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Scientific Innovation (23)
Find narratives by ethical themes or by technologies.
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- 7 min
- Chronicle
- 2021
The history of AI contains a pendulum which swings back and forth between two approaches to artificial intelligence; symbolic AI, which tries to replicate human reasoning, and neural networks/deep learning, which try to replicate the human brain.
- Chronicle
- 2021
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- 7 min
- Chronicle
- 2021
Artificial Intelligence Is a House Divided
The history of AI contains a pendulum which swings back and forth between two approaches to artificial intelligence; symbolic AI, which tries to replicate human reasoning, and neural networks/deep learning, which try to replicate the human brain.
Discussion Prompts:
Which approach to AI (symbolic or neural networks) do you believe leads to greater transparency? Which approach to AI do you believe might be more effective in accomplishing a certain goal? Does one approach make you feel more comfortable than the other? How could these two approaches be synthesized, if at all?
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- 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
- Wired
- 2020
In discussing the history of the singular Internet that many global users experience every day, this article reveals some dangers of digital technologies becoming transparent through repeated use and reliance. Namely, it becomes more difficult to imagine a world where there could be alternatives to the current digital way of doing things.
- Wired
- 2020
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- 7 min
- Wired
- 2020
Hello, World! It is ‘I’, the Internet
In discussing the history of the singular Internet that many global users experience every day, this article reveals some dangers of digital technologies becoming transparent through repeated use and reliance. Namely, it becomes more difficult to imagine a world where there could be alternatives to the current digital way of doing things.
Discussion Prompts:
Is it too late to imagine alternatives to the Internet? How could people be convinced to get on board with a radical redo of the internet as we know it? Do alternatives need to be imagined before forming a certain digital product or service, especially if they end up being as revolutionary as the internet? Are the most popular and powerful digital technologies and services “tools”, or have they reached the status of cultural norms and conduits?
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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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- 3 min
- Tech Crunch
- 2020
This narrative explains that the push for technology to help with accessibility for disabled groups, especially blind or visually impaired individuals, has spurred scientific innovation which is to the benefit of everyone.
- Tech Crunch
- 2020
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- 3 min
- Tech Crunch
- 2020
What will tomorrow’s tech look like? Ask someone who can’t see.
This narrative explains that the push for technology to help with accessibility for disabled groups, especially blind or visually impaired individuals, has spurred scientific innovation which is to the benefit of everyone.
Discussion Prompts:
What are the benefits of developing technologies and innovations which aim to solve a specific problem? How might this lead to unprecedented positive innovations? How can accessibility become a priority, and become adequately incentivized, in tech development, instead of other priorities such as profit?
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- 15 min
- Kinolab
- 1993
Dinosaurs are an extinct species that are revived and brought into the modern day in Jurassic Park. This is accomplished through a cloning process involving extracting dinosaur DNA from mosquitos preserved in amber, and using computational genomics to create replicants with certain properties, such as breeding only female dinosaurs. Three scientists are sent to audit the park, and all three find problems inherent with the use of technology in attempts to control life itself. Eventually, the park’s founder, John Hammond, admits that his idea to create entertainment out of this dangerous technological revival was a failure, which is seen in action during the subsequent dinosaur attack.
- Kinolab
- 1993
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- 15 min
- Kinolab
- 1993
Technological Revival of the Past
Dinosaurs are an extinct species that are revived and brought into the modern day in Jurassic Park. This is accomplished through a cloning process involving extracting dinosaur DNA from mosquitos preserved in amber, and using computational genomics to create replicants with certain properties, such as breeding only female dinosaurs. Three scientists are sent to audit the park, and all three find problems inherent with the use of technology in attempts to control life itself. Eventually, the park’s founder, John Hammond, admits that his idea to create entertainment out of this dangerous technological revival was a failure, which is seen in action during the subsequent dinosaur attack.
Discussion Prompts:
Is using computational genomics to alter the course of nature and natural selection itself inherently wrong? Are there contexts where this may be helpful or necessary? How should technology be used to tell the story of the past, and what limits should exist in this prospect? How can technological idealists like John Hammond be checked before their innovations lead to disaster?
Jurassic Park: Life Finds a Way