Machine Learning (83)
Find narratives by ethical themes or by technologies.
FILTERreset filters- ZDNet
- 2021
Facebook’s use of biometrics to develop facial recognition came under scrutiny from those skeptical of users’ privacy protection. The company has just filed a $650 million settlement to close the lawsuit regarding this issue.
- ZDNet
- 2021
- ZDNet
- 2021
Judge approves $650m settlement for Facebook users in privacy, biometrics lawsuit
Facebook’s use of biometrics to develop facial recognition came under scrutiny from those skeptical of users’ privacy protection. The company has just filed a $650 million settlement to close the lawsuit regarding this issue.
What role do you think the government should play in establishing precedent for violations of privacy by technology companies?
- Endgadget
- 2021
Article is an excerpt from book about the history of AI and the shift in AI research in 1990s from knowledge-based to context-based approaches to artificial intelligence.
- Endgadget
- 2021
- Endgadget
- 2021
Hitting the Books: The Brooksian revolution that led to rational robots
Article is an excerpt from book about the history of AI and the shift in AI research in 1990s from knowledge-based to context-based approaches to artificial intelligence.
-
- 10 min
- Engadget
- 2021
This article provides an excerpt from a book detailing the “Brooksian Revolution,” a movement in the 1980s pressing the idea that the “intelligence” of AI should start from a foundation of acute awareness of its environment, rather than “typical” indicators of intelligence such as pure logic or problem solving. By principle, a reasoning machine-learning loop that operates off of a one-time perception of its environment is inherently disconnected from its environment.
- Engadget
- 2021
-
- 10 min
- Engadget
- 2021
Hitting the Books: The Brooksian revolution that led to rational robots
This article provides an excerpt from a book detailing the “Brooksian Revolution,” a movement in the 1980s pressing the idea that the “intelligence” of AI should start from a foundation of acute awareness of its environment, rather than “typical” indicators of intelligence such as pure logic or problem solving. By principle, a reasoning machine-learning loop that operates off of a one-time perception of its environment is inherently disconnected from its environment.
Why is an environment important to cognition, both that of humans and machines? Will robots ever be able to abstract the world, or model it, in the same way that the human brain can? Are there dangers to robots being strictly “rational” and decoupled from their environments? Are there dangers to robots being too connected to their environments?
-
- 2 min
- azfamily.com
- 2018
Facial recognition technology has found a new application: reuniting dogs with their owners. A simple machine learning algorithm takes a photo of a dog and crawls through a database of photos of dogs in shelters in hopes of finding a match.
- azfamily.com
- 2018
-
- 2 min
- azfamily.com
- 2018
Facial recognition technology now used in Phoenix area to locate lost dogs
Facial recognition technology has found a new application: reuniting dogs with their owners. A simple machine learning algorithm takes a photo of a dog and crawls through a database of photos of dogs in shelters in hopes of finding a match.
How could this beneficial use of recognition technology find even broader use?
-
- 10 min
- MIT Technology Review
- 2020
This article explains the ethical warnings of Timnit Gebru against training Natural Language Processing algorithms on large language models developed on sets of textual data from the internet. Not only does this process have a negative environmental impact, it also still does not allow these machine learning tools to process semantic nuance, especially as it relates to burgeoning social movements or countries with lower internet access. Dr. Gebru’s refusal to retract this paper ultimately lead to her dismissal from Google.
- MIT Technology Review
- 2020
-
- 10 min
- MIT Technology Review
- 2020
We read the paper that forced Timnit Gebru out of Google. Here’s what it says.
This article explains the ethical warnings of Timnit Gebru against training Natural Language Processing algorithms on large language models developed on sets of textual data from the internet. Not only does this process have a negative environmental impact, it also still does not allow these machine learning tools to process semantic nuance, especially as it relates to burgeoning social movements or countries with lower internet access. Dr. Gebru’s refusal to retract this paper ultimately lead to her dismissal from Google.
How should models for training NLP algorithms be more closely scrutinized? What sorts of voices are needed at the design table to ensure that the impact of such algorithms are consistent across all populations? Can this ever be achieved?
-
- 7 min
- VentureBeat
- 2021
The GPT-3 Natural Language Processing model, created by the company open AI and released in 2020, is the most powerful of its kind, using a generalized approach to feed its machine learning algorithm in order to mirror human speech. The potential applications of such a powerful program are manifold, but this potential means that many tech monopolies may want to enter an “arms race” to get the most powerful model possible.
- VentureBeat
- 2021
-
- 7 min
- VentureBeat
- 2021
GPT-3: We’re at the very beginning of a new app ecosystem
The GPT-3 Natural Language Processing model, created by the company open AI and released in 2020, is the most powerful of its kind, using a generalized approach to feed its machine learning algorithm in order to mirror human speech. The potential applications of such a powerful program are manifold, but this potential means that many tech monopolies may want to enter an “arms race” to get the most powerful model possible.
Should AI be able to imitate human speech unchecked? Should humans be trained to be able to tell when speech or text might be produced by a machine? How might Natural Language Processing cheapen human writing and writing jobs?