I'm a cloud architect and writer, focused on Azure, Azure cost control, Azure Security and Azure Cognitive Services. Maximizing your cloud investment is the shared responsibility of business and IT but often, there's a disconnect; the teams speak past each other. I act as the bridge, speaking the language of both groups to help organizations achieve their goals and maximize value.
I propose that cybernetics, as a practice area and way of thinking, has been hijacked by the tech industry and the military industrial complex. This has robbed us of a vital tool because we associate its use with our enemies.
To shine a light on the past, to hopefully help build a way to the future, let’s study cybernetics as it was imagined by its pioneers. To help in this effort, I suggest using ‘The Cybernetic Brain‘ by Andrew Pickering, as a grounding text.
Here’s a description from the Univ of Chicago publisher:
“The Cybernetic Brain explores a largely forgotten group of British thinkers, including Grey Walter, Ross Ashby, Gregory Bateson, R. D. Laing, Stafford Beer, and Gordon Pask, and their singular work in a dazzling array of fields. Psychiatry, engineering, management, politics, music, architecture, education, tantric yoga, the Beats, and the sixties counterculture all come into play as Pickering follows the history of cybernetics’ impact on the world, from contemporary robotics and complexity theory to the Chilean economy under Salvador Allende. What underpins this fascinating history, Pickering contends, is a shared but unconventional vision of the world as ultimately unknowable, a place where genuine novelty is always emerging.”
Get a copy of the book (let me know if that’s a problem and I’ll try to find other ways to share the text)
Join the Discord for discussion (the invite will go to Patreon members once seriousness has been vetted; there are a lot of clowns out here, we need to be a good group)
When we have a ‘critical mass’ (at least five people) we can begin. The plan is to go through the book, chapter by chapter, meeting once a week to discuss what we’re learning and help each other understand and research.
Several years ago, there was a mini-trend of soft documentaries depicting what would happen to the built environment if humans somehow disappeared from the Earth. How long, for example, would untended skyscrapers punch against the sky before they collapsed in spectacular, downward cascading showers of steel and glass onto abandoned streets? These are the sorts of questions posed in these films.
As I watched these soothing depictions of a quieter world, I sometimes imagined a massive orbital tombstone, perhaps launched by the final rocket engineers, onto which was etched: Wasted Potential.
While I type these words, billions of dollars have been spent on and barely tabulated amounts of electrical power, water and human labor (barely tabulated, because deliberately obscured) have been devoted to large language model (LLM) systems such as ChatGPT. If you follow the AI critical space you’re familiar with the many problems produced by the use and promotion of these systems – including, on the hype end, the most recent gyration, a declaration of “existential risk” by a collection of tech luminaries (a category which, in a Venn diagram, overlaps with carnival barker). This use of mountains of resources to enhance the profit objectives of Microsoft, Amazon and Google, among other firms not occupying their olympian perches, is wasted potential in frenetic action.
But what of alternative visions? They exist, all is not despair. The dangerous nonsense relentlessly spewing from the AI industry is overwhelming and countering it is a full time pursuit. But we can’t stay stuck, as if in amber, in a state of debunking and critique. There must be more. I recommend the DAIR Institute and Logic(s) magazine as starting points for exploring other ways of thinking about applied computation. Ideologically, AI doomerism is fueled in large measure by dystopian pop sci-fi such as Terminator. You know the story, which is a tale as old as the age of digital computers: a malevolent supercomputer – Skynet (a name that sounds like a product) – launches, for some reason, a war on humanity, resulting in near extinction. The tech industry seems to love ripping dystopian yarns. Judging by the now almost completely forgotten metaverse push (a year ago, almost as distant as the pleistocene in hype cycle time), inspired by the less than sunny sci-fi novel Snow Crash, we can even say that dystopian storylines are a part of business plans (what is the idea of sitting for hours wearing VR goggles if not darkly funny?).
There are also less terrible, even hopeful, fictional visions, presented via pop science fiction such as Star Trek´s Library Computer Access/Retrieval System – LCARS.
In the Star Trek: The Next Generation episode, “Booby Trap” the starship Enterprise is caught in a trap, composed of energy sapping fields, that prevents it from using its most powerful mode of propulsion, warp drive. The ship’s chief engineer, Geordi LeForge, is given the urgent task of finding a solution. LeForge realizes that escaping this trap requires a re-configuration, perhaps even a new understanding, of the ship’s propulsion system. That’s the plot but most intriguing to me is the way LeForge goes about trying to find a solution.
