Every important concept, why it matters, and exactly how nonprofits use it with Squark.
Models learn from labeled examples (inputs paired with correct outputs).
Why it matters: It’s the basis of propensity scoring, forecasting, and most production models.
Nonprofit/Squark usage: Predict probability and amount of the next gift for targeted outreach.
Finds hidden structure in unlabeled data (clusters, patterns).
Why it matters: Drives donor segmentation and discovery of behaviors you didn’t label.
Nonprofit/Squark usage: Cluster donors by recency–frequency–monetary behavior to tailor journeys.
Trains on a small labeled set plus a large unlabeled set.
Why it matters: Cuts labeling cost while boosting accuracy.
Nonprofit/Squark usage: Label a few thousand donors for upgrade; leverage millions of unlabeled touches.
Learns actions by maximizing rewards over time.
Why it matters: Optimizes sequential decisions such as channel and timing.
Nonprofit/Squark usage: Learn a best next-touch policy to maximize incremental gifts.
Learns by predicting parts of the input (e.g., masked words).
Why it matters: Produces strong embeddings; powers LLMs and search.
Nonprofit/Squark usage: Better donor-note summaries and retrieval via rich representations.
Updates models continuously as new data arrives.
Why it matters: Adapts to seasonality, drift, and changing donor mix.
Nonprofit/Squark usage: Refresh scores weekly during peak seasons for up-to-date targeting.
Adapts a pretrained model to a new task with less data.
Why it matters: Cuts time and cost to reach production quality.
Nonprofit/Squark usage: Fine‑tune a base language model on winning appeals and style guides.
Performs well with only a handful of labeled examples.
Why it matters: Useful when data is sparse or new programs launch.
Nonprofit/Squark usage: Kick off a new campaign and still get useful predictions quickly.
Handles tasks with no labeled examples using general knowledge.
Why it matters: Rapid capability without long data collection cycles.
Nonprofit/Squark usage: Draft first‑time campaign copy constrained by brand rules.
‘Learning to learn’: fast adaptation across tasks/domains.
Why it matters: Accelerates tuning across programs, chapters, or affiliates.
Nonprofit/Squark usage: Adapt models across affiliates with minimal rework.
Trains across data silos without moving raw data.
Why it matters: Privacy and compliance while collaborating.
Nonprofit/Squark usage: Cross‑affiliate modeling without sharing PII.
The model selects the most informative items to label.
Why it matters: Minimizes labeling cost for maximum accuracy gains.
Nonprofit/Squark usage: Ask analysts to label only borderline donor records.
Predicts categories (yes/no or one among many).
Why it matters: Core for response targeting and routing decisions.
Nonprofit/Squark usage: Predict ‘will donate in 90 days’ or choose best channel.
Two-class (yes/no) prediction.
Why it matters: Simple, highly actionable modeling.
Nonprofit/Squark usage: Identify monthly‑donor conversion candidates.
Predicts one of many classes.
Why it matters: Routes donors to the best option.
Nonprofit/Squark usage: Choose best channel (email/SMS/mail).
Assigns multiple labels to a single record.
Why it matters: Reflects reality when supporters fit many themes.
Nonprofit/Squark usage: Tag donors with multiple cause affinities.
Predicts a numeric value.
Why it matters: Sets budgets and expectations on dollars and counts.
Nonprofit/Squark usage: Predict expected gift amount for ask ladders.
Predicts values over time.
Why it matters: Planning, cash‑flow, and inventory.
Nonprofit/Squark usage: Weekly donation forecasts with intervals.
Flags unusual behavior or outliers.
Why it matters: Protects data quality and fraud detection.
Nonprofit/Squark usage: Detect suspicious spikes or refunds.
Groups similar records without labels (e.g., K‑Means, Hierarchical, DBSCAN).
Why it matters: Data‑driven segmentation for better personalization.
Nonprofit/Squark usage: Create behavior‑driven donor segments.
Compresses features while retaining structure (PCA, t‑SNE, UMAP).
Why it matters: Speeds models and enables visualization.
Nonprofit/Squark usage: Plot donor universe for executive reviews.
Models time until an event (time‑to‑X).
Why it matters: Enables timing decisions.
Nonprofit/Squark usage: Estimate time to second gift or to lapse.
Estimates likelihood of an action.
Why it matters: Prioritizes spend and attention.
Nonprofit/Squark usage: Likelihood to upgrade, re‑activate, or become a sustainer.
Predicts failure before it happens.
Why it matters: Prevents downtime and hidden costs.
