vincenthwt

I have a similar idea, just on a smaller scale. Rather than searching for cheap Malaysian street food via Google Maps reviews, I built MakanIndex.com to track the 'disappointment rate'."

Thank you for your perspective. As a machine vision engineer in the semiconductor industry, I have seen a lot of hype around deep learning and AI for vision applications. From my experience, deep learning works well for OCR but less so for classification tasks.

I often achieve better results by focusing on good lighting and using classical computer vision techniques.

I agree with your point about the politics of technology adoption. To protect my career, I usually promote hybrid approaches that combine deep learning and traditional computer vision methods. In reality, many deep learning solutions still rely heavily on classical techniques. Your comments on political challenges and decision-making in technology are very relevant to my experience.

Yes, that's correct about OCR. I work as a machine vision engineer in the semiconductor industry, where each wafer usually has both OCR text and machine-readable codes such as barcodes, QR codes, or data matrix codes. The OCR typically uses the SEMI font standard.

To achieve accurate OCR results, I need to preprocess the image by isolating each character, sorting them from left to right, and using regular expressions (regex) to verify the output. However, I prefer machine-readable codes because they are simpler to use, feature built-in error detection, and are much more reliable. While deep-learning OCR solutions often perform well, they cannot guarantee the 100 percent accuracy required in our applications.

This approach is similar to how e-wallet payments use cameras to scan QR codes instead of OCR text, as QR codes provide greater reliability and accuracy.

Background Context: I am a machine vision engineer working with the Halcon vision library and HDevelop to write Halcon code. Below is an example of a program I wrote using Halcon:

* Generate a tuple from 1 to 1000 and name it 'Sequence'

tuple_gen_sequence (1, 1000, 1, Sequence)

* Replace elements in 'Sequence' divisible by 3 with 'Fizz', storing the result in 'SequenceModThree'

tuple_mod (Sequence, 3, Mod)

tuple_find (Mod, 0, Indices)

tuple_replace (Sequence, Indices, 'Fizz', SequenceModThree)

* Replace elements in 'Sequence' divisible by 5 with 'Buzz', storing the result in 'SequenceModFive'

tuple_mod (Sequence, 5, Mod)

tuple_find (Mod, 0, Indices)

tuple_replace (SequenceModThree, Indices, 'Buzz', SequenceModFive)

* Replace elements in 'Sequence' divisible by 15 with 'FizzBuzz', storing the final result in 'SequenceFinal'

tuple_mod (Sequence, 15, Mod)

tuple_find (Mod, 0, Indices)

tuple_replace (SequenceModFive, Indices, 'FizzBuzz', SequenceFinal)

Alternatively, this process can be written more compactly using inline operators:

tuple_gen_sequence (1, 1000, 1, Sequence)

tempThree:= replace(Sequence, find(Sequence % 3, 0), Fizz')

tempFive:= replace(tempThree, find(Sequence % 5, 0), 'Buzz')

FinalSequence := replace(tempFive, find(Sequence % 15, 0), 'FizzBuzz')

In this program, I applied a vectorization approach, which is an efficient technique for processing large datasets. Instead of iterating through each element individually in a loop (a comparatively slower process), I applied operations directly to the entire data sequence in one step. This method takes advantage of Halcon's optimized, low-level implementations to significantly improve performance and streamline computations.

Your latest response still does not provide any concrete data or evidence to support your claims about space exploration being a waste, self-sufficient colonies being impossible, or AI and synthetic biology being more promising alternatives.

I have already asked for specific data to back up your assumptions, but none has been provided.

Without evidence, this discussion remains purely speculative. I recommend looking into the significant advancements in reusable rockets, in-situ resource utilization, and fusion research before dismissing their potential. Unsupported claims about feasibility or value are simply unsubstantiated opinion.

I agree, Deep Learning OCR often outperforms traditional methods.

But as engineers, it’s essential to understand and maintain the systems we build. If everything is a black box, how can we control it? Without understanding, we risk becoming dependent on systems we can’t troubleshoot or improve. Don’t you think it’s important for engineers to maintain control and not rely entirely on something they don’t fully understand?

That said, there are scenarios where using a black-box system is justifiable, such as in non-critical applications where performance outweighs the need for complete control. However, for critical applications, black-box systems may not be suitable due to the risks involved. Ultimately, what is "responsible" depends on the potential consequences of a system failure.

