A yet unmet challenge in algorithmic fairness is the problem of intersectionality, that is, achieving fairness across the intersection of multiple groups -- and verifying that such fairness has been attained. Because intersectional groups tend to be small, verifying whether a model is fair raises statistical as well as moral-methodological challenges. This paper (1) elucidates the problem of intersectionality in algorithmic fairness, (2) develops desiderata to clarify the challenges underlying the problem and guide the search for potential solutions, (3) illustrates the desiderata and potential solutions by sketching a proposal using simple hypothesis testing, and (4) evaluates, partly empirically, this proposal against the proposed desiderata.

This paper introduces the \emph{Optimist}, an autonomous system developed to advance automated conjecture generation in graph theory. Leveraging mixed-integer programming (MIP) and heuristic methods, the \emph{Optimist} generates conjectures that both rediscover established theorems and propose novel inequalities. Through a combination of memory-based computation and agent-like adaptability, the \emph{Optimist} iteratively refines its conjectures by integrating new data, enabling a feedback process with minimal human (\emph{or machine}) intervention. Initial experiments reveal the \emph{Optimist}'s potential to uncover foundational results in graph theory, as well as to produce conjectures of interest for future exploration. This work also outlines the \emph{Optimist}'s evolving integration with a counterpart agent, the \emph{Pessimist} (a human \emph{or machine} agent), to establish a dueling system that will drive fully automated graph theory research.

We propose that a tree-like hierarchical structure represents a simple and effective way to model the emergent behaviour of financial markets, especially markets where there exists a pronounced intersection between social media influences and investor behaviour. To explore this hypothesis, we introduce an agent-based model of financial markets, where trading agents are embedded in a hierarchical network of communities, and communities influence the strategies and opinions of traders. Empirical analysis of the model shows that its behaviour conforms to several stylized facts observed in real financial markets; and the model is able to realistically simulate the effects that social media-driven phenomena, such as echo chambers and pump-and-dump schemes, have on financial markets.

Clustering-based nearest neighbor search is a simple yet effective method in which data points are partitioned into geometric shards to form an index, and only a few shards are searched during query processing to find an approximate set of top-$k$ vectors. Even though the search efficacy is heavily influenced by the algorithm that identifies the set of shards to probe, it has received little attention in the literature. This work attempts to bridge that gap by studying the problem of routing in clustering-based maximum inner product search (MIPS). We begin by unpacking existing routing protocols and notice the surprising contribution of optimism. We then take a page from the sequential decision making literature and formalize that insight following the principle of ``optimism in the face of uncertainty.'' In particular, we present a new framework that incorporates the moments of the distribution of inner products within each shard to optimistically estimate the maximum inner product. We then present a simple instance of our algorithm that uses only the first two moments to reach the same accuracy as state-of-the-art routers such as \scann by probing up to $50%$ fewer points on a suite of benchmark MIPS datasets. Our algorithm is also space-efficient: we design a sketch of the second moment whose size is independent of the number of points and in practice requires storing only $O(1)$ additional vectors per shard.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. The vice chair of Congress' artificial intelligence caucus says privacy regulations need to be a top short-term priority for Congress as Washington looks to get to grips with the rapidly emerging technology – which he says poses risks, but could be a catalyst for the next expansion of the U.S. economy. Rep. Jay Obernolte, R-Calif., told Fox News Digital in an interview that he is an optimist when it comes to the potential for artificial intelligence, but Congress needs to make sure it is protecting Americans from the potential negatives and disruption that AI brings. "I think in the short term, the ability of AI to pierce through digital data privacy and to re-aggregate data that has supposedly been disaggregated and use it to create behavioral models that could be used to influence behavior, that's very concerning, and that's something that the government definitely needs to play a role in mitigating," Obernolte said. Rep. Jay Obernolte has a graduate degree in artificial intelligence.

Bletchley Park, a milestone in Alan Turing's journey to technological immortality, heard warnings this week that the coming wave of artificial intelligence systems could threaten humanity. But for one of the world's leading tech investors, holding back AI development will be just as damaging in terms of deaths in car crashes, pandemics and poorly targeted munitions that could have been prevented by the technology. "We believe any deceleration of AI will cost lives. Deaths that were preventable by the AI that was prevented from existing is a form of murder," wrote Marc Andreessen, an early investor in Facebook, Pinterest and Twitter, in a blogpost last month titled The Techno-Optimist Manifesto. When it comes to AI, Andreessen is not the only techno-optimist out there, despite the pessimistic view of the technology dominating the agenda in the run-up to last week's AI safety summit at Bletchley.

Sam: I laugh at the stupid Prophecizers who are so certain of technological Singularity, for ex: Kurzweil. He is a fraud or worst, an inductivist idiot. Just plot a line of past progresses against time, and extend that "exponential" line to the future. And voila you have got Artificial General Intelligence and the fountain of youth. Chris: So you think AGI is a myth that will never happen?

Policy Optimization (PO) is a widely used approach to address continuous control tasks. In this paper, we introduce the notion of mediator feedback that frames PO as an online learning problem over the policy space. The additional available information, compared to the standard bandit feedback, allows reusing samples generated by one policy to estimate the performance of other policies. Based on this observation, we propose an algorithm, RANDomized-exploration policy Optimization via Multiple Importance Sampling with Truncation (RANDOMIST), for regret minimization in PO, that employs a randomized exploration strategy, differently from the existing optimistic approaches. When the policy space is finite, we show that under certain circumstances, it is possible to achieve constant regret, while always enjoying logarithmic regret. We also derive problem-dependent regret lower bounds. Then, we extend RANDOMIST to compact policy spaces. Finally, we provide numerical simulations on finite and compact policy spaces, in comparison with PO and bandit baselines.

The Covid-19 vaccine has been widely distributed, and business and consumer behavior has returned to a new normal. The recession isn't over, but things are looking brighter. Consumer spending and jobs are recovering. OK, so this may be wishful thinking because none of us really knows what will happen, but I'm an optimist. As president of a marketing firm with a focus on communications and customer engagement, I decided to predict what some of these changes might mean for brands and marketing so that the pragmatic optimists among us can start planning today.

Garry Kasparov is a political activist who's written books and articles on artificial intelligence, cybersecurity and online privacy, but he's best known for being the former World Chess Champion who took on the IBM computer known as Big Blue in the mid-1990s. I spoke to Kasparov before a speaking engagement at the Collision Conference last month where he was participating in his role as Avast Security Ambassador. Our discussion covered a lot of ground, from his role as security ambassador to the role of AI. (Transcribed questions and answers were edited for clarity.) TechCrunch: How did you become a security ambassador for Avast? Garry Kasparov: It started almost by accident.