Tag: ai

Turn On, Tune In, Boot Up! For MozFest 2023:

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AI-Musement Park and MONDO
Vanilli’s Blockchain Busting Musical Experience “R.U. Cyber.. R.U. Against NFTs?”

Immediate release from: 03/03/2023

“AI-Musement Park comprises a cornucopia of performances / talks / happenings /
documentary & discussion about AI, Intelligences, technocapitalism’s more than
pressing-ongoing urgencies.” -Eleanor Dare, Cambridge University & AI-Musement Park

R.U. Cyber.. R.U. Against NFTs? An original AI-Musement Park, PlayLa.bZ & MONDO 2000
History Project human and machine learning co-creation, taking the perspective of an AI that is
training itself on the R.U. Sirius & MONDO Vanilli ‘I’m Against NFT’s’ song lyrics, exploring a
surreal, mind melting and multi-dimensional 360 world of paradoxes and conflicting rules.

“Mondo Vanilli was originally intended to be a virtual reality band exploding all
assumptions about property and propriety in the 1990s. Today fabrication becomes de
rigueur as the connection to the real is intentionally confused by the banal political
tricksters of power and profitability… while storms pound our all-too-human bodies and
communities. I am thrilled to finally see MONDO Vanilli in it’s appropriate context.
Immersive. Come play in the simulacra one more time” -R.U. Sirius, MONDO 2000

R.U. Cyber.. R.U. Against NFTs? Is a satirical, irreverent block-chain busting commentary on
the propaganda relations fueled ‘Web 3’ hype around non-fungible tokens and the broader
issues that underpin our algorithmically massaged hyper-connected infinite scrolls and trolls
age. Challenging our assumptions about the nature of technology, creativity, and value,
reminding us that the digital world is shaped by powerful forces that determine what is valued
and what is not, and a click is not always for free.

Join Us! On Spring Solstice 2023 For “R.U. Cyber? :// Mondo 2000 History Project Salon”
at MozFest Virtual Plaza & Mozilla Hubs: AI-Musement Park 20th March / 8.30pm EU / GMT

R U Cyber Funzone ai-musement park

About R.U.Sirius & Mondo 2000 #Mondo2000 #RUSirius

R.U. Sirius is an American writer, editor, and media pioneer. Known for being one of key
psychedelic & cyberpunk movement figures. Best known as Mondo 2000 editor-in-chief and at
forefront of 1990s underground cyberculture movement.

About Mozilla Festival #TrustworthyAI #AIMusementPark

Since 2010, MozFest has fueled the movement to ensure the internet benefits humanity, rather
than harms it. This year, your part in the story is critical to our community’s mission: a better,
healthier internet and more Trustworthy AI.

About PlayLa.bZ CIC #PlayLabZ #SpatialCadetZ

Co-founded by PsychFi, FreekMinds & Squire Studios we’re a next generation multipotentiality
multi-award-winning, multi-dimensional motion arts experience design laboratory, developing
DIY changemaking createch immersive experiences & software applications for social good
storycraft. Supporters & Friends: Mozilla Festival, Jisc: Digifest, Beyond Games, Tate Modern,
Furtherfield, Boomtown Festival, Sci-Fi-London, Ravensbourne University London, UAL, East
London Dance, NESTA, Modern Panic, ArtFutura, Kimatica, National Gallery X, Kings College
London, Looking Glass Factory, SubPac, Ecologi, The JUMP, BOM Labs, Mondo 2000

PR Contact: James E. Marks, Tel: 07921 523438 @: jem@playla.bz Twitter: @GoGenieMo

There’s Still A Visionary Edge at the Intersection of Art and Technology: CODAME

Ken Goffman1 Comment

by Rachel Haywire

Since the early 90’s, publications such as Mondo 2000 have brought a subversive and countercultural relevance to the consistently evolving (yet not always radical) tech industry. Focusing on a passionate new world in which VR, AI, music, visual art, audio experimentation, and live performances combine; mutants/freaks/pioneers in this provocative space between art and technology have carried on the torch to create their own festivals, projects, and temporary autonomous zones.

From tech salons like the BIL Conference and the Extreme Futurist Festival, to electronic music events and communities such as GOGBOT and LoveTech, a new history is being written for makers and innovators who see outside the corporate world of tech. In the accelerating 2000’s, this evolving demographic is leading the charge through an intoxicating fusion of art, science, creativity, AI-generated music, experimental live performances, crypto-communities, biohacking, digital philosophy, fashion technology, and more.