Nonprofit/Squark usage: Anticipate mail‑ops or data pipeline failures.
Identifies who is likely to stop giving.
Why it matters: Retention ROI via early intervention.
Nonprofit/Squark usage: Trigger save series for at‑risk sustainers.
Anticipates future demand/volume.
Why it matters: Capacity and budgeting decisions.
Nonprofit/Squark usage: Forecast premiums/print volumes.
Assign scores (risk, value, priority) to records.
Why it matters: Simple decisioning at scale.
Nonprofit/Squark usage: Credit‑like pledge‑fulfillment risk or mid‑level prioritization.
Two‑network system (generator vs. discriminator) for high‑fidelity synthesis.
Why it matters: Produces realistic images and tabular samples.
Nonprofit/Squark usage: Synthetic data for creative testing or scarce segments.
Latent‑variable generative models that reconstruct and sample data.
Why it matters: Controlled variation for safe exploration.
Nonprofit/Squark usage: Generate donor‑persona variants for testing.
Generate images by denoising noise (e.g., Stable Diffusion, Imagen, DALL·E).
Why it matters: State‑of‑the‑art creative quality.
Nonprofit/Squark usage: Concept art variants for ads and landing pages.
Attention‑based sequence models powering GPT, BERT, T5, LLaMA, Mistral.
Why it matters: Backbone of LLMs and modern gen‑AI.
Nonprofit/Squark usage: Generate donor‑ready copy conditioned on segment.
Foundation models trained on vast text corpora.
Why it matters: Summarize, generate, classify, and reason.
Nonprofit/Squark usage: Draft appeals, landing pages, and social posts.
Models that process text + images (and sometimes audio/video) such as CLIP, Flamingo, Gemini.
Why it matters: Richer understanding and creative search.
Nonprofit/Squark usage: Auto‑tag images and align visuals to copy.
Generate text from text prompts or structured inputs.
Why it matters: Core capability for content creation.
Nonprofit/Squark usage: Appeal drafts from a brief; compliant stewardship letters.
Create images from text prompts.
Why it matters: Rapid visual ideation.
Nonprofit/Squark usage: Mockups for campaign creative.
Create audio/music from text prompts.
Why it matters: Scalable audio assets.
Nonprofit/Squark usage: Personalized thank‑you audio or background music.
Generate short videos from text prompts.
Why it matters: Compelling storytelling assets.
Nonprofit/Squark usage: Program highlight clips for web or social.
Transcribe audio to text; synthesize voice from text.
Why it matters: Accessibility and productivity.
Nonprofit/Squark usage: Transcribe donor calls; TTS for outbound messages.
Modify images conditionally (style or content).
Why it matters: Creative variations on‑brand.
Nonprofit/Squark usage: Adapt assets to channels without re‑shoots.
Apply one image’s style to another.
Why it matters: Consistent visuals across assets.
Nonprofit/Squark usage: Brand‑consistent look from diverse sources.
Linear classifier with a logistic link function.
Why it matters: Interpretable baseline with calibrated probabilities.
Nonprofit/Squark usage: Simple response models for compliance‑friendly reviews.
Predicts continuous outcomes using a linear relationship.
Why it matters: Transparent forecasting and ask‑amount modeling.
Nonprofit/Squark usage: Predict donation amount for ask ladders and budgets.
Tree‑based models; forests are ensembles of randomized trees.
Why it matters: Strong, interpretable baselines for tabular data.
Nonprofit/Squark usage: Rank households for phone outreach; explain splits.
Sequential trees that correct residuals (XGBoost, LightGBM, CatBoost).
Why it matters: Top performance on tabular data with speed and accuracy.
Nonprofit/Squark usage: Lift in response and amount models for mail.
Margin‑maximizing classifiers/regressors.
Why it matters: Strong on small/medium datasets.
Nonprofit/Squark usage: Classify content sentiment or intent.
Probabilistic classifier with independence assumptions.
Why it matters: Fast, simple text baseline.
Nonprofit/Squark usage: Classify donor emails by intent.
Predict by averaging nearest examples.
Why it matters: Simple, non‑parametric reference.
Nonprofit/Squark usage: Quick content‑tag inference for small sets.
Multi‑layer perceptrons for mixed features.
Why it matters: Flexible function approximators.
Nonprofit/Squark usage: Combine behavioral + textual features for uplift.
Apply convolutions for images and spatial data.
Why it matters: Vision powerhouse.
Nonprofit/Squark usage: Classify creatives for brand fit and compliance.
