Columbia Journalism Review, Bernat Ivancsics and Mark Hansen
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Ethical questions of introducing AI in journalism abound. But since AI has once again captured the public imagination, it is hard to have a clear-eyed discussion about the issues involved with journalism’s call to both report on and with these new computational tools. And so our article will alternate a discussion of issues facing the profession today with a “slant narrative” — indicated because these sections are in italics.
The slant narrative starts with the 1964 World’s Fair and a partnership between IBM and The New York Times, winds through commentary by Joseph Weizenbaum, a famed figure in AI research in the 1960s, and ends in 1983 with the shuttering of one of the most ambitious information delivery systems of the time. The simplicity of the role of computation in the slant narrative will help us better understand our contemporary situation with AI. But we begin our article with context for the use of data and computation in journalism — a short, and certainly incomplete, history before we settle into the rhythm of alternating narratives.
ESPN’s Daniel Dopp, co-host of “The Fantasy Show with Matthew Berry” on ESPN+, sat down with IBM Master Inventor Aaron Baughman at the IBM Innovation Lab in New York City to discuss Watson’s foray into the arcane science of fantasy football.
“It helps to have a compromise between the heart and the brain. We trained Watson on millions of fantasy football stories, blog posts and videos. We taught it to develop a scoring range for thousands of players with their upsides and their downsides. And we taught it to estimate the chances a player will exceed their upside or fall below the downside,” Baughman said.
American Academy of Sleep Medicine, Department of Energy
from
“The U.S. Energy Information Administration (EIA) within the Department of Energy (DOE) has issued a notice and request for comments describing its intent to reinstate the Residential Energy Consumption Survey. The federal agency is proposing to update the survey with changes including the addition of a question to collect information about the household use of energy-intensive medical equipment such as continuous positive airway pressure (CPAP) machines, a common treatment for obstructive sleep apnea (OSA).” Deadline to contribute is January 21, 2020.
This Monday, my first essay appeared in the Chronicle of Higher Education: 5 Ways to Welcome Women to Computer Science. Today, Friday, seems like a good day to reflect on responses. In particular, I want to talk about social media responses from institutions and individuals, and an in-person conversation with Whitman CS students and faculty.
Mayor Bill de Blasio today signed an Executive Order to establish an Algorithms Management and Policy Officer within the Mayor’s Office of Operations. The Officer will serve as a centralized resource on algorithm policy and develop guidelines and best practices to assist City agencies in their use of algorithms to make decisions. The new Officer will ensure relevant algorithms used by the City to deliver services promote equity, fairness and accountability. The creation of the position follows review of the recommendations from the Automated Decision Systems (ADS) Task Force Report required by Local Law 49 of 2018, published here.
“Fairness and equity are central to improving the lives of New Yorkers,” said Mayor Bill de Blasio. “With every new technology comes added responsibility, and I look forward to welcoming an Algorithms Management and Policy Officer to my team to ensure the tools we use to make decisions are fair and transparent.”
A team of researchers thinks they may have uncovered the Rosetta Stone of the US labor market.
They recently unveiled the US Private Sector Job Quality Index (or JQI for short), a new monthly indicator that aims to track the quality of jobs instead of just the quantity. The JQI measures the ratio of what the researchers call “high-quality” versus “low-quality” jobs, based on whether the work offer more or less than the average income.
In TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents, a group of Boston University researchers demonstrate an attack on machine learning systems trained with “reinforcement learning” in which ML systems derive solutions to complex problems by iteratively trying multiple solutions.
[Roger] McNamee was convinced that Facebook was different. Then, in February, 2016, shortly after he retired from full-time investing, he noticed posts in his Facebook feed that purported to support Bernie Sanders but struck him as fishy. That spring, the social-media-fuelled vitriol of the Brexit campaign seemed like further proof that Facebook was being exploited to sow division among voters—and that company executives had turned a blind eye. The more McNamee listened to Silicon Valley critics, the more alarmed he became: he learned that Facebook allowed facial-recognition software to identify users without their consent, and let advertisers discriminate against viewers. (Real-estate companies, for example, could exclude people of certain races from seeing their ads.)
Ten days before the Presidential election, McNamee sent an e-mail to Zuckerberg and Sandberg. “I am disappointed. I am embarrassed. I am ashamed,” he wrote.
Hewlett Packard Enterprises has announced an AI Ops R&D collaboration with the US Department of Energy’s National Renewable Energy Laboratory (NREL) to develop artificial intelligence (AI) and machine learning (ML) technologies to automate and improve operational efficiency, including resiliency and energy usage, in data centers for the exascale era. The effort is part of NREL’s ongoing mission as a world leader in advancing energy efficiency and renewable energy technologies to create and implement new approaches that reduce energy consumption and lower operating costs.
Yale School of Management, Yale Insights, Kevin Williams
from
The two biggest home improvement chains use zone pricing, setting common prices across large segments of the United States rather than in individual stores. A new study co-authored by Yale SOM’s Kevin Williams suggests that the strategy benefits some consumers at the expense of others—and costs one of the two giants potential profits.
This holiday season, Amazon will move millions of packages at dizzying speed. Internal injury reports suggest all that convenience is coming at the expense of worker safety.
“I’m one of the last people you’d expect to hear warning about the danger of conspiracies and lies,” the actor and comedian Sacha Baron Cohen said today in an op-ed in the Washington Post, adapted from last week’s viral speech about the dangers of social networks at an Anti-Defamation League conference.
In fact, Cohen is exactly the sort of person I’d expect to be warning us about social networks. As a rich celebrity who has no need for the free communication tools they provide, and who can thrive without relying on the promotional benefits that come with active use of the platforms, blasting Big Tech costs Cohen nothing.
Meanwhile, few people would have ever even heard of Cohen’s speech had it not thrived on social media — first on Twitter, then on YouTube — where social media critiques, particularly of Facebook, have grown increasingly popular. In coming to bury the big platforms, Cohen inadvertently proved their benefit.
San Diego, CA February 24-26, 2020. “Returning for the 3rd year to unite AI and Machine Learning experts from across Pharma, Biotech and Academia to discover the pragmatic case studies of AI and Machine Learning in action to augment your processes for increased efficiency and accuracy from target discovery to phase 2 clinical trials.” [$$$$]
New York, NY Through September 20, 2020 at Museum of the City of New York. “In anticipation of the 2020 census, Who We Are: Visualizing NYC by the Numbers showcases work not just by data analysts and demographers, but also by cutting-edge contemporary artists and designers who use these tools to enliven and humanize statistics and to shed new light on how we understand our urban environment and ourselves.” [$$]
“High School SIP (HS-SIP) provides an opportunity to spend a summer working at the NIH side-by-side with some of the leading scientists in the world, in an environment devoted exclusively to biomedical research.” Deadline to apply is February 1, 2020.
We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. We also provide a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them.
Recommendation systems are all around us and they are getting more sophisticated by the minute. While traditional recommender systems were focused on one-time recommendations based on user actions, new models effectively engage in sequential interactions to try to find the best recommendation based on the user behavior and preferences. This type of recommendation systems are known as collaborative interactive recommenders(CIRs) and have been triggered by advancements in areas such as natural language processing(NLP) and deep learning in general. However, building this systems remains a challenge. Recently, Google open sourced RecSim, a platform for creating simulation environment for CIRs.
“To get a sense of how people think about these systems, we interviewed 8 participants who shared their practices related to their use of Google Drive, OneDrive, and Dropbox. Some also briefly described their experiences with other services such as Apple’s iCloud and the Adobe’s Creative Cloud.”