Artificial Intelligence
Viewpoints
2023
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February:
2022
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December/January:
2022: The Year in Review
Look for These Developments in 2023 -
November:
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2021
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December/January:
2021: The Year in Review
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November:
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March:
Accelerating AI Deployment in Defense
The Commercial State of Quantum Computing -
February:
Toward Practical AI Development
Possible Futures for Artificial Intelligence
Archived Viewpoints
2020
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December/January:
2020: The Year in Review
Look for These Developments in 2021 -
November:
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October:
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September:
Progress in Intelligent Energy Monitoring
Big Picture: AI and Smart Grids -
August:
Quantum Machine Learning
Big Picture: Quantum Computing and AI -
July:
Enhancing Remote Health Care with AI
Machine Learning for Video Compression -
June:
The Pandemic Crisis: Scenarios for the Future of AI and Automation
Scenarios Presentation: The Pandemic Crisis: Scenarios for the Future of Technology Development
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May:
The Pandemic Crisis: Key Forces That Will Shape the Future of AI and Automation
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April:
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March:
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February:
Industry Insights from the Military's Tactical Edge
Progress in AI for Content Moderation
2019
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December/January:
2019: The Year in Review
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November:
Deep Neural-Network Vulnerabilities
The Road toward Autonomous Cybersecurity -
October:
Quantum Computing versus AI-Assisted Spintronics
New Questions for Natural-Language Understanding -
September:
Image Understanding
Raising the Bar for Natural-Language Processing -
August:
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July:
Governance for AI Developments
Emerging Tools for Combating AI Bias -
June:
Advanced Methods for Neuromorphic Computing
Advances in Affective-Computing Research -
May:
AI Software as a Medical Device
AI for Intelligent 5G Networks -
April:
Decision Trees and Random Forests
Distributed Machine Learning for Data Privacy -
March:
Deep Learning with Supercomputers
Deep Learning for Drug Discovery -
February:
2018
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December/January:
2018: The Year in Review
Look for These Developments in 2019 -
November:
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October:
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September:
Machine Learning for System and Infrastructure Management
Bias in Machine-Learning Processes -
August:
Intelligent Process Automation for Enterprises
Machine Learning for Satellite Images -
July:
Emerging Issues in Machine-Learning Systems for Cybersecurity
Update on Use of Machine Learning for Image-Quality Improvements -
June:
Conversational AI and Artificial Common Sense
Advanced Intelligence at IARPA -
May:
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April:
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March:
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February:
2017
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December/January:
2017: The Year in Review
Look for These Developments in 2018 -
November:
Secure Computation and Quantum Computing
AI for Fighting Financial Crime -
October:
Three-Dimensional Visualization
Spintronics for Neuromorphic Computing -
September:
Recalibrating Expectations
Machine Learning for Industrial Robotics -
August:
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July:
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June:
Fast Inference Algorithms
Update on Emotionally Intelligent Computing -
May:
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April:
The Quest for Quantum Supremacy
AI Start-Ups Specializing in Health Care -
March:
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February:
Intelligent Image Processing
Testing the Safety of Driverless Vehicles
2016
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December/January:
2016: The Year in Review
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November:
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October:
Intelligent Customer-Relationship Management
Artificial Music Composition -
September:
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August:
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July:
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February:
Graphics Processors Improve Their Deep-Learning Skills
Open-Source AI
2015
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December/January:
2015: The Year in Review
Look for These Developments in 2016 -
November:
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October:
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September:
Standardized Intelligence Tests for Computers
Neural Turing Machines -
August:
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July:
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June:
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May:
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April:
Siri's Offspring: Viv Labs
High Expectations for Sentient Technologies -
March:
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February:
2014
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December/January:
2014: The Year in Review
Look for These Developments in 2015 -
November:
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October:
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September:
The Quest for a Hardware Breakthrough
Open-Source Machine-Learning Software -
August:
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July:
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June:
A Quantum of Security
Progress in Speech-to-Speech Translation -
May:
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April:
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March:
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February:
2013
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December/January:
2013: The Year in Review
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November:
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2012
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December/January:
2012: The Year in Review
Look for These Developments in 2013 -
November:
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February:
2011
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December/January:
2011: The Year in Review
Look for These Developments in 2012 -
November:
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October:
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September:
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August:
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July:
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April:
Areas to Monitor: Reality Mining
Areas to Monitor: Self-Aware Machines -
March:
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February:
2010
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December/January:
2010: The Year in Review
Look for These Developments in 2011 -
November:
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October:
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March:
Formal Competition
Recent Developments: Finder's-Fee Business Model for Siri -
February:
2009
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December/January:
2009: The Year in Review
Look for These Developments in 2010 -
November:
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October:
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September:
Before September 2009, the Artificial Intelligence technology area was Knowledge-Based Systems.
About Artificial Intelligence
Virtual assistants that listen and speak, software that outperforms human masters of strategy games, and other newsworthy developments have led many people to wonder and worry about the future of artificially intelligent systems, especially systems that take advantage of recent innovations in machine learning for deep artificial neural networks. This Technology Map mainly defines AI as systems that perform activities that otherwise require an intelligent human. Although the methods that AI software uses to perform these activities are quite different from the way that human intelligence works, people have reason to monitor whether software and intelligent robots will introduce economic efficiencies, displace many workers, cause social discord, or have other combinations of disruptive effects.
All current AI-development work that shows measurable progress is for specialized systems that perform narrow tasks: Machine-learning algorithms seem to be driving AI toward uncanny capabilities that do not emulate human intelligence, as in the case of game-playing computers, whose styles of play differ greatly from those of human players. But a system that plays games does not recognize speech, and a system that identifies cancer in chest X-rays does not perform other tasks that occupy the majority of a radiologist's professional time. Commercial success stories in the field are all examples of narrow AI (application-specific solutions), including big-data analysis platforms that automatically generate insights about business operations, speech recognizers for virtual assistants, and face recognizers for automated tagging in social-media posts and police files. Some uses of AI have been counterproductive, confirming people's varying and mixed opinions about AI's promises and perils.
Although a faction of researchers' labors toward artificial general intelligence, current progress in AI mainly depends on efforts to solve particular problems. Active domains of development include transportation, robotics, office productivity and collaboration, security, transactions, logistics, entertainment and advertising, and social interaction. Some of the work promises or threatens to replace human workers, such as efforts to automate customer service or develop driverless trucks. Many other developments emphasize human-machine collaboration, such as machine-translation systems and intelligent cybersecurity tools for trusted personnel. Improved collaboration and increased autonomy alike could arise from efforts to produce explainable AI systems that provide a rational justification for their behaviors. AI researchers also aim for breakthroughs in efforts to incorporate the latest findings from the field of neuroscience, which has its own road map for discovering how natural brains work. Other hoped-for breakthroughs that involve quantum computers and other novel hardware might accelerate existing machine-learning processes by orders of magnitude. Whether AI technologies become generalized or remain specialized, whether they are mostly autonomous or mostly collaborative with humans, and whether or not their actions are sufficiently explainable to people, for good or ill the current technology road map seems to point to potential changes to people's roles in workplaces and communities. Monitoring AI developments will be important for evaluating expectations of business conditions and the factors that affect constructive AI-development and public-policy decisions.