Accomplished Automotive Technical Understanding Skills Can Help With AI Understanding and Troubleshooting

Before I built AI systems that outperform industry standards (like Google ASR NLP CC), I spent more than a decade as a multi-ASE-certified (Automotive Service Excellence) (as well as county/state certified emissions and safety inspector) automotive technician, diagnosing engine performance, transmission failures, engine problems, computer and electrical issues.

I became a mechanic in the 80s, in high school, I opted to try out the Vocational Automotive program, and found I had a talent with cards. After a decade working in the field, multiple ASE certifications, etc., I was later an instructor at the American Automotive Institute and also the Dodge Certification Center in Utah.

Most mechanics in the 1980s and 1990s hated the vacuum routings, electrical, and especially computer systems evolving in cars of the time, while I embraced them.

The diagnostic thinking I learned in auto repair shops later, surprisingly, turned out to become relevant in my evolution as a software developer creating Bulletin Board Service (BBS) and Internet Relay Chat (IRC) bots and agents, as well as Optical Character Recognition (OCR) and voice-to-text natural language processing systems in the 1990s. As we as my developing more advanced artificial intelligence, synthetic intelligence interactive matrices, neural networks, and machine learning systems in the 2000s.

Of course now, the cars have many complex computers systems, and most of the less-experienced (less than 20 years) technicians I run into now, no  longer know the fundamentals of the cars they work on, they are just relying on "pulling codes" and doing what the computer troubleshooting tells them to do. In too many instances, due to their lack of experience with the fundamentals, this often (mis)leads them try an excessive number of trial and error "shotgunning" troubleshooting, rather than well-refined methodical processes that I see the more senior technicians (20+ years experience) utilize effectively, struggling with when trying to track down various automotive issues.

Pattern Recognition Across Systems

Automotive diagnosis involves methodical logical (sometimes intricate multi-system) investigation of complex systems where multiple components interact in subtle ways.

An engine or transmission problem might actually trace to electrical issues, fluid channels, fluid chemistry, or mechanical wear patterns that affect seemingly unrelated subsystems.

Portions of AI system development requires similar thinking.

When neural networks aren't converging properly, the root cause might start with GIGO data wrangling, or later data preprocessing, hyperparameter tuning, architectural choices, or training procedures that create cascade effects throughout the learning process.

It can be challenging making the correct cognitive linkage when the causality of issues is separated by more and more layers of abstraction or interference, which requires having an immensely robust broad general knowledge of however everything works together, and not a hyper-focused/hyper-specialization that can't take all the issues into context (the adage of the different blind people trying to describe the creature they are feeling from different parts of the elephant, and all drawing the wrong conclusions).

Both domains reward systematic approaches that consider complex layers of systems and their interactions between components rather than focusing on individual elements in isolation.

A Whazzit-Whozit?

Let me break down some of the above terms in each of these AI system development challenges.

GIGO & Data Wrangling (Garbage In, Garbage Out)

This is your foundational data quality issue. Before any preprocessing happens, you need clean, relevant source data.

All too often people aren't careful with the fundamentals of data wrangling, and often the consequences might not be realized until months, or even years later, when it is eventually tracked down to the initial data input process.

Common problems include:

• Wrong mappings because of over-reliance on automated processes without taking the time to perform some sanity checks first on a smaller scale
• Biased or unrepresentative samples that don't reflect real-world distributions
• Mislabeled data where ground truth annotations are incorrect
• Inconsistent data collection methods across different sources
• Missing or corrupted values that weren't caught during collection
• Data leakage where test/validation information accidentally bleeds into training data

If your raw data is fundamentally flawed, no amount of sophisticated modeling will fix it.

Fundamentals matter, even in the 2000s.

Data Preprocessing

This transforms raw data into a format suitable for learning.

Issues here include:

• Feature scaling problems (standardization vs. normalization choices that don't match your data distribution)
• Feature engineering mistakes like creating features with high multicollinearity or low predictive power
• Improper handling of categorical variables (one-hot encoding creating dimension explosion, or losing ordinal relationships)
• Temporal data leaks in time-series when you accidentally use future information
• Imbalanced class handling (over/under-sampling at wrong pipeline stages)

Preprocessing errors often create subtle bugs that only manifest as poor generalization, and can be very challenging to isolate and rectify properly.

