Model steering represents a powerful technique that dynamically aligns large language models (LLMs) with human preferences during inference. However, conventional model-steering methods rely heavily on externally annotated data, not only limiting their adaptability to varying contexts but also tethering their effectiveness to annotation quality. In this paper, we present SIMS, the first self-improving model-steering framework that operates without relying on external supervision. At its core, SIMS autonomously generates and refines contrastive samples through iterative self-improvement cycles, enabling adaptive, context-specific steering. Additionally, SIMS employs novel strategies, including prompt ranking and contrast sampling, to further enhance steering efficacy. Extensive evaluation across diverse LLMs and benchmarks demonstrates that SIMS substantially outperforms existing methods in steering effectiveness and adaptability, highlighting self-improving model steering as a promising direction for future research on inference-time LLM alignment.

For the first time, we unveil discernible temporal (or historical) trajectory imprints resulting from adversarial example (AE) attacks. Standing in contrast to existing studies all focusing on spatial (or static) imprints within the targeted underlying victim models, we present a fresh temporal paradigm for understanding these attacks. Of paramount discovery is that these imprints are encapsulated within a single loss metric, spanning universally across diverse tasks such as classification and regression, and modalities including image, text, and audio. Recognizing the distinct nature of loss between adversarial and clean examples, we exploit this temporal imprint for AE detection by proposing TRAIT (TRaceable Adversarial temporal trajectory ImprinTs). TRAIT operates under minimal assumptions without prior knowledge of attacks, thereby framing the detection challenge as a one-class classification problem. However, detecting AEs is still challenged by significant overlaps between the constructed synthetic losses of adversarial and clean examples due to the absence of ground truth for incoming inputs. TRAIT addresses this challenge by converting the synthetic loss into a spectrum signature, using the technique of Fast Fourier Transform to highlight the discrepancies, drawing inspiration from the temporal nature of the imprints, analogous to time-series signals. Across 12 AE attacks including SMACK (USENIX Sec'2023), TRAIT demonstrates consistent outstanding performance across comprehensively evaluated modalities, tasks, datasets, and model architectures. In all scenarios, TRAIT achieves an AE detection accuracy exceeding 97%, often around 99%, while maintaining a false rejection rate of 1%. TRAIT remains effective under the formulated strong adaptive attacks.

Ising machines (IM) are physics-inspired alternatives to von Neumann architectures for solving hard optimization tasks. By mapping binary variables to coupled Ising spins, IMs can naturally solve unconstrained combinatorial optimization problems such as finding maximum cuts in graphs. However, despite their importance in practical applications, constrained problems remain challenging to solve for IMs that require large quadratic energy penalties to ensure the correspondence between energy ground states and constrained optimal solutions. To relax this requirement, we propose a self-adaptive IM that iteratively shapes its energy landscape using a Lagrange relaxation of constraints and avoids prior tuning of penalties. Using a probabilistic-bit (p-bit) IM emulated in software, we benchmark our algorithm with multidimensional knapsack problems (MKP) and quadratic knapsack problems (QKP), the latter being an Ising problem with linear constraints. For QKP with 300 variables, the proposed algorithm finds better solutions than state-of-the-art IMs such as Fujitsu's Digital Annealer and requires 7,500x fewer samples. Our results show that adapting the energy landscape during the search can speed up IMs for constrained optimization.

An accurate AI-based diagnostic system for induction motors (IMs) holds the potential to enhance proactive maintenance, mitigating unplanned downtime and curbing overall maintenance costs within an industrial environment. Notably, among the prevalent faults in IMs, a Broken Rotor Bar (BRB) fault is frequently encountered. Researchers have proposed various fault diagnosis approaches using signal processing (SP), machine learning (ML), deep learning (DL), and hybrid architectures for BRB faults. One limitation in the existing literature is the training of these architectures on relatively small datasets, risking overfitting when implementing such systems in industrial environments. This paper addresses this limitation by implementing large-scale data of BRB faults by using a transfer-learning-based lightweight DL model named ShuffleNetV2 for diagnosing one, two, three, and four BRB faults using current and vibration signal data. Spectral images for training and testing are generated using a Short-Time Fourier Transform (STFT). The dataset comprises 57,500 images, with 47,500 used for training and 10,000 for testing. Remarkably, the ShuffleNetV2 model exhibited superior performance, in less computational cost as well as accurately classifying 98.856% of spectral images. To further enhance the visualization of harmonic sidebands resulting from broken bars, Fast Fourier Transform (FFT) is applied to current and vibration data. The paper also provides insights into the training and testing times for each model, contributing to a comprehensive understanding of the proposed fault diagnosis methodology. The findings of our research provide valuable insights into the performance and efficiency of different ML and DL models, offering a foundation for the development of robust fault diagnosis systems for induction motors in industrial settings.

Strong artificial intelligence (AI) is envisioned to possess general cognitive abilities and scientific creativity comparable to human intelligence, encompassing both knowledge acquisition and problem-solving. While remarkable progress has been made in weak AI, the realization of strong AI remains a topic of intense debate and critical examination. In this paper, we explore pivotal innovations in the history of astronomy and physics, focusing on the discovery of Neptune and the concept of scientific revolutions as perceived by philosophers of science. Building on these insights, we introduce a simple theoretical and statistical framework of weak beliefs, termed the Transformational Belief (TB) framework, designed as a foundation for modeling scientific creativity. Through selected illustrative examples in statistical science, we demonstrate the TB framework's potential as a promising foundation for understanding, analyzing, and even fostering creativity -- paving the way toward the development of strong AI. We conclude with reflections on future research directions and potential advancements.

