Posts Tagged ‘MachineLearning’
[NDCOslo2024] Mirror, Mirror: LLMs and the Illusion of Humanity – Jodie Burchell
In the mesmerizing mirror maze of machine mimicry, where words weave worlds indistinguishable from wit, Jodie Burchell, JetBrains’ data science developer advocate, shatters the spell of sentience in large language models (LLMs). A PhD psychologist turned NLP pioneer, Jodie probes the psychological ploys that propel projections of personhood onto probabilistic parsers, dissecting claims from consciousness to cognition. Her inquiry, anchored in academia and augmented by anecdotes, advises acuity: LLMs as linguistic lenses, not living likenesses, harnessing their heft while heeding hallucinations.
Jodie greets with gratitude for her gritty slot, her hipster cred in pre-prompt NLP notwithstanding. LLMs’ 2022 blaze beguiles: why bestow brains on bytes when other oracles oblige? Her hypothesis: humanity’s hall of mirrors, where models mirror our mores, eliciting empathy from echoes.
Psychological Projections: Perceiving Personhood in Parsers
Humans, Jodie hazards, hallucinate humanity: anthropomorphism’s ancient artifice, from pets to puppets. LLMs lure with language’s liquidity—coherent confessions conjure companionship. She cites stochastic parrots: parleying patterns, not pondering profundities, yet plausibility persuades.
Extraordinary assertions abound: Blake Lemoine’s LaMDA “alive,” Google’s Gemini “godhead.” Jodie juxtaposes: sentience’s scaffold—selfhood, suffering—sans in silicon. Chalmers’ conundrum: consciousness connotes qualia, quanta qualms quell in qubits.
Levels of Luminescence: From Language to Luminary
DeepMind’s AGI arc: Level 1 chatbots converse convincingly; Level 2 reasons reactively; Level 3 innovates imaginatively. LLMs linger at 1-2, lacking Level 4’s abstraction or 5’s autonomy. Jodie jests: jackdaws in jester’s garb, juggling jargon sans judgment.
Illusions intensify: theory of mind’s mirage, where models “infer” intents from inferences. Yet, benchmarks belie: ARC’s abstraction stumps, BIG-bench’s breadth baffles—brilliance brittle beyond basics.
Perils of Projection: Phishing and Philosophical Pitfalls
Prompt injections prey: upstream overrides oust origins, birthing bogus bounties—”Amazon voucher via arcane URL.” Jodie demonstrates: innocuous inquiries infected, innocuousness inverted into inducements. Robustness rankles: rebuttals rebuffed, ruses reiterated.
Her remedy: recognize reflections—lossy compressions of lore, not luminous lives. Demystify to deploy: distill data, detect delusions, design defensively.
Dispelling the Delusion: Harnessing Heuristics Humanely
Jodie’s jeremiad: myths mislead, magnifying misuses—overreach in oracles, oversight in safeguards. Her horizon: LLMs as lucid lenses, amplifying analysis while acknowledging artifice.
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[DotJs2025] Using AI with JavaScript: Good Idea?
Amid the AI deluge reshaping codecraft, a tantalizing prospect emerges: harnessing neural nets natively in JavaScript, sidestepping Python’s quagmires or API tolls. Wes Bos, a prolific Canadian educator whose Syntax.fm podcast and courses have schooled half a million in JS mastery, probed this frontier at dotJS 2025. Renowned for demystifying ES6 and React, Wes extolled browser-bound inference via Transformers.js, weighing its virtues—privacy’s fortress, latency’s lightning—against hardware’s hurdles, affirming JS’s prowess for sundry smart apps.
Wes’s overture skewered the status quo: cloud fetches or Python purgatory, both anathema to JS purists. His heresy: embed LLMs client-side, ONNX Runtime fueling Hugging Face’s arsenal—sentiment sifters, translation tomes, even Stable Diffusion’s slimmer kin. Transformers.js’s pipeline paradigm gleams: import, instantiate (pipeline('sentiment-analysis')), infer (result = await pipe(input)). Wes demoed a local scribe: prompt yields prose, all sans servers, WebGPU accelerating where GPUs oblige. Onyx.js, his bespoke wrapper, streamlines: model loads, GPU probes, inferences ignite—be it code completion or image captioning.
