sition, such as moving a pawn one square over, may change a win to a loss (see Figure 10). Neural networks struggle to deal with non-smooth surfaces like this during training, especially when making predictions for positions they are unfamiliar with, and require vast quantities of data to compensate.</p>

        <figure>
            <img src="figure10.png" />
            <figcaption>Figure 10: White to play, winning (left); White to play, losing (right)</figcaption>
        </figure>

        <p>One of Alp