Ah, I think you work in the same industry as me, machine vision. I completely agree with you, most applications use grayscale images unless it’s color-based application.

Which vision library are you using? I’m using Halcon by MVTec.

You're absolutely right, deep learning OCR often delivers better results for complex tasks like handwriting or noisy text. It uses advanced models like CNNs or CRNNs to learn patterns from large datasets, making it highly versatile in challenging scenarios.

However, if I can’t understand the system, how can I debug it if there are any issues? Part of an engineer's job is to understand the system they’re working with, and deep learning models often act as a "black box," which makes this difficult.

Debugging issues in these systems can be a major challenge. It often requires specialized tools like saliency maps or attention visualizations, analyzing training data for problems, and sometimes retraining the entire model. This process is not only time-consuming but also may not guarantee clear answers.

It really depends on the application. If the illumination is consistent, such as in many machine vision tasks, traditional thresholding is often the better choice. It’s straightforward, debuggable, and produces consistent, predictable results. On the other hand, in more complex and unpredictable scenes with variable lighting, textures, or object sizes, AI-based thresholding can perform better.

That said, I still prefer traditional thresholding in controlled environments because the algorithm is understandable and transparent.

Debugging issues in AI systems can be challenging due to their "black box" nature. If the AI fails, you might need to analyze the model, adjust training data, or retrain, a process that is neither simple nor guaranteed to succeed. Traditional methods, however, allow for more direct tuning and certainty in their behavior. For consistent, explainable results in controlled settings, they are often the better option.

Thank you for sharing this. The part about Warren Buffett and the contrast with hustle culture is particularly delightful. It highlights the importance of competence and meaningful leadership over performative busyness.

Reasonable Cost

1. You didn’t address my main argument: reallocating 5% of the U.S. defense budget to NASA could double its budget without raising taxes. Instead, you reframed it as additional taxation. My point is about redistributing current resources, not increasing taxpayer obligations.

2. Do you believe reallocating 5% of defense spending would harm national security? Or could it be a reasonable way to reprioritize national spending towards long-term scientific advancement?

Moon Resources

1. You claim extraterrestrial mining could "crash the market," but cheaper, abundant resources typically foster innovation and develop new industries (e.g., space-based solar power or advanced batteries), which could benefit consumers. Can you provide examples where resource surpluses caused economic collapse instead of creating opportunities?

2. You argue helium-3 is "mostly worthless" because fusion is 50+ years away. However, companies like Helion Energy predict commercial fusion by the 2030s, and technologies like aneutronic fusion could make helium-3 a critical resource. What specific evidence supports your lengthy timeline?

Effectiveness and Feasibility of Moon/Mars Projects

1. You claim Moon/Mars projects would exceed the defense budget but provide no data. NASA’s Artemis program, for example, is projected to cost $93B over a decade, far below $842B in annual U.S. defense spending. What data supports your claim of higher costs?

2. Reusable rockets, such as SpaceX’s Starship, have already reduced launch costs by up to 90%, directly countering your cost concerns. Why did you not address this?

3. Advancements in in-situ resource utilization (ISRU), 3D printing, and automated production are already paving the way for sustainable off-world colonies. Why do you dismiss these technologies entirely when critiquing the concept of self-sustainability?

4. While you note "showmanship" is a factor, history shows symbolic exploration fuels technological advancement. Apollo, for example, spurred breakthroughs in computing, communications, and materials science. Moon/Mars exploration could provide similar transformative benefits.

Comparison to AI and Synthetic Biology

1. You claim synthetic biology is more promising than space exploration, but can you provide evidence to support this? Space exploration directly addresses existential risks like resource scarcity and planetary threats.

2. Do you agree that space research fuels advancements in robotics, AI, and materials science, which vastly benefit Earth and humanity’s long-term survival? Why can’t space exploration and other emerging technologies work together to create a stronger foundation for humanity’s future?

3. Delaying space exploration may result in lost opportunities for innovation that could directly impact Earthly and extraterrestrial problems.

Conclusion

You raise important points, but much of your argument lacks supporting evidence and is based on speculation. I encourage further consideration of current research and advancements like reusable rockets, ISRU, and fusion energy, which prove the feasibility and value of space exploration. I appreciate your thoughts and look forward to continuing the discussion.

