The most popular methods for measuring importance of the variables in a black box prediction algorithm make use of synthetic inputs that combine predictor variables from multiple subjects. These inputs can be unlikely, physically impossible, or even logically impossible. As a result, the predictions for such cases can be based on data very unlike any the black box was trained on. We think that users cannot trust an explanation of the decision of a prediction algorithm when the explanation uses such values. Instead we advocate a method called Cohort Shapley that is grounded in economic game theory and unlike most other game theoretic methods, it uses only actually observed data to quantify variable importance. Cohort Shapley works by narrowing the cohort of subjects judged to be similar to a target subject on one or more features. We illustrate it on an algorithmic fairness problem where it is essential to attribute importance to protected variables that the model was not trained on.

Modeling and control of epidemics such as the novel Corona virus have assumed paramount importance at a global level. A natural and powerful dynamical modeling framework to use in this context is a continuous time Markov decision process (CTMDP) that encompasses classical compartmental paradigms such as the Susceptible-Infected-Recovered (SIR) model. The challenges with CTMDP based models motivate the need for a more efficient approach and the mean field approach offers an effective alternative. The mean field approach computes the collective behavior of a dynamical system comprising numerous interacting nodes (where nodes represent individuals in the population). This paper (a) presents an overview of the mean field approach to epidemic modeling and control and (b) provides a state-of-the-art update on recent advances on this topic. Our discussion in this paper proceeds along two specific threads. The first thread assumes that the individual nodes faithfully follow a socially optimal control policy prescribed by a regulatory authority. The second thread allows the individual nodes to exhibit independent, strategic behavior. In this case, the strategic interaction is modeled as a mean field game and the control is based on the associated mean field Nash equilibria. In this paper, we start with a discussion of modeling of epidemics using an extended compartmental model - SIVR and provide an illustrative example. We next provide a review of relevant literature, using a mean field approach, on optimal control of epidemics, dealing with how a regulatory authority may optimally contain epidemic spread in a population. Following this, we provide an update on the literature on the use of the mean field game based approach in the study of epidemic spread and control. We conclude the paper with relevant future research directions.

Fox News correspondent Matt Finn has the latest on the impact of AI technology that some say could outpace humans on'Special Report.' China on Tuesday revealed its proposed assessment measures for prospective generative artificial intelligence (AI) tools, telling companies they must submit their products before launching to the public. The Cyberspace Administration of China (CAC) proposed the measures in order to prevent discriminatory content, false information and content with the potential to harm personal privacy or intellectual property, the South China Morning Press reported. Such measures would ensure that the products do not end up suggesting regime subversion or disrupting economic or social order, according to the CAC. A number of Chinese companies, including Baidu, SenseTime and Alibaba, have recently shown of new AI models to power a number of applications from chatbots to image generators, prompting concern from officials over the impending boom in use.

Lois Rosson is a historian of science and technology based in Los Angeles. She is currently writing a book about images of outer space and their legibility. In 1835, William Henry Fox Talbot finally succeeded in producing a crude photograph of his country estate. He triumphantly declared that his was the first house ever known to have drawn its own picture. Fox Talbot described the calotype, his contribution to the photomechanical process, as an eradication of human intervention.

In late March, the Future of Life Institute released an open letter (and a related FAQ) calling "on all AI labs to immediately pause for at least six months the training of AI systems more powerful than GPT-4. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium." The letter, which also stated that "Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable," was initially signed by over a thousand people, including many notable technology leaders. Many thousands more added their signatures after its publication.

Artificial Intelligence (AI) has come a long way in recent years, with rapid advancements in machine learning, natural language processing, and deep learning algorithms. These technologies have led to the development of powerful generative AI systems such as ChatGPT, Midjourney, and Dall-E, which have transformed industries and impacted our daily lives. However, alongside this progress, concerns over the potential risks and unintended consequences of AI systems have been growing. In response, the concept of AI capability control has emerged as a crucial aspect of AI development and deployment. In this blog, we will explore what AI capability control is, why it matters, and how organizations can implement it to ensure AI operates safely, ethically, and responsibly.

For fans of digital art in a more traditional sense – art produced by a human who is making conscious decisions about their use of digital technologies – new trends dominating the genre have been met with some skepticism. Whereas the general public seems to have gotten on board with these shifts with little hesitation, the complexities of this new wave are not lost on those with familiarity to art, technology, or its intersection. While the emergence of these art forms dates back to the 20th century, the tail-end of the last decade has seen significant advancements in machine learning and increased visibility with easy promotion via social media. This past year, non-fungible tokens, or NFTs, caused frenzied bidding wars among its fans, and harsh criticisms from its skeptics, in part due to the environmental impact of the medium – on average, the creation of one Ethereum-based NFT, not including the computational power required in the sale process, amounts to 120.7 pounds of CO2, which is equivalent to driving a car 200 miles or 322 kilometers. However, regardless of reason, backlash from artists was minimal, with many looking to capitalize on the massive potential gains – with the exception of David Hockney, a digital artist himself and one of only two people on Earth whose work has sold for more than Beeple's $69.3 million-dollar NFT, The First 5000 Days. Hockney is responsible for the scalding take that NFTs are "silly little things" and that a better acronym would be "I.C.S. […] international crooks and swindlers."

Artificial intelligence's rapid growth has led to advancements like autonomous vehicles, virtual reality, and ChatGPT. But AI technologies and training AI models require a lot of energy, increasing concerns about the environmental impact of AI and its sustainability. To put AI's energy usage into perspective, it took nine days to train one of OpenAI's early model chatbots, MegatronLM. According to TechTarget, during those nine days, 27,648 kilowatt hours of energy was used. That's about the same amount of energy used by three U.S. homes over the course of an entire year.

To halt the influx of bogus Ernie bot apps from surfacing in the App Store, Chinese technology company Baidu has filed a lawsuit against Apple and many app developers. Baidu is suing Apple and the creators of imitation Ernie bot apps in a lawsuit that was launched on Friday in Beijing Haidian People's Court. It aims to compel Apple to remove the problematic bogus apps and prevent app developers from distributing them. In its lawsuit, Baidu claimed that it has filed claims against Apple and the creators of the imitators of its Ernie bot in Beijing Haidian People's Court. In a statement published by its authorized "Baidu AI" WeChat account, Baidu stated that "Ernie does not currently have any official apps."

ChatGPT is an AI-powered conversational model developed by OpenAI that has revolutionized the way we communicate with machines. However, like any new technology, there are potential risks and dangers associated with its use. In this article, we will explore the dark side of ChatGPT AI technology and discuss the unseen dangers that lurk beneath its seemingly harmless exterior. The chatbot you are using has been trained on a lot of information from different sources like books, websites, social media posts, and articles on the internet. There's a chance that it has even been trained on your own social media posts. It's not clear if the company behind the chatbot got permission from the original authors to use their information.