The main built-in assumptions in neural network are usually extremely high-level and abstract things, such as &quot;this and this can be approximated with more or less smooth functions&quot;, or &quot;things that are near are more likely to matter than things that are far away&quot;.<p>Before, using machine learning to build models from the data was in some sense a last resort, for things you couldn&#x27;t get a grip on modelling by hand from your own understanding. When you could, you preferred hand-made models because they did better.<p>To take a concrete example, the top computer go program before Alphago, CrazyStone, still had hand-crafted patterns to prioritize which moves to explore during playouts. The author (and even more his competitors) tried to find better patterns with machine learning but they couldn&#x27;t. The <i>weights</i> of the patterns were tuned with machine learning, but any attempt to e.g. evolve patterns with genetic algorithms just did worse.<p>Now, it&#x27;s going the other direction, we&#x27;re seeing very general models only constrained by training data beat carefully hand-crafted models in area after area. And models are becoming more similar, more generalized as well - many people have noticed that state of the art recurrent and feedforward nets increasingly look more like each other.<p>I disagree that it&#x27;s a far cry from intelligence. I think people&#x27;s ideas of intelligence are fuzzy and inconsistent, and it&#x27;s far too often confused with things we <i>associate</i> with intelligence (from consciousness to independence and arrogance).