Machine intelligence
Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize.
Machine Intelligence at Google raises deep scientific and engineering challenges, allowing us to contribute to the broader academic research community through technical talks and publications in major conferences and journals. Contrary to much of current theory and practice, the statistics of the data we observe shifts rapidly, the features of interest change as well, and the volume of data often requires enormous computation capacity. When learning systems are placed at the core of interactive services in a fast changing and sometimes adversarial environment, combinations of techniques including deep learning and statistical models need to be combined with ideas from control and game theory.
Recent Publications
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Large Language Models utilizing reasoning techniques improve task performance but incur significant latency and token costs due to verbose generation. Existing automatic prompt optimization(APO) frameworks target task accuracy exclusively at the expense of generating long reasoning traces. We propose Cost-Regularized Optimization of Prompts (CROP), an APO method that introduces regularization on response length by generating textual feedback in addition to standard accuracy feedback. This forces the optimization process to produce prompts that elicit concise responses containing only critical information and reasoning. We evaluate our approach on complex reasoning datasets, specifically GSM8K, LogiQA and BIG-Bench Hard. We achieved an 80.6% reduction in token consumption while maintaining competitive accuracy, seeing only a nominal decline in performance. This presents a pragmatic solution for deploying token-efficient and cost-effective agentic AI systems in production pipelines.
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In "Elephants, Goldfish and the New Golden Age of Software Engineering," the author discusses how AI is changing knowledge work, especially software development. Written from the perspective of April 2026, the article points out that while AI speeds up coding, it can also quickly generate a lot of mistakes and messy code if it isn't carefully managed by human oversight and clear processes.
The paper outlines a practical approach to working with AI, broken down into three main sections:
Using AI as a Tool, Not a Toy:
The author notes that people often get poor results by asking AI to do everything in a single prompt. Instead, users should have back-and-forth conversations with AI to question assumptions, set clear grading rules, and guide the research. The main point is that humans must still provide the final judgment; AI is simply a way to speed up and record that thinking.
The Elephant-Goldfish Model:
As AI creates more code than humans can easily read, written design documents become more important than the code itself. To keep AI on track, the author suggests a two-part method:
* The Elephant: A long chat session where the human and AI discuss ideas and write a detailed design document *before* any code is written. This session holds all of the project's background information and decisions.
* The Goldfish: A brand-new AI chat session with no memory. The human asks this "goldfish" to read the design document. If the goldfish cannot understand the plan based only on that document, the document needs more details.
* Only after the design document is clear enough for the goldfish to understand does the human ask the AI to write the code based on those strict instructions.
* Managing AI and the Future of Work:
The author expects that regular employees will soon act like managers, overseeing multiple AI helpers. Because of this, workers need to learn basic management skills, like how to delegate tasks and set clear boundaries. Also, since AI will handle routine chores, humans will need to practice focusing for longer periods to do deeper, harder thinking. Ultimately, a worker's value will come from their planning and decision-making skills, rather than their ability to type code.
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The Ontic-Epistemic Distinction: Implications for General Intelligence
Master's Thesis (2026) (to appear)
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The current pursuit of robust machine intelligence is largely predicated on a substrate independent, computational functionalist view of cognition, where sufficiently complex computational processing is expected to eventually yield generalized reasoning. This paper explores the ontological distinctions between these computational frameworks and biological cognition, specifically how these differences impact the capacity for semantic understanding. By analyzing phenomena such as the "reversal curse" where models fail to generalize the symmetry in identity relations (A=B implies B=A), and performance on novel reasoning benchmarks (e.g., ARC-AGI), this paper examines whether current model limitations are transient artifacts of scale or indicative of a distinct architectural category. Integrating Stevan Harnad’s “symbol grounding problem” with Evan Thompson’s biological model of “intrinsic normativity,” I investigate whether robust general intelligence might require sense-making: a process distinct from information processing, whereby an agent’s internal states are causally coupled with its environment via survival or system-wide stakes which grounds symbols in meaning. Current Large Language Models (LLMs) appear to lack this intrinsic normativity, and consequently may operate primarily as epistemic instruments rather than ontic agents. By introducing the concept of “ontic grounding”, this paper presents a potential framework for distinguishing between the simulation of reasoning and true understanding, which could have implications for AI safety and governance.
