Jean-Paul Thorbjornsen, an Australian man in his mid-30s, with a rural Catholic upbringing, is a founder of THORChain, a blockchain through which users can swap one cryptocurrency for another and earn fees from making those swaps. THORChain is permissionless, so anyone can use it without getting prior approval from a centralized authority. As a decentralized network, the blockchain is built and run by operators located across the globe. During its early days, Thorbjornsen himself hid behind the pseudonym "leena" and used an AI-generated female image as his avatar. But around March 2024, he revealed his true identity as the mind behind the blockchain. If there is a central question around THORChain, it is this: Exactly who is responsible for its operations?

Consider a general formulation of the distributionally robust learning (DRL) problem of active interest.

We show the security risk associated with using machine learning classifiers in United States election tabulators. The central classification task in election tabulation is deciding whether a mark does or does not appear on a bubble associated to an alternative in a contest on the ballot. Barretto et al. (E-Vote-ID 2021) reported that convolutional neural networks are a viable option in this field, as they outperform simple feature-based classifiers. Our contributions to election security can be divided into four parts. To demonstrate and analyze the hypothetical vulnerability of machine learning models on election tabulators, we first introduce four new ballot datasets. Second, we train and test a variety of different models on our new datasets. These models include support vector machines, convolutional neural networks (a basic CNN, VGG and ResNet), and vision transformers (Twins and CaiT). Third, using our new datasets and trained models, we demonstrate that traditional white box attacks are ineffective in the voting domain due to gradient masking. Our analyses further reveal that gradient masking is a product of numerical instability. We use a modified difference of logits ratio loss to overcome this issue (Croce and Hein, ICML 2020). Fourth, in the physical world, we conduct attacks with the adversarial examples generated using our new methods. In traditional adversarial machine learning, a high (50% or greater) attack success rate is ideal. However, for certain elections, even a 5% attack success rate can flip the outcome of a race. We show such an impact is possible in the physical domain. We thoroughly discuss attack realism, and the challenges and practicality associated with printing and scanning ballot adversarial examples.

Like a mirage speeding across the dusty desert outside Pueblo, Colorado, the first hydrogen-fuel-cell passenger train in the United States is getting warmed up on its test track. It will soon be shipped to Southern California, where it is slated to carry riders on San Bernardino County's Arrow commuter rail service before the end of the year. The best way to decarbonize railroads is the subject of growing debate among regulators, industry, and activists. The debate is partly technological, revolving around whether hydrogen fuel cells, batteries, or overhead electric wires offer the best performance for different railroad situations. In the insular world of railroading, this hydrogen-powered train is a Rorschach test.

Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data. Various approaches have been proposed to address these challenges. One approach involves training specialized retrievers to select relevant information, thereby reducing the input length. Another approach is to transform diverse modalities of data into a single modality, simplifying the task difficulty and enabling more straightforward processing. In this paper, we propose HProPro, a novel program-based prompting framework for the hybrid question answering task. HProPro follows the code generation and execution paradigm. In addition, HProPro integrates various functions to tackle the hybrid reasoning scenario. Specifically, HProPro contains function declaration and function implementation to perform hybrid information-seeking over data from various sources and modalities, which enables reasoning over such data without training specialized retrievers or performing modal transformations. Experimental results on two typical hybrid question answering benchmarks HybridQA and MultiModalQA demonstrate the effectiveness of HProPro: it surpasses all baseline systems and achieves the best performances in the few-shot settings on both datasets.

There's a hilarious illustration from Paris in late 1839, mere months after an early type of photograph called a daguerreotype was announced to the world, that warned what this tiny picture portended. In Théodore Maurisset's imagination, the daguerreotype would bring about a collective hysteria, La Daguerréotypomanie, in which crazed masses arrive from the ends of the earth and overrun a small photo studio. Some in the crowd want pictures of themselves, but, mon Dieu, others demand cameras to take their own pictures--Maurisset shows them loading the machines like contraband onto steamships bound for foreign ports--and still others throng simply to ogle at this newfangled thing and all the lunatic proceedings surrounding it. The clamor is so feverish that it brings about a mass hallucination, in which nearly everything else in the landscape around the studio, including railroad cars, a clock tower, a basket for a hot air balloon, indeed anything remotely boxy in shape, morphs into cameras. As they march to the studio, the crowds pass by half a dozen gallows, where in response to the daguerreotype's appearance artists have hung themselves.

Jason Allen, a video game designer in Pueblo, Colorado, spent roughly 80 hours working on his entry to the Colorado State Fair's digital arts competition. Judges awarded him first place, which came with a $300 prize. But when Allen posted about his win on social media late last month, his artwork went viral--for all the wrong reasons. Allen's victory took a turn when he revealed online that he'd created his prize-winning art using Midjourney, an artificial intelligence program that can turn text descriptions into images. He says he also made that clear to state fair officials when he dropped off his submission, called Théâtre D'opéra Spatial.

The winning artwork was created using the AI tool Midjourney – which turns lines of text into astonishingly realistic graphics. The award came with a $300 cash prize. AI tools to generate images have been around for years with companies such as Google and OpenAI being notable investors in these text-to-image systems. "I'm not going to apologise for it … I won and I didn't break any rules," Allen, who is from Pueblo, Colorado, told The New York Times newspaper in an interview published on Friday. However, many have taken to social media to express their anger and despair over the award, arguing it took away from the hard work invested by humans to physically create noteworthy art.

Jason M. Allen was almost too nervous to enter his first art competition. Now, his award-winning image is sparking controversy about whether art can be generated by a computer, and what, exactly, it means to be an artist. In August, Allen, a game designer who lives in Pueblo West, Colorado, won first place in the emerging artist division's "digital arts/digitally-manipulated photography" category at the Colorado State Fair Fine Arts Competition. His winning image, titled "Théâtre D'opéra Spatial" (French for "Space Opera Theater"), was made with Midjourney -- an artificial intelligence system that can produce detailed images when fed written prompts. A $300 prize accompanied his win.