Trade-offs tempered triumph. Footprints fluctuate: 2MB wisps to 2GB behemoths, browser quotas (Safari’s 2GB cap) constraining colossi. Compute cedes to client: beefy rigs revel, mobiles murmur—Wes likened Roblox’s drain to LLM’s voracity. Yet, upsides dazzle: zero egress fees, data’s domicile (GDPR’s grace), offline oases. 2025’s tide—Chrome’s stable WebNN, Firefox’s flag—heralds ubiquity, Wes forecasting six-month Safari stability. His verdict: JS, with its ubiquity and ecosystem, carves niches where immediacy reigns—chatbots, AR filters—not every oracle, but myriad muses.
Wes’s zeal stemmed personal: from receipt printers to microcontroller React, JS’s whimsy fuels folly. Transformers.js empowers prototypes unbound—anime avatars, code clairvoyants—inviting creators to conjure without concessions.
Client-Side Sorcery Unveiled
Wes unpacked pipelines: sentiment sorters, summarizers—Hugging Face’s trove, ONNX-optimized. Onyx’s facade: await onnx.loadModel('gpt2'), GPU fallback, inferences instantaneous. WebGPU’s dawn (Chrome 2025 stable) unlocks acceleration, privacy paramount—no telemetry trails.
Balancing Bytes and Burdens
Models’ mass mandates moderation: slim variants suffice for mobile, diffusion downsized. Battery’s bite, CPU’s churn—Wes warned of Roblox parallels—yet offline allure and cost calculus compel. JS’s sinew: ecosystem’s expanse, browser’s bastion, birthing bespoke brains.
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[DevoxxUK2024] Is It (F)ake?! Image Classification with TensorFlow.js by Carly Richmond
Carly Richmond, a Principal Developer Advocate at Elastic, captivated the DevoxxUK2024 audience with her engaging exploration of image classification using TensorFlow.js. Inspired by her love for the Netflix show Is It Cake?, Carly embarked on a project to build a model distinguishing cakes disguised as everyday objects from their non-cake counterparts. Despite her self-professed lack of machine learning expertise, Carly’s journey through data gathering, pre-trained models, custom model development, and transfer learning offers a relatable and insightful narrative for developers venturing into AI-driven JavaScript applications.
Gathering and Preparing Data
Carly’s project begins with the critical task of data collection, a foundational step in machine learning. To source images of cakes resembling other objects, she leverages Playwright, a JavaScript-based automation framework, to scrape images from bakers’ websites and Instagram galleries. For non-cake images, Carly utilizes the Unsplash API, which provides royalty-free photos with a rate-limited free tier. She queries categories like reptiles, candles, and shoes to align with the deceptive cakes from the show. However, Carly acknowledges limitations, such as inadvertently including biscuits or company logos in the dataset, highlighting the challenges of ensuring data purity with a modest set of 367 cake and 174 non-cake images.
Exploring Pre-Trained Models
To avoid building a model from scratch, Carly initially experiments with TensorFlow.js’s pre-trained models, Coco SSD and MobileNet. Coco SSD, trained on the Common Objects in Context (COCO) dataset, excels in object detection, identifying bounding boxes and classifying objects like cakes with reasonable accuracy. MobileNet, designed for lightweight classification, struggles with Carly’s dataset, often misclassifying cakes as cups or ice cream due to visual similarities like frosting. CORS issues further complicate browser-based MobileNet deployment, prompting Carly to shift to a Node.js backend, where she converts images into tensors for processing. These experiences underscore the trade-offs between model complexity and practical deployment.
Building and Refining a Custom Model
Undeterred by initial setbacks, Carly ventures into crafting a custom convolutional neural network (CNN) using TensorFlow.js. She outlines the CNN’s structure, which includes convolution layers to extract features, pooling layers to reduce dimensionality, and a softmax activation for binary classification (cake vs. not cake). Despite her efforts, the model’s accuracy languishes at 48%, plagued by issues like tensor shape mismatches and premature tensor disposal. Carly candidly admits to errors, such as mislabeling cakes as non-cakes, illustrating the steep learning curve for non-experts. This section of her talk resonates with developers, emphasizing perseverance and the iterative nature of machine learning.