The engineer uses the ship’s computer – the LCARS system – to do a retrieval and rapid parsing of the text of research and engineering papers going back centuries. He interacts with the computer via a combination of audio and keyboard/monitor. Eventually, LeForge resorts to a synthetic, holo mockup of the designer of the ship’s engines, Dr. Leah Brahms, raising all manner of ethical issues but we needn’t bother with that plot element.
I’ve created a high level visualisation of how this fictional system is portrayed in the episode:
The ability to identify text via search, to summarize and read contents (with just enough contextual capability to be useful) and to output relevant results is rather close, conceptually, to the potential of language models. The difference between what we actually have – competing and discrete systems owned by corporations – and LCARS (besides many orders of magnitude of greater sophistication in the fictional system) is that LCARS is presented as an integrated, holistic and scoped system. LCARS’ design is to be a library that enables access to knowledge and retrieves results based on queried criteria.
There is a potential, latent within language models and hybrid systems – indeed, within almost the entire menagerie of machine learning methods – to create a unified computational model for a universally useful platform. This potential is being wasted, indeed, suppressed as oceans of capital, talent and hardware is poured into privately owned things such as ChatGPT. There are hints of this potential found within corporate spaces; Meta’s LLaMA, which leaked online, shows one avenue. There are surely others.
Among a dizzying collection of falsehoods, the tech industry’s greatest lie is that it is building the future. Or perhaps, I should sharpen my description: the industry may indeed be building the future but contrary to its claims, it is not a future with human needs centered. It is possible however, to imagine and build a different computation and we needn’t turn to Silicon Valley’s well thumbed library of dystopian novels to find it. Science fiction such as Star Trek (I’m sure there are others) provide more productive visions
There’s a temptation, if you are, or were, a Twitter user (and perhaps, even if you aren’t since we all must comment on everything, everywhere all the time now) to have an opinion about that platform and its current state.
For some, it’s a tale of paradise lost, of all yesterday’s parasocial parties, ruined by the jarring arrival of an off-putting, racist weirdo who, while lacking nearly all social skills, demands everyone’s attention. For others, there’s a deeper sense of impending loss; of online communities that were built against the odds and against the objections of a hostile world. And so, we have the agonies and ecstasies of Black Twitter and Philosophy Twitter and Literary Twitter and Trans Twitter and a universe of other groupings which came together (while also remaining open to other communities) as it all seems to be burning down.
Of course, I have my own Twitter story to tell which involves gaining some small degree of notice for my efforts dissecting the tech industry’s dangerous fantasies from a materialist, and indeed, Marxist perspective. For me, however, the larger concern, or really, observation, is that all of it – the good, the bad and the ugly of Twitter was built, like so many modern beliefs, upon a foundation of unreality.
What do I mean by unreality,what am I driving at? Here, we must take a detour to the past, borrowing a moment from Edward Gibbon´s Decline and Fall of the Roman Empire, (1782):
“To resume, in a few words, the system of the Imperial government; as it was instituted by Augustus, and maintained by those princes who understood their own interest and that of the people, it may be defined an absolute monarchy disguised by the forms of a commonwealth. The masters of the Roman world surrounded their throne with darkness, concealed their irresistible strength, and humbly professed themselves the accountable ministers of the senate, whose supreme decrees they dictated and obeyed.”
This fascinates me – the use of democratic forms to obscure tyranny; an “absolute monarchy disguised by the forms of a commonwealth.” When I think of the tech industry which, until very, very recently, was almost universally hailed as a sun kissed road to ‘The Future’, that vaguely defined territory, always just over the horizon, this potent phrase comes to mind.
When Elon Musk took command of Twitter, arriving at the company’s San Francisco office carrying a sink in a typically poor attempt at humor, we recoiled in keyboard-conveyed horror, waiting for the bad times to come. We all know what happened next: the mass firings of key people in moderation, compliance, software and data center infrastructure, and also, anyone who knows what to do with a bathroom fixture. This sort of anti-worker action, common in most other sectors (though not always quite so haphazardly) came as a shock to those in, and observers of, that shiny Mordor, the tech sector’s Silicon Valley heartland (particularly those who forgot, or weren’t around for the dot com crash of 2000).