On the front lines of this cultural movement is a collaborative organization in San Francisco called CODAME whose participants are gearing up to throw an ART+TECH Festival in early June of 2018. This ART+TECH event features gallery installations, screenings, and performances @ The Midway from June 4-7, 2018. Their theme this year is #ARTOBOTS, which zooms in on how automatons orchestrate so much of our lives through both our bodies and minds.

From the CODAME website:

“In the daily movements we make, the messages we send, and the sensations we experience, we already collaborate with [bots] in increasingly varied, tactile and tangible ways. Bots mediate our relationships with ourselves, each other and our environments. While many of these interactions are familiar to us, there are a myriad of ways to move, think, sense and feel with our lively machines.”

Events like CODAME are examples of the future going in a new radical direction envisioned by the early adapters of Mondo 2000. Dorkbot and RE/Search Publications also come to mind, as early Bay Area organizations that continue to influence this countercultural space today. Who says that the tech world must be full of boring and soulless robots who aren’t even literally robots? Who says that science can’t be a visionary world of spontaneous emergence and creative expression?

Technology must not lose its visionary edge. If we feel outnumbered, we can work to convert new markets into a more exciting world. We can show the public that technology is about conscious evolution and radical self expression as much as numbers and spreadsheets. We can paint the landscape with #ARTOBOTS and more, providing an electrifying future for our generation to participate in. Through these festivals and communities, our entire species can be radically transformed.

The Annual ARTS + TECH takes place June 4 – June 7 at The Midway in San Francisco

 

Pink Lexical Goop: The Dark Side of Autocorrect

Ken Goffman3 Comments

 

By Dmitry Mazin.

Illustrations by Sean McOmber.

We’ve awaited the age of artificial intelligence for decades. In our fantasies, AI is usually humanoid, straight out of the Jetsons. But while we anticipate the great arrival of the robotic butlers, AI has, in fact, already quietly permeated the fabric of our daily lives — from shopping, to driving, to communication.

Consider autocorrect, an AI-driven input assistant so ubiquitous that you likely don’t even realize how much it impacts your life. Without it, typing on a smartphone would be exceedingly difficult. That utility comes with a price, however, as autocorrect has begun to significantly alter the way we communicate.

Though you probably first encountered autocorrect as telltale squiggly red lines under your spelling mistakes, its breakthrough came with the smartphone. As you mash the tiny keys on your phone’s virtual keyboard, a sophisticated language model, working behind the scenes, determines which keys you actually intended to press. The iPhone, for example, invisibly enlarges those keys you are likely to hit next, so they are harder to miss[1]. Naturally, spelling is automatically checked in the process. This hybrid of input assistance and spellchecking is what we now know as autocorrect.

Prior to autocorrect, spellcheck was constrained to word processors. Its impact was limited, affecting primarily formal documents like letters and essays. Now, thanks to autocorrect, which mediates everything typed on a smartphone — casual and formal speech included — spellcheck is essentially universal. While the Standard English which spell check enforces may be preferable within the context of a formal document, this isn’t necessarily the case elsewhere.

Autocorrect’s insistence on “ducking” (instead of the much coarser exclamation) is infamous, but its rigidity goes beyond cursing. If you actually prefer the spelling “miniscule,” you must wrestle with autocorrect. And because actual humans adapt quickly to change (and even anticipate it), a human-edited dictionary like Merriam-Webster actually includes words that autocorrect doesn’t, such as “abridgement.”

Autocorrect fundamentally alters English. Since there are many ways to spell most English sounds, its spelling tends to drift. Autocorrect slows this evolution, enforcing Standard English in spaces where novel or informal spellings would have previously gone unmolested. Indeed, a 2011 study concluded that in a 20-year period prior to the introduction of autocorrect, spellcheck was already largely responsible for an accelerating death of English words, while the creation of new words contracted sharply, causing an actual shrinkage of the English lexicon[2].

Nevertheless, autocorrect undeniably provides a net benefit. Using our smartphones would simply be intractable without it. However, a new class of input assistant AIs operates on a level beyond spelling, affecting the very way we choose our words. These AIs cross into dangerous territory, threatening to render the English language into lexical pink slime.

