scholarly journals Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning

2017 ◽  
Vol 40 ◽  
Author(s):  
Pierre-Yves Oudeyer
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Social Learning ◽  
Intrinsic Motivation ◽  
Scaling Up ◽  
Learning Systems ◽  
Natural Interaction ◽  
Autonomous Choice ◽  
Autonomous Development ◽  
And Robotics

AbstractAutonomous lifelong development and learning are fundamental capabilities of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives.

scholarly journals Artificial Intelligence Hits the Barrier of Meaning

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 51 ◽  
Author(s):  
Melanie Mitchell
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
State Of The Art ◽  
Learning Systems ◽  
Human Intelligence ◽  
Interdisciplinary Science

Today’s AI systems sorely lack the essence of human intelligence: Understanding the situations we experience, being able to grasp their meaning. The lack of humanlike understanding in machines is underscored by recent studies demonstrating lack of robustness of state-of-the-art deep-learning systems. Deeper networks and larger datasets alone are not likely to unlock AI’s “barrier of meaning”; instead the field will need to embrace its original roots as an interdisciplinary science of intelligence.

A Study on Role of A.I. and Robotics in Health Care Sector: Its Impact and Contribution

2021 ◽  
Vol 07 (3&4) ◽  
pp. 7-14
Author(s):  
Devnath Jayaswal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Health Care Sector ◽  
Healthcare Sector ◽  
Current Scenario ◽  
Health Complications ◽  
And Robotics ◽  
Major Domain

Health Care is one of the major domain sectors of our country. As this domain has many different aspect of implementation, as per the current scenario of Diseases and health complications. This paper will discuss about how, the Artificial Intelligence (A.I.) and robotics can be beneficial and plays a major role on, health care domain with respect to the Efficiently Diagnose, Developing New Medicines, Earlier Detection of Diseases, Advance Treatment Care, A.I-Deep learning For the Critical Decision’s. As this Information will help to give more clarity on what, A.I. & Robotics contributes for the major Diseases Treatment by the advancement of Technology. This can be beneficial for not only Doctors, Patients, or Firm but can also be helpful for citizen people as well. The objective of this paper is to study the role of AI and Robotics in Healthcare Sector and its impact.

scholarly journals Uncovering the Politics Behind Adobe Stock

2021 ◽  
Vol 1 (1) ◽  
pp. 1-9
Author(s):  
Angelica Martinez Ochoa
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Learning Systems ◽  
Political Work

This paper explores how the categorization of images and the searching methods in the Adobe Stock database are culturally situated practices; they are a form of politics, filled with questions about who gets to decide what images mean and what kinds of social and political work those representations perform. Understanding the politics behind artificial intelligence, machine learning, and deep learning systems matters now more than ever, as Adobe is already using these technologies across all their products.

scholarly journals To Identify and Recognize the Object for Traffic Analysis System using Deep Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 1621-1624
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Traffic Analysis ◽  
High Accuracy ◽  
Object Identification ◽  
Learning Systems ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Analysis System

The object identification has been most essential field in development of machine vision which should be more efficient and accurate. Machine Learning & Artificial Intelligence, both are on their peak in today’s technology world. Playing with these can leads towards development. The field has actually replaced human efforts. With the approach of profound learning systems (i.e. deep learning techniques), the precision for object identification has expanded radically. This project aims to implement Object Identification for Traffic Analysis System in real time using Deep Learning Algorithms with high accuracy. The differentiation among objects such as humans, Traffic signs, etc. are identified. The dataset is so designed with specific objects which will be recognized by the camera and result will be shown within seconds. The project purely based on deep learning approaches which also includes YOLO object detection & Covolutionary Neural Network (CNN). The resulting system is fast and accurate, therefore can be implemented for smart automation across global stage

Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury

2021 ◽  
Vol 27 (6) ◽  
pp. 560-572 ◽  
Author(s):  
Tezcan Ozrazgat-Baslanti ◽  
Tyler J. Loftus ◽  
Yuanfang Ren ◽  
Matthew M. Ruppert ◽  
Azra Bihorac
Keyword(s):  
Artificial Intelligence ◽  
Acute Kidney Injury ◽  
Deep Learning ◽  
Kidney Injury ◽  
Learning Systems

Artificial intelligence and crime: A primer for criminologists

2020 ◽  
pp. 174165902091743
Author(s):  
Keith J Hayward ◽  
Matthijs M Maas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Criminal Justice ◽  
Potential Application ◽  
Scaling Up ◽  
General Review ◽  
The Third ◽  
Prediction And Prevention

This article introduces the concept of Artificial Intelligence (AI) to a criminological audience. After a general review of the phenomenon (including brief explanations of important cognate fields such as ‘machine learning’, ‘deep learning’, and ‘reinforcement learning’), the paper then turns to the potential application of AI by criminals, including what we term here ‘crimes with AI’, ‘crimes against AI’, and ‘crimes by AI’. In these sections, our aim is to highlight AI’s potential as a criminogenic phenomenon, both in terms of scaling up existing crimes and facilitating new digital transgressions. In the third part of the article, we turn our attention to the main ways the AI paradigm is transforming policing, surveillance, and criminal justice practices via diffuse monitoring modalities based on prediction and prevention. Throughout the paper, we deploy an array of programmatic examples which, collectively, we hope will serve as a useful AI primer for criminologists interested in the ‘tech-crime nexus’.

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