Sequence models capturing temporal dependencies.
Why it matters: Model sequences beyond static aggregates.
Nonprofit/Squark usage: Model sequences of donor actions over time.
RNNs with gates for long‑term dependencies.
Why it matters: Better long‑horizon sequence learning.
Nonprofit/Squark usage: Donation time‑series modeling.
Efficient gated RNN variant.
Why it matters: Fewer parameters; strong sequence baselines.
Nonprofit/Squark usage: Predict churn from engagement streams.
Weights focus on relevant tokens.
Why it matters: Improves long‑sequence learning and explainability.
Nonprofit/Squark usage: Highlight key events in donor summaries.
Map input sequences to output sequences.
Why it matters: Translation, summarization, dialog.
Nonprofit/Squark usage: Summarize donor calls into CRM notes.
Learn compressed representations by reconstruction.
Why it matters: Denoising, anomaly detection, feature learning.
Nonprofit/Squark usage: Spot anomalous donation patterns; pretrain features.
Make probabilities and intervals honest post‑training.
Why it matters: Improves decision integrity and budget allocation.
Nonprofit/Squark usage: Calibrate propensities for mailing volumes and risk.
Distribution‑free method to produce valid prediction intervals.
Why it matters: Risk‑aware decisions with guaranteed coverage.
Nonprofit/Squark usage: Donation forecast intervals and risk bands.
Balancing under/over‑fitting error sources.
Why it matters: Guides model/regularization choices.
Nonprofit/Squark usage: Choose simpler models for small segments; richer for big ones.
Too complex vs. too simple relative to the data.
Why it matters: Both harm generalization and ROI.
Nonprofit/Squark usage: Right‑size your model capacity for stable lift.
Rotate validation across folds for robust estimates.
Why it matters: Prevents luck from one split.
Nonprofit/Squark usage: K‑fold CV for stable lift curves.
Search settings that govern model behavior.
Why it matters: Large impact on accuracy and stability.
Nonprofit/Squark usage: Tune depth/learning rate/regularization systematically.
Three common strategies for tuning exploration.
Why it matters: Efficiently finds strong settings.
Nonprofit/Squark usage: Quick random baseline → Bayesian optimization for fine tuning.
Iterative optimization algorithms minimizing loss.
Why it matters: Enable training at scale.
Nonprofit/Squark usage: Default to Adam/RMSProp for deep models; SGD for large linear.
Objectives the model minimizes (MSE, Cross‑Entropy, Hinge, Contrastive, Perplexity).
Why it matters: Shape learning behavior and calibration.
Nonprofit/Squark usage: Use Tweedie/MSE for dollar amounts; Cross‑Entropy for response.
Measures of predictive quality and ranking.
Why it matters: Pick metrics aligned to decisions.
Nonprofit/Squark usage: Lift/PR‑AUC for targeting; RMSE/MAE for forecasts.
Basic classification metrics.
Why it matters: Each highlights different errors and costs.
Nonprofit/Squark usage: Balance them to select operating thresholds.
Global ranking quality across thresholds; PR‑AUC focuses on positives.
Why it matters: Robust comparison on imbalanced data.
Nonprofit/Squark usage: Compare models fairly before rollout.
Regression goodness‑of‑fit and error.
Why it matters: Quantifies forecast quality in natural units.
Nonprofit/Squark usage: Evaluate weekly revenue forecasts.
Probabilistic and generative evaluation scores.
Why it matters: Guide quality for text models.
Nonprofit/Squark usage: Compare fine‑tuned LLMs and generated copy.
Create informative variables from raw data.
Why it matters: Often the biggest accuracy gains.
Nonprofit/Squark usage: RFM, engagement depth, channel recency.
Keep only useful predictors.
Why it matters: Avoid overfitting and speed training.
Nonprofit/Squark usage: Drop noisy/collinear fields before scoring.
Normalize or standardize magnitudes.
Why it matters: Stabilizes training for many algorithms.
Nonprofit/Squark usage: Standardize numeric features for SVM/NN.
Dense vectors that capture meaning (Word2Vec, GloVe, FastText, Sentence Transformers).
Why it matters: Enable search, clustering, and RAG.
Nonprofit/Squark usage: Retrieve relevant impact stories on demand.
Split text into model‑processable tokens.
Why it matters: Affects cost and quality of LLM usage.
Nonprofit/Squark usage: Efficient chunking of donor notes and reports.
Represent text/items as numeric vectors.
Why it matters: Prerequisite for ML on text and search.
