate a cognitive functionalism for artificial intelligence. This view conflates the relationships between implicit and explici...

The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction.

The Behavioral and brain sciences
In this commentary, we highlight a crucial challenge posed by the proposal of Lake et al. to introduce key elements of human cognition into deep neural networks and future artificial-intelligence systems: the need to design effective sophisticated ar...

Deep Diabetologist: Learning to Prescribe Hypoglycemic Medications with Recurrent Neural Networks.

Studies in health technology and informatics
In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention. This sequential nature of EHR data make them wellmatched for the power of Recurrent Neural Network (RNN). In this poster, we propose "D...

Applying Risk Models on Patients with Unknown Predictor Values: An Incremental Learning Approach.

Studies in health technology and informatics
In clinical practice, many patients may have unknown or missing values for some predictors, causing that the developed risk models cannot be directly applied on these patients. In this paper, we propose an incremental learning approach to apply a dev...

Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students.

Studies in health technology and informatics
Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is bo...

Deep Learning: A Primer for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapp...

Introducing ZORA to Children with Severe Physical Disabilities.

Studies in health technology and informatics
The aim of the present study was to explore the potential of a ZORA-robot based intervention in rehabilitation and special education for children with (severe) physical disabilities from the professionals perspective. The qualitative results of this ...