Crossing oceans and building planes were not always seen as clearly useful by everyone. Skeptics at the time dismissed them as dangerous, impractical, or unnecessary, yet those who pursued these goals unlocked advancements that transformed human history. The same applies to the Moon. It is far more than just a big, dry ball of rock; it contains highly valuable resources with practical potential.

For instance, the Moon has helium-3, a rare isotope that could one day power clean nuclear fusion energy, a trillion-dollar industry waiting to happen. Lunar water ice can be converted into hydrogen and oxygen for rocket fuel and life support, making sustainable space exploration feasible and reducing the need for costly Earth-based resources. The Moon also has rare earth metals that are vital for technology and renewable energy systems, helping us address resource scarcity and reduce the environmental damage caused by terrestrial mining.

We do not explore the Moon for its own sake. The point of space exploration is to create a foundation for future industries and innovation while solving long-term challenges, such as resource depletion and planetary risks. Given the enormous technological, economic, and environmental benefits these resources could provide, the Moon is far more than just a barren rock; it holds the key to securing humanity's future.

1. What is a "reasonable cost," and who decides?

Reasonable cost is subjective, but NASA’s budget provides perspective. At 0.4 percent of the US federal budget, it amounts to just 27 billion dollars in 2023, while the defense budget is 842 billion dollars, or 13 percent of annual spending. Redirecting just 5 percent of defense funding, about 40 billion dollars, would more than double NASA's budget and allow for significant progress on Moon and Mars projects. This minor reallocation would not impact national security, making space exploration both affordable and worthwhile. When we consider the technological, scientific, and economic benefits, investing in space stands out as a smart, future-focused decision.

2. Are there any minerals on the Moon worth exploring?

The Moon holds valuable resources like helium-3 for clean fusion energy, water ice for fuel and life support, and rare earth metals for advanced technologies. Helium-3 could power nuclear fusion reactors and potentially yield trillions of dollars in energy benefits. Water ice can be converted into hydrogen and oxygen, creating rocket fuel that reduces reliance on costly Earth resupplies for space missions. Mining rare earth metals on the Moon could also lessen our dependency on Earth’s finite resources and help minimize ecological damage caused by terrestrial mining. The long-term financial value of these resources far outweighs the costs of extracting them.

3. Will Moon and Mars bases actually double NASA’s existing budget?

This claim is incorrect. The Artemis program, for example, is projected to cost 93 billion dollars over more than ten years, with yearly spending far below doubling NASA’s current 27 billion dollar budget. Additionally, technologies like reusable rockets, such as SpaceX’s Starship, have lowered launch costs by 90 percent, making Moon and Mars exploration increasingly achievable. With international collaborations and private investment, developing these projects is far less expensive than critics often assume, and will not significantly burden taxpayers.

4. What about other technologies, like AI or synthetic biology?

While AI and synthetic biology can offer exciting short-term benefits, they focus on Earth-based solutions and neglect humanity's long-term survival. Space exploration addresses critical long-term challenges, such as resource scarcity, reducing dependence on Earth, and avoiding extinction-level threats. Unlike efforts in Earth’s hostile environments like Antarctica or the deep sea, Moon and Mars exploration unlock completely new resources and pathways for innovation. Delaying investment in space exploration risks stagnating progress, and waiting for the "perfect time" could mean missing transformative opportunities that secure humanity's future.

"Yes, but why?" If humans had never ventured beyond perceived limits, like crossing oceans or building planes, where would we be today?

"We’re light-years away from being interplanetary; it’s too costly and our energy is laughable." If people doubted the Wright brothers or mocked the idea of landing on the Moon, should we have stopped trying?

"How far are we from affordable Moon travel that’s worth it?" Humanity thrives when it takes risks and embraces exploration. Space is where the next wave of innovation and opportunity lies, and waiting for "perfect timing" ensures we stay stagnant while others move ahead. Why choose doubt over progress?

Hopefully, your view is in the minority. If this mindset becomes prevalent in the US, nothing new will ever be invented, and no new regions of space will be explored.

Modern moon exploration isn’t about repeating Apollo but progressing toward resource extraction and establishing humanity’s long-term presence in space. These missions are designed to achieve goals that were previously impossible and lay the foundation for humanity’s future beyond Earth.

