# AI News, BOOK REVIEW: Why not just dump the neural networks and deep learning? [closed]

- On Monday, June 4, 2018
- By Read More

## Why not just dump the neural networks and deep learning? [closed]

As the comments to your question point out, there are a lot of people working on finding something better.

I would though like to answer this question by expanding the comment left by @josh All models are wrong but some are useful (Wiki) The above statement is a general truth used to describe the nature of statistical models.

Using a number of observations, we can fit a model to give us an approximate value for a dependent variable given any value(s) for the independent variable(s).

Deviations from our model (as can be seen in the image above) appear random, some observations are below the line and some are above, but our regression line shows a general correlation.

For example, imagine watching cars as they drove through a junction where they must turn either left or right to continue, the cars turn in no particular pattern.

In reality they are probably heading somewhere specific for a specific reason, and without attempting to stop each car to ask them about their reasoning, we can only describe their actions as random.

Where we are able to fit a model with minimal deviation, how certain can we be that an unknown, unnoticed or immeasurable variable wont at some point throw our model?

As you mention, there's an incomprehensible number of solutions which could fit the data using ANNs, but is this number really any different to the amount of solutions we would need to develop ourselves through trial and error?

- On Tuesday, June 18, 2019

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