Leveraging Transfer Learning
Recognizing the limitations of her dataset and custom model, Carly pivots to transfer learning, using MobileNet’s feature vectors as a foundation. By adding a custom classification head with ReLU and softmax layers, she achieves a significant improvement, with accuracy reaching 100% by the third epoch and correctly classifying 319 cakes. While not perfect, this approach outperforms her custom model, demonstrating the power of leveraging pre-trained models for specialized tasks. Carly’s comparison of human performance—90% accuracy by the DevoxxUK audience versus her model’s results—adds a playful yet insightful dimension, highlighting the gap between human intuition and machine precision.
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[DotAI2024] DotAI 2024: Steeve Morin – Revolutionizing AI Inference with ZML
Steeve Morin, a seasoned software engineer and co-founder of ZML, unveiled an innovative approach to machine learning deployment during his presentation at DotAI 2024. As the architect behind LegiGPT—a pioneering legal AI assistant—and a former VP of Engineering at Zenly (acquired by Snap Inc.), Morin brings a wealth of experience in scaling high-performance systems. His talk centered on ZML, a compiling framework tailored for Zig programming language, leveraging MLIR, XLA, and Bazel to streamline inference across diverse hardware like NVIDIA GPUs, AMD accelerators, and TPUs. This toolset promises to reshape how developers author and deploy ML models, emphasizing efficiency and production readiness.
Bridging Training and Inference Divides
Morin opened by contrasting the divergent demands of model training and inference. Training, he described, thrives in exploratory environments where abundance reigns—vast datasets, immense computational power, and rapid prototyping cycles. Python excels here, fostering innovation through quick iterations and flexible experimentation. Inference, however, demands precision in production settings: billions of queries processed with unwavering reliability, minimal resource footprint, and consistent latency. Here, Python’s interpretive nature introduces overheads that can compromise scalability.
This tension, Morin argued, underscores the need for specialized frameworks. ZML addresses it head-on by targeting inference exclusively, compiling models into optimized binaries that execute natively on target hardware. Built atop MLIR (Multi-Level Intermediate Representation) for portable optimizations and XLA (Accelerated Linear Algebra) for high-performance computations, ZML integrates seamlessly with Bazel for reproducible builds. Developers write models in Zig—a systems language prized for its safety and speed—translating high-level ML constructs into low-level efficiency without sacrificing expressiveness.
Consider a typical workflow: a developer prototypes a neural network in familiar ML dialects, then ports it to ZML for compilation. The result? A self-contained executable that bypasses runtime dependencies, ensuring deterministic performance. Morin highlighted cross-accelerator binaries as a standout feature—single artifacts that adapt to CUDA, ROCm, or TPU environments via runtime detection. This eliminates the provisioning nightmares plaguing traditional ML ops, where mismatched driver versions or library conflicts derail deployments.
Furthermore, ZML’s design philosophy prioritizes developer ergonomics. From a MacBook, one can generate deployable archives or Docker images tailored to Linux ROCm setups, all within a unified pipeline. This hermetic coupling of model and runtime mitigates version drift, allowing teams to focus on innovation rather than firefighting. Early adopters, Morin noted, report up to 3x latency reductions on edge devices, underscoring ZML’s potential to democratize high-fidelity inference.
Empowering Production-Grade AI Without Compromise
Morin’s vision extends beyond technical feats to cultural shifts in AI engineering. He positioned ZML for “AI-flavored backend engineers”—those orchestrating large-scale systems—who crave hardware agnosticism without performance trade-offs. By abstracting accelerator specifics into compile-time decisions, ZML fosters portability: a model tuned for NVIDIA thrives unaltered on AMD, fostering vendor neutrality in an era of fragmented ecosystems.
He demonstrated this with Mistral models, compiling them for CUDA execution in mere minutes, yielding inference speeds rivaling hand-optimized C++ code. Another showcase involved cross-compilation from macOS to ARM-based TPUs, producing a Docker image that auto-detects and utilizes available hardware. Such versatility, Morin emphasized, eradicates MLOps silos; models deploy as-is, sans bespoke orchestration layers.
Looking ahead, ZML’s roadmap includes expanded modality support—vision and audio alongside text—and deeper integrations with serving stacks. Morin invited the community to engage via GitHub, underscoring the framework’s open-source ethos. Launched stealthily three weeks prior, ZML has garnered enthusiastic traction, bolstered by unsolicited contributions that refined its core.
