How far are we from achieving true AGI? – Valentino Zocca

AGI solutions are being continuously investigated, though the current most promising mainstream technology, neural networks, while contributing to some extraordinary results, are still running short of achieving them. This criticism is not new, and, most recently Gary Marcus, in "Deep Learning: A Critical Appraisal", arXiv:1801.00631v1, has outlined many issues with current deep learning architectures, in particular their inability to'understand' the information they manipulate and their ability to mostly work in a'stable' world. As Marcus states in his article: 'The logic of deep learning is such that it is likely to work best in highly stable worlds, like the board game Go, which has unvarying rules, and less well in systems such as politics and economics that are constantly changing. To the extent that deep learning is applied in tasks such as stock prediction, there is a good chance that it will eventually face the fate of Google Flu Trends, which initially did a great job of predicting epidemological [sic] data on search trends, only to complete [sic] miss things like the peak of the 2013 flu season (Lazer, Kennedy, King & Vespignani, 2014)'. Even one of the so called'fathers' of Deep Learning architectures, Geoffrey Hinton, has recently voiced his concerns that deep learning needs to start over.