The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

Peloton adds new 'groups' feature as people work out together from home

The Independent - Tech

Peloton has added a new groups feature to allow people to exercise together despite being in lockdown. The company – which makes internet-enabled spinning bikes and treadmills, as well as running an app of online exercise classes – has seen a huge surge in users in recent months as people have looked for a way to work out at home. Now it has added a new groups feature, officially called "tags", allowing people to track their exercise alongside other people in communities they have formed off the bike. With the new feature, users are able to add hashtags to their profile, which designate certain groups: a certain set of people all from the same workplace, for instance, or one of the many "tribes" of users that have formed on other platforms such as Facebook and Reddit. If a user has a given hashtag in their profile, they will be able to see what classes other members have taken and when they are working out.

[Online] AI/Machine Learning for beginners


This is a 1-week/10 hours long, part-time and instructor-led training offered in evening time (New York Timezone) by, a San Francisco based technology company. This training program is built based on 6FS team's years of experience in building large-scale solutions using various various Big Data and AI/ML technologies. This is not a book-based training, rather a hands-on, interactive experience app building apps using AI/ML, delivered by experienced startup CTOs. While learning basic concepts like Python, Jupyter notebooks, and training models and human powered labeling, you'll also learn practical problems and solutions, based on how Dean and Adrian built technology stacks in their previous startups. Let's build a project to gather data from human labeling service like AWS Sage maker GroundTruth.

Online Diverse Learning to Rank from Partial-Click Feedback Machine Learning

Learning to rank is an important problem in machine learning and recommender systems. In a recommender system, a user is typically recommended a list of items. Since the user is unlikely to examine the entire recommended list, partial feedback arises naturally. At the same time, diverse recommendations are important because it is challenging to model all tastes of the user in practice. In this paper, we propose the first algorithm for online learning to rank diverse items from partial-click feedback. We assume that the user examines the list of recommended items until the user is attracted by an item, which is clicked, and does not examine the rest of the items. This model of user behavior is known as the cascade model. We propose an online learning algorithm, cascadelsb, for solving our problem. The algorithm actively explores the tastes of the user with the objective of learning to recommend the optimal diverse list. We analyze the algorithm and prove a gap-free upper bound on its n-step regret. We evaluate cascadelsb on both synthetic and real-world datasets, compare it to various baselines, and show that it learns even when our modeling assumptions do not hold exactly.

How to cover artificial intelligence and understand its impact on journalism: MOOC in Spanish, in partnership with Microsoft


The term "artificial intelligence" has been around since 1956, and yet many journalists are unfamiliar with its history and impact on the world today, even as its influence grows everywhere, including on how we gather and report the news. The next massive open online course (MOOC) in Spanish, and the Knight Center's first in partnership with Microsoft, will familiarize students with the foundations of artificial intelligence (AI) and how it impacts the news industry. "Artificial Intelligence: How to cover AI and understand its impact on journalism," will run from Oct. 22 to Nov. 25, 2018 and will be taught by Sandra Crucianelli, a veteran instructor for Knight Center MOOCs and a member of the International Consortium of Investigative Journalists (ICIJ). "The course will be a wonderful opportunity for those who have not yet become familiar with artificial intelligence technologies," Crucianelli said. "We will be sharing definitions, but also analyzing applications, examples and there also will be online discussions.

Applications of artificial intelligence - Wikipedia


Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of A.I. where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more. AI for Good is a movement in which institutions are employing AI to tackle some of the world's greatest economic and social challenges. For example, the University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address socially relevant problems such as homelessness. At Stanford, researchers are using AI to analyze satellite images to identify which areas have the highest poverty levels.[1] The Air Operations Division (AOD) uses AI for the rule based expert systems. The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.[2]

Flipboard on Flipboard


There are more than 8,000 online courses out there. These are some of the best. More than 50 million students signed up for one this year. When scientists announce they've made a breakthrough, they usually promise we'll see the full effects of those discoveries--anything from a better understanding of how the universe works to a drug ready for use in patients--in about five years. TULSA -- Tom Coomer has retired twice: once when he was 65, and then several years ago.

How Machine Learning Affects Everyday Life


Enterprises today are finding it exceedingly meaningful and resourceful in the massive amounts of data they generate and save every day. The required algorithms, applications and frameworks to bring greater predictive accuracy and value to enterprises' data sets are available; therefore, businesses need to make sure they have data sets of sufficient size and quality. It is due to the excessive need to do a better job in capturing and utilizing data. The rise of deep learning and neural networks has spread in everyday lives. It took about six years for neural nets to show impressive results, first in speech recognition, then computer vision, images, image detection and diagnostics, and more recently, in natural language processing.

Elon Pew Future of the Internet Survey Report: Impacts of AI, Robotics by 2025


Internet experts and highly engaged netizens participated in answering an eight-question survey fielded by Elon University and the Pew Internet Project from late November 2013 through early January 2014. Self-driving cars, intelligent digital agents that can act for you, and robots are advancing rapidly. Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025? Describe your expectation about the degree to which robots, digital agents, and AI tools will have disrupted white collar and blue collar jobs by 2025 and the social consequences emerging from that. Among the key themes emerging from 1,896 respondents' answers were: - Advances in technology may displace certain types of work, but historically they have been a net creator of jobs. This page holds the content of the survey report, which is an organized look at respondents elaborations derived from 250 single-spaced pages of responses from ...