Spotting Machine-Written Text
Creating 3D Models from Flat 2D Images
March 10, 2020Ambient Computing Expands
March 10, 2020Spotting Machine-Written Text
It turns out that A.I. can also be used to detect when text was machine generated, even if we humans can’t spot the fake.
In the past year, researchers showed how A.I. could be used to compose text so good that humans couldn’t tell it was machine written. The team at OpenAI demonstrated the many reasons why this was problematic, from mass-generating salacious social media posts and fake reviews to forging documents by world leaders.
It turns out that A.I. can also be used to detect when text was machine generated, even if we humans can’t spot the fake. That’s because an essay written by A.I. tends to rely on statistical patterns in text and doesn’t have much linguistic variation.
Researchers at the MIT-IBM Watson AI Lab and at Harvard University developed the Giant Language Model Test Room (GLTR), which looks for words that are likely to appear in a particular order.
This is good news for those concerned about the spread of automated misinformation campaigns.
Related trends
This trend is part of our section on Artificial Intelligence. Other trends in this section include:
- A.I. and Digital Dividends
- Prioritizing Accountability and Trust
- Ambient Computing Expands
- Ubiquitous Digital Assistants
- A.I. for Drug Development
- A.I. for Interviews
- Consumer-Grade A.I. Applications
- The New Mil-Tech Industrial Complex
- National A.I. Strategies
- The Race to Establish A.I. Rules
- Algorithmic Warfare
- Making A.I. Explain Itself
- Using A.I. in Critical Systems
- China’s A.I. Rules
- Artificial Emotional Intelligence
- Personal Digital Twins
- Problematic Data Sets
- A.I. to Catch Cheaters
- Algorithms Designed to Target Vulnerable Populations
- A.I. Still Has a Bias Problem
- A.I. Systems Intentionally Hiding Data
- The Rise of Undocumented A.I. Accidents
- Multitask Learning
- Generative Adversarial Networks (GANs)
- New Generative Modeling Techniques
- Probabilistic Programming Languages
- Machine Image Completion
- Hybrid Human-Computer Vision Analysis
- Predictive Machine Vision
- Automated Machine Learning (AutoML)
- Customized Machine Learning
- Graph Neural Networks
- Intelligent Optical Character Recognition
- A.I. for the Creative Process
- Generative Algorithms for Content Production
- Generating Virtual Environments from Short Videos
- Automated Versioning
- Automatic Voice Cloning and Dubbing
- Spotting Machine-Written Text
- Algorithmic Fact Checking
- Datamining Crowds
- Deep Linking
- Corporate Biometric Spoofing
- Bots
- Global Rush to Fund A.I.
- Algorithm Marketplaces
- Marketplace Consolidation
- Fragmentation
- Liability Insurance for A.I.
- Ambient Surveillance
- Creating 3D Models from Flat 2D Images
- Neuro-Symbolic A.I. Algorithms and Systems
- Real-Time Machine Learning
- Natural Language Understanding (NLU)
- Machine Reading Comprehension (MRC)
- Natural Language Generation (NLG)
- Real-Time Context in Machine Learning
- General Reinforcement Learning Algorithms
- Deep Learning Scales
- Faster and More Powerful Open Source Frameworks
- Reinforcement Learning and Hierarchical RL
- Continuous Learning
- Using A.I. to Speed the Process of Scientific Discovery
- A.I. in the Cloud
- Robotic Process Automation
- Digital Twins and Cognitive Twins in the Enterprise
- Robots with Cognitive Skills
- Advanced A.I. Chipsets
- Serverless Computing
- Proprietary, Homegrown A.I. Languages
- Proliferation of Franken-Algorithms
- Companies Manipulating A.I. Systems for Competitive Advantage
This trend is related to the following industries:
Finance, Human Resources, Information Technology, National Security, Professional Services, Work (Future of)