Automotive-related parallels explanation:

TODO

Hyperparameter Tuning

These are the "knobs and dials" that aren't learned from data:

• Learning rate - too high causes divergence, too low means never converging
• Batch size - affects gradient noise and generalization (small batches = noisy gradients, large batches = potential overfitting)
• Regularization parameters (L1/L2 weight decay, dropout rates) - balancing underfitting vs. overfitting
• Optimizer choices (SGD vs. Adam vs. AdamW) and their momentum/beta parameters
• Number of epochs and early stopping criteria
• Architecture-specific params (number of layers, hidden units, attention heads, etc.)

Poor hyperparameter choices can make an otherwise sound architecture completely fail to learn.

Automotive-related parallels explanation:

TODO

Architectural Choices

The fundamental structure of your neural network:

• Depth vs. width trade-offs - deeper networks can learn hierarchical features but risk vanishing gradients
• Layer types - when to use convolutional, recurrent, transformer, or attention mechanisms
• Skip connections and residual blocks - crucial for training very deep networks
• Activation functions - ReLU, GELU, Swish each have different gradient flow properties
• Bottleneck layers - can compress information too aggressively
• Output layer design - ensuring it matches your task (softmax for classification, sigmoid for multi-label, etc.)

Wrong architectural choices create fundamental capacity or inductive bias mismatches with your problem.

Automotive-related parallels explanation:

TODO

Training Procedures

The actual learning process orchestration:

• Gradient accumulation and mixed precision training - memory vs. computational efficiency
• Learning rate scheduling (warmup, cosine annealing, step decay) - affects convergence trajectory
• Gradient clipping - prevents exploding gradients but can mask other problems
• Curriculum learning - order of data presentation can drastically affect learning
• Transfer learning and fine-tuning strategies - which layers to freeze, when to unfreeze
• Distributed training synchronization - batch norm statistics, gradient aggregation across devices

Automotive-related parallels explanation:

TODO

The Cascade Effect

Here's why this matters: each stage builds on the previous one, and can have exponential compounding of problems.

You might have:

• Slightly noisy labels (GIGO)
• That get amplified by poor normalization (preprocessing)
• Leading to gradient instability (requiring aggressive regularization in hyperparameters)
• Which masks the fact that your architecture is too shallow (architectural choice)
• So you compensate with an overly complex training schedule (training procedure)

Now you're debugging in the wrong layer of the stack. The real fix was back at step 1 or 2, but you're tweaking learning rates and adding dropout everywhere.

All too often folks are trying to fix the symptoms and not the causality a 30 steps up the chain.

TODO:

Automotive-related parallels explanation, starting with the fuel supply to the car, then into the system, compounded by trying to compensate electrically/computer/emissiones devices, and fouling out the catalytic converter (and replacing it), and inadequate vacuum giving false postives that the automatic transmission is failing, wen it was all because they got cheap gas where the management didn't maintain their filters or water removal processes.

I've seen these scenarios play out many times, and usually these "possessed demon" "Christine" (Stephen King) cars were often brought to me as a last resort to save them from being scrapped completely.

TODO

The "Simple First" (K.I.S.S.) Principle 

Experienced mechanics learn to check simple explanations before assuming complex failures. This can save hours, days, or even weeks of work and unnecessary expensive billable hours (or if working flat rate or blue book based - going broke).

A car that won't start might have a dirty battery connection that just needs cleaning, or just a dead battery rather than starter relay, starter, or engine problems.

A transmission that's slipping might just need fluid topped-off, or a new filter, rather than major repairs (all too often I've seen a majority of mechanics lie to customers - that was how I became a mechanic in the 80s, they kept ripping off my mother). There were some that just lied and said it was a $400 sensor when it was just a broken/arcing spark plug wire, they'd fix the real problem (replace the wire), but charge them for the sensor anyway. I am also disturbingly seeing increasing parallels in the software development world, worsening the past 10-15 years with accelerating levels of dishonest and obfuscation as the social contract between management and developers continues to disintegrate the relationship on both sides. But that is another topic.

This methodological, approach, often called in the auto shops and elsewhere as the K.I.S.S. (Keep It Simple Stupid (or Keep It Stupid Simple)) is also known as Occam's Razor.

This principle also applies directly to AI development.

Before redesigning neural network architectures or implementing complex optimization algorithms, check data quality, feature engineering, and basic hyperparameter settings.

Most machine learning problems trace to straightforward issues rather than exotic technical challenges.

