Now we're getting somewhere that actually has some vague relevance to
the list.
On 2 May 2010, at 22:39, Randall Reetz wrote:
I had assumed your questions were rhetorical.
If I ask the same questions multiple times you can be sure that
they're not rhetorical.
When I say that software hasn't changed I mean to say that it hasn't
jumped qualitative categories. We are still living in a world where
computing exists as pre-written and compiled software that is
blindly executed by machines and stacked foundational code that has
no idea what it is processing, can only process linearly, all
semantics have been stripped, it doesn't learn from experience or
react to context unless this too has been pre-codified and frozen in
binary or byte code, etc. etc etc. Hardware has been souped up. So
our little wrote tricks can be made more elaborate within the
substantial confines mentioned. These same in-paradigm restrictions
apply to both the software users slog through and the software we
use to write software.
As a result, these very plastic machines with mercurial potential
are reduced to simple players that react to user interrupts. They
are sequencing systems, not unlike the lead type setting racks of
Guttenburg-era printing presses. Sure we have taught them some
interesting seeming tricks – if you can represent something as
digital media, be it sound, video, multi-dimentional graph space,
markup – our sequencer doesn't know enough to care.
So for you, for something to be 'revolutionary' it has to involve a
full paradigm shift? That's a more extreme definition than most people
use.
Current processors are capable of 6.5 million instructions per
second but are used less than a billionth of available cycles by the
standard users running standard software.
From a pedantic, technical point of view, these days if the processor
is being used that little then it will ramp down the clock speed,
which has some environmental and practical benefits in itself. ;-)
As regards photo editing software, anyone aware of the history of
image processing will recognize that most of the stuff seen in
photoshop and other programs was proposed and executed on systems
long before some guys in france democratized these algorithms for
consumer use and had their code acquired by adobe. It used to be
called array arithmetic and applied smoothly to images divided up
into a grid of pixels. None of these systems "see" an image for its
content except as an array of numbers that can be crunched
sequentially like a spread sheet.
It was only when object recognition concepts were applied to photos
that any kind of compositional grammar could be extracted from an
image and compared as parts to other images similarly decomposed.
This is a form of semantic processing and has its parallels in other
media like text parsers and sound analysis software.
You haven't looked up what content-aware fill *is*, have you? It's
based on the same basic concepts of pattern-matching/feature detection
that facial recognition software is based on but with a different
emphasis.
To paraphrase, it's not facial recognition that you think is the only
revolutionary feature in photography in twenty years, it's pattern-
matching/detection/eigenvectors. A lot of time and frustration would
have been saved if you'd said that in the first place.
Semantics opens the door to the building of systems that
"understand" the content they process. That is the promised second
revolution in computation that really hasn't seen any practical
light of day as of yet.
You're jumping too many steps here - object recognition concepts are
in *widespread* use in consumer software and devices, whether it's the
aforementioned 'focus-on-a-face' digital cameras, healing brushes in
many different pieces of software, feature recognition in panoramic
stitching software or even live stitching in some of the new Sony
cameras.
Semantic processing of content doesn't magically enable a computer to
initiate action.
Data mining really isn't semantically mindful, simply uses
statistical reduction mechanisms to guess at the existence of the
location of pattern ( a good first step but missing the grammatical
hierarchy necessary to work towards a self optimized and domain
independent ability to detect and represent salience in the stacked
grammar that makes up any complex system.
Combining pattern-matching with adaptive systems, whether they be
neural networks or something else is another matter - but it's been a
long hard slog to find out that this is what you're talking about.
Adaptive systems themselves are also quite widespread by now, from
Tivos learning what programmes you watch to predictive text on an
iPhone, from iTunes 'Genius' playlists & recommendations through to
Siri (just bought up by Apple, as it happens).
Such systems will need to work all of the time. ALL OF THE TIME!
Only pausing momentarily to pay attention to our interactions as
needed. Once they are running, these systems will subsume all of
the manual activity we have been made to perform to this day. Think
"fly by wire" for processing.