In essence, ZML liberates inference from Python’s constraints, enabling lean, predictable deployments that scale effortlessly. As Morin quipped, “Build once, run anywhere”—a mantra that could redefine production AI, empowering engineers to deliver intelligence at the edge of possibility.
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[DevoxxBE2023] How Sand and Java Create the World’s Most Powerful Chips
Johan Janssen, an architect at ASML, captivated the DevoxxBE2023 audience with a deep dive into the intricate process of chip manufacturing and the role of Java in optimizing it. Johan, a seasoned speaker and JavaOne Rock Star, explained how ASML’s advanced lithography machines, powered by Java-based software, enable the creation of cutting-edge computer chips used in devices worldwide.
From Sand to Silicon Wafers
Johan began by demystifying chip production, starting with silica sand, an abundant resource transformed into silicon ingots and sliced into wafers. These wafers, approximately 30 cm in diameter, serve as the foundation for chips, hosting up to 600 chips per wafer or thousands for smaller sensors. He passed around a wafer adorned with Java’s mascot, Duke, illustrating the physical substrate of modern electronics.
The process involves printing multiple layers—up to 200—onto wafers using extreme ultraviolet (EUV) lithography machines. These machines, requiring four Boeing 747s for transport, achieve precision at the nanometer scale, with transistors as small as three nanometers. Johan likened this to driving a car 300 km and retracing the path with only 2 mm deviation, highlighting the extraordinary accuracy required.
The Role of EUV Lithography
Johan detailed the EUV lithography process, where tin droplets are hit by a 40-kilowatt laser to generate plasma at sun-like temperatures, producing EUV light. This light, directed by ultra-flat mirrors, patterns wafers through reticles costing €250,000 each. The process demands cleanroom environments, as even a single dust particle can ruin a chip, and involves continuous calibration to maintain precision across thousands of parameters.
ASML’s machines, some over 30 years old, remain in use for producing sensors and less advanced chips, demonstrating their longevity. Johan also previewed future advancements, such as high numerical aperture (NA) machines, which will enable even smaller transistors, further enhancing chip performance and energy efficiency.
Java-Powered Analytics Platform
At the heart of Johan’s talk was ASML’s Java-based analytics platform, which processes 31 terabytes of data weekly to optimize chip production. Built on Apache Spark, the platform distributes computations across worker nodes, supporting plugins for data ingestion, UI customization, and processing. These plugins allow departments to integrate diverse data types, from images to raw measurements, and support languages like Julia and C alongside Java.
The platform, running on-premise to protect sensitive data, consolidates previously disparate applications, improving efficiency and user experience. Johan highlighted a machine learning use case where the platform increased defect detection from 70% to 92% without slowing production, showcasing Java’s role in handling complex computations.
Challenges and Solutions in Chip Manufacturing
Johan discussed challenges like layer misalignment, which can cause short circuits or defective chips. The platform addresses these by analyzing wafer plots to identify correctable errors, such as adjusting subsequent layers to compensate for misalignments. Non-correctable errors may result in downgrading chips (e.g., from 16 GB to 8 GB RAM), ensuring minimal waste.
He emphasized a pragmatic approach to tool selection, starting with REST endpoints and gradually adopting Kafka for streaming data as needs evolved. Johan also noted ASML’s collaboration with tool maintainers to enhance compatibility, such as improving Spark’s progress tracking for customer feedback.
Future of Chip Manufacturing
Looking ahead, Johan highlighted the industry’s push to diversify chip production beyond Taiwan, driven by geopolitical and economic factors. However, building new factories, or “fabs,” costing $10–20 billion, faces challenges like equipment backlogs and the need for highly skilled operators. ASML’s customer support teams, working alongside clients like Intel, underscore the specialized knowledge required.
Johan concluded by stressing the importance of a forward-looking mindset, with ASML’s roadmap prioritizing innovation over rigid methodologies. This approach, combined with Java’s robustness, ensures the platform’s scalability and adaptability in a rapidly evolving industry.
