Disney Asked Character.AI to Go Bankrupt for a Get-Out-of-Lawsuits Card The entertainment giant hopes to get trust and safety tech, among other IP, from a startup whose chatbots dozens of states allege encouraged minors to self-harm and kill their parents. This week, Character.AI declared bankruptcy. While some saw the fall of a chatbot platform as a harbinger for the industry, court filings indicate that the decision was actually a key part of a deal that has let Disney hire most of Character.AI's staff--including its now former CEO Karandeep Anand --potentially acquire most of Character.AI's technology, and avoid the beleaguered startup's many legal liabilities and obligations. In the past few years, Character.AI has faced multiple private lawsuits over the safety of its chatbots, as well as investigations from regulators around the world. In a declaration filed as part of the bankruptcy, Character.AI's newly appointed CEO explained that the goal of the bankruptcy was to reach a "consensual resolution" with personal injury claimants and a group of states investigating the company, while maximizing the value of the company's assets for shareholders.

Trending Now Say More Look Up In My Bag Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Mashable Selects Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook AI at School Safety Net All Series Character.AI bankruptcy filing reveals the rapid climb, stunning collapse of a once-major AI player The AI chatbot company says its platform is still running, but it could shut down if no buyer comes forward. Chance Townsend is the General Assignments Editor at Mashable, covering tech, video games, dating apps, digital culture, and whatever else comes his way. He has a Master's in Journalism from the University of North Texas and is a proud orange cat father. His writing has also appeared in PC Mag and . Character.AI, the chatbot platform that lets people create and talk to AI characters, has filed for Chapter 11 bankruptcy.

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The best-known manufacturer of autonomous vacuums declared bankruptcy this week, and no one should be surprised. The home-vacuum robot began, like most things, with war. In August 1990, the same month and year Saddam Hussein invaded Kuwait, three MIT roboticists incorporated the company that would eventually become iRobot, the maker of the Roomba. In its first decade, iRobot began to assemble a small-droid A-team for the theater of combat. The Ariel defused mines; the PackBot handled bomb disposal.

After Amazon's acquisition fell apart, it ran out of options. It plans to sell all assets to its primary supplier, the Chinese company Picea Robotics. Investors "will experience a total loss and not receive recovery on their investment" if the deal is approved, iRobot said. The company didn't discuss how the move might affect its employees in the US or elsewhere. Amazon dropped its $1.7 billion acquisition of the company last year after a veto threat from European regulators, leaving the Roomba maker with no other option.

The Roomba maker is considering handing over control to its main Chinese supplier.

The US firm behind the Roomba smart vacuum cleaner, iRobot, has filed for bankruptcy protection after facing competition from Chinese rivals and being hit by tariffs. Under the so-called pre-packaged Chapter 11 process, the main manufacturer of its devices, Shenzhen-based Picea Robotics, will take ownership of the firm. The tough commercial landscape had forced iRobot to cut its prices and make major investments in new technology, according to documents filed on Sunday. US import duties of 46% on goods from Vietnam, where most of iRobot's devices for the American market are made, increased its costs by $23m (£17.2m) this year, the firm said. The loss-making company was valued at $3.56bn in 2021 after the pandemic helped to drive strong demand for its products.

The proliferation of complex, black-box AI models has intensified the need for techniques that can explain their decisions. Feature attribution methods have become a popular solution for providing post-hoc explanations, yet the field has historically lacked a formal problem definition. This paper addresses this gap by introducing a formal definition for the problem of feature attribution, which stipulates that explanations be supported by an underlying probability distribution represented by the given dataset. Our analysis reveals that many existing model-agnostic methods fail to meet this criterion, while even those that do often possess other limitations. To overcome these challenges, we propose Distributional Feature Attribution eXplanations (DFAX), a novel, model-agnostic method for feature attribution. DFAX is the first feature attribution method to explain classifier predictions directly based on the data distribution. We show through extensive experiments that DFAX is more effective and efficient than state-of-the-art baselines.

A flotilla of uncrewed wolf pack drone boats has successfully been used to escort warships in a Royal Navy and Army trial. The Navy said it was a milestone demonstration of how it could utilise such technology in a real-life scenario. With camera and sensor data being fed back to Patrick Blackett, five 7.2m autonomous Rattler boats safely escorted the two ships playing the role of foreign warships during the 72-hour milestone training exercise, it said. The demonstration was a culmination of months of trials by the Navy's Disruptive Capabilities and Technology Office (DCTO) and the Fleet Experimentation Squadron (FXS). Each of the Rattler boats were operated by a two-person team, with one responsible for piloting the drone and the other monitoring and operating onboard systems, as well as helping to manage live data streams.

Large Reasoning Models (LRMs) such as DeepSeek-R1 and OpenAI o1 have demonstrated remarkable capabilities in various reasoning tasks. Their strong capability to generate and reason over intermediate thoughts has also led to arguments that they may no longer require extensive prompt engineering or optimization to interpret human instructions and produce accurate outputs. In this work, we aim to systematically study this open question, using the structured task of event extraction for a case study. We experimented with two LRMs (DeepSeek-R1 and o1) and two general-purpose Large Language Models (LLMs) (GPT-4o and GPT-4.5), when they were used as task models or prompt optimizers. Our results show that on tasks as complicated as event extraction, LRMs as task models still benefit from prompt optimization, and that using LRMs as prompt optimizers yields more effective prompts. Our finding also generalizes to tasks beyond event extraction. Finally, we provide an error analysis of common errors made by LRMs and highlight the stability and consistency of LRMs in refining task instructions and event guidelines.