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XProf: An Open, Scalable and Extensible Profiling System for the Modern ML Stack
Robert Hundt
Naveen Kumar
Jose Baiocchi Paredes
Scott Goodson
Kelvin Le
Yin Zhang
Kan Cai
Sai Ganesh Bandiatmakuri
Yogesh SY
Ani Udipi
Vikas Aggarwal
Ninth Conference on Machine Learning and Systems (2026)
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Optimizing Large Models across thousands of accelerators requires deep system expertise. To address modern machine learning (ML) optimization needs, we present XProf, the ML profiler for the OpenXLA ecosystem. XProf delivers actionable optimization suggestions and in-depth performance analysis, empowering ML researchers and framework users to improve efficiency without specialized systems knowledge. XProf provides a unified, full-stack view of both host (CPU) and device (accelerator - TPUs/GPUs) performance, leveraging tools like the Roofline Model for comprehensive analysis. XProf’s distributed architecture is designed to monitor thousands of chips with minimal workload overhead (<1%). This architecture is made pluggable through the open-source PJRT C API extension, which has facilitated its adoption by third-party accelerator vendors. XProf has been instrumental in achieving significant efficiency gains at Google and winning MLPerf submissions. This paper presents the design and architecture of XProf, showcases its differentiating tools and capabilities, and highlights its impact within Google and across the industry as a state of the art ML profiler. XProf is available as part of the OpenXLA project at https://github.com/openxla/xprof.
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ToolGrad: Efficient Tool-use Dataset Generation with Textual "Gradients"
Kohei Uehara
Haoyu Zhang
Jingtao Zhou
Lin Gu
Zheng Xu
Tatsuya Harada
ACL 2026 (2026)
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Prior work synthesizes tool-use LLM datasets by first generating a user query, followed by complex tool-use annotations like depth-first search (DFS). This leads to inevitable annotation failures and low efficiency in data generation. We introduce ToolGrad, an agentic framework that inverts this paradigm. ToolGrad first constructs valid tool-use chains through an iterative process guided by textual "gradients", and then synthesizes corresponding user queries. This "answer-first" approach led to ToolGrad-500, a dataset generated with more complex tool use, lower cost, and almost 100% pass rate. Experiments show that ToolGrad models outperform those trained on expensive baseline datasets and proprietary LLMs.
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Optimizing large-language model (LLM) training and serving on large-sacle distributed systems with hundreds and thousands of accelerators is always a challenging task due to the fast evloving LLMs, strong domain expertise required, and various optimization goals from different worklaods. Existing methods rely on either handcrafted optimization performed by human experts, which is tedious and time-consuming or resource-intensive black-box searches, which lack the extensibility to keep pace with evolving models and hardware. To address this, we introduce PROMPTS, a novel multi-agent framework that complements traditional search methods with expert-informed reasoning. It automates the diagnosis of performance bottlenecks by synthesizing profiler data and leverages a knowledge base to propose optimized sharding configurations with detailed justifications.
Across eight real-world production workloads, PROMPTS demonstrated remarkable efficiency and accuracy, delivering performance improvements of up to 434%. These workloads spanned diverse model architectures, hardware platforms, computational scales, and various stages of the machine learning lifecycle (pre-training, serving, and post-training). In every case, the configuration adopted by human engineers was identified within the agent's top three proposals from a single invocation. Furthermore, the agent's top-ranked recommendation was the one ultimately adopted in 87.5% of cases, showcasing its ability to not only find optimized solutions, but also to correctly prioritize them. Our work establishes PROMPTS as a scalable, extensible, and explainable methodology for AI-assisted performance engineering in large-scale ML systems.
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