I think definitely for the next decade, more of A.I. is going to be around augmenting humans to make them better. It’s going to be around filling in gaps in jobs that are dangerous or boring or things people don’t want to do
The abbreviation A.I. along with its phrase “artificial intelligence” for many invokes fantasies of sentient overlord robots everywhere or benevolent machine assistants or sexy operating systems.
It can be difficult to separate hype from reality. Newsfeed cycles don’t help either. Stories circulate with titillating headlines as to what A.I. has just done without context as to where it’s failed.
Regardless we should pay attention because the ultimate goal of A.I. is to not only make machines intelligent – but as intelligent as humans.
Hence my guest, Rob May. Rob is the CEO and Co-founder of Talla, a Boston-based company offering AI-Powered automation for service and support Teams. Prior to Talla, Rob was the CEO and Co-founder of Backupify, the world’s first cloud-to-cloud backup company acquired by Datto in 2014 Backupify was acquired by Datto.
He is an angel investor in 50 A.I. companies and the Managing Director at Half Court Ventures and also writes InsideAI, the world’s most popular email newsletter on artificial intelligence.
Narrow A.I. have been an interest of mine since my googler days when Alpha testing Google Home. So I was very interested in talking with Rob not only because of his deep expertise running his own company but his broad exposure to many different types of A.I. forward companies. I wanted the scoop as to what he is seeing in the real world currently, not DeepMind or CMU experiments but what C Corps, LLCs or Partnerships, etc, we’re actually running or launching.
Bear with me as I geek out a bit but hang in as we bring it home on how A.I. might impact your company and your work.
Here are some of the highlights of our discussion.
- how he defines and discerns between A.I. and Machine Learning
- what he sees as hype and the current reality
- where he sees A.I. working and where / why companies fail with A.I.
- his perspective on the near and long term futures of A.I.
- measuring application disruption vs augmentation
- how companies and people can prepare for broad adoption
- where he see most of A.I.’s value creation coming from
- how he evaluates companies to invest in who say they use AI
- and much more …
Resources / References