Model

/bɜːrt/

n. "Test instrument measuring bit error ratios in high-speed serial links using known PRBS patterns."

BERT, short for Bit Error Rate Tester, comprises pattern generator and error detector validating digital communication systems by transmitting known sequences through DUT (Device Under Test) and comparing received bits against expected, quantifying performance as BER = errors/total_bits (target 1e-12 for SerDes). Essential for characterizing CTLE, DFE, and CDR under stressed PRBS-31 patterns with added sinusoidal jitter/SJ.

Key characteristics of BERT include: Pattern Generator produces PRBS7/15/23/31 via LFSR or user-defined CDR-lock patterns; Error Counter accumulates bit mismatches over test time (hours for 1e-15 BER); Jitter Injection adds TJ/SJ/RJ stressing receiver tolerance; Loopback Mode single-unit testing via DUT TX→RX shorting; Bathtub Analysis sweeps voltage/jitter revealing BER contours.

Conceptual example of BERT usage:

# BERT automation script (Keysight M8040A API example)
import pyvisa

rm = pyvisa.ResourceManager()
bert = rm.open_resource('TCPIP::BERT_IP::inst0::INSTR')

# Configure PRBS-31 + 0.5UI SJ @ 20% depth
bert.write(':PAT:TYPE PRBS31')
bert.write(':JITT:TYPE SINU; FREQ 2e9; AMPL 0.1')  # 2GHz SJ, 0.1UI

# Run 1e12 bit test targeting 1e-12 BER
bert.write(':TEST:START')
bert.write(':TEST:BITS 1e12')
bert.query(':TEST:BER?')  # Returns '1.23e-13'

# Bathtub sweep: Vth vs RJ
bert.write(':SWEEp:VTH 0.4,0.8,16')  # 16 voltage steps
bert.write(':SWEEp:RUN')
bathtub_data = bert.query(':TRACe:DATA?')  # BER contours

Conceptually, BERT functions as truth arbiter for USB4/DisplayPort PHYs—injects PRBS through stressed channel, counts symbol errors post-CTLE/DFE while plotting Q-factor bathtub curves. Keysight M8040A/MSO70000 validates 224G Ethernet hitting 1e-6 BER pre-FEC, correlating eye height to LFSR error floors; single-unit loopback mode transforms FPGA SerDes into self-tester, indispensable for PCIe5 compliance unlike protocol analyzers measuring logical errors.

/lʊk-ɛm-ɛl/

n. “The language that teaches Looker how to see your data.”

LookML is a modeling language used in Looker to define relationships, metrics, and data transformations within a data warehouse. It allows analysts and developers to create reusable, structured definitions of datasets so that business users can explore data safely and consistently without writing raw SQL queries.

Unlike traditional SQL, LookML is declarative rather than procedural. You describe the structure and relationships of your data — tables, joins, dimensions, measures, and derived fields — and Looker generates the necessary queries behind the scenes. This separation ensures consistency, reduces duplication, and enforces business logic centrally.

Key concepts in LookML include:

• Views: Define a single table or dataset and its fields (dimensions and measures).
• Explores: Configure how users navigate and join data from multiple views.
• Dimensions: Attributes or columns users can query, such as “customer_name” or “order_date.”
• Measures: Aggregations like COUNT, SUM, or AVG, defined once and reused throughout analyses.

Here’s a simple LookML snippet defining a view with a measure and a dimension:

view: users {
  sql_table_name: public.users ;;

dimension: username {
sql: ${TABLE}.username ;;
}

measure: total_users {
type: count
sql: ${TABLE}.id ;;
}
}

In this example, the view users represents the database table public.users. It defines a dimension called username and a measure called total_users, which counts the number of user records. Analysts can now explore and visualize these fields without writing SQL manually.

LookML promotes centralized governance, reducing errors and inconsistencies in reporting. By abstracting SQL into reusable models, organizations can ensure that all users are working with the same definitions of metrics and dimensions, which is critical for reliable business intelligence.

In essence, LookML is a bridge between raw data and meaningful insights — it teaches Looker how to understand, organize, and present data so teams can focus on analysis rather than query mechanics.

/ˌeɪˈaɪ/

n. “Machines pretending to think… sometimes convincingly.”

AI, short for Artificial Intelligence, is a broad field of computer science focused on building systems that perform tasks normally associated with human intelligence. These tasks include learning from experience, recognizing patterns, understanding language, making decisions, and adapting to new information. Despite the name, AI is not artificial consciousness, artificial emotion, or artificial intent. It is artificial behavior — behavior that appears intelligent when observed from the outside.

At its core, AI is about models. A model is a mathematical structure that maps inputs to outputs. The model does not “understand” in the human sense. It calculates. What makes AI interesting is that these calculations can approximate reasoning, perception, and prediction well enough to be useful — and occasionally unsettling.

Modern AI is dominated by machine learning, a subfield where systems improve performance by analyzing data rather than following rigid, hand-written rules. Instead of telling a program exactly how to recognize a face or translate a sentence, engineers feed it large datasets and let the model infer patterns statistically. Learning, in this context, means adjusting parameters to reduce error, not gaining insight or awareness.

Within machine learning sits deep learning, which uses multi-layered neural networks inspired loosely by biological neurons. These networks excel at handling unstructured data such as images, audio, and natural language. The “deep” part refers to the number of layers, not depth of thought. A deep model can be powerful and still profoundly wrong.

AI systems are often categorized by capability. Narrow AI performs a specific task — recommending videos, detecting fraud, generating text, or playing chess. This is the only kind of AI that exists today. General AI, a hypothetical system capable of understanding and learning any intellectual task a human can, remains speculative. It is a concept, not a product.

In practical systems, AI is embedded everywhere. Search engines rank results using learned relevance signals. Voice assistants convert sound waves into meaning. Recommendation engines predict what you might want next. Security tools flag anomalies. These systems rely on pipelines involving data collection, preprocessing, training, evaluation, and deployment — often supported by ETL processes and cloud infrastructure such as Cloud Storage.

A critical property of AI is probabilistic behavior. Outputs are based on likelihoods, not certainties. This makes AI flexible but also brittle. Small changes in input data can produce surprising results. Bias in training data can become bias in decisions. Confidence scores can be mistaken for truth.

Another defining feature is opacity. Many advanced AI models function as black boxes. They produce answers without easily explainable reasoning paths. This creates tension between performance and interpretability, especially in high-stakes domains like medicine, finance, and law.

It is important to separate AI from myth. AI does not “want.” It does not “believe.” It does not possess intent, values, or self-preservation. Any appearance of personality or agency is a projection layered on top by interface design or human psychology. The system executes optimization objectives defined by humans, sometimes poorly.

Used well, AI amplifies human capability. It accelerates analysis, reduces repetitive labor, and uncovers patterns too large or subtle for manual inspection. Used carelessly, it automates mistakes, scales bias, and obscures accountability behind math.

AI is not magic. It is applied statistics, software engineering, and compute power arranged cleverly. Its power lies not in thinking like a human, but in doing certain things humans cannot do fast enough, consistently enough, or at sufficient scale.

In the end, AI is best understood not as an artificial mind, but as a mirror — reflecting the data, goals, and assumptions we feed into it, sometimes with uncomfortable clarity.