We show that these conditions are closely connected: all sufficiently high-entropy distributions are instantaneously realizable at anyhistory of selected items.

The Recommender then observes the Agent's selected item

In 2023, a Belgian man reportedly ended his life after developing eco-anxiety and confiding in an AI chatbot over six weeks about the future of the planet. Without those conversations, his widow reportedly told the Belgian outlet La Libre, "he would still be here". In April this year, a 35-year-old Florida man was shot and killed by police in another chatbot-related incident: his father later told media that the man had come to believe an entity named Juliet was trapped inside ChatGPT, and then killed by OpenAI. When the man, who reportedly struggled with bipolar disorder and schizophrenia, was confronted by police, he allegedly charged at them with a knife. The wide availability of chatbots in the past few years has apparently led some to believe there is a ghost in the machine – one that is conscious, capable of loving and being loved.

In 2024, online conspiracy theories can feel almost impossible to avoid. Podcasters, prominent public figures, and leading political figures have breathed oxygen into once fringe ideas of collusion and deception. Nationwide, nearly half of adults surveyed by the polling firm YouGov said they believe there is a secret group of people that control world events. Nearly a third (29%) believe voting machines were manipulated to alter votes in the 2020 presidential election. A surprising amount of Americans think the Earth is flat.

The quality of pre-training data plays a critical role in the performance of foundation models. Popular foundation models often design their own recipe for data filtering, which makes it hard to analyze and compare different data filtering approaches. DataComp is a new benchmark dedicated to evaluating different methods for data filtering. This paper describes our learning and solution when participating in the DataComp challenge. Our filtering strategy includes three stages: single-modality filtering, cross-modality filtering, and data distribution alignment. We integrate existing methods and propose new solutions, such as computing CLIP score on horizontally flipped images to mitigate the interference of scene text, using vision and language models to retrieve training samples for target downstream tasks, rebalancing the data distribution to improve the efficiency of allocating the computational budget, etc. We slice and dice our design choices, provide in-depth analysis, and discuss open questions. Our approach outperforms the best method from the DataComp paper by over 4% on the average performance of 38 tasks and by over 2% on ImageNet.

Around the time of the 2016 election, YouTube became known as a home to the rising alt-right and to massively popular conspiracy theorists. The Google-owned site had more than 1 billion users and was playing host to charismatic personalities who had developed intimate relationships with their audiences, potentially making it a powerful vector for political influence. At the time, Alex Jones's channel, Infowars, had more than 2 million subscribers. And YouTube's recommendation algorithm, which accounted for the majority of what people watched on the platform, looked to be pulling people deeper and deeper into dangerous delusions. The process of "falling down the rabbit hole" was memorably illustrated by personal accounts of people who had ended up on strange paths into the dark heart of the platform, where they were intrigued and then convinced by extremist rhetoric--an interest in critiques of feminism could lead to men's rights and then white supremacy and then calls for violence. Most troubling is that a person who was not necessarily looking for extreme content could end up watching it because the algorithm noticed a whisper of something in their previous choices.

Social media is a modern person's digital voice to project and engage with new ideas and mobilise communities $\unicode{x2013}$ a power shared with extremists. Given the societal risks of unvetted content-moderating algorithms for Extremism, Radicalisation, and Hate speech (ERH) detection, responsible software engineering must understand the who, what, when, where, and why such models are necessary to protect user safety and free expression. Hence, we propose and examine the unique research field of ERH context mining to unify disjoint studies. Specifically, we evaluate the start-to-finish design process from socio-technical definition-building and dataset collection strategies to technical algorithm design and performance. Our 2015-2021 51-study Systematic Literature Review (SLR) provides the first cross-examination of textual, network, and visual approaches to detecting extremist affiliation, hateful content, and radicalisation towards groups and movements. We identify consensus-driven ERH definitions and propose solutions to existing ideological and geographic biases, particularly due to the lack of research in Oceania/Australasia. Our hybridised investigation on Natural Language Processing, Community Detection, and visual-text models demonstrates the dominating performance of textual transformer-based algorithms. We conclude with vital recommendations for ERH context mining researchers and propose an uptake roadmap with guidelines for researchers, industries, and governments to enable a safer cyberspace.

It seems like AI-generated art has suddenly exploded everywhere in the last month. A friend showed me what he was creating with DALL·E 2, and down a rabbit hole I fell. And man, what a fun rabbit hole it is too for photography and illustration alike! AI-generated art is literally that: Artworks created by an AI generator. Fed millions upon millions of images and trained by humans and machines, these tools can produce incredibly detailed, sophisticated and realistic images based on natural language text input.

I distinctly remember the moment I was first intrigued by the possibilities of AI Art. On December 13th, 2021 I was scrolling through Twitter and breaking up the endless parade of apocalyptic news stories, vicious wisecracks and obscure movie trivia fighting for space on my feed was a puzzling and bizarre comic-grotesquerie "Elon Musk Dying in Space," created by one @wagface. No malice towards Mr. Musk, but one thing I love about Twitter is how the rich and poor, the powerful and anonymous are all trapped together in the same canvas sack, and this was a striking example of an anonymous online wise-guy turning the tools of cutting edge technology against one of the world's most pre-eminent technologists. I was also totally baffled, "What, exactly, am I looking at?" The image's melted plastic, smeared pixel future-primitive aesthetic couldn't've been more appropriate towards the subject at hand, but how was this nightmare created and what else is it capable of creating?

Are robots coming for your job? New York Times tech columnist Kevin Roose says companies and governments are increasingly using automation and artificial intelligence to cut costs, transform workplaces and eliminate jobs -- and more changes are coming. "We need to prepare for the possibility that a lot of people are going to fall through the cracks of this technological transformation," Roose says. "It's happened during every technological transformation we've ever had, and it's going to happen this time. And in fact, it already is happening."