Data

What to do first after scoping out and starting a data science project?

I’ve started an unsupervised learning project based on textual data. The first thing I like to do is actually look at the data. Is it noisy? What are the features—complex feature engineering needed? How heterogeneous? What generalization and overfitting challenges?

Analysis can take many forms: actually looking at the numbers, using visualization tools, Excel spreadsheet, Jupyter notebooks with Matplotlib, computing various statistics on the whole dataset or portions of it.

Some may believe this is not important. Just throw a barrage of classification or regression methods at the data, treat the data as a black box. Of course testing on a suite of ML methods is not a bad thing. But I can’t imagine not using every avenue available, including looking at the data. I’m certainly not alone in this view (see for example here, here and here).

I spent a few hours developing a simple custom data viewer for my problem that colored different parts of the textual data to give insight as to what was going on. I used ChatGPT to develop parts of this tool; some of it was incorrect and needed fixing, but having at least a draft of the code definitely saved time. Seeing the actual data in person was insightful and generated ideas for solving the problem.

While inspecting the data can help identify issues, it also risks biasing the modeling process by imposing assumptions that a flexible model might otherwise uncover on its own. One must also beware of data leakage. That being said—in general I think understanding as much as you can about the data is not a bad thing.

Many are voicing concern that the world is running out of data and that this will be a blocker to progress toward smarter AI models. One paper in fact projects timelines for when we will run out.

AI researchers are looking for ways to adapt. Nvidia has trained a specific model to generate synthetic data for training other models. Some use this approach, though using AI-generated data to train AI is not without risk.

Others have asked a bigger question, namely, is something fundamentally missing in our approach that relies so heavily on data. Certainly the bitter lesson thesis and the position long advocated by Geoffrey Hinton argue for a data-first approach with “as few” prior assumptions as possible (though every model has a bias).

But it’s currently simply unknown whether just adding more data and compute will do the trick for achieving general intelligence or whether something else is needed. Neurosymbolic approaches are being experimented with, in various forms. But it’s unclear whether these can scale up to the level needed. And the frontier labs, laser-focused on the current paradigm, may not have adequate time or resources to investigate high-risk/high-reward alternatives.

From a theoretical standpoint, sometimes more data is simply not enough. As discussed in a previous post, some problems in mathematics and engineering require exponentially large amount of data to train neural network models. Exponentials can work in your favor, but also can work against you (think of the Tower of Hanoi problem or the Wheat and Chessboard problem). Some problems on certain models cannot be solved by any amount of data available in the entire universe.

The requirements for solving these problems can grow much more quickly than expected. The strength of neural networks, their flexibility, their universal approximation property, can also be a weakness. It can take so much data to nail down all the parameters so that the model is completely error free. Thankfully, many other problems that people want to solve (such as human language modeling) are fundamentally lower dimensional and thus less vulnerable to this problem.

We just don’t know whether the current data-hungry approach will be enough—or whether we’ll need to learn another bitter lesson.

Looking at Your Data

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