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[NodeCongress2021] Machine Learning in Node.js using Tensorflow.js – Shivay Lamba
The fusion of machine learning capabilities with server-side JavaScript environments opens intriguing avenues for developers seeking to embed intelligent features directly into backend workflows. Shivay Lamba, a versatile software engineer proficient in DevOps, machine learning, and full-stack paradigms, illuminates this intersection through his examination of TensorFlow.js within Node.js ecosystems. As an open-source library originally developed by the Google Brain team, TensorFlow.js democratizes access to sophisticated neural networks, allowing practitioners to train, fine-tune, and infer models without forsaking the familiarity of JavaScript syntax.
Shivay’s narrative commences with the foundational allure of TensorFlow.js: its seamless portability across browser and Node.js contexts, underpinned by WebGL acceleration for tensor operations. This universality sidesteps the silos often encountered in traditional ML stacks, where Python dominance necessitates cumbersome bridges. In Node.js, the library harnesses native bindings to leverage CPU/GPU resources efficiently, enabling tasks like image classification or natural language processing to unfold server-side. Shivay emphasizes practical onboarding—install via npm, import tf, and instantiate models—transforming abstract algorithms into executable logic.
Consider a sentiment analysis endpoint: load a pre-trained BERT variant, preprocess textual inputs via tokenizers, and yield probabilistic outputs—all orchestrated in asynchronous handlers to maintain Node.js’s non-blocking ethos. Shivay draws from real-world deployments, where such integrations power recommendation engines or anomaly detectors in e-commerce pipelines, underscoring the library’s scalability for production loads.
Streamlining Model Deployment and Inference
Deployment nuances emerge as Shivay delves into optimization strategies. Quantization shrinks model footprints, slashing latency for edge inferences, while transfer learning adapts pre-trained architectures to domain-specific corpora with minimal retraining epochs. He illustrates with a convolutional neural network for object detection: convert ONNX formats to TensorFlow.js via converters, bundle with webpack for serverless functions, and expose via Express routes. Monitoring integrates via Prometheus metrics, tracking inference durations and accuracy drifts.
Challenges abound—memory constraints in containerized setups demand careful tensor management, mitigated by tf.dispose() invocations. Shivay advocates hybrid approaches: offload heavy training to cloud TPUs, reserving Node.js for lightweight inference. Community extensions, like @tensorflow/tfjs-node-gpu, amplify throughput on NVIDIA hardware, aligning with Node.js’s event-driven architecture.
Shivay’s exposition extends to ethical considerations: bias audits in datasets ensure equitable outcomes, while federated learning preserves privacy in distributed training. Through these lenses, TensorFlow.js transcends novelty, evolving into a cornerstone for ML-infused Node.js applications, empowering creators to infuse intelligence without infrastructural overhauls.
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[DevoxxPL2022] Successful AI-NLP Project: What You Need to Know
At Devoxx Poland 2022, Robert Wcisło and Łukasz Matug, data scientists at UBS, shared insights on ensuring the success of AI and NLP projects, drawing from their experience implementing AI solutions in a large investment bank. Their presentation highlighted critical success factors for deploying machine learning (ML) models into production, addressing common pitfalls and offering practical guidance across the project lifecycle.
Understanding the Challenges
The speakers noted that enthusiasm for AI often outpaces practical outcomes, with 2018 data indicating only 10% of ML projects reached production. While this figure may have improved, many projects still fail due to misaligned expectations or inadequate preparation. To counter this, they outlined a simplified three-phase process—Prepare, Build, and Maintain—integrating Software Development Lifecycle (SDLC) and MLOps principles, with a focus on delivering business value and user experience.
Prepare Phase: Setting the Foundation
Łukasz emphasized the importance of the Prepare phase, where clarity on business needs is critical. Many stakeholders, inspired by AI hype, expect miraculous solutions without defining specific outcomes. Key considerations include:
- Defining the Output: Understand the business problem and desired results, such as labeling outcomes (e.g., fraud detection). Reduce ambiguity by explicitly defining what the application should achieve.
- Evaluating ML Necessity: ML excels in areas like recommendation systems, language understanding, anomaly detection, and personalization, but it’s not a universal solution. For one-off problems, simpler analytics may suffice.
- Red Flags: ML models rarely achieve 100% accuracy, requiring more data and testing for higher precision, which increases costs. Highly regulated industries may demand transparency, posing challenges for complex models. Data availability is also critical—without sufficient data, ML is infeasible, though workarounds like transfer learning or purchasing data exist.
