A verifiable quantum advantage (opens in new tab)

Google Quantum AI researchers have introduced "Quantum Echoes," a new algorithm designed to measure Out-of-Time-Order Correlators (OTOCs) to characterize quantum chaos. By demonstrating this task on the 103-qubit Willow chip, the team has achieved a verifiable quantum advantage that surpasses the limitations of previous random circuit sampling techniques. This work establishes a direct path toward solving practical problems in physics and chemistry, such as Hamiltonian learning, through the use of stable and reproducible quantum expectation values. ## Limitations of Random Circuit Sampling * While the 2019 "quantum supremacy" milestone proved quantum computers could outperform classical ones, the bitstring sampling method used was difficult to verify and lacked practical utility. * In large-scale quantum systems, specific bitstrings rarely repeat, which restricts the ability to extract useful, actionable information from the computation. * The Quantum Echoes approach shifts focus to quantum expectation values—such as magnetization, density, and velocity—which remain consistent across different quantum computers and are computationally verifiable. ## The Quantum Echoes Algorithm and OTOCs * The algorithm measures OTOCs, which represent the state of a single qubit after a series of "forward" ($U$) and "backward" ($U^\dagger$) evolutions. * In the experiment, 103 qubits on the Willow processor underwent evolution through random quantum circuits to reach a highly chaotic state. * A perturbation (gate $B$) is applied between the forward and backward evolutions; if the system is chaotic, this small change triggers a "butterfly effect," resulting in a final state significantly different from the initial one. * Higher-order OTOCs involve multiple "round trips" of these evolutions, increasing the system's sensitivity to the perturbation and allowing for a more detailed characterization of the quantum dynamics. ## Many-Body Interference and Signal Amplification * The researchers discovered that higher-order OTOCs function like many-body interferometers, where the quantum states of many particles interfere with one another. * The perturbation gates ($B$ and $M$) act as mirrors; when a resonance condition is met (where $U^\dagger$ is the exact inverse of $U$), constructive interference occurs. * This constructive interference amplifies specific quantum correlations, allowing the OTOC signal magnitude to scale as a negative power of the system size, rather than the exponential decay typically seen in chaotic systems. * This amplification makes the OTOC a sensitive instrument for identifying the specific correlations generated between two different qubits during the evolution of the circuit. ## Practical Applications and Future Research The success of the Quantum Echoes algorithm on the Willow chip marks a transition toward using quantum computers for tasks that are both beyond-classical and physically relevant. This method is particularly well-suited for Hamiltonian learning in Nuclear Magnetic Resonance (NMR) and studying the flow of electrons in high-temperature superconductors. Moving forward, the ability to measure verifiable expectation values in the chaotic regime will be essential for researchers looking to simulate complex quantum materials that are impossible to model on classical hardware.

Behind the Streams: Real-Time Recommendations for Live Events Part 3 | by Netflix Technology Blog | Netflix TechBlog (opens in new tab)

Netflix manages the massive surge of concurrent users during live events by utilizing a hybrid strategy of prefetching and real-time broadcasting to deliver synchronized recommendations. By decoupling data delivery from the live trigger, the system avoids the "thundering herd" effect that would otherwise overwhelm cloud infrastructure during record-breaking broadcasts. This architecture ensures that millions of global devices receive timely updates and visual cues without requiring linear, inefficient scaling of compute resources. ### The Constraint Optimization Problem To maintain a seamless experience, Netflix engineers balance three primary technical constraints: time to update, request throughput, and compute cardinality. * **Time:** The specific duration required to coordinate and push a recommendation update to the entire global fleet. * **Throughput:** The maximum capacity of cloud services to handle incoming requests without service degradation. * **Cardinality:** The variety and complexity of unique requests necessary to serve personalized updates to different user segments. ### Two-Phase Recommendation Delivery The system splits the delivery process into two distinct stages to smooth out traffic spikes and ensure high availability. * **Prefetching Phase:** While members browse the app normally before an event, the system downloads materialized recommendations, metadata, and artwork into the device's local cache. * **Broadcasting Phase:** When the event begins, a low-cardinality "at least once" message is broadcast to all connected devices, triggering them to display the already-cached content instantaneously. * **Traffic Smoothing:** This approach eliminates the need for massive, real-time data fetches at the moment of kickoff, distributing the heavy lifting of data transfer over a longer period. ### Live State Management and UI Synchronization A dedicated Live State Management (LSM) system tracks event schedules in real time to ensure the user interface stays perfectly in sync with the production. * **Dynamic Adjustments:** If a live event is delayed or ends early, the LSM adjusts the broadcast triggers to preserve accuracy and prevent "spoilers" or dead links. * **Visual Cues:** The UI utilizes "Live" badging and dynamic artwork transitions to signal urgency and guide users toward the stream. * **Frictionless Playback:** For members already on a title’s detail page, the system can trigger an automatic transition into the live player the moment the broadcast begins, reducing navigation latency. To support global-scale live events, technical teams should prioritize edge-heavy strategies that pre-position assets on client devices. By shifting from a reactive request-response model to a proactive prefetch-and-trigger model, platforms can maintain high performance and reliability even during the most significant traffic peaks.

