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Category: Lechuck
Analytical vs Empirical
Analytical vs Empirical Approaches
Analytical Approach
- Theory Driven: Based on mathematical theories and logical reasoning
- Programmable with Design: Implemented through explicit rules and algorithms
- Sequential by CPU: Tasks are processed one at a time in sequence
- Precise & Explainable: Results are accurate and decision-making processes are transparent
Empirical Approach
- Data Driven: Based on real data and observations
- Deep Learning with Learn: Neural networks automatically learn from data
- Parallel by GPU: Multiple tasks are processed simultaneously for improved efficiency
- Approximate & Unexplainable: Results are approximations and internal workings are difficult to explain
Summary
This diagram illustrates the key differences between traditional programming methods and modern machine learning approaches. The analytical approach follows clearly defined rules designed by humans and can precisely explain results, while the empirical approach learns patterns from data and improves efficiency through parallel processing but leaves decision-making processes as a black box.
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Thinking time makes ..
It’s in deep thought that growth begins—now, take action.
Sequential vs Parallel
This image illustrates a crucial difference in predictability between single-factor and multi-factor systems.
In the Sequential (Serial) model:
- Each step (A→B→C→D) proceeds independently without external influences.
- All causal relationships are clearly defined by “100% accurate rules.”
- Ideally, with no other associations, each step can perfectly predict the next.
- The result is deterministic (100%) with no uncertainty.
- However, such single-factor models only truly exist in human-made abstractions or simple numerical calculations.
In contrast, the Parallel model shows:
- Multiple factors (a, b, c, d) exist simultaneously and influence each other in complex ways.
- The system may not include all possible factors.
- “Not all conditions apply” – certain influences may not manifest in particular situations.
- “Difficult to make all influences into one rule” – complex interactions cannot be simplified into a single rule.
- Thus, the result becomes probabilistic, making precise predictions impossible.
- All phenomena in the real world closely resemble this parallel model.
In our actual world, purely single-factor systems rarely exist. Even seemingly simple phenomena consist of interactions between various elements. Weather, economics, ecosystems, human health, social phenomena – all real systems comprise numerous variables and their complex interrelationships. This is why real-world phenomena exhibit probabilistic characteristics, which is not merely due to our lack of knowledge but an inherent property of complex systems.
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Computing is ..
This image illustrates the core concept of “Computing.” The key message is that computing is a process of transforming data to make people’s next decisions easier.
In the center, there is a circle with the title “Computing” along with calculator and computer chip icons. On the left side, there is binary input data (0s and 1s), which is shown being transformed through the central computing process into different binary output on the right side. Next to the binary output on the right is blue italic text saying “To make the next decision a little easier,” emphasizing that the purpose of this data transformation is to aid human decision-making.
At the bottom of the image, there is a section titled “Data Change” with cycling arrows representing data transformation. Below that, there’s a monitor displaying charts and graphs with descriptions “Based on the correlation between data” and “Monitoring changes & analysis,” showing that analyzing relationships between data is important for supporting decision-making.
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Think by a Vector
This image presents a concept titled “Think by a Vector” that compares two approaches to handling data.
The image shows a data processing flow starting from what appears to be a network or system diagram on the left, which outputs binary data (represented as 0s and 1s) labeled as “Data Explosion.” This data can then be processed in two different ways:
- Raster approach (top path):
- Labeled as “Checking All data one by one”
- Described as “Impossible to handle all data”
- Represented by squares/pixels, suggesting pixel-by-pixel processing
- Vector approach (bottom path):
- Labeled as “Extract Features”
- Uses “prediction with Features”
- Includes text stating “must apply the perfect basic rules (Feature)”
- Represented by a node/vector diagram showing connected points
The main message appears to be advocating for vector-based thinking or processing, which focuses on extracting and working with key features rather than processing every individual data point. This approach is presented as more efficient and effective than the raster-based approach.
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Rainy morning
