This article is adapted from a lecture series I gave at Deeplearn 2025: From Prototype to Production: Evaluation Strategies for Agentic Applications. Task-based evaluations, which measure an AI system’s performance in use-case-specific, real-world settings, are underadopted and understudied. There is…
Here’s a guide to all the models—plus Pixel case recommendations and smart software tricks to try.
Find and fix security vulnerabilities. Deploy your app to the cloud. All without leaving your command-line. Today, we’re closing the gap between your terminal and the cloud with a first look at the future of Gemini CLI, delivered through two…
On Wednesday morning, Poland shot down several Russian drones that entered its airspace—a first since Moscow’s invasion of Ukraine. The incident disrupted air travel and set the region on edge.
Drawing from his 8 years of experience in AI, Paul Iusztin breaks down the core components of a scalable architecture, emphasizing the importance of RAG. He shares practical patterns, including the Feature Training Inference architecture, and provides a detailed use…
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In this tutorial, we walk through an advanced yet practical workflow using SpeechBrain. We start by generating our own clean speech samples with gTTS, deliberately adding noise to simulate real-world scenarios, and then applying SpeechBrain’s MetricGAN+ model to enhance the audio. Once the audio is denoised, we run automatic speech recognition with a language model–rescored […] The post Building a Speech Enhancement and Automatic Speech Recognition (ASR) Pipeline in Python Using SpeechBrain appeared first on MarkTechPost.
arXiv:2509.07108v1 Announce Type: new Abstract: Survival analysis is a fundamental tool for modeling time-to-event outcomes in healthcare. Recent advances have introduced flexible neural network approaches for improved predictive performance. However, most of these models do not provide interpretable insights into the association between exposures and the modeled outcomes, a critical requirement for decision-making in clinical practice. To address this limitation, we propose Additive Deep Hazard Analysis Mixtures (ADHAM), an interpretable additive survival model. ADHAM assumes a conditional latent structure that defines subgroups, each characterized by a combination of covariate-specific hazard functions. To select the number of subgroups, we introduce a post-training refinement that reduces the number of equivalent latent subgroups by merging similar groups. We perform comprehensive studies to demonstrate ADHAM's interpretability at the population, subgroup, and individual levels. Extensive experiments on real-world datasets show that ADHAM provides novel insights into the association between exposures and outcomes. Further, ADHAM remains on par with existing state-of-the-art survival baselines in terms of predictive performance, offering a scalable and interpretable approach to time-to-event prediction in healthcare.
arXiv:2509.06213v2 Announce Type: replace-cross Abstract: We investigate reinforcement learning in the Game Of Hidden Rules (GOHR) environment, a complex puzzle in which an agent must infer and execute hidden rules to clear a 6$times$6 board by placing game pieces into buckets. We explore two state representation strategies, namely Feature-Centric (FC) and Object-Centric (OC), and employ a Transformer-based Advantage Actor-Critic (A2C) algorithm for training. The agent has access only to partial observations and must simultaneously infer the governing rule and learn the optimal policy through experience. We evaluate our models across multiple rule-based and trial-list-based experimental setups, analyzing transfer effects and the impact of representation on learning efficiency.
