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AI Factory Glossary

9,967 technical terms and definitions

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Showing page 125 of 200 (9,967 entries)

packaging,chiplet,interposer

Advanced packaging connects multiple dies. Chiplets enable mix-and-match. Interposers and 3D stacking.

packed sequences, llm optimization

Packed sequences concatenate examples without padding for memory efficiency.

packnet, continual learning

Prune and freeze for tasks.

packnet,continual learning

Iteratively prune and train for new tasks.

pad token, pad, nlp

Padding for equal-length sequences.

padding mask, llm optimization

Padding masks prevent attention to padded positions during processing.

padding token,nlp

Token used to fill sequences to same length in batches.

padding, llm optimization

Padding extends shorter sequences to batch length enabling parallel processing.

page-attention, optimization

Efficiently manage paged KV cache.

paged attention, llm optimization

Paged attention manages KV cache in non-contiguous memory blocks like virtual memory.

paged attention,vllm,memory

PagedAttention (vLLM) manages KV cache like virtual memory. Reduces fragmentation. Enables larger batches.

pagedattention,inference optimization

Memory-efficient attention mechanism used in vLLM for serving.

pagerank algorithm, graph algorithms

Measure node importance via random walks.

painn, chemistry ai

Equivariant molecular network.

painn, graph neural networks

Polarizable Atom Interaction Neural Network uses equivariant message passing for molecular property prediction.

paired t-test, quality & reliability

Paired t-tests compare related measurements on same units.

pairwise comparison, training techniques

Pairwise comparisons judge relative quality between two responses.

pairwise comparison,evaluation

Compare two outputs and pick better one.

pairwise ranking, recommendation systems

Pairwise ranking learns relative preferences between item pairs for recommendation ordering.

pairwise ranking,machine learning

Learn from item comparisons.

palm (pathways language model),palm,pathways language model,foundation model

Google's large-scale language model.