Understanding Failure Modes

Automotive systems usually fail in predictable patterns based on design limitations, wear characteristics, physics, driver habits, and environmental stresses.

Experienced technicians learn to recognize these patterns and eventually develop the wisdom to where they can often diagnose problems from symptoms alone.

AI systems also have characteristic failure modes. Overfitting, vanishing gradients, data leakage, and distribution shift, can all create recognizable symptom patterns.

Understanding these failure modes helps identify problems quickly rather than spending hours, days, weeks, or even months, of time on haphazard uninformed trial-and-error debugging.

The Importance of Measurable Diagnostics

Good automotive diagnosis relies on quantitative measurements: fluid pressures, electrical voltages, temperature readings, timing specifications.

Subjective observations ("it sounds funny") matter, but objective measurements provide definitive answers.

AI development benefits from similar emphasis on quantitative evaluation. Loss curves, accuracy metrics, validation performance, and statistical significance tests provide objective evidence about system performance that subjective impressions can't match.

Systems Thinking

Automotive systems involve mechanical, electrical, hydraulic, fuel, exhaust, emissions, and computer-controlled components that must all work together seamlessly. Changes to one subsystem can create unexpected effects elsewhere in the vehicle.

Many a driver (and mechanic) has been fooled by a vibration in the steering wheel making them think it was something in the front-end suspension or front tires, when it turned out to be rear drive axle bearing or drive shaft issue (on some car configurations).

Modern AI systems similarly involve data pipelines, preprocessing steps, model architectures, training procedures, and deployment infrastructure that must integrate effectively. Optimizing individual components without considering system-level interactions often creates problems rather than solutions.

Real-World Performance vs. Lab Performance

Cars that perform perfectly in controlled shop conditions might fail under real-world driving conditions: temperature extremes, aggressive driving, poor maintenance, or unexpected loads.

AI systems that achieve excellent performance on clean benchmark datasets often struggle with real-world data: missing values, distribution shift, adversarial inputs, or edge cases that weren't represented in training data.

Both domains require testing under realistic conditions rather than just optimal scenarios.

The Value of Experience-Based Intuition

It is dangerous and unprofessional to rely solely on intution, in these type of tech industries, automotive or computers, that is a path to chaos and madness, but a technician devoid of experience-based intuition to help narrow down the highest probability of where to start looking, rather than randomly, can have dramatic impact.

Experienced mechanics develop intuition about system behavior that goes beyond technical specifications. They can often identify problems through subtle cues that novice technicians miss entirely.

I've had cars come in with frazzled owners that have "taken it to everyone", multiple dealership, specialty shops, and after hundreds, or thousands of dollars, none actually solved it. I cannot count how many times I, and other mechanics I really look up to far better than I, have been able to solve these mystery cards within minutes. It takes years to build this "technical intuition" to high efficacy, but it comes across as "magic" to others, and thus , like the Unix masters becoming called "gurus", this actually logical process built on experience, seems, as per Arthur C. Clarke's Third Law "Any sufficiently advanced technology is indistinguishable from magic."

AI development similarly benefits from experience-based intuition about what approaches are likely to work for specific types of problems.

This intuition develops through repeated exposure to different system architectures, data types, and application domains.

Those of us that have been deep in the bowels of the AI development for many decades, often do not experience the same feel of "magic" from interacting with AI that others do, but the results are still the same. And as we develop our own intuition, we have to keep figuring out how to bring this into the AI ecosystem to get to that next level of advancement in synthetic intelligence evolution.

Continuous Learning and Adaptation

Automotive technology evolves constantly: new engine designs, electronic systems, materials, and diagnostic tools. Successful technicians must continuously update their knowledge and adapt their diagnostic approaches.

AI development requires similar commitment to continuous learning. New architectures, training techniques, research findings, and application domains emerge regularly.

Success requires staying current with developments while maintaining solid foundations in core principles.

Some of My Current Applications Of AI

These diagnostic principles now inform my work on AI systems that process 10+ million data points of industrial supply chain parts per transaction, power my NeuroRPG neurotechnology platforms controlled by brain signals, and enhance the educational technologies that adapts to individual learning patterns.

The systematic thinking learned in automotive diagnosis translates directly to building reliable, scalable AI systems that work under real-world conditions rather than just laboratory demonstrations.

QUESTION: What unexpected experiences have shaped your approach to technical problem-solving? I'd be interested to hear how your atypical diverse background influences your development methodology.