💥 AI business models; the maker movement; creative destruction; spacecraft engineering & Dyson shells++ #31

Revue
 
Networked artificial intelligence and defensible business models; Tesla's update; the maker movement
 
October 18 - Issue #31
The Exponential View
Networked artificial intelligence and defensible business models; Tesla’s update; the maker movement & creative destruction; the long cycle of innovation; Apple’s insurmountable advantage; the science of orgasm.
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Dept of the near future
🔨 Understanding the maker movement: one of the best things I’ve read on the maker movement, by EV subscriber @jhagel (from last year) Must read
💥 Creative destruction: only 12% of Fortune 500 firms of 1955 have survived to 2015, and the rate of turnover is accelerating. Great data
🍃 True exponential change is hard to fathom - and explains why estimates for solar and wind have been consistently wrong for 30 years. Excellent
😠Blame the “engineers and MBAs and engineer-MBAs that are technoculture’s cardinals and archbishops” for the abuse culture killing Twitter & the social web. Powerful polemic by @umair
🍎 On Apple’s insurmountable platform advantage: the platform business and vertical integration.
Dept of pay-it-forward
Hi everyone! Exponential View grows solely and exclusively by your referrals and recommendations. It’s a huge personal motivator when the community grows. 
So as it is October, please take the time to tweet a recommendation and forward this to four colleagues at work. Muchas gracias.
Dept of networked intelligence
Earlier this week, Tesla updated the software in its popular Model S to introduce a vast swathe of new artificial intelligence. The auto-pilot mode brings autonomous driving to a mainstream road vehicle for the first time. But at its heart is the arrival of a stunningly effective business model for artificial intelligence.
There are some jaw-dropping elements at play:
* It works. I mean it actually works. See this fun short video by a quirky New Yorker as he goes autonomous. And a longer more detailed one by Loic.
* It was delivered as software over-the-air without a visit to a garage. Ponder on that, within the frame of software eating the world. We turned a car into an AI self-driving robot through the equivalent of a glorified text message.
* Most interestingly, the new Teslas are not just artificially intelligent, they are networked. They learn from each other. 💡Every Tesla driven teaches every other Tesla to drive better. Good read.
There are two implications of this networked intelligence. One a business implication, the second about the nature of non-human intelligence. I’ll explore the first in more detail.
The business impact is crucial. Musk has created a method where every single human driver is generating a training set of data for the ecosystem of algorithms that allow a car to self-drive. Forget Google’s 1m miles of autonomous driving, Tesla can get a million miles of driving every week from 25,000 average drivers. The training set - which includes statistically rare weird edge cases - will become enormous. Tesla’s AI will literally be better able to navigate the world than competitor AIs.
And from great data, come effective AI systems. Google, of all, know this, perhaps best espoused in their classic “The Unreasonable Effectiveness of Data” (five years old, but worth a read.)
So Tesla’s AI systems will get better and better than the competition. And they will have a substantial lead time on anyone else putting autonomous vehicles on the road.
That lead time is, in Warren Buffet’s term, the ‘economic moat’. At PeerIndex, we spent a lot of time thinking about how to build and generate proprietary data around topic-based expertise rankings, and by-and-large succeeded through a range of simple and complex methods to generate this data. 
My friend, Michael Chalfen, 👉 beautifully articulates how the availability of proprietary data is what digs the moat for artificial intelligence apps. Highly recommended. No moat, no defensibility. With one, a chance to build a profitable business.
And to the second point: no exploration or hypothesis, just things to ponder. What questions does this network of AIs raise for our understanding of artificial or other non-human AIs? The car is not like a humanoid robot. It is embodied in an entirely different way. Where will the car’s intelligence located? In your own vehicle; in a 'cloud’ or shared across all vehicles in the network? As these systems develop, does one ever 'own’ one’s car if core parts of its navigation systems reside in a network or a higher-order emergent attribute of that network?
🐬 Is a Tesla alone, even a Tesla? (Read this on dolphin intelligence to explore some notions of non-human social/networked intelligence.)
😱* Also: it isn’t AI until it can go on an acid trip.
Short morsels for dinner parties
🚀 Working mother, spaceship programmer and inventor of the term “software engineering”, a profile of the amazing Margaret Hamilton. Must read
👽 Did we really discover an alien mega-structure around a mature star? Good discussion
💗 The mind-blowing science of orgasms: they “take near-total control of your brain and nervous system
An anti-drone weapon (with video)
Powerlaw economics: the top 1% now owns half the wealth of the entire world. Dubya be proud. 
💰 Also, and kind of amazing, visible wealth inequality has signficant negative impacts on overall welfare and cooperation in networks. (Visible inequality is more adverse that inequality on its own.) 
🚢 On the humble shipping container: the box that build the world.
Paralyzed person uses human-brain interface to type.
🏯 Robots learn judo techniques to stop falling over.
As design gets more important that technology (see the Neumann piece above), here is how Uber conducts field anthropology.
End notes
Thanks for reading and thanks to Greg Yardley for recommended content this week. Don’t forget to hit reply if you want to chat. Gets lots of email so give me a few days to respond. 
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