The Unreasonable Reputation of Neural Networks
<i>&quot;Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks.<p>By the same token, the ability of neural networks to learn interpretable word embeddings, say, does not remotely suggest that they are the right kind of tool for a human-level understanding of the world. It is impressive and surprising that these general-purpose, statistical models can learn meaningful relations from text alone, without any richer perception of the world, but this may speak much more about the unexpected ease of the task itself than it does about the capacity of the models. Just as checkers can be won through tree-search, so too can many semantic relations be learned from text statistics. Both produce impressive intelligent-seeming behaviour, but neither necessarily pave the way towards true machine intelligence.&quot;</i><p>So true, and this is why I don&#x27;t listen when Elon Musk or Stephen Hawkings spread fear about the impending AI disaster; they think because a neural network can recognize an image like a human can, that it&#x27;s not a huge leap to say it will be able to soon think and act like a human, but in reality this is just not the case.