Teaching Gemini to spot exploding stars with just a few examples (opens in new tab)

Researchers have demonstrated that Google’s Gemini model can classify cosmic events with 93% accuracy, rivaling specialized machine learning models while providing human-readable explanations. By utilizing few-shot learning with only 15 examples per survey, the model addresses the "black box" limitation of traditional convolutional neural networks used in astronomy. This approach enables scientists to efficiently process the millions of alerts generated by modern telescopes while maintaining a transparent and interactive reasoning process. ## Bottlenecks in Modern Transient Astronomy * Telescopes like the Vera C. Rubin Observatory are expected to generate up to 10 million alerts per night, making manual verification impossible. * The vast majority of these alerts are "bogus" signals caused by satellite trails, cosmic rays, or instrumental artifacts rather than real supernovae. * Existing specialized models often provide binary "real" or "bogus" labels without context, forcing astronomers to either blindly trust the output or spend hours on manual verification. ## Multimodal Few-Shot Learning for Classification * The research utilized few-shot learning, providing Gemini with only 15 annotated examples for three major surveys: Pan-STARRS, MeerLICHT, and ATLAS. * Input data consisted of image triplets—a "new" alert image, a "reference" image of the same sky patch, and a "difference" image—each 100x100 pixels in size. * The model successfully generalized across different telescopes with varying pixel scales, ranging from 0.25" per pixel for Pan-STARRS to 1.8" per pixel for ATLAS. * Beyond simple labels, Gemini generates a textual description of observed features and an interest score to help astronomers prioritize follow-up observations. ## Expert Validation and Self-Assessment * A panel of 12 professional astronomers evaluated the model using a 0–5 coherence rubric, confirming that Gemini’s logic aligned with expert reasoning. * The study found that Gemini can effectively assess its own uncertainty; low self-assigned "coherence scores" were strong indicators of likely classification errors. * This ability to flag its own potential mistakes allows the model to act as a reliable partner, alerting scientists when a specific case requires human intervention. The transition from "black box" classifiers to interpretable AI assistants allows the astronomical community to scale with the data flood of next-generation telescopes. By combining high-accuracy classification with transparent reasoning, researchers can maintain scientific rigor while processing millions of cosmic events in real time.

A picture's worth a thousand (private) words: Hierarchical generation of coherent synthetic photo albums (opens in new tab)

Researchers at Google have developed a hierarchical method for generating differentially private (DP) synthetic photo albums, providing a way to share representative datasets while protecting sensitive individual information. By utilizing an intermediate text representation and a two-stage generation process, the approach maintains thematic coherence across multiple images in an album—a significant challenge for traditional synthetic data methods. This framework allows organizations to apply standard, non-private analytical techniques to safe synthetic substitutes rather than modifying every individual analysis method for differential privacy. ## The Hierarchical Generation Process * The workflow begins by converting original photo albums into structured text; an AI model generates detailed captions for each image and a summary for the entire album. * Two large language models (LLMs) are privately fine-tuned using DP-SGD: the first is trained to produce album summaries, and the second generates individual photo captions based on those summaries. * Synthetic data is then produced hierarchically, where the model first generates a global album summary to serve as context, followed by a series of individual photo captions that remain consistent with that context. * The final step uses a text-to-image AI model to transform the private, synthetic text captions back into a set of coherent images. ## Benefits of Intermediate Text Representations * Text summarization is inherently privacy-enhancing because it is a "lossy" operation, meaning the text description is unlikely to capture the exact unique details of an original photo. * Using text as a midpoint allows for more efficient resource management, as generated albums can be filtered and curated at the text level before undergoing the computationally expensive process of image generation. * The hierarchical approach ensures that photos within a synthetic album share the same characters and themes, as every caption in a set is derived from the same contextual summary. * Training two separate models with shorter context windows is significantly more efficient than training one large model, because the computational cost of self-attention scales quadratically with the length of the context. This hierarchical, text-mediated approach demonstrates that high-level semantic information and thematic coherence can be preserved in synthetic datasets without sacrificing individual privacy. Organizations should consider this workflow—translating complex multi-modal data into structured text before synthesis—to scale differentially private data generation for advanced modeling and analysis.