That's a really REALLY bad analogy. FBW is a pilot-initiated control
system. It's smaller/lighter (the initial reason for it's use) and it
reacts to changes faster than the pilot can to stop stalls etc, in a
similar way to ABS systems in a car reducing the chances of a skid. It
doesn't *initiate* anything in itself, it's 'just' a moderated control/
feedback system.
Gone is the need to discreetly encode every single bit in exactly
the only possible sequence.
This sentence makes no sense. Did you mean 'process' rather than
'encode'?
What it means is the difference between writing a letter and our
computer interceding by understanding the meta-intent of the wrote
and inefficient processes we engage in today – what are letters
for? What resources is this user or entity after and why? Who has
those resources? Whom of those who have the desired resources need
something that we might have in exchange? How are the vectors of
intent among all entities entangled and grouped and how can our
systems work towards the optimization of this global intent matrix?
I like William Gibson or Stuart & Cohen as much as the next SF fan,
but again you're taking too many steps at once.
Emergent behaviour from a complex system (such as bypassing letter
writing by finding another way of communicating or reason for doing
things) is *emergent behaviour* - by definition you can't predict what
form it will take and you can't *plan* for it. You can't even plan
that it will *happen*.
In the same way, setting up a protocol for a network doesn't let you
predict that most internet traffic some years later will be via
Facebook or MMORPGs.
So, when I use the word "revisionist" I am calling attention to the
old sheep dressed up in new clothing but still being sheep.
Having now looked in a number of dictionaries on and offline, I stand
with Richmond's response. In common usage it's a word with very
specific connotations and they aren't ones that people associate with
software. With Steve Jobs, perhaps, but not with software. ;-)
Software feature creep is not really evolving software.
That's a matter of definition. Within the photography field, apps like
Aperture and LightRoom have had huge impacts on people's ways of
working (often making whole suites of other apps redundant in one go),
looking at a wider field the explosion of geolocation features and
services has revolutionised mobile devices and our interactions with
them, multi-touch devices are giving us new ways to physically
interact with computing systems.
That the jump is so long in coming is understandable. It is easy to
send a punch card through a machine and have it react accordingly
every time. The jump from wrote execution of static code to self
aware semantically self optimized pattern engines is a big big big
jump. But it isn't as big as it might at first seem. It is
happening. It will happen. And computing will finally result in
the kind of substantial increase in productivity that its expense
requires.
1. Much of what you're talking about as a final aim is emergent
behaviour - you *can't* predict what will or won't happen, or when.
2. We've already been through the substantial increases in
productivity that the expense of computing requires. Increased
productivity isn't the problem - *expected* productivity is the
problem because it automatically increases as productivity increases.
3. Adaptive systems don't just happen. They need to be trained, and
for the level of abstraction you're talking about they have to be
trained *a lot*. From a pragmatic point of view, much of that
increased productivity will be swallowed up by learning to be a good
system trainer, in the same way that certain types of information
research have been vastly increased via the net, only to be swallowed
up in learning to use search engines efficiently and learning how to
winnow out all the chaff.
4. The level of independent action you appear to be happy with in a
computer gives most people the screaming heebie-jeebies and flashbacks
to "I'm sorry Dave, I can't do that".
5. Most importantly, your entire body of communications on the list
appears to miss out a vital step - how do adaptive systems magically
morph into systems capable of initiating actions without user or
programmer input? I'm uncomfortably reminded of S. Harris's 'then a
miracle occurs' cartoon. :-(
http://www.sciencecartoonsplus.com/pages/gallery.php
6. On a slightly more tongue-in-cheek note, enjoy the minutes between
the first ubiquitous 'self aware semantically self optimized pattern
engine' and dying/transcending/experiencing "It's life Jim, but not as
we know it" in the ensuing technological singularity. ;-)
Ian
P.S. It's 'rote', not 'wrote'. I know it's just a typo, but it's one
that drastically alters some of your sentences._______________________________________________
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