Collaborating Authors


What AI cannot do


The following is an excerpt adapted from AI 2041 by Kai-Fu Lee and Chen Qiufan. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher. Artificial intelligence can perform many tasks better than people can, at essentially zero cost. This simple fact is poised to generate tremendous economic value but also to cause unprecedented job displacement -- a wave of disruption that will hit blue- and white-collar workers alike. In the future, AI will be doing everything from underwriting our loans to building our homes, and even hiring and firing us.

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.

Teaching a robot to do my job (and grimly cheering my obsolescence) Ellen Wengert


It is put to us on that first Monday morning as an exciting innovation which will streamline our processes and free up time for the important stuff. This happens in our morning "huddle cuddle", where at 9.15am on the dot our manager has us gather around in a loose circle and run through the day ahead – how many pieces of work there are to be processed, which queues will be prioritised, who is going to take lunch when. This is the kind of place where our team of nine refers to each other as family. The kind of place with an A4 printout stuck to the kitchenette fridge that says "if Britney Spears can get through 2007, you can get through today". The job is a data-processing role at a small member-owned health insurance fund.

Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches Machine Learning

We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model. Both balance between exploration, where we choose prices in order to learn the distribution of demands & claims for the insurance product, and exploitation, where we myopically choose the best price from the information gathered so far. The performance of the pricing policies is measured in terms of regret: the expected revenue loss caused by not using the optimal price. As is commonplace in insurance, we model demand and claims by GLMs. In our adaptive GLM design, we use the maximum quasi-likelihood estimation (MQLE) to estimate the unknown parameters. We show that, if prices are chosen with suitably decreasing variability, the MQLE parameters eventually exist and converge to the correct values, which in turn implies that the sequence of chosen prices will also converge to the optimal price. In the adaptive GP regression model, we sample demand and claims from Gaussian Processes and then choose selling prices by the upper confidence bound rule. We also analyze these GLM and GP pricing algorithms with delayed claims. Although similar results exist in other domains, this is among the first works to consider dynamic pricing problems in the field of insurance. We also believe this is the first work to consider Gaussian Process regression in the context of insurance pricing. These initial findings suggest that online machine learning algorithms could be a fruitful area of future investigation and application in insurance.

Digital transformation: online guide to digital transformation


Digital transformation is the profound transformation of business and organizational activities, processes, competencies and models to fully leverage the changes and opportunities of a mix of digital technologies and their accelerating impact across society in a strategic and prioritized way, with present and future shifts in mind. While digital transformation is predominantly used in a business context, it also impacts other organizations such as governments, public sector agencies and organizations which are involved in tackling societal challenges such as pollution and aging populations by leveraging one or more of these existing and emerging technologies. In some countries, such as Japan, digital transformation even aims to impact all aspects of life with the country's Society 5.0 initiative, which goes far beyond the limited Industry 4.0 vision in other countries. In the scope of this digital transformation overview, we mainly look at the business dimension.

Artificial Intelligence in insurance: How to make insurance more personal, affordable and adaptable – DXC Blogs


When was the last time your insurance company realized you had a life change, like getting married or buying a house or having a baby, and designed a policy just for you? And presented it to you without being asked? Without encroaching on personal privacy, this is the vision of insurance in the future: personalized, affordable and adaptable. Artificial intelligence (AI) can help insurance companies understand their customers in powerful new ways and be proactive -- and competitive -- about serving their needs. You may have heard the terms analytics, advanced analytics, machine learning and AI.

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 ...