DNA sequence classification is a fundamental task in computational biology with vast implications for applications such as disease prevention and drug design. Therefore, fast high-quality sequence classifiers are significantly important. This paper introduces ClaPIM, a scalable DNA sequence classification architecture based on the emerging concept of hybrid in-crossbar and near-crossbar memristive processing-in-memory (PIM). We enable efficient and high-quality classification by uniting the filter and search stages within a single algorithm. Specifically, we propose a custom filtering technique that drastically narrows the search space and a search approach that facilitates approximate string matching through a distance function. ClaPIM is the first PIM architecture for scalable approximate string matching that benefits from the high density of memristive crossbar arrays and the massive computational parallelism of PIM. Compared with Kraken2, a state-of-the-art software classifier, ClaPIM provides significantly higher classification quality (up to 20x improvement in F1 score) and also demonstrates a 1.8x throughput improvement. Compared with EDAM, a recently-proposed SRAM-based accelerator that is restricted to small datasets, we observe both a 30.4x improvement in normalized throughput per area and a 7% increase in classification precision.

Recently, many reactive trajectory planning approaches were suggested in the literature because of their inherent immediate adaption in the ever more demanding cluttered and unpredictable environments of robotic systems. However, typically those approaches are only locally reactive without considering global path planning and no guarantees for simultaneous collision avoidance and goal convergence can be given. In this paper, we study a recently developed circular field (CF)-based motion planner that combines local reactive control with global trajectory generation by adapting an artificial magnetic field such that multiple trajectories around obstacles can be evaluated. In particular, we provide a mathematically rigorous analysis of this planner in a planar environment to ensure safe motion of the controlled robot. Contrary to existing results, the derived collision avoidance analysis covers the entire CF motion planning algorithm including attractive forces for goal convergence and is not limited to a specific choice of the rotation field, i.e., our guarantees are not limited to a specific potentially suboptimal trajectory. Our Lyapunov-type collision avoidance analysis is based on the definition of an (equivalent) two-dimensional auxiliary system, which enables us to provide tight, if and only if conditions for the case of a collision with point obstacles. Furthermore, we show how this analysis naturally extends to multiple obstacles and we specify sufficient conditions for goal convergence. Finally, we provide a challenging simulation scenario with multiple non-convex point cloud obstacles and demonstrate collision avoidance and goal convergence.

Data science has been described as the fourth paradigm for scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges - our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI have sparked debates about the socio-technical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for "data science for social good" (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of socio-technical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the special issue. We hope that this article and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are garnering proportionately less attention with each passing day.

The Guardian has accused Microsoft of damaging its journalistic reputation by publishing an AI-generated poll speculating on the cause of a woman's death next to an article by the news publisher. Microsoft's news aggregation service published the automated poll next to a Guardian story about the death of Lilie James, a 21-year-old water polo coach who was found dead with serious head injuries at a school in Sydney last week. The poll, created by an AI program, asked: "What do you think is the reason behind the woman's death?" Readers were then asked to choose from three options: murder, accident or suicide. Readers reacted angrily to the poll, which has subsequently been taken down – although highly critical reader comments on the deleted survey were still online as of Tuesday morning.

A godfather of AI is among hundreds of tech bosses and academics calling for an international treaty to avoid the technology's'catastrophic' risk to humanity. On the eve of the AI Safety Summit, Turing award winner Yoshua Bengio has signed an open letter warning the danger it poses'warrants immediate and serious attention'. It cites a survey that found over half of AI researchers estimate there is more than a 10 per cent chance advances in machine learning could lead to human extinction. Notably, among the signatories is one of China's leading AI academics, Professor Yui Zeng, a key representative of Beijing who is set to lead one of the sessions at the event in Bletchley Park. Government officials may well see his backing as a positive signal that China – whose invitation to the summit has proven highly controversial – is willing to cooperate on international regulation.

Welcome to our October 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we talk AI, bias, and ethics with Aylin Caliskan, learn more about probabilistic logic shields, knowledge bases, and sparse reward tasks, and find out why everyone should learn a little programming. AIhub ambassador Andrea Rafai met with Aylin Caliskan at this year's International Joint Conference on Artificial Intelligence (IJCAI 2023), where she was giving an IJCAI Early Career Spotlight talk, and asked her about her work on AI, bias, and ethics. In this interview they discuss topics including bias in generative AI tools and the associated research and societal challenges. In their IJCAI article, Safe Reinforcement Learning via Probabilistic Logic Shields, which won a distinguished paper award at the conference, Wen-Chi Yang, Giuseppe Marra, Gavin Rens and Luc De Raedt provide a framework to represent, quantify, and evaluate safety.

Echoing the history of search engines and social media content rankings, the advent of large language models (LLMs) has led to a push for increased personalization of model outputs to individual users. In the past, personalized recommendations and ranking systems have been linked to the development of filter bubbles (serving content that may confirm a user's existing biases) and affective polarization (strong negative sentiment towards those with differing views). In this work, we explore how prompting a leading large language model, ChatGPT-3.5, with a user's political affiliation prior to asking factual questions about public figures and organizations leads to differing results. We observe that left-leaning users tend to receive more positive statements about left-leaning political figures and media outlets, while right-leaning users see more positive statements about right-leaning entities. This pattern holds across presidential candidates, members of the U.S. Senate, and media organizations with ratings from AllSides. When qualitatively evaluating some of these outputs, there is evidence that particular facts are included or excluded based on the user's political affiliation. These results illustrate that personalizing LLMs based on user demographics carry the same risks of affective polarization and filter bubbles that have been seen in other personalized internet technologies. This ``failure mode" should be monitored closely as there are more attempts to monetize and personalize these models.

While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively. To facilitate development of such systems, we release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent. To collect this dataset, each user utterance from MultiWOZ 2.2 was replaced with a small chat between two users that is semantically and pragmatically consistent with the original user utterance, thus resulting in the same dialogue state and system response. These dialogues reflect interesting dynamics of collaborative decision-making in task-oriented scenarios, e.g., social chatter and deliberation. Supported by this data, we propose the novel task of multi-user contextual query rewriting: to rewrite a task-oriented chat between two users as a concise task-oriented query that retains only task-relevant information and that is directly consumable by the dialogue system. We demonstrate that in multi-user dialogues, using predicted rewrites substantially improves dialogue state tracking without modifying existing dialogue systems that are trained for single-user dialogues. Further, this method surpasses training a medium-sized model directly on multi-user dialogues and generalizes to unseen domains.

Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity. We introduce FANToM, a new benchmark designed to stress-test ToM within information-asymmetric conversational contexts via question answering. Our benchmark draws upon important theoretical requisites from psychology and necessary empirical considerations when evaluating large language models (LLMs). In particular, we formulate multiple types of questions that demand the same underlying reasoning to identify illusory or false sense of ToM capabilities in LLMs. We show that FANToM is challenging for state-of-the-art LLMs, which perform significantly worse than humans even with chain-of-thought reasoning or fine-tuning.

CyberGuy explains a new factory in Oregon that can produce 10,000 robots a year. Maine, a state often admired for its serenity and scenic beauty, recently witnessed an unimaginable nightmare. Robert Card, an assault rifle-carrying gun instructor with documented mental troubles, gunned down 18 innocent people. The 40-year-old suspect was found dead two days later after an intense search by law enforcement. As families and communities mourn the loss, an important question is raised: Could alerting the police mere minutes earlier than the first 911 call have changed the outcome?