As Musk smashed his way through a complex system and tweeted like the synthesis of an angrily divorced uncle and a 14 year old manifesto writer, revealing in near real time his unsuitability for the role of CEO (or even to lead a bake sale) some of us thought: if only another, more competent and nicer person took the reigns; if only the terrible billionaire with his Saudi funders and sweaty style of presentation, could be replaced by that most hallowed of modern types, a professional, a good CEO who cared about Twitter as a ‘town square.’
Given the severe limitations of our barbarous era, a time in which we’re told that it’s easier to imagine the end of the world than the end of capitalism, it’s not surprising that our most commonly proposed solution to the problem of bad, even destructive management of a social media platform is its replacement by good management – still within the framework of privately owned companies – that is, a capitalist solution to a capitalist problem.
At the heart of the Musk problem (and the Dorsey problem before it, and the Google problem, and on and on) is the reality these platforms are not subject to democratic control and not answerable – except in a crude market feedback sense – to the needs of the people using them. We cry out for a better CEO, a better billionaire because the actual solution, that these platforms not be private at all but public utilities we control as citizens, not as consumers, has been purged from our minds as a possibility, let alone a goal (we’ll talk about Mastodon another time).
We have been trained, to borrow once again from Gibbon, to accept “absolute monarchy disguised by the forms of a commonwealth”. The ‘commonwealth’ disguise in this case, being the idea of a tech industry which, alone amongst capitalist sectors, somehow has our best interest at heart because… well, one isn’t sure; perhaps all the nice words about inclusion, expensive t-shirts, and California sunshine, shining down on the forgotten bones of the murdered indigenous population, oil rigs and hidden industrial waste.
AT THE END of ´The Wizard of OZ´, Metro-Goldwyn-Mayer´s 1939-released, surrealist musical fantasy, our heroine Dorothy and her loyal comrades complete a long, arduous (but song filled) journey, finally reaching the fabled city of OZ. In OZ, according to a tunefully stated legend, there’s a wizard who possesses the power to grant any wish, no matter how outlandish. Dorothy, marooned in OZ, only wishes to return home and for her friends to receive their various hearts´ desire.
Who Dares Approach Silicon Valley!
As they cautiously approach the Wizard’s chamber, Dorothy and her friends are met with a display of light, flame and sound; ¨who dares!?¨ a deafening voice demands. It’s quite a show of apparent fury but illusion crumbles when it’s revealed (by Dorothy´s dog, Toto) that behind it all is a rather ordinary man, hidden on the other side of a velvet curtain, frantically pulling levers and spinning dials to keep the machinery powering the illusion going while shouting, “pay no attention to that man behind the curtain!“
Behind the appearance of magic, there was a noisy industrial process, belching smoke. Instead of following the Wizard’s advice to pay no attention, let’s pay very close attention indeed to what lies behind appearances.
THERE’S AN INESCAPABLE MATERIALITY behind what’s called ‘AI’ deliberately obscured under a mountain of hype, flashy images and claims of impending ‘artificial general intelligence’ or ‘AGI’ as it’s known in sales brochures disguised as scientific papers.
At the heart of the success of techniques such as large language models, starting in the latter 2010s, is the graphics processing unit or GPU (in this essay about Meta´s OPT-175B, I provide an example of how GPUs are used). These devices use a parallel architecture, which enables greater performance than the general purpose processors used for your laptop; this vastly greater capability is the reason GPUs are commonly used for demanding applications such as games and now, the hyper-scale pattern matching behind so-called ´AI´ systems.
Typical GPU Architecture – ResearchGate
All of the celebrated feats of ‘AI’ – platforms such as Dall-E, GPT-3 and so on, are completely dependent on the use of some form of GPU, most likely provided by NVIDIA, the leading company in this space. OpenAI, a Microsoft partner, uses that company’s Azure cloud but within those ´cloud´ data centers, there are thousands upon thousands of GPUs, consuming power and requiring near constant monitoring to replace failed units.
GPUs are constructed as the result of a long and complex supply chain involving resource extraction, manufacturing, shipping and distribution; even a sales team. ‘AI’ luminaries and their camp followers, the army of bloggers, podcasters and researchers who promote the field, routinely and self-indulgently debate a variety of esoteric topics (if you follow the ´AI´ topic on Twitter, for example, odds are you have observed and perhaps participated in these discussions about vague topics such as, ´the nature of intelligence´) but it’s GPUs and their dependencies all the way down
GPU raw and processed material inputs are aluminum, copper, clad laminates, glass, fibers, thermal silica gel, tantalum and tungsten. Every time an industry partisan tries to ‘AI’-splain the field, declaring it to be a form of magic, ignore their over-determination and confusion of feedback loops with cognition and think of those raw materials, ripped from the ground.
