January 19, 2020

142 words 1 min read

Quantifying forgiveness

Quantifying forgiveness

Algorithms are increasingly arbiters of forgiveness. Julia Angwin discusses what she has learned about forgiveness in her series of articles on algorithmic accountability and the lessons we all need to learn for the coming AI future.
Talk Title Quantifying forgiveness
Speakers Julia Angwin (ProPublica)
Conference Strata Data Conference
Conf Tag Make Data Work
Location New York, New York
Date September 11-13, 2018
URL Talk Page
Slides
Video Talk Video

Algorithms are increasingly arbiters of forgiveness. They are used to decide who will get let out of jail, the amount that people pay in auto insurance premiums, and which neighborhoods will be overrun with cars sent there by a mapping algorithm. Investigative reporter Julia Angwin discusses what she has learned about forgiveness in her series of articles on algorithmic accountability and the lessons we all need to learn for the coming AI future.

algorithm ai
comments powered by Disqus
Intelligent Network Infrastructure: The "Next Hop" of Internet

Intelligent Network Infrastructure: The "Next Hop" of Internet

January 19, 2020

An AI + Network proposal called Intelligence Driven Network will be proposed. The network, especially the OAM, will develop toward intelligent and high-level automatic. The next hop of Internet potent …
AI is my copilot.

AI is my copilot.

January 15, 2020

Much attention in enterprise AI today is focused on automation. Jake Saper explains why the more interesting applications focus on worker augmentation and offers an overview of coaching networks, which gather data from a distributed network of workers and identify the best techniques for getting things done.
Executive Briefing: Best practices for human in the loopThe business case for active learning

Executive Briefing: Best practices for human in the loopThe business case for active learning

January 12, 2020

Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.
How Captricity built a human-level handwriting recognition engine using data-driven AI

How Captricity built a human-level handwriting recognition engine using data-driven AI

January 11, 2020

Captricity has deployed a machine learning pipeline that can read handwriting at human-level accuracy. Ramesh Sridharan discusses the big ideas the company learned building and deploying this system, using data to identify specific problems to solve using AI and to evaluate and validate the algorithm itself and the overall system once deployed.
Neural Network Distiller: A PyTorch environment for neural network compression

Neural Network Distiller: A PyTorch environment for neural network compression

January 11, 2020

Neta Zmora offers an overview of Distiller, an open source Python package for neural network compression research. Neta discusses the motivation for compressing DNNs, outlines compression approaches, and explores Distiller's design and tools, supported algorithms, and code and documentation. Neta concludes with an example implementation of a compression research paper.
The role of a decentralized data marketplace in the future of AI

The role of a decentralized data marketplace in the future of AI

January 10, 2020

Caroline Sofiatti explains how a token economy and a privacy-centric approach can reduce analysis times, cut data preparation costs, solve insufficient data problems, improve data security and governance, and enable the next generation of AI algorithms.