Essential Element for App Success: (opens in new tab)

Effective mobile app management requires proactive outage monitoring to prevent user churn caused by failures in critical flows like registration or payment. Relying on user reports is often too late, so developers must implement systematic event collection and real-time dashboards to identify issues the moment they arise. By integrating tools like Sentry or Firebase, teams can maintain high quality through immediate response and detailed performance analysis. ### Implementing Sentry in Flutter * **Dependency and Initialization**: Integration begins by adding `sentry_flutter` and `sentry_dio` to the project. The initialization process involves setting the Data Source Name (DSN), environment tags (e.g., production vs. staging), and release versions to ensure logs are correctly categorized. * **Performance and Privacy**: Developers should configure `tracesSampleRate` and `profilesSampleRate` to balance monitoring depth with costs. Additionally, the `beforeSend` callback allows for masking sensitive user data like authorization headers or IP addresses before they are transmitted. * **Contextual Tracking**: To aid debugging, the system captures user IDs via `Sentry.configureScope` and tracks user movement using `SentryNavigatorObserver`. Utilizing `SentryInterceptor` with the Dio library allows for automatic tracking of HTTP request performance and API bottlenecks. ### Strategic Log Level Design * **Debug and Info**: Debug logs remain local to the terminal to save resources. Info logs are reserved for significant user actions that change data, such as successful sign-ups or purchases, while high-frequency read actions like "viewing a product list" are excluded to reduce noise and costs. * **Warning**: This level tracks external system failures, such as failed API calls or push notification losses. To prevent "alert fatigue," client-side network issues (e.g., timeouts or offline status) are ignored, and alerts are triggered only when specific thresholds are met, such as 100 failures within 10 minutes. * **Error**: Error logs represent internal logic failures that bypass defensive coding, such as null object errors, parsing failures, or unreachable code branches. These require immediate notification to the development team to facilitate rapid hotfixes. * **Fatal**: This level is dedicated to application crashes and unhandled exceptions. When configured at the app's entry point, the system automatically captures these critical failures to provide a comprehensive "crash-free users" metric. ### Creating Effective Dashboards * **Naming Conventions**: Logs should follow a strict structure, using tags for modules and event names (e.g., `[API] [postLogin] success`). This consistency allows for granular querying and clearer visualization on monitoring dashboards. * **Data Enrichment**: Using the `extra` field in log events provides vital context for troubleshooting, such as including the specific endpoint, request body, and response status code for a failed transaction. * **Actionable Metrics**: Effective monitoring focuses on key performance indicators like API error rates and the failure percentage of core business events (login, registration, payment) rather than just raw crash counts. A robust monitoring strategy shifts the focus from simple crash reporting to comprehensive service health. By standardizing log levels and automating event collection, development teams can distinguish between transient network blips and critical logic errors, ensuring they spend their time fixing high-impact issues.

Solving virtual machine puzzles: How AI is optimizing cloud computing (opens in new tab)

Google researchers have developed LAVA, a scheduling framework designed to optimize virtual machine (VM) allocation in large-scale data centers by accurately predicting and adapting to VM lifespans. By moving beyond static, one-time predictions toward a "continuous re-prediction" model based on survival analysis, the system significantly improves resource efficiency and reduces fragmentation. This approach allows cloud providers to solve the complex "bin packing" problem more effectively, leading to better capacity utilization and easier system maintenance. ### The Challenge of Long-Tailed VM Distributions * Cloud workloads exhibit a extreme long-tailed distribution: while 88% of VMs live for less than an hour, these short-lived jobs consume only 2% of total resources. * The rare VMs that run for 30 days or longer account for a massive fraction of compute resources, meaning their placement has a disproportionate impact on host availability. * Poor allocation leads to "resource stranding," where a server's remaining capacity is too small or unbalanced to host new VMs, effectively wasting expensive hardware. * Traditional machine learning models that provide only a single prediction at VM creation are often fragile, as a single misprediction can block a physical host from being cleared for maintenance or new tasks. ### Continuous Re-prediction via Survival Analysis * Instead of predicting a single average lifetime, LAVA uses an ML model to generate a probability distribution of a VM's expected duration. * The system employs "continuous re-prediction," asking how much longer a VM is expected to run given how long it has already survived (e.g., a VM that has run for five days is assigned a different remaining lifespan than a brand-new one). * This adaptive approach allows the scheduling logic to automatically correct for initial mispredictions as more data about the VM's actual behavior becomes available over time. ### Novel Scheduling and Rescheduling Algorithms * **Non-Invasive Lifetime Aware Scheduling (NILAS):** Currently deployed on Google’s Borg cluster manager, this algorithm ranks potential hosts by grouping VMs with similar expected exit times to increase the frequency of "empty hosts" available for maintenance. * **Lifetime-Aware VM Allocation (LAVA):** This algorithm fills resource gaps on hosts containing long-lived VMs with jobs that are at least an order of magnitude shorter. This ensures the short-lived VMs exit quickly without extending the host's overall occupation time. * **Lifetime-Aware Rescheduling (LARS):** To minimize disruptions during defragmentation, LARS identifies and migrates the longest-lived VMs first while allowing short-lived VMs to finish their tasks naturally on the original host. By integrating survival-analysis-based predictions into the core logic of data center management, cloud providers can transition from reactive scheduling to a proactive model. This system not only maximizes resource density but also ensures that the physical infrastructure remains flexible enough to handle large, resource-intensive provisioning requests and essential system updates.

