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.

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

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.

Context: Financial system stability is determined by the condition of the banking system. A bank failure can destroy the stability of the financial system, as banks are subject to systemic risk, affecting not only individual banks but also segments or the entire financial system. Calculating the probability of a bank going bankrupt is one way to ensure the banking system is safe and sound. Existing literature and limitations: Statistical models, such as Altman's Z-Score, are one of the common techniques for developing a bankruptcy prediction model. However, statistical methods rely on rigid and sometimes irrelevant assumptions, which can result in low forecast accuracy. New approaches are necessary. Objective of the research: Bankruptcy models are developed using machine learning techniques, such as logistic regression (LR), random forest (RF), and support vector machines (SVM). According to several studies, machine learning is also more accurate and effective than statistical methods for categorising and forecasting banking risk management. Present Research: The commercial bank data are derived from the annual financial statements of 44 active banks and 21 bankrupt banks in Turkey from 1994 to 2004, and the rural bank data are derived from the quarterly financial reports of 43 active and 43 bankrupt rural banks in Indonesia between 2013 and 2019. Five rural banks in Indonesia have also been selected to demonstrate the feasibility of analysing bank bankruptcy trends. Findings and implications: The results of the research experiments show that RF can forecast data from commercial banks with a 90% accuracy rate. Furthermore, the three machine learning methods proposed accurately predict the likelihood of rural bank bankruptcy. Contribution and Conclusion: The proposed innovative machine learning approach help to implement policies that reduce the costs of bankruptcy.

Claire's on brink of collapse putting 2,150 jobs at risk 15 minutes agoShareSaveTom EspinerBusiness reporter, BBC NewsShareSaveEPA Claire's will appoint administrators after struggles with online competition. Fashion accessories chain Claire's is on the brink of collapse after the retailer said it will appoint administrators in the UK and Ireland, putting 2,150 jobs at risk. The company has 278 stores in the UK and 28 in Ireland but has been struggling with falling sales and fierce competition. All the shops will continue trading while administrators at Interpath, once appointed, will "assess options for the company". Interpath chief executive Will Wright, said options include "exploring the possibility of a sale which would secure a future for this well-loved brand". Claire's in the US filed for bankruptcy in the US earlier this month.

Uplift modeling has achieved significant success in various fields, particularly in online marketing. It is a method that primarily utilizes machine learning and deep learning to estimate individual treatment effects. This paper we apply uplift modeling to analyze the effect of company adjustment on their financial status, and we treat these adjustment as treatments or interventions in this study. Although there have been extensive studies and application regarding binary treatments, multiple treatments, and continuous treatments, company adjustment are often more complex than these scenarios, as they constitute a series of multiple time-dependent actions. The effect estimation of company adjustment needs to take into account not only individual treatment traits but also the temporal order of this series of treatments. This study collects a real-world data set about company financial statements and reported behavior in Luxembourg for the experiments. First, we use two meta-learners and three other well-known uplift models to analyze different company adjustment by simplifying the adjustment as binary treatments. Furthermore, we propose a new uplift modeling framework (MTDnet) to address the time-dependent nature of these adjustment, and the experimental result shows the necessity of considering the timing of these adjustment.

Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI systems. On the other hand, however, it also opens the door to locating possible vulnerabilities in an AI model. Traditional adversarial text attack uses word substitution, data augmentation techniques and gradient-based attacks on powerful pre-trained Bidirectional Encoder Representations from Transformers (BERT) variants to generate adversarial sentences. These attacks are generally whitebox in nature and not practical as they can be easily detected by humans E.g. Changing the word from "Poor" to "Rich". We proposed a simple yet effective Grey-box cum Black-box approach that does not require the knowledge of the model while using a set of surrogate Transformer/BERT models to perform the attack using Explainable AI techniques. As Transformers are the current state-of-the-art models for almost all Natural Language Processing (NLP) tasks, an attack generated from BERT1 is transferable to BERT2. This transferability is made possible due to the attention mechanism in the transformer that allows the model to capture long-range dependencies in a sequence. Using the power of BERT generalisation via attention, we attempt to exploit how transformers learn by attacking a few surrogate transformer variants which are all based on a different architecture. We demonstrate that this approach is highly effective to generate semantically good sentences by changing as little as one word that is not detectable by humans while still fooling other BERT models.

Following a final hearing on its bankruptcy plan Thursday morning, the coworking pioneer will have fewer locations, a new influx of capital, and 4 billion in debt wiped from its books. In a packed courtroom in Newark, New Jersey, Judge John Sherwood approved WeWork's restructuring plan. WeWork expects to finally exit bankruptcy in mid-June. The plan also staved off a bid by WeWork's controversial founder Adam Neumann, who had sought to buy back the company he founded before he was infamously ousted. WeWork's clean slate will coincide with a new era of working, one in which office workers have pushed back against returning to offices full-time.