Nonprofit/Squark usage: TF‑IDF/BOW baselines; embeddings for semantics.
Create varied training samples synthetically.
Why it matters: Improves generalization when data is scarce.
Nonprofit/Squark usage: Paraphrase small‑segment texts.
Produce realistic data for privacy or scarcity.
Why it matters: Enables safe modeling and QA.
Nonprofit/Squark usage: Create safe tabular samples for testing pipelines.
Assign ground‑truth labels for supervised learning.
Why it matters: Quality labels determine outcomes.
Nonprofit/Squark usage: Use clear instructions; spot‑check with active learning.
Build models from data with chosen objectives.
Why it matters: The start of the production lifecycle.
Nonprofit/Squark usage: Reproducible scripts and tracked experiments.
Deliver predictions via batch or APIs.
Why it matters: Turns models into business value.
Nonprofit/Squark usage: Batch for mail lists; real‑time for web personalization.
Track quality, latency, and drift after deployment.
Why it matters: Maintains trust and reliability.
Nonprofit/Squark usage: Alert when lift or inputs shift beyond bounds.
Detects input/relationship shifts over time.
Why it matters: Prompts retraining and review.
Nonprofit/Squark usage: Trigger retrain when channel effectiveness changes.
Automated build/test/deploy for ML systems.
Why it matters: Reliability and speed to production.
Nonprofit/Squark usage: Nightly scoring pipelines and safe rollouts.
Log runs, metrics, params, and artifacts.
Why it matters: Reproducibility and governance.
Nonprofit/Squark usage: Trace which model scored a list.
Catalog/version approved models.
Why it matters: Supports governance and rollbacks.
Nonprofit/Squark usage: Promote champion models to production.
Orchestrated steps to prepare inputs and serve outputs.
Why it matters: Consistency and reuse.
Nonprofit/Squark usage: Shared features and scoring steps across programs.
Compare control vs. variant with significance.
Why it matters: Ground‑truth impact measurement.
Nonprofit/Squark usage: Validate modeled lift before scaling.
Make decisions understandable.
Why it matters: Trust and oversight.
Nonprofit/Squark usage: SHAP for board reviews; quick LIME checks during QA.
Policies and controls for safe, fair systems.
Why it matters: Reduces risk and protects brand.
Nonprofit/Squark usage: Guardrails, PII minimization, and audits.
Three pillars of responsible AI practice.
Why it matters: Mitigate harm and comply with regulation.
Nonprofit/Squark usage: Audit targeting parity and document decisions.
Design inputs that steer LLM outputs.
Why it matters: Quality and control for generation.
Nonprofit/Squark usage: Standard prompts for compliant appeal drafts.
Learned soft prompts specialized to a task.
Why it matters: Cheaper than full fine‑tuning.
Nonprofit/Squark usage: Create brand‑specific adapters per program.
Adapt a base model to your domain.
Why it matters: Improves accuracy and tone.
Nonprofit/Squark usage: Train on past winning campaigns and guidelines.
Fine‑tune small parameter sets on top of a base model.
Why it matters: Low cost, fast iteration and deployment.
Nonprofit/Squark usage: Per‑brand adapters without retraining the base.
Reinforcement learning from human feedback.
Why it matters: Aligns models to human preferences and policy.
Nonprofit/Squark usage: On‑brand generation for stewardship and appeals.
Retrieve trusted context before generating.
Why it matters: Grounds outputs in facts and keeps them fresh.
Nonprofit/Squark usage: Pull donor history and current stats into copy.
The span a model can attend to plus the mechanism.
Why it matters: Limits usable history and coherence.
Nonprofit/Squark usage: Use long‑context models for multi‑year donor histories.
Confident but incorrect generations.
Why it matters: Trust, compliance, and risk.
Nonprofit/Squark usage: Block unfounded claims and require citations.
Decoding controls that trade precision for diversity.
Why it matters: Balance creativity vs. accuracy.
Nonprofit/Squark usage: Low temperature for stewardship; higher for ideation.
Induce stepwise reasoning traces.
Why it matters: Improves complex problem solving.
Nonprofit/Squark usage: Internal tools for complex allocation logic.
Tool‑using and orchestrated agents that plan/act.
Why it matters: Automation and reliability for end‑to‑end tasks.
Nonprofit/Squark usage: Compose retrieval + copy + send with approvals.
Combine signals across text, image, audio, and tabular.
Why it matters: Richer understanding and relevance.
Nonprofit/Squark usage: Text+image alignment in creative QA.
