  the great filter, in the context of the fermi paradox, is whatever prevents "dead matter" from giving rise, in time, to "expanding lasting life". the concept originates in robin hanson's argument that the failure to find any extraterrestrial civilizations in the observable universe implies the possibility something is wrong with one or more of the arguments from various scientific disciplines that the appearance of advanced intelligent life is probable; this observation is conceptualized in terms of a "great filter" which acts to reduce the great number of sites where intelligent life might arise to the tiny number of intelligent species actually observed (currently just one: human).hanson 1998: "no alien civilizations have substantially colonized our solar system or systems nearby. thus among the billion trillion stars in our past universe, none has reached the level of technology and growth that we may soon reach. this one data point implies that a great filter stands between ordinary dead matter and advanced exploding lasting life. and the big question is: how far along this filter are we?" this probability threshold, which could lie behind us (in our past) or in front of us (in our future), might work as a barrier to the evolution of intelligent life, or as a high probability of self-destruction. the main counter-intuitive conclusion of this observation is that the easier it was for life to evolve to our stage, the bleaker our future chances probably are. the idea was first proposed in an online essay titled "the great filter - are we almost past it?", written by economist robin hanson. the first version was written in august 1996 and the article was last updated on september 15, 1998. since that time, hanson's formulation has received recognition in several published sources discussing the fermi paradox and its implications. == main argument == ===fermi paradox===  there is no reliable evidence aliens have visited earth and we have observed no intelligent [[ex
Extraterrestrial_life Fermi_paradox Open_problems