Using AI to identify genetic variants in tumors with DeepSomatic (opens in new tab)

DeepSomatic is an AI-powered tool developed by Google Research to identify cancer-related mutations by analyzing a tumor's genetic sequence with higher accuracy than current methods. By leveraging convolutional neural networks (CNNs), the model distinguishes between inherited genetic traits and acquired somatic variants that drive cancer progression. This flexible tool supports multiple sequencing platforms and sample types, offering a critical resource for clinicians and researchers aiming to personalize cancer treatment through precision medicine. ## Challenges in Somatic Variant Detection * Somatic variants are genetic mutations acquired after birth through environmental exposure or DNA replication errors, making them distinct from the germline variants found in every cell of a person's body. * Detecting these mutations is technically difficult because tumor samples are often heterogeneous, containing a diverse set of variants at varying frequencies. * Sequencing technologies often introduce small errors that can be difficult to distinguish from actual somatic mutations, especially when the mutation is only present in a small fraction of the sampled cells. ## CNN-Based Variant Calling Architecture * DeepSomatic employs a method pioneered by DeepVariant, which involves transforming raw genetic sequencing data into a set of multi-channel images. * These images represent various data points, including alignment along the chromosome, the quality of the sequence output, and other technical variables. * The convolutional neural network processes these images to differentiate between three categories: the human reference genome, non-cancerous germline variants, and the somatic mutations driving tumor growth. * By analyzing tumor and non-cancerous cells side-by-side, the model effectively filters out sequencing artifacts that might otherwise be misidentified as mutations. ## System Versatility and Application * The model is designed to function in multiple modes, including "tumor-normal" (comparing a biopsy to a healthy sample) and "tumor-only" mode, which is vital for blood cancers like leukemia where isolating healthy cells is difficult. * DeepSomatic is platform-agnostic, meaning it can process data from all major sequencing technologies and adapt to different types of sample processing. * The tool has demonstrated the ability to generalize its learning to various cancer types, even those not specifically included in its initial training sets. ## Open-Source Contributions to Precision Medicine * Google has made the DeepSomatic tool and the CASTLE dataset—a high-quality training and evaluation set—openly available to the global research community. * This initiative is part of a broader effort to use AI for early detection and advanced research in various cancers, including breast, lung, and gynecological cancers. * The release aims to accelerate the development of personalized treatment plans by providing a more reliable way to identify the specific genetic drivers of an individual's disease. By providing a more accurate and adaptable method for variant calling, DeepSomatic helps researchers pinpoint the specific drivers of a patient's cancer. This tool represents a significant advancement in deep learning for genomics, potentially shortening the path from biopsy to targeted therapeutic intervention.

Coral NPU: A full-stack platform for Edge AI (opens in new tab)

Coral NPU is a new full-stack, open-source platform designed to bring advanced AI directly to power-constrained edge devices and wearables. By prioritizing a matrix-first hardware architecture and a unified software stack, Google aims to overcome traditional bottlenecks in performance, ecosystem fragmentation, and data privacy. The platform enables always-on, low-power ambient sensing while providing developers with a flexible, RISC-V-based environment for deploying modern machine learning models. ## Overcoming Edge AI Constraints * The platform addresses the "performance gap" where complex ML models typically exceed the power, thermal, and memory budgets of battery-operated devices. * It eliminates the "fragmentation tax" by providing a unified architecture, moving away from proprietary processors that require costly, device-specific optimizations. * On-device processing ensures a high standard of privacy and security by keeping personal context and data off the cloud. ## AI-First Hardware Architecture * Unlike traditional chips, this architecture prioritizes the ML matrix engine over scalar compute to optimize for efficient on-device inference. * The design is built on RISC-V ISA compliant architectural IP blocks, offering an open and extensible reference for system-on-chip (SoC) designers. * The base design delivers performance in the 512 giga operations per second (GOPS) range while consuming only a few milliwatts of power. * The architecture is tailored for "always-on" use cases, making it ideal for hearables, AR glasses, and smartwatches. ## Core Architectural Components * **Scalar Core:** A lightweight, C-programmable RISC-V frontend that manages data flow using an ultra-low-power "run-to-completion" model. * **Vector Execution Unit:** A SIMD co-processor compliant with the RISC-V Vector instruction set (RVV) v1.0 for simultaneous operations on large datasets. * **Matrix Execution Unit:** A specialized engine using quantized outer product multiply-accumulate (MAC) operations to accelerate fundamental neural network tasks. ## Unified Developer Ecosystem * The platform is a C-programmable target that integrates with modern compilers such as IREE and TFLM (TensorFlow Lite Micro). * It supports a wide range of popular ML frameworks, including TensorFlow, JAX, and PyTorch. * The software toolchain utilizes MLIR and the StableHLO dialect to facilitate the transition from high-level models to hardware-executable code. * Developers have access to a complete suite of tools, including a simulator, custom kernels, and a general-purpose MLIR compiler. SoC designers and ML developers looking to build the next generation of wearables should leverage the Coral NPU reference architecture to balance high-performance AI with extreme power efficiency. By utilizing the open-source documentation and RISC-V-based tools, teams can significantly reduce the complexity of deploying private, always-on ambient sensing.