Aluminum mining
The ‘AI’ industrial complex is beset by two self-serving fantasies:
1.) We are building intelligence
2.) The supply chain feeding the industry is infinite and can ‘scale is all you need’ its way forever to a brave new world.
For now, this industry has been able to keep the levers and dials moving, but the amount of effort required will only grow as the uses to which this technology is put expand (Amazon alone seems determined to find as many ways to consume computational infrastructure as possible with a devil take the hindmost disregard for consequences), the need for processors grows and global supply chains are stressed by factors such as climate change, and geopolitical fragmentation.
The Wizards, out of tricks, curtains pulled, will be revealed as the ordinary (mostly) men they are. What comes next, will be up to us.
As I type this, it’s hot here in Amsterdam. In the past, one might have said ‘unseasonably warm’ a phrase that, in our current circumstances, seems like a form of wishful thinking, an echo of an earlier time – not more innocent but not as burdened with the hyper-problem of C02. I mention the temperature, and add the fact that I don’t have air conditioning (because traditionally, it wasn’t needed), to set expectations; this may not be the sharpest bit of work. But then again, perhaps I’ll rise, like the temperature, to the occasion.
But enough preamble.
In a recent edition of educator and angry Marxist uncle Derrick Varn’s ever excellent Youtube program, Varn Vlog, the subject of ‘kludges’ is discussed. Merriam Webster states that a kludge is “a haphazard or makeshift solution to a problem and especially to a computer or programming problem” which is precisely right. Varn builds on the theme of kludges to explain the provisional character of modern systems – not just technical but bureaucracies, corporations, and so on.
I know a thing or two about kludges, having worked in the technology industry for decades – an industry that is essentially a massive ziggurat of kludges, covered by a polished surface to hide the stone knives, bear skins, rubber bands, glue and endless scurrying about. Apple, for example, pretends to run with the smooth efficiency of a Borg cube but is really an assemblage of various kludges, deployed to increase market share and profitability (the only real goals).
Now I’m going to tell you a brief story about a kludge I was compelled to put into place, forced, as Varn would note, by path dependencies. The story’s point isn’t to elicit sympathy (or perhaps, considering the system I’m describing, horror) but to give you a glimpse into just how right Varn is and how sloppy things can be. This is only one of many such stories I could tell.
The Saga of Phil’s Server
Once upon a time, never mind how many years ago, I was consulting at an energy company, let’s call it SPARK which, in addition to owning a variety of power generation systems – hydro, fossil, nuclear, across the continental US – also had an energy futures trading division. This division, which I’ll call SPARK-HYPERION because it captures the degree of self regard and the amount of money generated (many billions) was responsible for calculating the available excess generating capacity of SPARK’s fleet of assets alongside weather conditions in various markets and the correspondingly forecasted need. SPARK is part of the PJM Interconnection network which makes it possible to send power between regions.
PJM Interconnect – Example Data
Here’s the scenario:
Let’s say that a weather event in a neighboring region (perhaps a heat wave) increases electricity demand in excess of that region’s capacity. Through the interconnection of regions, SPARK could send spare power from its assets to the region in need, but of course, for a price, generating a profit from the trade. The forecasting of potential need in neighboring regions, based on a combination of real-time weather satellite data, and real-time power generation data was considered a key strategic capability and millions were spent on keeping this at a state of the art level (one project I led was creating a method for distributing the computational requirements for analysis across the spare capacity of idle office PCs at night – that was fun).
Forecasting.
Keep this word in mind because it explains what happens next in our story.
Energy futures traders needed data from power plants to determine what was available on the market. This is why Phil (of course, not his real name) had access to a live feed of the megawatt output of a nuclear power plant that was part of SPARK’s generating portfolio. What Phil’s requirement didn’t explain was the reason this system, which connected to the nuke plant’s SCADA, showing, via a web interface, coolant levels and other critical things, was under his desk. What his job requirement also didn’t explain was why that system was available to pretty much everyone on the corporate network. Just drop the address of the server into your browser and poof! Instant access to nuke plant data.
