Describes limitations and shortfalls of current digital technologies, particularly when compared to human capabilities.
Limitations of Digital Technologies (22)
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- 10 min
- The Washington Post
- 2021
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
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- 10 min
- The Washington Post
- 2021
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.
Discussion Prompts:
Why are humanities perspectives needed in computer science and artificial intelligence fields? What would it take for data barons and/or technology users to listen to the predictions and ethical concerns of whistleblowers?
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- 7 min
- VentureBeat
- 2021
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
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- 7 min
- VentureBeat
- 2021
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.
Discussion Prompts:
What does “robustness” in a natural language processing algorithm mean to you? Should machines always be taught to automatically associate certain words or terms? What are the consequences of large corporations not using the most robust training data for their NLP algorithms?
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- 6 min
- Vox
- 2020
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
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- 6 min
- Vox
- 2020
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.
Discussion Prompts:
Should virtual reality ever reach a point where it is indistinguishable from true reality in terms of graphic design or other sensory information? How could such technology be weaponized or abused? How accessible should the most immersive virtual reality technologies be to the general public?
How Virtual Reality Tricks Your Brain: How Virtual Reality Tricks Your Brain-
- 7 min
- MIT Technology Review
- 2020
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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- 7 min
- MIT Technology Review
- 2020
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.
Discussion Prompts:
Does more efficiency mean more data would be wanted or needed? Would that be a good thing, a bad thing, or potentially both?
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- 7 min
- ZDNet
- 2020
Dr. Gary Marcus explains that deep machine learning as it currently exists is not maximizing the potential of AI to collect and process knowledge. He essentially argues that these machine “brains” should have more innate knowledge than they do, similar to how animal brains function in processing an environment. Ideally, this sort of baseline knowledge would be used to collect and process information from “Knowledge graphs,” a semantic web of information available on the internet which can sometimes be hard for an AI to process without translation to machine vocabularies such as RDF.
- ZDNet
- 2020
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- 7 min
- ZDNet
- 2020
Rebooting AI: Deep learning, meet knowledge graphs
Dr. Gary Marcus explains that deep machine learning as it currently exists is not maximizing the potential of AI to collect and process knowledge. He essentially argues that these machine “brains” should have more innate knowledge than they do, similar to how animal brains function in processing an environment. Ideally, this sort of baseline knowledge would be used to collect and process information from “Knowledge graphs,” a semantic web of information available on the internet which can sometimes be hard for an AI to process without translation to machine vocabularies such as RDF.
Discussion Prompts:
Does giving a machine similar learning capabilities to humans and animals bring artificial intelligence closer to singularity? Should humans ultimately be in control of what a machine learns? What is problematic about leaving AI less capable of understanding semantic webs?
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- 5 min
- CNN
- 2010
Algorithms and machines can struggle with facial recognition, and need ideal source images to perform it consistently. However, its potential use in monitoring and identifying citizens is concerning.
- CNN
- 2010
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- 5 min
- CNN
- 2010
Why face recognition isn’t scary — yet
Algorithms and machines can struggle with facial recognition, and need ideal source images to perform it consistently. However, its potential use in monitoring and identifying citizens is concerning.
Discussion Prompts:
How have the worries regarding facial recognition changed since 2010? Can we teach machines to identify human faces? How can facial recognition pose a danger/worry when use for governmental purposes?