This is a list of published arguments that we know of that current methods in artificial intelligence will not lead to human-level AI. Details Clarifications We take ‘current methods’ to mean techniques for engineering artificial
An analysis of historical growth supports the possibility of radical increases in growth rate. Naive extrapolation of long-term trends would suggest massive increases in growth rate over the coming century, although growth over the last
Survey participants (n = 83) were given anonymized descriptions of behavior in the wild for four animals: one bird species and one primate species with a similar neuron count, and one bird species and one
According to experience and data from the Good Judgment Project, the following are associated with successful forecasting, in rough decreasing order of combined importance and confidence: Past performance in the same broad domain Making more
The human brain’s approximately 86 billion neurons are probably connected by something like 850,000 km of axons and dendrites. Of this total, roughly 80% is short-range, local connections (averaging 680 microns in length), and approximately
We aren’t convinced by any of the arguments we’ve seen to expect large discontinuity in AI progress above the extremely low base rate for all technologies. However this topic is controversial, and many thinkers on
The cheapest hardware prices (for single precision FLOPS/$) appear to be falling by around an order of magnitude every 10-16 years. This rate is slower than the trend of FLOPS/$ observed over the past quarter century,
The largest number factored to date grew by about 4.5 decimal digits per year over the past roughly half-century. Between 1988, when we first have good records, and 2009, when the largest number to date was factored, progress was
Published June 2016; last substantial update before Oct 2017 The 2016 Expert Survey on Progress in AI is a survey of machine learning researchers that Katja Grace and John Salvatier of AI Impacts ran in
Participants Professor Tom Griffiths, Director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences at the University of California, Berkeley. Finan Adamson, AI Impacts. Note: These notes were