The Tru64 Unix system that connected to a nuclear power plant was under Phil’s desk, within easy reach of anyone strolling by with their terrible office coffee; not in a data center. That was the first kludge; a rushed together ‘solution’ designed to give Phil the data he needed with minimal latency but also, as a knock on effect, minimal security. I discovered this troublesome computer during a security assessment of the corporate network using a Nessus vulnerability scanner system I created from a spare PC running the Linux operating system. There I was, sitting at my desk, sipping tea like a character in a BBC murder mystery. The results showed a system, on the corporate network, running a web server. How interesting. I browsed to the site, saw the status of a nuclear power plant, and nearly spat out my tea. Quietly, I walked into the office of the VP of information technology. ‘If you don’t want an unpleasant visit from Homeland Security and the Federal Energy Regulatory Commission‘, I started, ‘I suggest you listen to what I’m about to tell you.’ What a marvelous, completely normal day.
The second kludge was from me, a forced mitigation compelled by path dependencies including palpable executive fear of disrupting, for even the shortest of moments, Phil’s multimillion dollar generating workflow by moving the system to where it should have been all along, the data center (no reader, not even an after hour or weekend move was permitted – no one wanted to be the exec who said yes to that in case anything went wrong). I couldn’t move the system to a more secure location, with all that would have meant for enhanced monitoring and control so, of necessity, I had to bring more security to Phil’s desk.
I received authorization to install a multi-thousand dollar Cisco firewall, designed to sit comfortably in a professionally managed data center, providing network security services to hundreds if not thousands of systems, under Phil’s already busy desk. This was a kludge on top of a kludge. Ladies and gentlemen, this was a multi-billion dollar firm.
Classic Cisco Network Topology
A Fable of Competence
In modern mythology, by which I mean marketing, technologies are deployed in companies with a cool competence building on past perfection with new perfection: shiny and flawless. In reality, despite our best efforts, complex systems accrue debts: past compromises force new compromises to ensure the entire system continues to function. Keep this in mind the next time you think about your bank or credit card company or Meta or Google or the world as a whole.
There are words, such as ‘freedom’ and ‘democracy’ which get tossed around like a cat’s toy, tumbling between meanings depending on the speaker. To this list of floating signifiers, we can add ‘automation’ which, when mentioned by business types, is meant to depict a bright and shining future but, when used by people on the left who, one would hope, are concerned about the prospects for labor, is typically employed as a warning of trouble ahead (there’s an exception to this: the ‘fully automated luxury communism’ folks, some of whom, seeing their robot butler dreams fade, are now turning to the polar opposite idea of degrowth).
The trouble with floating signifiers is that they float, making it difficult to speak, and perhaps think, with precision – actually, this also explains their appeal; any malcontent can shout they’re defending ‘freedom’ and fool at least some of the people, some of the time, via a socially agreed-upon vagary.
One of my quixotic preoccupations is a struggle against imprecise language and thought. It’s silly; we’re all over the place as a species and wouldn’t be human if it were otherwise (among my many arguments against the ‘AI’ industry crowd is its collective failure to understand that imprecision is a key element of our cognition, beyond duplication in electronic machinery)
So, with my quest for precision in mind, let’s spend a few moments contemplating automation, trying to put some bones and flesh on an ideological mist.
Check out the graphic shown below:
The Things to Think About and Study
I cooked up this image to visualize what I see as the appropriate areas of material concern for left politics. How do things work? And, for me, because this is my area of expertise, what role does computation play in the performance and command and control of labor in these various sectors of production?
In this post, I focus on automation in farming. Oh and by the way, my focus here is also on method, on how to think; that is, how to think in material terms about things which are presented in vague ways.
Drones, Robot Tractors and Harvestors
For me, the foundational, 21st century work on the real-world impacts of automation on labor is ‘Automation and the Future of Work’ by Aaron Benanav. Here’s a link to an article Benanav wrote for the New Left Review outlining his argument which can be summarized as: yes, of course, there’s automation and it has an impact but not as profound and far reaching, and not in the ways we are encouraged to think.
To look at farming specifically, I visited PlugandPlay, an industry and venture capitalist boosting website (trade publications, properly analyzed, are an excellent source of information) that published “How Automation is Transforming the Farming Industry”.
