Not sure if it is really the biggest but certainly one of the most pressing problems: Out-of-distribution generalization. It is explained as

Imagine, for example, an AI that's trained to identify cows in images. Ideally, we'd want it to learn to detect cows based on their shape and colour. But what if the cow pictures we put in the training dataset always show cows standing on grass? In that case, we have a spurious correlation between grass and cows, and if we're not careful, our AI might learn to become a grass detector rather than a cow detector.

As an epidemiologist I would have simply said, it is colliding or confounding, so every new field is rediscovering the same problems over and over again.

Not unexpected AI just running randomly over pixels is leading to spurious association. Once shape and colour of cows has been detected, surrounding environment, like grass or stable is irrelevant. That means that after getting initial results we have to step back, simulate different lighting conditions from sunlight to lightbulb and environmental conditions from grass to slatted floor (invariance principle). Also shape and size matters – cow spots will keep to some extent size and form irrespective if it is a real animal or children toy (scaling principle). I am a bit more sceptical about including also multimodal data (eg smacking sound) as the absence of these features is no proof of non-existence while this sound can also be imitated by other animals.

And yes, less is more.