XR Blocks: Accelerating AI + XR innovation (opens in new tab)

XR Blocks is an open-source, cross-platform framework designed to bridge the technical gap between mature AI development ecosystems and high-friction extended reality (XR) prototyping. By providing a modular architecture and high-level abstractions, the toolkit enables creators to rapidly build and deploy intelligent, immersive web applications without managing low-level system integration. Ultimately, the framework empowers developers to move from concept to interactive prototype across both desktop simulators and mobile XR devices using a unified codebase. ### Core Design Principles * **Simplicity and Readability:** Drawing inspiration from the "Zen of Python," the framework prioritizes human-readable abstractions where a developer’s script reflects a high-level description of the experience rather than complex boilerplate code. * **Creator-Centric Workflow:** The architecture is designed to handle the "plumbing" of XR—such as sensor fusion, AI model integration, and cross-platform logic—allowing creators to focus entirely on user interaction and experience. * **Pragmatic Modularity:** Rather than attempting to be a perfect, all-encompassing system, XR Blocks favors an adaptable and simple architecture that can evolve alongside the rapidly changing fields of AI and spatial computing. ### The Reality Model Abstractions * **The Script Primitive:** Acts as the logical center of an application, separating the "what" of an interaction from the "how" of its underlying technical implementation. * **User and World:** Provides built-in support for tracking hands, gaze, and avatars while allowing the system to query the physical environment for depth, estimated lighting conditions, and object recognition. * **AI and Agents:** Facilitates the integration of intelligent assistants, such as the "Sensible Agent," which can provide proactive, context-aware suggestions within the XR environment. * **Virtual Interfaces:** Offers tools to augment blended reality with virtual UI elements that respond to the user's physical context. ### Technical Implementation and Integration * **Web-Based Foundation:** The framework is built upon accessible, standard technologies including WebXR, three.js, and LiteRT (formerly TFLite) to ensure a low barrier to entry for web developers. * **Advanced AI Support:** It features native integration with Gemini for high-level reasoning and context-aware applications. * **Cross-Platform Deployment:** Developers can prototype depth-aware, physics-based interactions in a desktop simulator and deploy the exact same code to Android XR devices. * **Open-Source Resources:** The project includes a comprehensive suite of templates and live demos covering specific use cases like depth mapping, gesture modeling, and lighting estimation. By lowering the barrier to entry for intelligent XR development, XR Blocks serves as a practical starting point for researchers and developers aiming to explore the next generation of human-centered computing. Interested creators can access the source code on GitHub to begin building immersive, AI-driven applications that function seamlessly across the web and specialized XR hardware.

​​Speech-to-Retrieval (S2R): A new approach to voice search (opens in new tab)