Train smaller models from larger teachers.
Why it matters: Efficiency with near‑teacher quality.
Nonprofit/Squark usage: Deploy lightweight copy assistants.
Shrink models via lower precision and weight removal.
Why it matters: Lower latency and cost.
Nonprofit/Squark usage: Serve LLMs cheaply for peak campaigns.
Parallel processors that speed training and inference.
Why it matters: Critical for deep and generative workloads.
Nonprofit/Squark usage: Serve LLMs under Giving‑Tuesday load.
Train across multiple devices/nodes.
Why it matters: Scales models and reduces wall‑clock time.
Nonprofit/Squark usage: Meet deadlines for new models before appeals.
Split work across data shards, model parts, or staged pipelines.
Why it matters: Enables very large models and datasets.
Nonprofit/Squark usage: Parallelize gradient boosting and deep training.
Stores embeddings for fast similarity search (Pinecone, Weaviate, FAISS, Milvus).
Why it matters: Backbone for production RAG.
Nonprofit/Squark usage: Retrieve donor knowledge instantly during generation.
Scalable storage/analytics platforms (Snowflake, BigQuery, Redshift).
Why it matters: Central engine for analytics/ML.
Nonprofit/Squark usage: Train/score directly in your warehouse.
Extract‑Transform‑Load (or load‑then‑transform) patterns.
Why it matters: Keeps data reliable and fresh.
Nonprofit/Squark usage: Normalize CRM exports and campaign logs.
Reuse results to cut response time and cost.
Why it matters: Better UX and scale.
Nonprofit/Squark usage: Cache common snippets and search results.
Choose mode by latency/volume needs.
Why it matters: Cost/performance trade‑offs.
Nonprofit/Squark usage: Batch for mail; real‑time for web personalization.
Estimate cause‑effect, not just correlation.
Why it matters: Improves policy and marketing decisions.
Nonprofit/Squark usage: Quantify causal lift of welcome kits on LTV.
Combine learning with logical rules.
Why it matters: Greater reliability and reasoning.
Nonprofit/Squark usage: Rule‑aware compliant generation with ML.
Systems that continuously refine themselves.
Why it matters: Better performance with less manual retraining.
Nonprofit/Squark usage: Automated prompt/model updates under governance.
Generated archetypes and simulations.
Why it matters: Safe testing and planning.
Nonprofit/Squark usage: Pre‑test appeals on persona panels; simulate journeys.
Large pretrained models adaptable to many tasks.
Why it matters: Versatile, cost‑effective bases.
Nonprofit/Squark usage: Adapt for fundraising copy and decisioning.
Policies, controls, and logs for responsible AI.
Why it matters: Enterprise‑grade trust and compliance.
Nonprofit/Squark usage: Audit trails for decisions and content.
Keep models within intended behaviors.
Why it matters: Risk reduction and reputation.
Nonprofit/Squark usage: Red‑team prompts; enforce guardrails.
Reduce compute/energy via efficient models.
Why it matters: Lower cost and footprint.
Nonprofit/Squark usage: Smaller tuned models for daily operations.
Accuracy, completeness, and consistency of data.
Why it matters: Trustworthy models start with trustworthy data.
Nonprofit/Squark usage: Deduplicate records, fix imbalance, detect outliers.
GAN/LLM‑based tabular/text data for scarce or sensitive classes.
Why it matters: Enables safe modeling and QA where data is limited.
Nonprofit/Squark usage: Create rare‑class examples (e.g., major‑gift positives) for testing.
Mechanisms to protect personal information (differential privacy, homomorphic encryption, secure aggregation).
Why it matters: Legal and ethical obligations.
Nonprofit/Squark usage: Hash emails, minimize PII, use federated secure aggregation.
Lineage, versioning, and compliance frameworks (GDPR/CCPA, HIPAA).
Why it matters: Stability and audit‑readiness.
Nonprofit/Squark usage: Lock export formats and honor data subject requests.
Combine multiple models (stacking, blending, bagging).
Why it matters: Higher accuracy and robustness.
Nonprofit/Squark usage: Blend uplift and amount models for better net revenue.
Learning over nodes/edges (GNNs, knowledge‑graph embeddings).
Why it matters: Captures relationships.
Nonprofit/Squark usage: Identify influencer hubs among volunteers or fundraisers.
Time‑to‑event models with different assumptions.
Why it matters: Crucial where timing is key.
Nonprofit/Squark usage: Time to lapse; upgrade timing for mid‑level.
Bayesian inference, Gaussian Processes, HMMs/MRFs.