From the article:
“Farm automation, often associated with “smart farming”, is technology that makes farms more efficient and automates the crop or livestock production cycle. An increasing number of companies are working on robotics innovation to develop drones, autonomous tractors, robotic harvesters, automatic watering, and seeding robots. Although these technologies are fairly new, the industry has seen an increasing number of traditional agriculture companies adopt farm automation into their processes.”
You can imagine a futuristic farm, abuzz with robotic activity, all watched over, to paraphrase the poet Richard Brautigan, by machines of sublime grace, producing the food we need while the once over-worked farmer relaxes in front of a panel of screens watching devices do all the labor.
Let’s dig a little deeper to list the categories of systems mentioned in the article:
Drones
Autonomous tractors
Robotic harvesters
Automatic watering
Seeding robots
For each of these categories, the PlugandPlay article, very helpfully, provides an example company. This gives us an opportunity to review the claims, methods and production readiness (i.e., can you buy a product and receive shipment and technical support for setup or are only pre-orders available?) of individual firms in each area of activity. This information enables us to add more precision to our understanding.
With this information at-hand, we’re not just saying ‘farming automation’ we’re looking at the sector’s operational mechanics.
For drones, American Robotics’ aerial survey systems are mentioned. As is my habit, I checked out their job listings to see the sort of research and engineering efforts they’re hiring for which is a solid indicator of real or aspirational capabilities. I’ve written about drone-based analysis before; it does have real world applications but isn’t as autonomous as often claimed.
The three examples of robotic harvesters listed are from Abundant Robotics, which is building specialized apple harvesting systems, Bear Flag Robotics, which seems to have retrofitted existing tractors with sensors to enable navigation through farming fields (and perhaps remote operation, the marketing material isn’t very clear about this) and Rabbit Tractors, which appears to be out of business.
There are a few other examples offered but hopefully, a picture is forming; there are, at this point, some purpose built systems – some more demonstration platform than production ready – which show the limitations, and potential usefulness of automation in the farming sector: perfect for bounded, repetitive applications (a weed sprayer that follows assigned paths comes to mind) not so great at situations requiring flexible action. Keep this principle in mind as a rule of thumb when evaluating automation claims.
It also isn’t clear how well any of these systems work in varying weather conditions, what the failure modes and maintenance schedules are and lots of other critical questions. It may seem cheaper, in concept, to replace workers with automated or semi-automated harvesters (for example) but these machines aren’t cheap and introduce new cost factors which may complicate profitability goals and it follows, adoption by agribusiness, which, like all other capitalist sectors, is always in search of profits.
…
So, yes, automation is indeed coming to, or is already present in farming but not, it appears, in the hands-off, labor smashing way we tend to think of when the word, ‘automation’ is tossed around, like a cat’s toy.
Next, time, I’ll take a look at automation in logistics. How far has it gone? How far will it go?
Usually, I try to start these essays with an anecdote to lead you, the esteemed reader, into my topic. These anecdotes lead me into a subject too; a warm up to get the writing process flowing.
For this brief essay, which is about yet another video posted to Twitter about a supposedly autonomous killing machine, I’m thinking of the classic shell game, which is described in this (obligatory) Wikipedia article:
The shell game (also known as thimblerig, three shells and a pea, the old army game) is often portrayed as a gambling game, but in reality, when a wager for money is made, it is almost always a confidence trick used to perpetrate fraud. In confidence trick slang, this swindle is referred to as a short-con because it is quick and easy to pull off. The shell game is related to the cups and balls conjuring trick, which is performed purely for entertainment purposes without any purported gambling element.
A confidence trick, a fraud. What do I mean when I use these words to describe the machine gun toting, robot ‘dog’?
After all, there is the machine and it’s gun. Where is the game?
Let’s look at the original post:
Tweet showing video of robot dog
The video shows a machine, very similar to Boston Dynamics’ Spot (which I discuss in this essay, Boston Dynamics, A Brief Inquiry) moving on a course with its spindly legs, firing an automatic weapon.
It’s implied that what we’re seeing is, as in that often cited Black Mirror episode ‘Metal Head‘, an autonomous machine which can roam on its own, killing people using some form of silicon intelligence, tuned for lethality.
What we’re really seeing is a remote controlled system, whose true purpose is obscuring the bullet’s source, the hand pulling the trigger.
The evidence is in the video.
The Controller and The Controlled
Take a close look at what we see in this still excerpt; a military transport vehicle, sitting idly by for no apparent reason. This is the controller, hidden in plain sight (the operator could just as easily have been outside) to give the appearance of autonomy.