In 2014, years after the iPhone’s initial release, typing on a smartphone apparently remained too slow[3]. With iOS 9, Apple launched a new product called QuickType, a small bar above the keyboard which automatically suggests the next word in a sentence, dramatically reducing the need for typing itself. “Typing as you know it might soon be a thing of the past,” Apple promised[4]. For simple phrases like “on my way,” QuickType works perfectly, and for more complex phrases, its suggestions are often good enough. Read more “Pink Lexical Goop: The Dark Side of Autocorrect”

Semi-autonomous software agents: A personal perspective.

Bryce LynchLeave a comment

by: The Doctor [412/724/301/703/415]

So, after going on for a good while about software agents you’re probably wondering why I have such an interest in them. I started experimenting with my own software agents in the fall of 1996 when I first started undergrad. When I went away to college I finally had an actual network connection for the first time in my life (where I grew up the only access I had was through dialup) and I wanted to abuse it. Not in the way that the rest of my classmates were but to do things I actually had an interest in. So, the first thing I did was set up my own e-mail server with Qmail and subscribed to a bunch of mailing lists because that’s where all of the action was at the time. I also rapidly developed a list of websites that I checked once or twice a day because they were often updated with articles that I found interesting. It was through those communication fora that I discovered the research papers on software agents that I mentioned in earlier posts in this series. I soon discovered that I’d bitten off more than I could chew, especially when some mailing lists went realtime (which is when everybody started replying to one another more or less the second they received a message) and I had to check my e-mail every hour or so to keep from running out of disk space. Rather than do the smart thing (unsubscribing from a few ‘lists) I decided to work smarter and not harder and see if I could use some of the programming languages I was playing with at the time to help. I’ve found over the years that it’s one thing to study a programming language academically, but to really learn one you need a toy project to learn the ins and outs. So, I wrote some software that would crawl my inbox, scan messages for certain keywords or phrases and move them into a folder so I’d see them immediately, and leave the rest for later. I wrote some shell scripts, and when those weren’t enough I wrote a few Perl scripts (say what you want about Perl, but it was designed first and foremost for efficiently chewing on data). Later, when that wasn’t enough I turned to C to implement some of the tasks I needed Leandra to carry out.

Due to the fact that Netscape Navigator was highly unreliable on my system for reasons I was never quite clear on (it used to throw bus errors all over the place) I wasn’t able to consistently keep up with my favorite websites at the time. While the idea of update feeds existed as far back as 1995 they didn’t actually exist until the publication of the RSS v0.9 specification in 1999, and ATOM didn’t exist until 2003, so I couldn’t just point a feed reader at them. So I wrote a bunch of scripts that used lynx -dump http://www.example.com/ > ~/websites/www.example.com/`date ‘+%Y%m%d-%H:%M:%S’`.txt and diff to detect changes and tell me what sites to look at when I got back from class.

That was one of the prettier sequences of commands I had put together, too. This kept going on for quite a few years, and the legion of software agents I had running on my home machine grew out of control. As opportunities presented themselves, I upgraded Leandra as best I could, from an 80486 to an 80586 to P-III, with corresponding increases in RAM and disk space. As one does. Around 2005 I discovered the novel Accelerando by Charles Stross and decided to call this system of scripts and daemons my exocortex, after the network of software agents that the protagonist of the first story arc had much of his mind running on through the story. In early 2014 I got tired of maintaining all of the C code (which was, to be frank, terrible because they were my first C projects), shell scripts (lynx and wget+awk+sed+grep+cut+uniq+wc+…) and Perl scripts to keep them going as I upgraded my hardware and software and had to keep on top of site redesign after site redesign… so I started rewriting Exocortex as an object-oriented framework in Python, in part as another toy project and in part because I really do find myself enjoying Python as a thing that I do. I worked on this project for a couple of months and put my code on Github because I figured that eventually someone might find it helpful. When I had a first cut more or less stable I showed it to a friend at work, who immediately said “Hey, that works just like Huginn!”

I took one look at Huginn, realized that it did everything I wanted plus more by at least three orders of magnitude, and scrapped my codebase in favor of a Huginn install on one of my servers.

