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Month: June 2018

500 Ways to Be Inspired by Wikis

by Marshall Kirkpatrick

When I visualize a positive future for the world, it’s networked, interconnected, creative, and emergent.  It’s collaborative and aware of its history.  It looks like a wiki.

Like podcasting has, wikis (beyond Wikipedia) may still prove to be a technology that saw a burst of nerdly excitement, then a long period of obscurity, and then a break into the mainstream.  I’m hoping so.

The first wiki was created by Ward Cunningham in Portland, Oregon in 1995.  It’s still up online!  Cunningham has now been working on something called Federated Wiki, a very cool project that celebrated its 7th birthday this week. Wikipedia was launched in 2001 and in an era of rapidly increasing information warfare, there are few communities as experienced and prepared to help as the community of Wikipedia. Google, Facebook, and all of us should give it a lot more money.

One year ago this Summer, I was reading that first wiki, WikiWikiWeb, and thought – I want a wiki of my own!  And so my private, personal, mobile-responsive wiki was born.  (I use PMWiki software unloaded to my web host account.) It’s now my second-most visited site on the web (after Twitter) and I adore it so much! I put all my notes from reading in there, my notes from meetings, personal brainstorms, lots of things. I LOVE MY WIKI! I usually edit and read it on my phone. I love my “all recent pages” page, I love my “randomized list of 3 other pages” page.

(Read more...)

Simplicity, Gut, and Complex Decisions

by Marshall Kirkpatrick

There’s a saying that simple decisions are best made with rational thought alone, but complex decisions benefit from a big dose of gut feeling as well.

I’ve been employing two methods for dealing with both types of decisions that I thought I’d share here.  I think of things like these as tools I can learn, practice with, get better at, and then deploy in my work.  I typically pick them up reading online, record them on my personal wiki page for reading notes, then transfer them monthly into a variable repetition mobile flashcard app where I review and learn them over time.

For simple, but hard, decisions, I’ve been using for several years a method I call “write it all down and pick 6.”  I learned it in a print edition of HBR that I picked up at an airport , I think it was Stanford’s Baba Shiv that suggested it but to be honest I didn’t write that part down!

The idea is: write down all the factors to take into consideration in your decision.  As many as you can possibly think of.  This feels great, like you’ve really given it a good thought.  Then, pick a very small number of those factors that are most important – at most 6.  Now look just at those 6 most important factors and honestly ask yourself what decision they support making.  This may be more powerful than it sounds.  It’s great.

Second, when you’ve got a complex decision, it can be helpful (Read more...)

166. Techstars: What Worked, What Didn’t, What’s Next (David Cohen)

by Nick Moran

David Cohen of Techstars joins Nick to discuss Techstars: What Worked, What Didn't, What's Next. In this episode, we cover:

  • "What was the original vision for Techstars, when you started?
  • How has that vision changed and evolved to where it is now?
  • Techstars Ventures-- any concern that it sends a bad signal for those cohort companies that the fund does not invest in? What does winning look like for Techstars? How do you measure success?
  • Do you measure common VC fund metrics like TVPI, DPI, IRR, etc?
  • Do you compare yourself against the other top accelerators? If so, where do you excel?
  • Started Techstars Anywhere in 2017, first full class in '18...
  • How does one run a remote accelerator w/ the same quality of an in person one?
  • I'm going to put you on the spot here-- some founders do not have a positive experience going through Techstars or other accelerators for that matter. What type of founder is the program a great fit for and what is it a poor fit for?
  • I've noticed a focus both from you and Techstars on Mental health and wellness-- what are your thoughts on this area as an opportunity for startup innovation?
  • How about the Cannabis industry. You’re based in Boulder-- what are your thoughts on the sector and opportunity for founders?
  • Do More Faster is one of the must-have books for every founder. Super pragmatic, actionable insights. What lesson or piece of advice doesn't appear in the book that, now in (Read more...)