Google Research has introduced Speech-to-Retrieval (S2R), a direct speech-to-intent engine designed to overcome the fundamental limitations of traditional cascade-based voice search. By bypassing the error-prone intermediate step of text transcription, S2R significantly reduces information loss and prevents minor phonetic errors from derailing search accuracy. This shift from identifying literal words to understanding underlying intent represents an architectural change that promises faster and more reliable search experiences globally. ## Limitations of Cascade Modeling * Traditional systems rely on Automatic Speech Recognition (ASR) to convert audio into a text string before passing it to a search engine. * This "cascade" approach suffers from error propagation, where a single phonetic mistake—such as transcribing "The Scream painting" as "The Screen painting"—leads to entirely irrelevant search results. * Textual transcription often results in information loss, as the system may strip away vocal nuances or contextual cues that could help disambiguate the user's actual intent. ## The S2R Architectural Shift * S2R interprets and retrieves information directly from spoken queries, treating the audio as the primary source of intent rather than a precursor to text. * The system shifts the technical focus from "What words were said?" to "What information is being sought?", allowing the model to bridge the quality gap between current voice search and human-level understanding. * This approach is designed to be more robust across different languages and audio conditions by mapping speech features directly to a retrieval space. ## Evaluating Performance with the SVQ Dataset * Researchers used Mean Reciprocal Rank (MRR) to evaluate search effectiveness, comparing real-world ASR systems against "Cascade Groundtruth" models that use perfect, human-verified text. * The study found that Word Error Rate (WER) is often a poor predictor of search success; a lower WER does not always result in a higher MRR, as the nature of the error matters more than the frequency. * To facilitate further research, Google has open-sourced the Simple Voice Questions (SVQ) dataset, which includes audio queries in 17 languages and 26 locales. * The SVQ dataset is integrated into the new Massive Sound Embedding Benchmark (MSEB) to provide a standardized way to measure direct speech-to-intent performance. The transition to Speech-to-Retrieval signifies a major evolution in how AI handles human voice. For developers and researchers, the release of the SVQ dataset and the focus on MRR over traditional transcription metrics provide a new roadmap for building voice interfaces that are resilient to the phonetic ambiguities of natural speech.

IUI 20 (opens in new tab)

The IUI 2025 conference highlighted a significant shift in the AI landscape, moving away from a sole focus on model performance toward "human-centered AI" that prioritizes collaboration, ethics, and user agency. The prevailing consensus across key sessions suggests that for AI to be sustainable and trustworthy, it must transcend simple automation to become a tool that augments human perception and decision-making through transparent, interactive, and socially aware design. ## Reality Design and Human Augmentation The concept of "Reality Design" suggests that Human-Computer Interaction (HCI) research must expand beyond screen-based interfaces to design reality itself. As AI, sensors, and wearables become integrated into daily life, technology can be used to directly augment human perception, cognition, and memory. * Memory extension: Systems can record and reconstruct personal experiences, helping users recall details in educational or professional settings. * Sensory augmentation: Technologies like selective hearing or slow-motion visual playback can enhance a user's natural observational powers. * Cognitive balance: While AI can assist with task difficulty (e.g., collaborative Lego building), designers must ensure that automation does not erode the human will to learn or remember, echoing historical warnings about technology-induced "forgetfulness." ## Bridging the Socio-technical Gap in AI Transparency Transparency in AI, particularly for high-risk areas like finance or medicine, should not be limited to showing mathematical model weights. Instead, it must bridge the gap between technical complexity and human understanding by focusing on user goals and social contexts. * Multi-faceted communication: Effective transparency involves model reporting (Model Cards), sharing safety evaluation results, and providing linguistic or visual cues for uncertainty rather than just numerical scores. * Counterfactual explanations: Users gain better trust when they can see how a decision might have changed if specific input conditions were different. * Interaction-based transparency: Transparency must be coupled with control, allowing users to act as "adjusters" who provide feedback that the model then reflects in its future outputs. ## Interactive Machine Learning and Human-in-the-Loop The framework of Interactive Machine Learning (IML) challenges the traditional view of AI as a static black box trained on fixed data. Instead, it proposes an interactive loop where the user and the model grow together through continuous feedback. * User-driven training: Users should be able to inspect model classifications, correct errors, and have those corrections immediately influence the model's learning path. * Beyond automation: This approach reframes AI from a replacement for human labor into a collaborative partner that adapts to specific user behaviors and professional expertise. * Impact on specialized tools: Modern applications include educational platforms where students manipulate data directly and research tools that integrate human intuition into large-scale data analysis. ## Collaborative Systems in Specialized Professional Contexts Practical applications of human-centered AI are being realized in sensitive fields like child counseling, where AI assists experts without replacing the human element. * Counselor-AI transcription: Systems designed for counseling analysis allow AI to handle the heavy lifting of transcription while counselors manage the nuance and contextual editing. * Efficiency through partnership: By focusing on reducing administrative burdens, these systems enable professionals to spend more time on high-level cognitive tasks and emotional support, demonstrating the value of AI as a supportive infrastructure. The future of AI development requires moving beyond isolated technical optimization to embrace the complexity of the human experience. Organizations and developers should focus on creating systems where transparency is a tool for "appropriate trust" and where design is focused on empowering human capabilities rather than simply automating them.