- Universal Performance Metric: Establish a metric aligned with business goals (e.g., click-through rate, precision/recall) to measure success, unify stakeholder expectations, and guide development priorities for cost efficiency.
- Tooling and Infrastructure: Align software and data science teams with shared tools (e.g., Git, data access, experiment logs). Ensure compliance with data restrictions (e.g., GDPR, cross-border rules) and secure access to production-like data and infrastructure (e.g., GPUs).
- Automation Levels: Decide the role of AI—ranging from no AI (human baseline) to full automation. Partial automation, where models handle clear cases and humans review uncertain ones, is often practical. Consider ethical principles like fairness, compliance, and no-harm to avoid bias or regulatory issues.
- Model Utilization: Plan how the model will be served—binary distribution, API service, embedded application, or self-service platform. Each approach impacts user experience, scalability, and maintenance.
- Scalability and Reuse: Design for scalability and consider reusing datasets or models to enhance future projects and reduce costs.
Build Phase: Crafting the Model
Robert focused on the Build phase, offering technical tips to streamline development:
- Data Management: Data evolves, requiring retraining to address drift. For NLP projects, cover diverse document templates, including slang or errors. Track data provenance and lineage to monitor sources and transformations, ensuring pipeline stability.
- Data Quality: Most ML projects involve smaller datasets (hundreds to thousands of points), where quality trumps quantity. Address imbalances by collaborating with clients for better data or using simpler models. Perform sanity checks to ensure representativeness, avoiding overly curated data that misaligns with production (e.g., professional photos vs. smartphone images).
- Metadata and Tagging: Use tags (e.g., source, date, document type) to simplify debugging and maintenance. For instance, identifying underperforming data (e.g., low-quality German PDFs) becomes easier with metadata.
- Labeling Strategy: Noisy or ambiguous labels (e.g., misinterpreting “bridges” as Jeff Bridges or drawings vs. physical bicycles) degrade model performance. Aim for human-level performance (HLP), either against ground truth (e.g., biopsy results) or inter-human agreement. A consistent labeling strategy, documented with clear examples, reduces ambiguity and improves data quality. Tools like AWS Mechanical Turk or in-house labeling platforms can streamline this process.
- Training Tips: Use transfer learning to leverage pre-trained models, reducing data needs. Active learning prioritizes labeling hard examples, while pseudo-labeling uses existing models to pre-annotate data, saving time if the model is reliable. Ensure determinism by fixing seeds for reproducibility during debugging. Start with lightweight models (e.g., BERT Tiny) to establish baselines before scaling to complex models.
- Baselines: Compare against prior models, heuristic-based systems, or simple proofs-of-concept to contextualize progress toward HLP. An 85% accuracy may be sufficient if it aligns with HLP, but 60% after extensive effort signals issues.
Maintain Phase: Sustaining Performance
Maintenance is critical as ML models differ from traditional software due to data drift and evolving inputs. Strategies include:
- Deployment Techniques: Use A/B testing to compare model versions, shadow mode to evaluate models in parallel with human processes, canary deployments to test on a small traffic subset, or blue-green deployments for seamless rollbacks.
- Monitoring: Beyond system metrics, monitor input (e.g., image brightness, speech volume, input length) and output (e.g., exact predictions, user behavior like query frequency). Detect data or concept drift to maintain relevance.
- Reuse: Reuse models, data, and experiences to reduce uncertainty, lower costs, and build organizational capabilities for future projects.
Key Takeaways
The speakers stressed reusing existing resources to demystify AI, reduce costs, and enhance efficiency. By addressing business needs, data quality, and operational challenges early, teams can increase the likelihood of delivering impactful AI-NLP solutions. They invited attendees to discuss further at the UBS stand, emphasizing practical application over theoretical magic.
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[KotlinConf2018] Mathematical Modeling in Kotlin: Optimization, Machine Learning, and Data Science Applications
Lecturer
Thomas Nield is a Business Consultant at Southwest Airlines, balancing technology with operations research in airline scheduling and optimization. He is an author with O’Reilly Media, having written “Getting Started with SQL” and “Learning RxJava,” and contributes to open-source projects like RxJavaFX and RxKotlin. Relevant links: O’Reilly Profile (publications); LinkedIn Profile (professional page).
