6 Minute Read

#6things: Prisoners Code, A Dissenting View of AI and Photography


Tammy Colson (@TLColson) recently asked on Facebook which of her talent photog friends had photography she could buy or use for projects. Why didn’t I think of that?! Victorio Milian (@Victorio_M), Doug Shaw (@dougshaw1), Heather Bussing (@heatherbussing) and apparently Neil Morrison (@neilmorrison) (I’m sure there are more…) all take lovely pictures or make some incredible art that we could all be supporting instead of some faceless behemoth stock service. With the prevalence of more “real” photography, I think this idea is due to come to fruition! (FBizzle)


Todd Raphael (@ToddRaphael) is the quiet, watchful steward of our industry. A longtime leader at industry mainstay ERE, he needs YOUR help identifying amazing startups to feature at this spring’s ERE event. What’s that you say? You want to reach out to him? Well here’s the call for companies and here’s his contact info.

I’m looking for companies that:

  • Fit into a future-oriented conference about the future of talent acquisition, representing where the field is going and improving
  • Make an attendee think, “wow, I hadn’t thought of recruiting that way” or “that’d really help me” or “that’d be a time saver” and so on
  • Are early stage, but more than “just an idea”; people are actually working on it
  • Are new and different
  • Aren’t already established and publicized and marketed

Shoot me an email (todd@ere.net) or a phone call (212-671-1181, x806). (ERE)


Dina Medeiros (@socalgirl) has worked for LinkedIn for almost as long as I’ve known her. Before that, she was a Monster gal and a Hodes leader. But all that just changed. This dynamo of a sales and talent leader is moving on to Blizzard to lead their talent attraction efforts. For those that don’t know, Dina is the one who made LinkedIn Talent Connect happen for me and I can only hope my performance had nothing to do with her departure 🙂 Dina brings a strong game no matter WHERE she is. Best wishes Dina! Blizzard is lucky to have you! (Facebook)


  • From 1972 to 2010, the number of people in prison in the US had increased 700%.
  • 25% of the world’s incarcerated population is in the US.
  • In California, we spend more on prisons than on higher education.
  • It costs around $47,000 to keep one prisoner in jail in California for one year.
  • More than 67% of the state prisoners released in 2005 were arrested within the next three years.

Scary stats, huh? Well, James Cavitt is doing something about it. He’s teaching prisoners to code and not ONE of his students has gone back to jail.

“In 2014, we launched Code.7370 San Quentin, the first computer programming curriculum in a US prison. The results have been extraordinary. Some of our graduates will be released this year and we are confident they will be hired as software engineers. With hard work and determination, these men have overcome serious obstacles and created a positive path for their future.”

We need more business leaders coming up with concepts like this. (Ideas.Ted.Com)




You guys, we are living in the friggin FUTURE.

Google Brain, was founded five years ago on this very principle: that artificial “neural networks” that acquaint themselves with the world via trial and error, as toddlers do, might in turn develop something like human flexibility. This notion is not new — a version of it dates to the earliest stages of modern computing, in the 1940s — but for much of its history most computer scientists saw it as vaguely disreputable, even mystical.

While my family enjoys our new Amazon Echo, it’s becoming ever clearer that these breakthroughs have more to do yet than just consumer-focused applications. True, these applications will enhance consumer life but also move beyond that to impact our professional functions:

Artificial general intelligence will not involve dutiful adherence to explicit instructions, but instead will demonstrate a facility with the implicit, the interpretive. It will be a general tool, designed for general purposes in a general context.

Moving from symbolic AI, where humans first input logic and a map for the computer to follow to computers learning from the ground up, is where we’re going next:

There has always been another vision for A.I. — a dissenting view — in which the computers would learn from the ground up (from data) rather than from the top down (from rules)…Humans don’t learn to understand language by memorizing dictionaries and grammar books, so why should we possibly expect our computers to do so?

Anyway, I am not doing this article justice. You should read it. (NYTimes)