palo alto,stanford,stanford university,hp,hewlett packard

# Palo Alto & Stanford Research Park ## Palo Alto | Property | Value | |----------|-------| | **Location** | Santa Clara County, California, USA | | **Coordinates** | $37.4419° \text{ N}, 122.1430° \text{ W}$ | | **Founded** | 1894 | | **Area** | $67.2 \text{ km}^2$ $(25.95 \text{ mi}^2)$ | | **Population (2020)** | $\approx 68,572$ | | **Elevation** | $9 \text{ m}$ $(30 \text{ ft})$ | ## 1. Origin - **Name Origin:** "Palo Alto" = Spanish for "tall tree" - Refers to: *El Palo Alto* — a historic coastal redwood (*Sequoia sempervirens*) - Location: Along San Francisquito Creek - Height estimate: $h \approx 33.5 \text{ m}$ $(110 \text{ ft})$ - Age estimate: $t \approx 1,000 \text{ years}$ - **Mathematical representation of tree growth:** $$ H(t) = H_{max} \cdot \left(1 - e^{-kt}\right) $$ Where: - $H(t)$ = height at time $t$ - $H_{max}$ = maximum potential height - $k$ = growth rate constant - $t$ = time in years ## 2. Historical Timeline | Year | Event | Significance Index $S_i$ | |------|-------|--------------------------| | 1769 | Spanish expedition discovers El Palo Alto | $S_1 = 1.0$ | | 1876 | Leland Stanford purchases land | $S_2 = 2.5$ | | 1891 | Stanford University opens | $S_3 = 5.0$ | | 1894 | Palo Alto incorporated | $S_4 = 3.0$ | | 1939 | HP founded in garage | $S_5 = 8.0$ | | 1951 | Stanford Research Park established | $S_6 = 9.5$ | | 1970 | Xerox PARC established | $S_7 = 9.0$ | **Cumulative Historical Impact:** $$ I_{total} = \sum_{i=1}^{n} S_i \cdot w_i = \sum_{i=1}^{7} S_i \cdot w_i $$ ## 3. Stanford Research Park ### 3.1 Overview - **Established:** 1951 - **Area:** $A = 700 \text{ acres} = 2.83 \text{ km}^2$ - **Buildings:** $n \approx 150$ - **Companies:** $N_{companies} \approx 140$ - **Employees:** $E \approx 23,000$ ### 3.2 Key Figures - **Frederick Terman** — "Father of Silicon Valley" - Dean of Engineering, Stanford University - Concept: University-industry symbiosis - **Innovation Model:** $$ \text{Innovation Rate} = \frac{dI}{dt} = \alpha \cdot R \cdot C \cdot U $$ Where: - $R$ = Research investment (USD) - $C$ = Corporate collaboration factor - $U$ = University knowledge transfer coefficient - $\alpha$ = Regional multiplier constant ### 3.3 Notable Tenants (Historical & Current) ``` - Company Timeline & Market Cap Evolution ======================================== 1. Hewlett-Packard (1939) - Founded: Garage at 367 Addison Ave - Initial investment: $538 - Split (2015): HP Inc. + HPE 2. Varian Associates (1948) - First SRP tenant - Focus: Microwave technology 3. Xerox PARC (1970) - Inventions: GUI, Ethernet, Laser printing - Impact factor: I_PARC → ∞ 4. Tesla (HQ moved 2010) - Market Cap (peak): M ≈ $1.2 × 10¹² USD 5. VMware - Founded: 1998 - Virtualization pioneer ``` ### 3.4 Economic Impact Model **Land Lease Revenue:** $$ R_{annual} = \sum_{i=1}^{n} A_i \cdot r_i $$ Where: - $A_i$ = area leased by company $i$ (sq ft) - $r_i$ = rate per sq ft for company $i$ - $n$ = total number of tenants **Estimated Annual Revenue to Stanford:** $$ R_{Stanford} \approx 50\,\text{ million USD/year} $$ ## 4. Silicon Valley Genesis ### 4.1 The HP Garage - **Address:** 367 Addison Avenue, Palo Alto - **Designation:** California Historical Landmark #976 - **Title:** "Birthplace of Silicon Valley" **Initial Capital:** $$ C_0 = 538\,\text{ USD} \quad \text{(1939)} $$ **Inflation-adjusted (2024):** $$ C_{2024} = C_0 \cdot \left(\frac{CPI_{2024}}{CPI_{1939}}\right) \approx 11{,}800\,\text{ USD} $$ ### 4.2 Venture Capital Ecosystem **Sand Hill Road Statistics:** - **Location:** Adjacent to Stanford Research Park - **VC Firms:** $n_{VC} > 50$ - **Assets Under Management (AUM):** $$ AUM_{SandHill} \approx 100\,\text{ billion USD} $$ **Investment Return Model:** $$ IRR = \left(\frac{FV}{PV}\right)^{\frac{1}{n}} - 1 $$ Where: - $IRR$ = Internal Rate of Return - $FV$ = Future Value - $PV$ = Present Value (initial investment) - $n$ = years ## 5. Geographic & Demographic Data ### 5.1 Geographic Boundaries ``` Cardinal Boundaries =================== North: Menlo Park, East Palo Alto South: Mountain View, Los Altos East: San Francisco Bay West: Portola Valley, Los Altos Hills ``` ### 5.2 Area Calculations **Total Area:** $$ A_{total} = A_{land} + A_{water} = 66.4 + 0.8 = 67.2 \text{ km}^2 $$ **Population Density:** $$ \rho = \frac{P}{A_{land}} = \frac{68,572}{66.4} \approx 1,033 \text{ people/km}^2 $$ ### 5.3 Demographics (2020 Census) | Demographic | Percentage | Population | |-------------|------------|------------| | White | $57.5\%$ | $\approx 39,429$ | | Asian | $32.9\%$ | $\approx 22,560$ | | Hispanic/Latino | $5.9\%$ | $\approx 4,046$ | | Black/African American | $1.6\%$ | $\approx 1,097$ | | Other/Mixed | $2.1\%$ | $\approx 1,440$ | **Diversity Index (Simpson's Index):** $$ D = 1 - \sum_{i=1}^{n} p_i^2 = 1 - (0.575^2 + 0.329^2 + 0.059^2 + 0.016^2 + 0.021^2) $$ $$ D \approx 1 - 0.439 = 0.561 $$ ## 6. Economic Indicators ### 6.1 Housing Market **Median Home Price (2024):** $$ P_{median} \approx 3.5 \times 10^6\,\text{ USD} $$ **Price-to-Income Ratio:** $$ \text{PIR} = \frac{P_{median}}{I_{median}} = \frac{3,500,000}{180,000} \approx 19.4 $$ *(National average PIR $\approx 5$)* ### 6.2 Income Distribution **Median Household Income:** $$ I_{median} \approx 180{,}000\,\text{ USD/year} $$ **Gini Coefficient (Income Inequality):** $$ G_{PaloAlto} \approx 0.52 $$ $$ G = \frac{\sum_{i=1}^{n}\sum_{j=1}^{n}|x_i - x_j|}{2n^2\bar{x}} $$ ## 7. Education & Research ### 7.1 Stanford University Statistics | Metric | Value | |--------|-------| | **Founded** | 1891 | | **Endowment** | approx USD 36.3 billion | | **Students** | $\approx 17,000$ | | **Faculty** | $\approx 2,300$ | | **Nobel Laureates** | $> 85$ | ### 7.2 Research Output Model **Publications per Year:** $$ P(t) = P_0 \cdot e^{rt} $$ **Citation Impact Factor:** $$ IF = \frac{C_{t-1} + C_{t-2}}{N_{t-1} + N_{t-2}} $$ Where: - $C$ = citations received - $N$ = citable items published - $t$ = current year ## 8. Innovation Metrics ### 8.1 Patent Activity **Patents originating from Palo Alto (annual):** $$ N_{patents} \approx 2,500 \text{ patents/year} $$ **Patent Density:** $$ \rho_{patent} = \frac{N_{patents}}{P} = \frac{2,500}{68,572} \approx 0.036 \text{ patents/capita} $$ ### 8.2 Startup Formation Rate **New Startups per Year:** $$ S_{new} \approx 150-200 \text{ startups/year} $$ **Survival Rate (5-year):** $$ P(survival \geq 5) \approx 0.45 $$ **Expected Value of Startup:** $$ E[V] = \sum_{i} P(outcome_i) \cdot V_i $$ ## 9. Transportation & Infrastructure ### 9.1 Commute Statistics **Average Commute Time:** $$ \bar{t}_{commute} \approx 25 \text{ minutes} $$ **Modal Split:** | Mode | Percentage | |------|------------| | Drive alone | $58\%$ | | Carpool | $8\%$ | | Public transit | $7\%$ | | Bicycle | $8\%$ | | Walk | $6\%$ | | Work from home | $13\%$ | ### 9.2 Caltrain Service **Stations in Palo Alto:** 1. Palo Alto Station 2. California Avenue Station **Daily Ridership (pre-pandemic):** $$ R_{daily} \approx 8,000 \text{ passengers} $$ ## 10. Environmental Data ### 10.1 Climate Classification **Köppen Classification:** $Csb$ (Mediterranean) **Average Temperature:** $$ \bar{T}_{annual} = \frac{1}{12}\sum_{m=1}^{12} T_m \approx 15°C \text{ } (59°F) $$ ### 10.2 Precipitation **Annual Rainfall:** $$ P_{annual} \approx 400 \text{ mm} \text{ } (15.7 \text{ in}) $$ **Monthly Distribution:** $$ P_m = P_{annual} \cdot f_m $$ Where $f_m$ = fraction of annual precipitation in month $m$

