Limitations of Digital Technologies (18)

Describes limitations and shortfalls of current digital technologies, particularly when compared to human capabilities.

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Themes
  • Privacy
  • Accountability
  • Transparency and Explainability
  • Human Control of Technology
  • Professional Responsibility
  • Promotion of Human Values
  • Fairness and Non-discrimination
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Technologies
  • AI
  • Big Data
  • Bioinformatics
  • Blockchain
  • Immersive Technology
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  • Year
    • 1916 - 1966
    • 1968 - 2018
    • 2019 - 2069
  • Duration
  • 7 min
  • The Verge
  • 2019
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AI ‘Emotion Recognition’ Can’t Be Trusted

Reliance on “emotion recognition” algorithms, which use facial analysis to infer feelings. Credibility of the results in question based on inability of machines to recognize abstract nuances.

  • The Verge
  • 2019
  • 1 min
  • Kinolab
  • 2019
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Limitations of Biometrics

In an imagined future of London, citizens all across the globe are connected to the Feed, a device and network accessed constantly through a brain-computer interface. Eric is able to use Biometrics to keep Evelyn and Max hostage and get high-level access to the Feed hub. This highlights an example of how computerized security systems might not be able to pick up on hostage situations or forced activity. The Biometrics can recognize their faces, but is unable to pick up on the ‘distress’ visible on Max and Evelyn’s faces that indicate they are in trouble.

  • Kinolab
  • 2019
  • 10 min
  • The Washington Post
  • 2021
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He predicted the dark side of the Internet 30 years ago. Why did no one listen?

The academic Philip Agre, a computer scientist by training, wrote several papers warning about the impacts of unfair AI and data barons after spending several years studying the humanities and realizing that these perspectives were missing from the field of computer science and artificial intelligence. These papers were published in the 1990s, long before the data-industrial complex and the normalization of algorithms in the everyday lives of citizens. Although he was an educated whistleblower, his predictions were ultimately ignored, the field of artificial intelligence remaining closed off from outside criticism.

  • The Washington Post
  • 2021
  • 7 min
  • VentureBeat
  • 2021
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Salesforce researchers release framework to test NLP model robustness

New research and code was released in early 2021 to demonstrate that the training data for Natural Language Processing algorithms is not as robust as it could be. The project, Robustness Gym, allows researchers and computer scientists to approach training data with more scrutiny, organizing this data and testing the results of preliminary runs through the algorithm to see what can be improved upon and how.

  • VentureBeat
  • 2021
  • 6 min
  • Vox
  • 2020
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How Virtual Reality Tricks Your Brain

Even virtual realities with unrealistic yet believable graphics are able to fool the brain’s sense of perception into believing that the digital environment still operates under the same rules as the real world. Connecting the technologies directly to one’s senses is more immersive than looking at a screen; although human brains have been able to process flat images for a long time, the direct sight connection to two screens with virtual reality makes perception a bit more muddled.

  • Vox
  • 2020
  • 7 min
  • MIT Technology Review
  • 2020
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Tiny four-bit computers are now all you need to train AI

This article details a new approach emerging in AI science; instead of using 16 bits to represent pieces of data which train an algorithm, a logarithmic scale can be used to reduce this number to four, which is more efficient in terms of time and energy. This may allow machine learning algorithms to be trained on smartphones, enhancing user privacy. Otherwise, this may not change much in the AI landscape, especially in terms of helping machine learning reach new horizons.

  • MIT Technology Review
  • 2020
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