RAISING >2 BILLION HUMANS INTELLIGENCES BY 25 YEARS. After helping with recovery 1970 cyclone killing half a million of his compatriots, Fazle Abed was nearly assassinated by his employer Royal Dutch Shell and the Pakistani army. Fortunately he spent his remaining 50 years celebrating intelligence development of the poorest 2 billion parents notably growth of 1billiongirls. For over quarter of a century all networking was done by word of mouth and sight of book because in Asia 20th c village life still meant no access to electricity grids or telephone lines. Fortunately both Computing Whizs Jobs & Gates were both partly dis-satisfied with western apps of pc networks which they had begun in 1984. Around 2001 they both hosted silicon valley 65th birthday wish parties for Abed as global village tech envoy. Partners in life critical challenges had begun to bring abed's village mothers solar and mobile to co-create with. Abed changed the way Jobs saw tech futures of education (see ) and how Gates saw global health fund foundations and overall the valley's university stanford started to see as far as intelligence of Women and Youth goes the most life critical knowhow for 2 billion humans wasnt directly measurable in 90 day monetary flows; it was measurable in increased life expectancy by over 25 years during Abed's community servant leadership. Probably the greatest lift in intelligence until celebrations of what Fei-Fei Li opened the worlds eyes to in 2012, and Melinda Gates and Nvidia's Jensen Huang were first to helped AIforall lift since 2014.

Tuesday, December 31, 2002

erkely presentations including brac ultra poor

 ultra poor

machine earning alg to find out who ultra poor are

Reajul Chowdhury, University of Illinois Agricultural and Applied Economics | Improved Targeting in BRAC’s Targeting the Ultra Poor (TUP) Graduation Model

originally brac had 40 branches - where it could lok  for ultar poor but unless you are bootom up network finding the poor iws contextual challenge

-- 2007 -2009 2011 3 surveys in bangladesh

this paper draws on brac data - cargoriatsions - housing expenditure

claims estimate traeatment effect by machine elarning targeting (who would benefot, who wouldnt)


his findings similar to original literature

surveyed 50 baseline characteristics

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