We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing. These systems often support communities disproportionately affected by systemic racial, gender, or other injustices, so it is crucial to design these systems with fairness considerations in mind. To address this problem, we propose a framework for evaluating fairness in contextual resource allocation systems that is inspired by fairness metrics in machine learning. This framework can be applied to evaluate the fairness properties of a historical policy, as well as to impose constraints in the design of new (counterfactual) allocation policies. Our work culminates with a set of incompatibility results that investigate the interplay between the different fairness metrics we propose. Notably, we demonstrate that: 1) fairness in allocation and fairness in outcomes are usually incompatible; 2) policies that prioritize based on a vulnerability score will usually result in unequal outcomes across groups, even if the score is perfectly calibrated; 3) policies using contextual information beyond what is needed to characterize baseline risk and treatment effects can be fairer in their outcomes than those using just baseline risk and treatment effects; and 4) policies using group status in addition to baseline risk and treatment effects are as fair as possible given all available information. Our framework can help guide the discussion among stakeholders in deciding which fairness metrics to impose when allocating scarce resources.

Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign that sample to an in-class label significantly compromises the reliability of a model. The problem has gained significant attention due to its importance for safety deploying models in open-world settings. Detecting OOD samples is challenging due to the intractability of modeling all possible unknown distributions. To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection. Despite having similar and shared concepts, out-of-distribution, open-set, and anomaly detection have been investigated independently. Accordingly, these research avenues have not cross-pollinated, creating research barriers. While some surveys intend to provide an overview of these approaches, they seem to only focus on a specific domain without examining the relationship between different domains. This survey aims to provide a cross-domain and comprehensive review of numerous eminent works in respective areas while identifying their commonalities. Researchers can benefit from the overview of research advances in different fields and develop future methodology synergistically. Furthermore, to the best of our knowledge, while there are surveys in anomaly detection or one-class learning, there is no comprehensive or up-to-date survey on out-of-distribution detection, which our survey covers extensively. Finally, having a unified cross-domain perspective, we discuss and shed light on future lines of research, intending to bring these fields closer together.

With greater scrutiny of tech practices and calls for transparency, businesses must manage the deployment of smart AI while ensuring privacy safeguards, preventing bias in algorithmic decision-making, and meeting guidelines in highly regulated industries. In this article, I look at five ways leaders can future-proof their businesses against these risks. Regulating AI is a multifaceted and difficult challenge, and as a result, the regulatory landscape is a consistently evolving environment. However, the issue of unethical and biased AI is becoming critical as organizations are increasingly relying on algorithms to support their decisions – and we will undoubtedly see the ramp-up of regulatory scrutiny in the coming years as a result. To avoid the consequences of financial and reputational damage from unethical AI, organizations will need to get ahead of the curve.

"Viewership is absolutely critical to the success of this ecosystem," wrote Tahir "Tashi" Hasandjekic, head of esports at 343 Industries, the subsidiary of Microsoft that developed "Infinite," in a blog post Jan. 2022. At the time, the HCS was riding high on the popularity of its debut tournament, and the competitive scene was a bright spot in an otherwise dimming constellation. Some of the biggest esports teams in the world, like OpTic Gaming and FaZe Clan, had eagerly joined up for "Halo Infinite," signing veteran stars and top emerging talent. At peak vewiership, more than 267,000 Halo fans were tuned into the game's first major tournament concurrently.

This article is from Big Technology, a newsletter by Alex Kantrowitz. A chatbot that meets the hype is finally here. On Thursday, OpenAI released ChatGPT, a bot that converses with humans via cutting-edge artificial intelligence. The bot can help you write code, compose essays, dream up stories, and decorate your living room. And that's just what people discovered on day one.

There's no reason you should have ever heard of Simon Squibb, the "chief purpose officer of the Purposeful Project". Mr Squibb, who describes himself as an "Elon Musk wanna-be" in his Twitter profile, is one of those tirelessly energetic mid-life influencers who proliferate on the petri dish of LinkedIn. Displaying the sort of enthusiasm that Matt Hancock reserves for a Bushtucker Challenge, Mr Squibb is on a mission. "I want to fix the education system", he says. This fix entails removing something that many of us consider quite an important part of the education system: the learning part.

It's been well over a decade since I finished a Cognitive Architectures course at MIT (9.364) under the late professor Whitman Richards. My final project was a little thing called "Agent Collaboration Using Anigrafs." Anigrafs were a pedagogical cognitive architecture that Richards defined. I wrote some code to implement Anigrafs and hooked it to simulated robots, which I called "Anibots." What follows is essentially my 2008 final report to Prof. Richards. I noticed several years later that he published Anigrafs: Experiments in Cooperative Cognitive Architecture as a book from MIT Press. TLDR: Identical robots can cooperate if they use a type of mental network that votes (with the Condorcet method) on what behavior to do next. The development goal is to achieve collaboration of situated agents to perform shared tasks and/or goals.

For the past six years, Elon Musk has been working on a chip designed to be implanted into human brains, with his neurotechnology company Neuralink. His ultimate goal is to develop a'brain-computer interface' that will initially be used to help people with paralysis or motor neurone disease to communicate. It will allegedly allow them to operate computers and mobile devices using their thoughts, but could have further uses in years to come. So what exactly is the chip? How does it work and how will it cure all medical problems?

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Last year, Andreessen Horowitz published a provocative blog post entitled "The Cost of Cloud, a Trillion Dollar Paradox." In it, the venture capital firm argued that out-of-control cloud spending is resulting in public companies leaving billions of dollars in potential market capitalization on the table. An alternative, the firm suggests, is to recalibrate cloud resources into a hybrid model. Such a model can boost a company's bottom line and free capital to focus on new products and growth.

CIRCLES Consortium research is supported by the National Science Foundation, the U.S. Department of Transportation and the U.S. Department of Energy. Additional funding was provided by Nissan, Toyota North America, General Motors, the Federal Highway Administration, the Tennessee Department of Transportation, the California Department of Transportation, the Nashville Department of Transportation, Gresham Smith, Siemens, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Amazon Web Services (AWS), C3.ai Digital Transformation Institute, the UC Berkeley Institute of Transportation Studies, Vanderbilt University, the University of Arizona, Rutgers University, Temple University, Ecole des Ponts ParisTech and the Université Gustave Eiffel.