Maneesh Agrawala

Stanford University

For contributions to visual communication through computer graphics, human-computer interaction, and information visualization

Anima Anandkumar

California Institute of Technology

For contributions to tensor methods for probabilistic models and neural operators

David Atienza Alonso


For contributions to the design of high-performance integrated systems and ultra-low power edge circuits and architectures

Boaz Barak

Harvard University

For contributions to theoretical computer science, in particular cryptography and computational complexity, and service to the theory community

Michel Beaudouin-Lafon

Université Paris-Saclay

For contributions to human-computer interaction, instrumental interaction and generative theory, and community leadership

Peter Boncz

Centrum Wiskunde & Informatica (CWI), Vrije Universiteit Amsterdam

For contributions to the design of columnar, main-memory, and vectorized database systems

Luis H. Ceze University of Washington

For contributions to developing new architectures and programming systems for emerging applications and computing technologies

Ranveer Chandra


For contributions to software-defined wireless networking and applications to agriculture and rural broadband

Nitesh Chawla

University of Notre Dame

For contributions to machine learning research for imbalanced data, graphs, and interdisciplinary innovations

Ed H. Chi


For contributions to machine learning and data mining techniques for social computing and recommender systems

Corinna Cortes


For theoretical and practical contributions to machine learning, industrial leadership, and service to the field

Bill Curtis

CAST Software/ Consortium for Information and Software Quality (CISQ)

For contributions to software process, software measurement, and human factors in software engineering

Constantinos Daskalakis

Massachusetts Institute of Technology

For fundamental contributions to algorithmic game theory, mechanism design, sublinear algorithms, and theoretical machine learning

Kalyanmoy Deb

Michigan State University

For technical contributions in evolutionary multi-objective optimization algorithms and multi-criterion decision support

Bronis R. de Supinski

Lawrence Livermore National Laboratory

For contributions to the design of large-scale systems and their programming systems and software

Sebastian Elbaum

University of Virginia

For contributions to the analysis and testing of evolving systems and robotic systems

Yuguang “Michael” Fang

City University of Hong Kong

For contributions to wireless networks and mobile computing

Kevin Fu

Northeastern University

For contributions to computer security, and especially to the secure engineering of medical devices

Craig Gotsman

New Jersey Institute of Technology

For contributions to computer graphics, geometry processing, and visual computing

Ahmed E. Hassan

Queen’s University

For contributions to the quality assurance of large-scale software systems

Abdelsalam (Sumi) Helal
University of Florida

For contributions to mobile and pervasive computing, and their applications in graceful aging and accessibility

Jörg Henkel

Karlsruhe Institute of Technology For contributions to hardware/software co-design of power and thermal efficient embedded computing

Manuel V. Hermenegildo

Universidad Politecnica de Madrid & IMDEA SW Institute

For contributions to program analysis, verification, parallelism, logic programming, and the IMDEA Software Institute

Michael Hicks

University of Maryland, Amazon Web Services

For contributions to programming language design and implementation, program analysis, and software security

Torsten Hoefler

ETH Zurich

For foundational contributions to High-Performance Computing and the application of HPC techniques to machine learning

Jason Hong

Carnegie Mellon University

For contributions to ubiquitous computing and to usable privacy and security

Sandy Irani

University of

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Global Reinforcement Learning in Computer Vision to Reach $34.7 billion by 2027

DUBLIN, Feb. 21, 2022 /PRNewswire/ — The “AI in Computer Vision Market by Technology, Solutions, Use Cases, Deployment Model and Industry Verticals 2022 – 2027” report has been added to’s offering.

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This report assesses the application of AI in computer vision systems used in conjunction with connected devices, hardware components, embedded software, AI platforms, and analytics. The report analyzes machine learning models and APIs used in computer vision systems along with the application of neural networks in AI analytics systems.

This research also evaluates the causal relationship of computer vision systems with IoT, Edge computing, and connected machines along with core hardware and software technology. The report also analyzes the relation of emotion AI with computer vision systems along with the market factors.

Select Report Findings:

  • The global market for AI in computer vision will reach $73.7 billion by 2027

  • Global reinforcement learning in computer vision will reach $34.7 billion by 2027

  • Global 2D and 3D machine vision will reach $3.4 billion and $7.4 billion respectively by 2027

  • Global AI in computer vision by unit volume expansion will grow at 37.8% CAGR through 2027

  • Global market for cameras with greater than 125 frame rate per second will exceed $10 billion by 2027

  • Asia Pacific software market in support of AI in computer vision will reach $11.8 billion by 2027 with 33.7% CAGR

Computer vision systems are dedicated to simulate the human visual system while analyzing the information extracted from photos and videos. They do this by way of mathematical operations in conjunction with signal processing systems to process both digital and analog images. These systems leverage both two dimensional and three-dimensional processes.

AI represents the ability to organize information and create outcomes in learning, decision-making, and problem-solving using a computer-enabled robotic system in the same way a human brain does. The integration of AI and computer vision systems enhance the accuracy of object identification, classification, and analysis of information.

Through leveraging AI, computer vision systems provide a robotic system in which vision sensing capabilities provide information about the environment. One of the best examples of this in practice is autonomous vehicles, which rely on computer vision and AI-based decision making for safe travel.

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction
2.1 Defining AI in Computer Vision
2.2 Artificial General Intelligence and Super Intelligence
2.3 AI and Computer Vision Market Predictions
2.4 AI Outcomes and Enterprise Benefits
2.5 Cognitive Computing and Swarm Intelligence
2.6 Market Driver and Opportunity Analysis
2.7 Market Challenge Analysis
2.8 Covid-19 Impact
2.9 Value Chain Analysis
2.10 Pricing Analysis
2.11 Hs Code 854231
2.12 AI Patent and Regulatory Framework
2.13 AI Public Policy Issues

3.0 Technology and Application Analysis
3.1 Technology Analysis
3.2 IoT Device Ecosystem: Consumer, Enterprise, Industrial, and Government
3.3 Machine Learning Model
3.4 Artificial Neural Networks
3.5 Emotion AI Analysis
3.6 Edge Computing and 5G Networks
3.7 Smart Machine and Virtual Twinning
3.8 Factory Automation and Industry 4.0
3.9 Building Automation and Smart Workplace
3.10 Cloud Robotics

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