166. Techstars: What Worked, What Didn’t, What’s Next (David Cohen)

by Nick Moran

David Cohen of Techstars joins Nick to discuss Techstars: What Worked, What Didn't, What's Next. In this episode, we cover:

  • "What was the original vision for Techstars, when you started?
  • How has that vision changed and evolved to where it is now?
  • Techstars Ventures-- any concern that it sends a bad signal for those cohort companies that the fund does not invest in? What does winning look like for Techstars? How do you measure success?
  • Do you measure common VC fund metrics like TVPI, DPI, IRR, etc?
  • Do you compare yourself against the other top accelerators? If so, where do you excel?
  • Started Techstars Anywhere in 2017, first full class in '18...
  • How does one run a remote accelerator w/ the same quality of an in person one?
  • I'm going to put you on the spot here-- some founders do not have a positive experience going through Techstars or other accelerators for that matter. What type of founder is the program a great fit for and what is it a poor fit for?
  • I've noticed a focus both from you and Techstars on Mental health and wellness-- what are your thoughts on this area as an opportunity for startup innovation?
  • How about the Cannabis industry. You’re based in Boulder-- what are your thoughts on the sector and opportunity for founders?
  • Do More Faster is one of the must-have books for every founder. Super pragmatic, actionable insights. What lesson or piece of advice doesn't appear in the book that, now in (Read more...)

Reinforcement Learning Progress

by Sam Altman
This post is by Sam Altman from Sam Altman

Today, OpenAI released a new result.  We used PPO (Proximal Policy Optimization), a general reinforcement learning algorithm invented by OpenAI, to train a team of 5 agents to play Dota and beat semi-pros.

This is the game that to me feels closest to the real world and complex decision making (combining strategy, tactics, coordinating, and real-time action) of any game AI had made real progress against so far.

The agents we train consistently outperform two-week old agents with a win rate of 90-95%.  We did this without training on human-played games—we did design the reward functions, of course, but the algorithm figured out how to play by training against itself.

This is a big deal because it shows that deep reinforcement learning can solve extremely hard problems whenever you can throw enough computing scale and a really good simulated environment that captures the problem you’re solving.  We hope to use this same approach to solve very different problems soon.  It's easy to imagine this being applied to environments that look increasingly like the real world.

There are many problems in the world that are far too complex to hand-code solutions for.  I expect this to be a large branch of machine learning, and an important step on the road towards general intelligence.

Reinforcement Learning Progress

by Sam Altman
This post is by Sam Altman from Sam Altman

Today, OpenAI released a new result.  We used PPO (Proximal Policy Optimization), a general reinforcement learning algorithm invented by OpenAI, to train a team of 5 agents to play Dota and beat semi-pros.

This is the game that to me feels closest to the real world and complex decision making (combining strategy, tactics, coordinating, and real-time action) of any game AI had made real progress against so far.

The agents we train consistently outperform two-week old agents with a win rate of 90-95%.  We did this without training on human-played games—we did design the reward functions, of course, but the algorithm figured out how to play by training against itself.

This is a big deal because it shows that deep reinforcement learning can solve extremely hard problems whenever you can throw enough computing scale and a really good simulated environment that captures the problem you’re solving.  We hope to use this same approach to solve very different problems soon.  It's easy to imagine this being applied to environments that look increasingly like the real world.

There are many problems in the world that are far too complex to hand-code solutions for.  I expect this to be a large branch of machine learning, and an important step on the road towards general intelligence.

Investor Stories 89: The Strange & Unusual (Farmer, Kaden, Narasin)

by Nick Moran

On this special segment of The Full Ratchet, the following Investors are featured:

  • Chris Farmer
  • Rebecca Kaden
  • Ben Narasin

Each investor describes the most unusual situation or pitch that they've encountered as an investor.

 

To listen more, please visit http://fullratchet.net/podcast-episodes/ for all of our other episodes.

Also, follow us on twitter  for updates and more information.

Investor Stories 89: The Strange & Unusual (Farmer, Kaden, Narasin)

by Nick Moran

On this special segment of The Full Ratchet, the following Investors are featured:

  • Chris Farmer
  • Rebecca Kaden
  • Ben Narasin

Each investor describes the most unusual situation or pitch that they've encountered as an investor.

 

To listen more, please visit http://fullratchet.net/podcast-episodes/ for all of our other episodes.

Also, follow us on twitter  for updates and more information.