A month-long task in just (opens in new tab)

This blog post explores how LY Corporation reduced a month-long development task to just five days by leveraging "vibe coding" with Generative AI tools like ChatGPT and Cursor. By shifting from traditional, rigid documentation to an iterative, demo-first approach, developers can rapidly validate multiple UI/UX solutions for complex problems like restaurant menu registration. The author concludes that AI's ability to handle frequent re-work makes it more efficient to "build fast and iterate" than to aim for perfection through long-form specifications. ### Strategic Shift to Rapid Prototyping * Traditional development cycles (spec → design → dev → fix) are often too slow to keep up with market trends due to heavy documentation and impact analysis. * The "vibe coding" approach prioritizes creating "working demos" over perfect specifications to find "good enough" answers through rapid feedback loops. * AI reduces the psychological and logistical burden of "starting over," allowing developers to refine the context and quality of outputs through repeated interaction without the friction of manual re-documentation. ### Defining Requirements and Solution Ideation * Initial requirements are kept minimal, focusing only on the core mission, top priorities, and essential data structures (e.g., product name, image, description) to avoid limiting AI creativity. * ChatGPT is used to generate a wide range of solution candidates, which are then filtered into five distinct approaches: Stepper Wizards, Live Previews with Quick Add, Template/Cloning, Chat Input, and OCR-based photo scanning. * This stage emphasizes volume and variety, using AI-generated pros and cons to establish selection criteria and identify potential UX bottlenecks early in the process. ### Detailed Design and Multi-Solution Wireframing * Each of the five chosen solutions is expanded into detailed screen flows and UI elements, such as progress bars, bottom sheets, and validation logic. * Prompt engineering is used iteratively; if an AI-generated result lacks a specific feature like "temporary storage" or "mandatory field validation," the prompt is adjusted to regenerate the design instantly. * The focus remains on defining the "what" (UI elements) and "how" (user flow) through textual descriptions before moving to actual coding. ### Implementation with Cursor and Flutter * Cursor is utilized to generate functional code based on the refined wireframes, using Flutter as the framework to ensure rapid cross-platform development for both iOS and Android. * The development follows a "skeleton-first" approach: first creating a main navigation hub with five entry points, then populating each individual solution module one by one. * Technical architecture decisions, such as using Riverpod for state management or SQLite for data storage, are layered onto the demo post-hoc, reversing the traditional "stack-first" development order to prioritize functional validation. ### Recommendation To maximize efficiency, developers should treat AI as a partner for high-speed iteration rather than a one-shot tool. By focusing on creating functional demos quickly and refining them through direct feedback, teams can bypass the bottlenecks of traditional software requirements and deliver user-centric products in a fraction of the time.

A collaborative approach to image generation (opens in new tab)

Google Research has introduced PASTA (Preference Adaptive and Sequential Text-to-image Agent), a reinforcement learning agent designed to transform image generation from a single-prompt task into a collaborative, multi-turn dialogue. By learning individual user preferences through sequential interactions, the system eliminates the frustration of trial-and-error prompting to achieve a specific creative vision. ## Data Strategy and User Simulation * Researchers collected a foundational dataset featuring over 7,000 human interactions, using Gemini Flash for prompt expansion and Stable Diffusion XL (SDXL) for image generation. * To overcome the scarcity of real-world interaction data, the team developed a user simulator that generated over 30,000 additional interaction trajectories. * The simulator is built on two primary components: a utility model that predicts how much a user will like an image, and a choice model that predicts which image a user will select from a given set. ## Latent Preference Discovery * The architecture utilizes pre-trained CLIP encoders paired with user-specific components to capture nuanced aesthetic tastes. * An expectation-maximization (EM) algorithm is employed to identify "user types," allowing the system to cluster users with similar interests, such as a preference for specific artistic styles or subject matter like "Food" or "Animals." * This approach enables the model to generalize preferences quickly, allowing it to adapt to new users based on minimal initial feedback. ## The Collaborative Generation Loop * PASTA operates as a value-based reinforcement learning model that aims to maximize cumulative user satisfaction across an entire interaction session. * The workflow begins with a candidate generator creating diverse prompt expansions; a candidate selector then picks an optimal "slate" of four variations to present to the user. * Each user selection provides a feedback signal that guides the agent’s next set of suggestions, iteratively narrowing the gap between the generated output and the user's intent. ## Training and Performance Validation * The agent was trained using Implicit Q-learning (IQL) to optimize decision-making without requiring online interaction during the training phase. * Performance was measured using several metrics, including Pick-a-Pic accuracy, Spearman’s rank correlation, and cross-turn accuracy. * Results indicated that agents trained on a combination of real-world and simulated data significantly outperformed baseline models and versions trained on only one data type. PASTA demonstrates that integrating iterative feedback loops and reinforcement learning can effectively bridge the "intent gap" in generative AI. For developers building creative tools, this research suggests that move-away from static prompting toward adaptive, simulation-trained agents can provide a more satisfying and intuitive user experience.