Why it matters: Uncertainty‑aware decisions.
Nonprofit/Squark usage: Forecast gifts with credible intervals; model donor states.
Libraries and methods for causal effects (DoWhy, Causal Forests, Counterfactuals).
Why it matters: Target interventions that actually change behavior.
Nonprofit/Squark usage: Mail only persuadables; quantify lift truly attributable to outreach.
Predicts causal lift at individual/segment level.
Why it matters: Spend only where it moves the needle.
Nonprofit/Squark usage: Trim waste print/postage and raise net revenue.
Learn 3D scenes from 2D images.
Why it matters: Immersive storytelling assets.
Nonprofit/Squark usage: Visualize project sites or installations for donors.
Extensions that control, speed, or expand diffusion.
Why it matters: Finer control and new modalities.
Nonprofit/Squark usage: Guide layouts (ControlNet) and produce 3D objects from imagery.
Routes tokens to specialized subnetworks.
Why it matters: Efficient scaling with specialization.
Nonprofit/Squark usage: Specialists for major donors vs. sustainers.
Architectures and memory tricks for long sequences (RNN‑T, memory‑aug transformers).
Why it matters: Use more history per decision.
Nonprofit/Squark usage: Write appeals using years of interactions.
Orchestrate retrieval, tools, and agents in pipelines.
Why it matters: Faster prototyping and reliability.
Nonprofit/Squark usage: Compose RAG + generation + sending with approvals.
Models that call APIs and emit JSON/XML schemas.
Why it matters: Moves from text to action.
Nonprofit/Squark usage: Fetch segment lists, create assets, and push to ESP safely.
Automated quality measures for generated text.
Why it matters: Compare drafts quickly before human review.
Nonprofit/Squark usage: Select best appeal drafts prior to QA.
Assess realism and alignment of generated images.
Why it matters: Quantify visual quality.
Nonprofit/Squark usage: Select creative variants for ads/social.
Perceived and objective speech quality metrics.
Why it matters: Ensure clarity for TTS/voice messages.
Nonprofit/Squark usage: Evaluate donor thank‑you audio quality.
Human assessment for alignment, usefulness, and safety.
Why it matters: Final guard against subtle errors.
Nonprofit/Squark usage: Review marquee appeals and policy‑sensitive content.
Indexing strategies for fast, accurate similarity search.
Why it matters: Right structure = speed + recall.
Nonprofit/Squark usage: Low‑latency retrieval of donor knowledge during generation.
Structured domain knowledge used with or without RAG.
Why it matters: Improves grounding and consistency.
Nonprofit/Squark usage: Enforce donor/program terminology across content.
Parallelism, experiments, and pipelines for ML.
Why it matters: Scalable, reliable operations.
Nonprofit/Squark usage: Run nightly scoring DAGs and track all models.
Techniques to shrink models for cheaper serving.
Why it matters: Lower latency and cost without losing too much quality.
Nonprofit/Squark usage: Serve assistants economically at scale.
Ways to quantify fairness across groups.
Why it matters: Detect and mitigate inequity.
Nonprofit/Squark usage: Audit response models for parity before rollout.
Approaches to reduce bias at data, model, or output stages.
Why it matters: Improves equity and compliance.
Nonprofit/Squark usage: Re‑weight training data; constrain models; post‑process scores.
Explain local/global model behavior.
Why it matters: Build trust and find issues.
Nonprofit/Squark usage: Show why a household was targeted; highlight drivers.
Frameworks and standards for AI governance.
Why it matters: Procurement‑friendly, audit‑ready AI.
Nonprofit/Squark usage: Map each use‑case to risk class and required controls.
Hardening models against misuse and attacks.
Why it matters: Protects brand and data.
Nonprofit/Squark usage: Probe prompts, sanitize inputs, and enforce policies.
Agents improve by playing themselves; complex behaviors emerge.
Why it matters: Inspires sequential decision strategies.
Nonprofit/Squark usage: Research for multi‑touch donor sequencing.
Multiple agents coordinate toward goals.
Why it matters: Robust exploration and coordination.
Nonprofit/Squark usage: Divide work across targeting, copy, QA agents.
Combine neural models with symbolic reasoning.
Why it matters: Reliability plus reasoning.
Nonprofit/Squark usage: Rule‑aware reasoning over donor policies.
Techniques for efficient training/serving.
Why it matters: Reduce cost with minimal quality loss.
Nonprofit/Squark usage: Use LoRA and sparse attention to scale responsibly.
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