Quick links

Some of the tech news that caught my attention lately:

• Humans working for the AI: How we get ground truth for machine learning ([1])
  
  
• Deep learning helping on diagnostic medical imaging with accuracy at human level ([1] [2] [3] [4])
  
  
• BHAG from Intel: "Intel aims to deliver up to 100x reduction in the time to train a deep learning model over the next three years compared to GPU" ([1])
  
  
• Deep learning's success is mostly a lot of data paired with an algorithm that can take advantage of a lot of data ([1])
  
  
• Fun! "A software platform for evaluating and training intelligent agents across the world’s supply of games, websites and other applications ... Agents use the same senses and controls as humans: seeing pixels and using a keyboard and mouse." ([1])
  
  
• Details on Duolingo's learning algorithms, including that they found what worked best for students using A/B tests ([1])
  
  
• A rant on hype-driven development ([1] [2])
  
  
• Building finished products is hard ([1])
  
  
• "There is an optimal newness for ideas -- advanced yet acceptable" ([1])
  
  
• Massive expansion of Facebook in Seattle. Seattle is increasingly becoming a mini Silicon Valley ([1] [2])
  
  
• Andy Jassy optimistic Amazon Web Services will become a $100B business ([1])
  
  
• Detailed comparison of pricing on Google, AWS, and Azure. To summarize, it's complicated, and what your cheapest option will be depends a lot on what you're doing. ([1])
  
  
• "Google has been carbon neutral since 2007, and [in 2017] we'll be powered by 100% renewable energy as our newest wind and solar farms come online" ([1])
  
  
• Likely to see truly massive wind turbines in the near future ([1])
  
  
• "The Waffle House Index also stands for something less obvious. It is an indicator of how complex and long supply chains are — for food, for fuel, for power — and of what it takes to plan around infrastructure that can be fragile in unexpected ways." ([1])
  
  
• Xkcd: "Of course, 'Number of times I've gotten to make a decision twice to know for sure how it would have turned out' is still at 0." ([1])
  
  
• "Not one, nor two, but five major VC funds reached out about investing in Rocket AI ... The ultimate fake AI company ... AI is at peak hype, and everyone in the community knows it." ([1])

Book review: Chaos Monkeys

Cynical, mercenary, and dark, this book aptly serves as an opposing view for any idealism you may have been feeling about Silicon Valley startups or their bigger brethren. Some of us work in technology to make a difference. That is not what you will find in this book.

It is a tale of a startup that wasn't really a startup, three people with no real product acquired after 10 months. It is a tale of sales and personal marketing, spinning unfavorable realities into golden-sounding tales capable of jumping the next hurdle and moving on to the next deal. It is a tale of greed and personal ambition, everything viewed through a Wall Street lens of climbing a hierarchy of wealth and power, some in the world of venture capital, and particularly detailed at Facebook.

Facebook comes out of the book particularly poorly, as if Zuck is a some kind of fickle boy king holding court with his sycophants. During his time at Facebook, the author appears to try to join this clique, only to grow bitter when entry is rebuffed.

Most interesting is the description of Facebook's struggle with advertising revenue, especially after its IPO. As the author describes it, Facebook couldn't figure out how to make the promised revenue. Eventually, in mid-2013 or so, they found a way, not by using data on what people do, but knowing who most people are, which turned out to be particularly important on mobile ("basic targeting like age and gender was a godsend to data-starved marketers ... data-wise, you have a first-party relationship with [only] a few apps"). The real value of Facebook turned out not to be its data on what people are doing, but merely being able to identify most people consistently and willing to exploit that to its fullest.

It helps if you know at least a few of the personalities featured in the book. Paul Graham, Sam Altman, Chris Sacca, Greg Badros, and many others make at least brief appearances, usually to get splattered with the slime that drips from these pages. Many VCs and people at Facebook and Twitter are also mentioned, mostly described as the amoral who's who of the rich and powerful of Silicon Valley.

Like many who got lucky, the author confuses luck with skill. Sure, that pitch meeting went well, but that meeting almost didn't happen. Success often was a result of a chance connection at the right time. In cases where the author angered someone with his arrogance or foolishness, someone should have killed the deal, and might have had they been in a slightly different mood that day. This startup was almost stillborn, barely making it into Y-combinator. The acqui-hire almost didn't happen, almost killed by lack of customer growth and shenanigans by the author. That everything worked out even as well as it did was mostly good fortune.

To his credit, the author realizes some of this in the end. In the acknowledgments, he writes, "Let's be blunt: ours was a relationship of pure convenience, and I exploited you as much as you did me." But he also writes of some he encountered, "In a Valley world awash with mammoth greed and opportunism masquerading as beneficent innovation, you were the only real loyalty and idealism I ever encountered." I'd like to think mammoth greed and opportunism have much smaller representation than idealistic innovation.

Some may call me wishful, but I think pushing for that idealistic world to be true is part of making it true. This book is not going to stop me from thinking that tech companies should be a force for idealistic innovation and promise for the future. At least in my circles, most people I talk with are awash with idealism, a genuine belief that what they are working on can make things better for others. It saddens me to see that the author's perception of the tech industry is so different than my own.