Chapter 1, Becoming:
Everything, without exception, requires additional energy and order to maintain itself. Even
nonmaterial world of bits can degrade when no energy is put into it. The more complex the
gear, the more attention it will require. You may not want to upgrade, but you must because
everyone else is. It’s an upgrade arms race. If you neglect ongoing minor upgrades, the
change backs up so much that the eventual big upgrade reaches traumatic proportions. You
do it regularly to keep your tech healthy. Behind the scenes, the machines will upgrade
themselves, slowly changing their features over time. This happens gradually, so we
don’t notice they are “becoming.”
Every one of us will be endless newbies in the future simply trying to keep up because first
of all, most of the important technologies that will dominate life 30 years from now have not
yet been invented, so naturally you’ll be a newbie to them. Second, because the new
technology requires endless upgrades, you will remain in the newbie state. Third, because
the cycle of obsolescence is accelerating (the average lifespan of a phone app is a mere 30
days!), you won’t have time to master anything before it is displaced, so you will remain in
the newbie mode forever.
technology is taking us to protopia. More accurately, we have already arrived in protopia.
Protopia is a state of becoming, rather than a destination. It is a process. In the protopian
mode, things are better today than they were yesterday, although only a little better. It is
incremental improvement or mild progress. The “pro” in protopian stems from the notions
of process and progress. A protopia generates almost as many new problems as new
benefits. The problems of today were caused by yesterday’s technological successes, and
the technological solutions to today’s problems will cause the problems of tomorrow. This
circular expansion of both problems and solutions hides a steady growth of small net
benefits over time. But that few percent positive difference is compounded over decades
into what we might call civilization.
Some have adopted the perspective of believers in a Singularity who claim that imagining
the future in 100 years is technically impossible. That makes us future-blind. This future-
blindness may simply be the inescapable affliction of our modern world. Perhaps at this
stage in civilization and technological advance, we enter into a permanent and ceaseless
present, without past or future. Utopia, dystopia, and protopia all disappear. There is only
the Blind Now.
The problem with constant becoming (especially in a protopian crawl) is that constant
change can blind us to its incremental changes. In constant motion we no longer notice the
motion.
With the rise of tv networks and media, the problem was that content was expensive to
produce, and 5,000 channels of it would be 5,000 times as costly. No company was rich
enough, no industry large enough to carry off such an enterprise.
The great telecom companies, which were supposed to wire up the digital revolution, were
paralyzed by the uncertainties of funding the net.
,The fear of commercialization was strongest among hard-core programmers who were
actually building the web: the coders, Unix weenies, and selfless volunteer IT folk who kept
the ad hoc network running. The techy administrators thought of their work as noble, a gift
to humanity. They saw the internet as an open commons, not to be undone by greed or
commercialization.
The WELL, an early text-only online system.
Intertwingularity is a term coined by Ted Nelson to express the complexity of interrelations
in human knowledge. Intertwingularity is related to Nelson's inventing of the term hypertext
(Hypertext is text displayed on a computer display or other electronic devices with
references (hyperlinks) to other text that the reader can immediately access.).
transclusion is the inclusion of part or all of an electronic document into one or more other
documents by hypertext reference.
Docuverse is a global distributed electronic library of interconnected documents, in other
words, a global metadocument.
The disruption ABC could not imagine was that this “internet stuff” enabled the formerly
dismissed passive consumers to become active creators.
People who take advantage of these capabilities are no longer a company’s customers;
they’re the company’s developers, vendors, laboratories, and marketers.
, Chapter 2, Cognifying:
Any device that touches networked AI will share—and contribute to—its intelligence. A
lonely off-the-grid AI cannot learn as fast, or as smartly, as one that is plugged into 7 billion
human minds, plus quintillions of online transistors, plus hundreds of exabytes of real-life
data, plus the self-correcting feedback loops of the entire civilization. So the network itself
will cognify into something that uncannily keeps getting better.
We’ll use its growing smartness for all kinds of chores, but it will be faceless, unseen. We will
be able to reach this distributed intelligence in a million ways, through any digital screen
anywhere on earth, so it will be hard to say where it is.
Ai: will make us the best versions of ourself. It will do the dangerous jobs for us.
Because AI improves as people use it, Watson is always getting smarter; anything it learns in
one instance can be quickly transferred to the others.
AI in different sectors:
- IBM provides Watson’s medical intelligence to partners like CVS, the retail pharmacy
chain, helping it develop personalized health advice for customers with chronic
diseases based on the data CVS collects.
- All the major cloud companies, plus dozens of startups, are in a mad rush to launch a
Watson-like cognitive service as it is attracts many investments. Private investment in
the AI sector has been expanding 70 percent a year on average for the past four
years, a rate that is expected to continue.
- Deepmind taught an AI to learn to play 1980s-era arcade video games. They did not
teach it how to play the games, but how to learn to play the games. At first, the AI
plays nearly randomly, but it gradually improves. Algorithms in gaming, called deep
reinforcement machine learning.
Now everything that we formerly electrified we will cognify. There is almost nothing we can
think of that cannot be made new, different, or more valuable by infusing it with some extra
IQ. the business plans of the next 10,000 startups are easy to forecast: Take X and add AI.
- By adding AI to chemistry, scientists can perform virtual chemical experiments. They
can smartly search through astronomical numbers of chemical combinations to
reduce them to a few promising compounds worth examining in a lab.
- The X might be something low-tech, like interior design. Add utility AI to a system
that matches levels of interest of clients as they walk through simulations of interiors.
The design details are altered and tweaked by the pattern-finding AI based on
customer response, then inserted back into new interiors for further testing. Through
constant iterations, optimal personal designs emerge from the AI.
- You could also apply AI to law, using it to uncover evidence from mountains of paper
to discern inconsistencies between cases, and then have it suggest lines of legal
- arguments.
The list of Xs is endless. The more unlikely the field, the more powerful adding AI will be.