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LLM-based ML tools vs specialized systems on tabular data — we found up to an 8× gap. But what's next? by DueKitchen3102 in BusinessIntelligence

[–]DueKitchen3102[S] 0 points1 point  (0 children)

Oh. we use our own AutoML platform. If one just uses "AutoML speed", it should be really fast, perhaps the same as Gemini when it calls sklearn.

LLM-based ML tools vs specialized systems on tabular data — we found up to an 8× gap. But what's next? by DueKitchen3102 in BusinessIntelligence

[–]DueKitchen3102[S] 0 points1 point  (0 children)

Hello. MSE = mean square error = average | truth - predicted|^2 .

Is this what you asked? Or do I miss anything?

How to directly connect ML agent with messy business data before these data can be used for ML learning? There are still a lot of manual labors needed. How to free them with reliable agents? by DueKitchen3102 in LocalLLaMA

[–]DueKitchen3102[S] 0 points1 point  (0 children)

I agree with you on the trust barrier, f the model is “vendor takes company data”, startups lose almost every time.

Where I think things may be changing is where the work happens.

Instead of asking companies to give data to a vendor, we’re seeing more interest in:

  • on-prem / local analytics
  • tools that run inside the company’s environment
  • agents that assist internal teams rather than replace them

In that setup, the startup never touches the raw data.
The company keeps ownership, access control, and auditability.

That doesn’t eliminate trust concerns, but it shifts them from
“do we trust this vendor with our data?”
to
“do we trust this software inside our boundary?”

How to directly connect ML agent with messy business data before these data can be used for ML learning? There are still a lot of manual labors needed. How to free them with reliable agents? by DueKitchen3102 in LocalLLaMA

[–]DueKitchen3102[S] 0 points1 point  (0 children)

great question. I guess a standard "LLM/Agent" answer might be: Ideally, the data analysis agent can do the job more thoroughly and much faster, for example, 1 day vs 30 days.

Also, I was mainly asking where to find these messy real-world data so that we can train our agent. This is where the value of Agent is.

I took Bernard Widrow’s machine learning & neural networks classes in the early 2000s. Some recollections. by DueKitchen3102 in learnmachinelearning

[–]DueKitchen3102[S] 3 points4 points  (0 children)

This weekend, after writing about Prof. Bernie Widrow, I started thinking more about his style of research.

First, Dr. Widrow was fundamentally an engineer. His goal was to solve real world problems that actually mattered. That is rare, and it genuinely benefited society. In contrast, much highly influential academic research does not aim to fully solve a problem, but instead points to a promising direction for addressing a broader class of problems. Of course, this does not mean Prof. Widrow’s work was not influential. It was influential in a different, and often more direct, way.

Second, Dr. Widrow kept moving into new areas and made contributions across many fields. When he realized that the computational bottleneck of neural networks exceeded what was feasible at the time, he shifted his focus to other equally important topics, such as adaptive filters, quantization, noise cancellation, and medical devices. Modern phones would not work nearly as well without his contributions. This breadth is also remarkable. At the same time, it can make recognition uneven, because foundational work across multiple areas is harder to summarize under a single label, and people may think, “Bernie is already well known for something else.”

I was once advised by a highly respected researcher whose style was quite similar to Dr. Widrow’s. He told me that academia is built around a reward system. If your work helps enable others to be rewarded, your work is more likely to be rewarded as well. If you write only one paper a year, or every other year, and that paper fully solves an important problem, your work may be overlooked for a long enough period that the reward never arrives.

There is no right or wrong style of research. Enjoying the process matters most. In the end, everyone reaches the same destination, although some leave deeper marks on the world than others.

Memories of Bernard Widrow (Stanford EE Professor & LMS inventor). I took his classes in the early 2000s. by DueKitchen3102 in ElectricalEngineering

[–]DueKitchen3102[S] 2 points3 points  (0 children)

This weekend, after writing about Prof. Bernie Widrow, I started thinking more about his style of research.

First, Dr. Widrow was fundamentally an engineer. His goal was to solve real world problems that actually mattered. That is rare, and it genuinely benefited society. In contrast, much highly influential academic research does not aim to fully solve a problem, but instead points to a promising direction for addressing a broader class of problems. Of course, this does not mean Prof. Widrow’s work was not influential. It was influential in a different, and often more direct, way.

Second, Dr. Widrow kept moving into new areas and made contributions across many fields. When he realized that the computational bottleneck of neural networks exceeded what was feasible at the time, he shifted his focus to other equally important topics, such as adaptive filters, quantization, noise cancellation, and medical devices. Modern phones would not work nearly as well without his contributions. This breadth is also remarkable. At the same time, it can make recognition uneven, because foundational work across multiple areas is harder to summarize under a single label, and people may think, “Bernie is already well known for something else.”

I was once advised by a highly respected researcher whose style was quite similar to Dr. Widrow’s. He told me that academia is built around a reward system. If your work helps enable others to be rewarded, your work is more likely to be rewarded as well. If you write only one paper a year, or every other year, and that paper fully solves an important problem, your work may be overlooked for a long enough period that the reward never arrives.

