sarah guo @saranormous·Apr 27, 20171/ An unpopular point of view: many enterprise "AI products" and "machine intelligence" products built today have limited appeal or impact18231401
sarah guo @saranormous·Apr 27, 20172/ My friends @shivon & @jamescham have done a fantastic job curating this ecosystem maporeilly.comThe current state of machine intelligence 3.0Watching the appeal and applications of machine intelligence expand.214106
sarah guo @saranormous·Apr 27, 20173/ ...but many enterprise "functions" or "intelligence" companies on the map haven't moved the needle, relative to other business tech3334
sarah guo @saranormous·Apr 27, 20174/ 99% of companies will say O365/Gsuite migration or AWS has a bigger impact on their businesses3656
sarah guo @saranormous·Apr 27, 20175/ AI-washing doesn't count. The reason co's adopt Salesforce isn't Einstein, it's configurable workflows, ecosystem and GTM dominance2868
sarah guo @saranormous·Apr 27, 20176/ Why is there such a gap b/w the significant multi-domain advances that Google has seen with ML, DeepMind, etc., vs. every other company?51137
sarah guo @saranormous·Apr 27, 20177/ Some reasons: A) inability to implement dramatic process change, B) lack of usable data, C) lack of access to both ML, engineering talent81172
sarah guo @saranormous·Apr 27, 20178/ 1 more reason: businesses today largely have structured or text data, not images, voice. NLU is the least far along of those 3 AI domains61882
Jeremy Stanley@jeremystanReplying to @saranormous @shivon and @jameschamMuch of enterprise structured data is sequence rich, and so may have greater opportunity for deep learning (recursive, memory) than expected2:21 PM · Apr 27, 2017 from Chicago, IL·Twitter for iPhone1 Like