Abstract
This article explores mathematical modeling in Kotlin, addressing complex problems through discrete optimization, Bayesian techniques, and neural networks. It analyzes methodologies for scheduling, regression, and classification, contextualized in data science and operations research. Implications for production deployment, library selection, and problem-solving efficiency are discussed, emphasizing Kotlin’s refactorable features.
Introduction and Context
Mathematical modeling solves non-deterministic problems beyond brute force, such as scheduling 190 classes or optimizing train costs. Kotlin’s pragmatic features enable clear, evolvable models for production.
Context: Models underpin data science, machine learning, and operations research. Examples include constraint programming for puzzles (Sudoku) and real-world applications (airline schedules).
Methodological Approaches
Discrete optimization uses libraries like OjAlgo for linear programming (e.g., minimizing train costs with constraints). Bayesian classifiers (e.g., Naive Bayes) model probabilities for spam detection.
Neural networks: Custom implementations train on MNIST for digit recognition, using activation functions (sigmoid) and backpropagation. Kotlin’s extensions and lambdas facilitate intuitive expressions.
Graph optimization: Dijkstra’s algorithm for shortest paths, applicable to logistics.
Analysis of Techniques and Examples
Optimization: Linear models minimize objectives under constraints; graph models solve routing (e.g., traveling salesman via genetic algorithms).
Bayesian: Probabilistic inference for sentiment/email classification, leveraging word frequencies.
Neural networks: Multi-layer perceptrons for fuzzy problems (image recognition); Kotlin demystifies black boxes through custom builds.
Innovations: Kotlin’s type safety and conciseness aid refactoring; libraries like Deeplearning4j for production.
Implications and Consequences
Models enable efficient solutions; choose based on data/problem nature (optimization for constraints, networks for fuzzy data).
Consequences: Custom implementations build intuition but libraries optimize; Kotlin enhances maintainability for production.
Conclusion
Kotlin empowers mathematical modeling, bridging optimization and machine learning for practical problem-solving.
Links
- Lecture video: https://www.youtube.com/watch?v=-zTqtEcnM7A
- Lecturer’s X/Twitter: @thomasnield
- Lecturer’s LinkedIn: Thomas Nield
- Organization’s X/Twitter: @SouthwestAir
- Organization’s LinkedIn: Southwest Airlines
[DevoxxUS2017] Continuous Optimization of Microservices Using Machine Learning by Ramki Ramakrishna
At DevoxxUS2017, Ramki Ramakrishna, a Staff Engineer at Twitter, delivered a compelling session on optimizing microservices performance using machine learning. Collaborating with colleagues, Ramki shared insights from Twitter’s platform engineering efforts, focusing on Bayesian optimization to tune microservices in data centers. His talk addressed the challenges of managing complex workloads and offered a vision for automated optimization. This post explores the key themes of Ramki’s presentation, highlighting innovative approaches to performance tuning.
Challenges of Microservices Performance
Ramki Ramakrishna opened by outlining the difficulties of tuning microservices in data centers, where numerous parameters and workload variations create combinatorial complexity. Drawing from his work with Twitter’s JVM team, he explained how continuous software and hardware upgrades exacerbate performance issues, often leaving resources underutilized. Ramki’s insights set the stage for exploring machine learning as a solution to these challenges.
Bayesian Optimization in Action
Delving into technical details, Ramki introduced Bayesian optimization, a machine learning approach to automate performance tuning. He described its application in Twitter’s microservices, using tools derived from open-source projects like Spearmint. Ramki shared practical examples, demonstrating how Bayesian methods efficiently explore parameter spaces, outperforming manual tuning in scenarios with many variables, ensuring optimal resource utilization.
Lessons and Pitfalls
Ramki discussed pitfalls encountered during Twitter’s optimization projects, such as the need for expert-defined parameter ranges to guide machine learning algorithms. He highlighted the importance of collaboration between service owners and engineers to specify tuning constraints. His lessons, drawn from real-world implementations, emphasized balancing automation with human expertise to achieve reliable performance improvements.
Vision for Continuous Optimization
Concluding, Ramki outlined a vision for a continuous optimization service, integrating machine learning into DevOps pipelines. He noted plans to open-source parts of Twitter’s solution, building on frameworks like Spearmint. Ramki’s forward-thinking approach inspired developers to adopt data-driven optimization, ensuring microservices remain efficient amidst evolving data center demands.