Porting my existing legion of software agents over to Huginn took about four hours… it used to take me between two and twelve hours to get a new agent up and running due to the amount of new code I had to write, even if I used an existing agent as a template. Just looking at the effort/payoff tradeoff there really wasn’t any competition. So, since that time I’ve been a dedicated Huginn user and hacker, and it’s been more or less seamlessly integrated into my day to day life as an extension of myself. I find it strange, somewhat alarming when I don’t hear from any of them (which usually means that something locked up, which still happens from time to time) but I’ve been working with the core development team to make it more stable. I find it’s a lot more stable than my old system was simply due to the fact that it’s an integrated framework, and not a constantly mutating patchwork of code in several languages sharing a back-end. Additionally, my original exocortex network was comprised of many very complex agents: One was designed to monitor Slashdot, another Github, another a particular IRC network; Huginn offers a large number of agents, each of which carries out a specific task (like providing an interface to an IMAP account or scraping a website). By customizing the configurations of instances of each agent type and wiring them together, you can build an agent network which can carry out very complex tasks which would otherwise require significant amounts of programming time. Read more “Semi-autonomous software agents: A personal perspective.”

Semi-autonomous agents: What are they, exactly?

Bryce LynchLeave a comment

by: The Doctor [412/724/301/703/415]

This post is intended to be the first in a series of long form articles (how many, I don’t yet know) on the topic of semi-autonomous software agents, a technology that I’ve been using fairly heavily for just shy of twenty years in my everyday life. My goals are to explain what they are, go over the history of agents as a technology, discuss how I started working with them between 1996e.v. and 2000e.v., and explain a little of what I do with them in my everyday life. I will also, near the end of the series, discuss some of the software systems and devices I use in the nebula of software agents that comprises what I now call my Exocortex (which is also the name of the project), make available some of the software agents which help to expand my spheres of influence in everyday life, and talk a little bit about how it’s changed me as a person and what it means to my identity.

So, what are semi-autonomous agents?

One working definition is that they are utility software that acts on behalf of a user or other piece of software to carry out useful tasks, farming out busywork that one would have to do oneself to free up time and energy for more interesting things. A simple example of this might be the pop-up toaster notification in an e-mail client alerting you that you have a new message from someone; if you don’t know what I mean play around with this page a little bit and it’ll demonstrate what a toaster notification is. Another possible working definition is that agents are software which observes a user-defined environment for changes which are then reported to a user or message queuing system. An example of this functionality might be Blogtrottr, which you plug the RSS feeds of one or more blogs into, and whenever a new post goes up you get an e-mail containing the article. Software agents may also be said to be utility software that observes a domain of the world and reports interesting things back to its user. A hypothetical software agent may scan the activity on one or more social networks for keywords which a statistically unusual number of users are posting and send alerts in response. I’ll go out on a limb a bit here and give a more fanciful example of what software agents can be compared to, the six robots from the Infocom game Suspended.

In the game, you the player are unable to act on your own because your body is locked in a cryogenic suspension tank, but the six robots (Auda, Iris, Poet, Sensa, Waldo, and Whiz) carry out orders given them, subject to their inherent limitations but are smart enough to figure out how to interpret those orders (Waldo, for example, doesn’t need to be told exactly how to pick up a microsurgical arm, he just knows how to do it). So, now that we have some working definitions of software agents, what are some of the characteristics that make them different from other kinds of software? For starters, agents run autonomously after they’re started up. In some ways you can compare them to daemons running on a UNIX system or Windows services, but instead of carrying out system level tasks (like sending and receiving e-mail) they carry out user level tasks. Agents may event in a reactive fashion in response to something they encounter (like an e-mail from a particular person) or in a proactive fashion (on a schedule, when certain thresholds are reached, or when they see something that fits a set of programmed parameters). Software agents may be adaptive to new operational conditions if they are designed that way. There are software agents which use statistical analysis to fine tune their operational parameters, sometimes in conjunction with feedback from their user, perhaps by turning down keywords or flagging certain things as false positives or false negatives. Highly sophisticated software agent systems may incorporate machine learning techniques to operate more effectively for their users over time, such as artificial neural networks, perceptrons, and Bayesian reasoning networks. Software agent networks may under some circumstances be considered to be implementations of machine learning systems because they can exhibit the functional architectures and behaviors of machine learning mechanisms. I wouldn’t make this characterization of every semi-autonomous agent system out there, though. Read more “Semi-autonomous agents: What are they, exactly?”