Reinforcing or even exacerbating societal biases and inequalities will increase significantly as generative AI increasingly produces useful artifacts, from text to images and beyond, for the real world. We address these issues by formally characterizing the notion of fairness for generative AI as a basis for monitoring and enforcing fairness. We define two levels of fairness using the notion of infinite sequences of abstractions of AI-generated artifacts such as text or images. The first is the fairness demonstrated on the generated sequences, which is evaluated only on the outputs while agnostic to the prompts and models used. The second is the inherent fairness of the generative AI model, which requires that fairness be manifested when input prompts are neutral, that is, they do not explicitly instruct the generative AI to produce a particular type of output. We also study relative intersectional fairness to counteract the combinatorial explosion of fairness when considering multiple categories together with lazy fairness enforcement. Finally, fairness monitoring and enforcement are tested against some current generative AI models.

Large pretrained models, coupled with fine-tuning, are slowly becoming established as the dominant architecture in machine learning. Even though these models offer impressive performance, their practical application is often limited by the prohibitive amount of resources required for every inference. Early-exiting dynamic neural networks (EDNN) circumvent this issue by allowing a model to make some of its predictions from intermediate layers (i.e., early-exit). Training an EDNN architecture is challenging as it consists of two intertwined components: the gating mechanism (GM) that controls early-exiting decisions and the intermediate inference modules (IMs) that perform inference from intermediate representations. As a result, most existing approaches rely on thresholding confidence metrics for the gating mechanism and strive to improve the underlying backbone network and the inference modules. Although successful, this approach has two fundamental shortcomings: 1) the GMs and the IMs are decoupled during training, leading to a train-test mismatch; and 2) the thresholding gating mechanism introduces a positive bias into the predictive probabilities, making it difficult to readily extract uncertainty information. We propose a novel architecture that connects these two modules. This leads to significant performance improvements on classification datasets and enables better uncertainty characterization capabilities. The dominant approach to improve machine learning models is to develop larger networks that can handle every potential sample. As a result, despite very impressive performance, the resource overhead is huge (Scao et al., 2023). The push for larger model size is often driven by the need to handle a small percentage of samples that are particularly challenging to infer (Bolukbasi et al., 2017); most inferences do not need the full power of a large network to be successfully executed. Nonetheless, most traditional neural network (NN) models have a fixed processing pipeline. This means that every sample, simple or complex, is processed the same way. To tackle this inefficiency, dynamic networks have been introduced (see (Han et al., 2022a) for a review).

In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult. Existing work on meme classification and tracking has focused on black-box methods that do not explicitly consider the semantics of the memes or the context of their creation. In this paper, we pursue a modular and explainable architecture for Internet meme understanding. We design and implement multimodal classification methods that perform example- and prototype-based reasoning over training cases, while leveraging both textual and visual SOTA models to represent the individual cases. We study the relevance of our modular and explainable models in detecting harmful memes on two existing tasks: Hate Speech Detection and Misogyny Classification. We compare the performance between example- and prototype-based methods, and between text, vision, and multimodal models, across different categories of harmfulness (e.g., stereotype and objectification). We devise a user-friendly interface that facilitates the comparative analysis of examples retrieved by all of our models for any given meme, informing the community about the strengths and limitations of these explainable methods.

This blog post is co-authored by Vittorio Accomazzi, Chief Technical Officer (CTO) at IMS. This blog is part of a series in collaboration with our partners and customers leveraging the newly announced Azure Health Data Services. Azure Health Data Services, a platform as a service (PaaS) offering designed to support Protected Health Information (PHI) in the cloud, is a new way of working with unified data--providing care teams with a platform to support both transactional and analytical workloads from the same data store and enabling cloud computing to transform how we develop and deliver AI across the healthcare ecosystem. The first implementation of digital imaging techniques in clinical use started in the 1970s. Since then, the medical imaging industry has grown exponentially--over the last two and a half decades, there has been a significant development in image acquisition solutions, which has boosted image quality and adoption in different clinical applications.

As cloud services are growing and generating high revenues, the cost of downtime in these services is becoming significantly expensive. To reduce loss and service downtime, a critical primary step is to execute incident triage, the process of assigning a service incident to the correct responsible team, in a timely manner. An incorrect assignment risks additional incident reroutings and increases its time to mitigate by 10x. However, automated incident triage in large cloud services faces many challenges: (1) a highly imbalanced incident distribution from a large number of teams, (2) wide variety in formats of input data or data sources, (3) scaling to meet production-grade requirements, and (4) gaining engineers' trust in using machine learning recommendations. To address these challenges, we introduce DeepTriage, an intelligent incident transfer service combining multiple machine learning techniques - gradient boosted classifiers, clustering methods, and deep neural networks - in an ensemble to recommend the responsible team to triage an incident. Experimental results on real incidents in Microsoft Azure show that our service achieves 82.9% F1 score. For highly impacted incidents, DeepTriage achieves F1 score from 76.3% - 91.3%. We have applied best practices and state-of-the-art frameworks to scale DeepTriage to handle incident routing for all cloud services. DeepTriage has been deployed in Azure since October 2017 and is used by thousands of teams daily.