I love lookup tables. Thanks for sharing!

That’s a great question. While NNs are revolutionary, they’re just one tool. In industrial Machine Vision, tasks like measurement, counting, code reading, and pattern matching often don’t need NNs.

In fact, illumination and hardware setup are often more important than complex algorithms. Classical techniques remain highly relevant, especially when speed and accuracy are critical.

You claim that 'most people' will be upset—are you their appointed spokesperson, or is this just your personal assumption? What I find strange is that I complimented and thanked you for your thoughts on machine vision, yet you responded with hostility. Is this how you communicate in real life too?

If 'most people' are upset about others using LLMs to improve their written communication, maybe they should reflect on why they hold such outdated views—or consider that the person replying might not be a native English speaker. Are platforms like Hacker News meant only for native English speakers?

Warning: The statement above was written by an LLM, so don’t be surprised—I’m letting you know in advance.

Are you talking about the high wages of software engineers or management? Makes sense to me— the glory days of high management and CEO salaries are over.

Your clever remark highlights poor emotional intelligence and weak communication skills. Sarcasm might have its place in casual conversation, but in professional discussions, it signals insecurity and a lack of respect—neither of which contribute to meaningful dialogue.

Your disdain for LLMs is equally puzzling. Are you seriously suggesting I shouldn’t use tools to improve my grammar and delivery simply because they don’t align with your engineering view? Ironically, LLM-based tools likely support your own work—whether through coding assistance, debugging, or other tasks—even if you choose not to acknowledge it.

By the way, I used an LLM to craft this reply too—who doesn’t?

Thank you for your thoughtful comment! I completely agree.

It’s great to see someone emphasize the importance of mastering the fundamentals—like calibration, optics, and lighting—rather than just chasing trendy topics like LLM or deep learning. Your examples are a great reminder of the depth and diversity in machine vision.

Here are two examples where the right camera, optics, and lighting make a huge difference:

Semiconductor Wafer Inspection: Detecting tiny defects like scratches or edge chips requires high-resolution cameras, precision optics, and specific lighting (e.g., darkfield) to highlight defects on reflective surfaces. Poor choices here can easily miss critical flaws.

Food Packaging Quality Control: Ensuring labels, seals, and packaging are error-free relies on the right camera and lighting. For instance, polarized lighting reduces glare on shiny surfaces, helping detect issues that might otherwise go unnoticed.

Can anyone recommend a good book on Machine Vision? I believe the foundation of effective machine vision, and even computer vision, lies in selecting the right camera, optics, and lighting. High-quality images are essential because poor input leads to poor output.

It's quite simple. What is the country of origin of the current Microsoft CEO and the founder of the bankrupted company?

In other words, software engineers need to "sell" the value of their work.

You're right, modern design often favors neutral bases with pops of color.

However, if everyone followed that same "modern taste," everything would look alike. Just as the technicolor era had its appeal, so do bold color palettes.

The best approach depends on the desired effect and overall design.

I hear you, and I agree that coding is still core to being a software engineer. However, with tools like GitHub Copilot already automating parts of coding, it’s clear that the role of software engineers is evolving.

While coding remains important, skills like problem-solving, communication, and selling ideas are becoming just as essential. LLMs can assist with tasks, but they can’t replace the creativity and critical thinking that only humans bring. Focusing on these skills ensures we stay ahead as the industry changes.

I worked as a Technical Sales Engineer and have been a Software Engineer for three years. I’ve realized many technical professionals overlook the importance of selling and writing.

With LLMs advancing and may take over coding tasks, software engineers should focus on skills like selling, writing, and problem-solving.

Much earlier. Ever since Google, I’ve been 'vibe' coding—just a different buzzword, I guess.

For clean, classical edges in standard images, classical algorithms(Canny, Sobel) are hard to beat in terms of accuracy, efficiency, and clarity.

For domain-specific edges (e.g., medical images, low-light, or noisy industrial setups), a small, well-trained neural network may perform better by learning more complex patterns.

In summary, "Garbage In, Garbage Out" applies to both classical algorithms and neural networks. Good camera setup, lighting, and optics solve 90% of machine vision or computer vision problems before the software—whether classical or neural network-based—comes into play.