Introducing interactive on-device segmentation in Snapseed (opens in new tab)

Google has introduced a new "Object Brush" feature in Snapseed that enables intuitive, real-time selective photo editing through a novel on-device segmentation technology. By leveraging a high-performance interactive AI model, users can isolate complex subjects with simple touch gestures in under 20 milliseconds, bridging the gap between professional-grade editing and mobile convenience. This breakthrough is achieved through a sophisticated teacher-student training architecture that prioritizes both pixel-perfect accuracy and low-latency performance on consumer hardware. ### High-Performance On-Device Inference * The system is powered by the Interactive Segmenter model, which is integrated directly into the Snapseed "Adjust" tool to facilitate immediate object-based modifications. * To ensure a fluid user experience, the model utilizes the MediaPipe framework and LiteRT’s GPU acceleration to process selections in less than 20ms. * The interface supports dynamic refinement, allowing users to provide real-time feedback by tracing lines or tapping to add or subtract specific areas of an image. ### Teacher-Student Model Distillation * The development team first created "Interactive Segmenter: Teacher," a large-scale model fine-tuned on 30,000 high-quality, pixel-perfect manual annotations across more than 350 object categories. * Because the Teacher model’s size and computational requirements are prohibitive for mobile use, researchers developed "Interactive Segmenter: Edge" through knowledge distillation. * This distillation process utilized a dataset of over 2 million weakly annotated images, allowing the smaller Edge model to inherit the generalization capabilities of the Teacher model while maintaining a footprint suitable for mobile devices. ### Training via Synthetic User Prompts * To make the model universally capable across all object types, the training process uses a class-agnostic approach based on the Big Transfer (BiT) strategy. * The model learns to interpret user intent through "prompt generation," which simulates real-world interactions such as random scribbles, taps, and lasso (box) selections. * During training, both the Teacher and Edge models receive identical prompts—such as red foreground scribbles and blue background scribbles—to ensure the student model learns to produce high-quality masks even from imprecise user input. This advancement significantly lowers the barrier to entry for complex photo manipulation by moving heavy-duty AI processing directly onto the mobile device. Users can expect a more responsive and precise editing experience that handles everything from fine-tuning a subject's lighting to isolating specific environmental elements like clouds or clothing.

100X Faster: How We Supercharged Netflix Maestro’s Workflow Engine | by Netflix Technology Blog | Netflix TechBlog (opens in new tab)

Netflix has significantly optimized Maestro, its horizontally scalable workflow orchestrator, to meet the evolving demands of low-latency use cases like live events, advertising, and gaming. By redesigning the core engine to transition from a polling-based architecture to a high-performance event-driven model, the team achieved a 100x increase in speed. This evolution reduced workflow overhead from several seconds to mere milliseconds, drastically improving developer productivity and system efficiency. ### Limitations of the Legacy Architecture The original Maestro architecture was built on a three-layer system that, while scalable, introduced significant latency during execution. * **Polling Latency:** The internal flow engine relied on calling execution functions at set intervals, creating a "speedbump" where tasks waited seconds to be picked up by workers. * **Execution Overhead:** The process of translating complex workflow graphs into parallel flows and sequentially chained tasks added internal processing time that hindered sub-hourly and ad-hoc workloads. * **Concurrency Issues:** A lack of strong guarantees from the internal flow engine occasionally led to race conditions, where a single step might be executed by multiple workers simultaneously. ### Transitioning to an Event-Driven Engine To support the highest level of user needs, Netflix replaced the traditional flow engine with a custom, high-performance execution model. * **Direct Dispatching:** The engine moved away from periodic polling in favor of an event-driven mechanism that triggers state transitions instantly. * **State Machine Optimization:** The new design manages the lifecycle of workflows and steps through a more streamlined state machine, ensuring faster transitions between "start," "restart," "stop," and "pause" actions. * **Reduced Data Latency:** The team optimized data access patterns for internal state storage, reducing the time required to write Maestro data to the database during high-volume executions. ### Scalability and Functional Improvements The redesign not only improved speed but also strengthened the engine's ability to handle massive, complex data pipelines. * **Isolation Layers:** The engine maintains strict isolation between the Maestro step runtime (integrated with Spark and Trino) and the underlying execution logic. * **Support for Heterogeneous Workflows:** The supercharged engine continues to support massive workflows with hundreds of thousands of jobs while providing the low latency required for iterative development cycles. * **Reliability Guarantees:** By moving to a more robust internal event bus, the system eliminated the race conditions found in the previous distributed job queue implementation. For organizations managing large-scale Data or ML workflows, moving toward an event-driven orchestration model is essential for supporting sub-hourly execution and low-latency ad-hoc queries. These performance improvements are now available in the Maestro open-source project for wider community adoption.