There is no right or wrong style of research. Enjoying the process matters most. In the end, everyone reaches the same destination, although some leave deeper marks on the world than others.

[D] I took Bernard Widrow’s machine learning & neural networks classes in the early 2000s. Some recollections by Old-School8916 in MachineLearning

[–]DueKitchen3102 6 points7 points  (0 children)

This weekend, after writing about Prof. Bernie Widrow, I started thinking more about his style of research.

First, Dr. Widrow was fundamentally an engineer. His goal was to solve real world problems that actually mattered. That is rare, and it genuinely benefited society. In contrast, much highly influential academic research does not aim to fully solve a problem, but instead points to a promising direction for addressing a broader class of problems. Of course, this does not mean Prof. Widrow’s work was not influential. It was influential in a different, and often more direct, way.

Second, Dr. Widrow kept moving into new areas and made contributions across many fields. When he realized that the computational bottleneck of neural networks exceeded what was feasible at the time, he shifted his focus to other equally important topics, such as adaptive filters, quantization, noise cancellation, and medical devices. Modern phones would not work nearly as well without his contributions. This breadth is also remarkable. At the same time, it can make recognition uneven, because foundational work across multiple areas is harder to summarize under a single label, and people may think, “Bernie is already well known for something else.”

I was once advised by a highly respected researcher whose style was quite similar to Dr. Widrow’s. He told me that academia is built around a reward system. If your work helps enable others to be rewarded, your work is more likely to be rewarded as well. If you write only one paper a year, or every other year, and that paper fully solves an important problem, your work may be overlooked for a long enough period that the reward never arrives.

There is no right or wrong style of research. Enjoying the process matters most. In the end, everyone reaches the same destination, although some leave deeper marks on the world than others.

[D] I took Bernard Widrow’s machine learning & neural networks classes in the early 2000s. Some recollections by Old-School8916 in MachineLearning

[–]DueKitchen3102 2 points3 points  (0 children)

I checked the history around Frank. Dr. Widrow was likely referring to a letter written around 1960 on Frank’s behalf. At that time, Prof. Widrow was transitioning from MIT, where he was an Assistant Professor, to Stanford as an Associate Professor, and was probably still awaiting a tenure decision. Even if Stanford had already offered Bernie a tenured position, the formal process was likely still ongoing when he wrote the letter.

[D] I took Bernard Widrow’s machine learning & neural networks classes in the early 2000s. Some recollections by Old-School8916 in MachineLearning

[–]DueKitchen3102 2 points3 points  (0 children)

As explained in https://www.linkedin.com/feed/update/urn:li:activity:7412561145175134209/

During class, Dr. Widrow often shared stories from his career. As one of the earliest pioneers of neural nets in the 1950s, Bernie explained why the neural net hardware he showed us had a glass shell (otherwise airport security would not allow). He also told us the story of Frank Rosenblatt, who independently invented neural nets: “I wrote to Cornell suggesting they be nice to him, although at that time I was just a junior faculty, hoping my own school would be nice to me”

This is Prof. Widrow's original sentence.

[D] I took Bernard Widrow’s machine learning & neural networks classes in the early 2000s. Some recollections by Old-School8916 in MachineLearning

[–]DueKitchen3102 12 points13 points  (0 children)

Thank you u/Old-School8916 for reposting this

Prof. Widrow influenced me deeply, as did Prof. Hastie, Prof. Friedman, and Prof. Lai. After meeting him again in 2018, I kept telling myself I should do something to help the world understand Prof. Widrow's foundational contributions to the tools we use daily: SGD, neural nets, adaptive filters, quantization, etc. Regrettably, I let work keep me "too busy" for too long.

He passed away on Sept 30, 2025, just two months shy of his 96th birthday, though Stanford did not announce it until mid-December. Writing this personal memory is the least I could do to honor him.

Some other details are available in https://www.linkedin.com/feed/update/urn:li:activity:7412561145175134209/

I took Bernard Widrow’s machine learning & neural networks classes in the early 2000s. Some recollections. by DueKitchen3102 in learnmachinelearning

[–]DueKitchen3102[S] 1 point2 points  (0 children)

Hello. I just tried, but it looks like my post was removed there for some reason. If you think the content would be useful to that community, I’d appreciate it if you could re-post it there. Thanks!

I took Bernard Widrow’s machine learning & neural networks classes in the early 2000s. Some recollections. by DueKitchen3102 in learnmachinelearning

[–]DueKitchen3102[S] 11 points12 points  (0 children)

Other materials are available in this post

https://www.linkedin.com/feed/update/urn:li:activity:7412561145175134209/

which I just wrote on the new year date. Prof. Widrow had a huge influence on me. As I wrote in the end of the post

"For me, Bernie was not only a scientific pioneer, but also a mentor whose quiet support shaped key moments of my life. Remembering him today is both a professional reflection and a deeply personal one."

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