pandas,dataframe,tabular

Pandas DataFrames handle tabular data. Data manipulation, analysis. Memory-intensive for large data.

panorama generation, generative models

Create wide panoramic images.

paperspace,gradient,ml

Paperspace Gradient provides ML platform. Notebooks, workflows.

parallel corpora, data

Aligned text in multiple languages.

parallel decoding, inference

Generate multiple tokens simultaneously.

parallel file systems, infrastructure

Distributed file systems (Lustre GPFS).

parallel inheritance hierarchies, code ai

Duplicate inheritance structures.

parallel sampling, llm optimization

Parallel sampling generates multiple sequence candidates simultaneously.

parallel system reliability, reliability

Redundant components.

parallel termination, signal & power integrity

Parallel termination connects resistor to supply or ground at receiver matching line impedance.

parallel wavegan, audio & speech

Parallel WaveGAN combines adversarial training with multi-resolution STFT loss for fast non-autoregressive neural vocoding.

parallelism,simd,simt

SIMD/SIMT: Single Instruction Multiple Data/Threads. GPUs excel at parallel operations. Key for matrix math.

parameter binding, ai agents

Parameter binding fills function arguments with appropriate values from context.

parameter count vs training tokens, planning

Tradeoff in resource allocation.

parameter count,model training

Total number of trainable weights in the model.

parameter design, quality & reliability

Parameter design selects factor levels minimizing sensitivity to noise factors.

parameter sharing, model optimization

Parameter sharing applies same weights to multiple operations enabling efficient architectures with fewer parameters.

parametric activation functions, neural architecture

Activations with learnable parameters.

parametric design,engineering

Design with adjustable parameters.

parametric ocv (pocv),parametric ocv,pocv,design

Path-based statistical variation.

parametric test, advanced test & probe

Parametric testing measures electrical characteristics like threshold voltage leakage current and transconductance to verify device specifications.

parametric test,metrology

Measure electrical parameters to monitor process health.

parametric testing,testing

Measure electrical parameters at test structures.

parametric yield analysis, manufacturing

Yield limited by parameter variation.

parametric yield loss, production

Loss from parameters out of spec.

paraphrase detection, nlp

Identify paraphrased sentences.

paraphrase,rewrite,simplify

Paraphrase text. Simplify complex language.

parasitic extraction, signal & power integrity

Parasitic extraction computes unintended resistance capacitance and inductance for circuit simulation.