A grounded, materialist, less science fiction informed examination of this image tells the story (well that, and the fact there is no machine Intelligence and certainly not within the form factor of this device): this is a remote controlled device, a drone. What appears to be a VHF whip antenna is clearly visible along with control interfaces and a camera for navigation.
One additional bit of information can be found in this image:
This shot, probably by accident because someone thought it was cool, reveals what’s behind the curtain: the camera’s view is directly of the gunsight which is certainly what the controller, sitting in the military transport vehicle, sees via a display. The robot ‘dog’ though it exhibits dangerous potential, is not the harbinger of a new form of self-directed killbot but rather, the harbinger of a new class of remote controlled drone, designed, like their UAV cousins, to obscure culpability.
What is the True Danger?
The 21st century isn’t going well.
In addition to climate change, the lingering possibility of nuclear war and the unraveling of neoliberal capitalism which, at the height of its power as a social form, was sold as being history’s last stage, we face the coming to earth of the military drone, long a menace to people around the world and arriving, as all military ideas eventually do, to a street near you.
So, we should agree there is a danger. But it’s not the science fiction danger of sinister machines, free of human control. It’s the danger of remote operated systems, used to harass and kill people while obscuring the source of this harassment and death. It’s easy to imagine a scenario: someone is killed by a police officer but the tools of body cams and eyewitness testimony are removed; the device from which the bullets flew is controlled by an unseen operator, indemnified from responsibility like the drone operators remotely flying machines over contested territory.
Earlier I mentioned the shell game which is this: the sleight of hand, performed via carefully shot marketing material, which leads our thoughts away from who is pulling the trigger into talking endlessly, and in terrified circles, about the same, tired science fiction tropes.
It’s time to put Black Mirror away to see the true danger taking shape, right before our eyes.
When I was young, the word, ‘supercomputer’ evoked images of powerful, intelligent systems, filling the cavities of mountains with their humming electronic menace.
Science fiction encouraged this view, which is as far from the (still impressive, yet grounded) reality of supercomputing as the Earth is from some distant galaxy. The distance between marketing hype and actually existing machines is like that: vast and unbridgeable, except in dreams.
Which brings me to this Verge story, posted on 24 January, 2022:
Social media conglomerate Meta is the latest tech company to build an “AI supercomputer” — a high-speed computer designed specifically to train machine learning systems. The company says its new AI Research SuperCluster, or RSC, is already among the fastest machines of its type and, when complete in mid-2022, will be the world’s fastest.
“Meta has developed what we believe is the world’s fastest AI supercomputer,” said Meta CEO Mark Zuckerberg in a statement. “We’re calling it RSC for AI Research SuperCluster and it’ll be complete later this year.”
The phrase, “AI supercomputer” is obviously designed to sell the idea that this supercomputer, unlike others, is optimized for AI. And to give the devil his due, the fact it’s reportedly composed of NVIDIA game processing units, which, since the mid 2000’s have found extensive use powering tasks such as building large language models, gives some amount of credibility to the claim.
Some, but not as much as it might seem. Consider this hyperventilating article:
“Mind boggling”
This is clearly the tone Meta (and others) is hoping to cultivate via the use of ‘AI supercomputer’ as a descriptor. The assumption is that if enough computational power is thrown at the task of building machine learning models, those models will, in some not sharply defined way, reach unprecedented heights of…well, one isn’t sure.
Schematic of Typical Supercomputing Infrastructure from ResearchGate
The category, ‘supercomputer’, created to describe a class of tightly integrated, high performance computational platforms, has existed for over 60 years. The first supercomputers were developed for nuclear research (weapons and energy) at Lawrence Livermore Labs in the US at the height of the Cold War (maybe we should call it Cold War Classic) and have also been applied to demanding tasks such as modeling the Earth’s climate. It’s a venerable technology with clearly defined parameters such as the use of symmetric multiprocessing. In all these decades, no supercomputer has managed to exhibit intelligence or plot our demise, except in fiction.
Adding ‘AI’ to the mix doesn’t change that reality since ever larger statistical pattern matching techniques do not cognition make. Oh and Meta’s claim is that these types of supercomputing data centers will, in addition to serving as development platforms, also host the haunted cartoon castle they call the “Metaverse’.
Attentive readers will note that this blog is primarily focused on dissecting and highlighting the political economy and social impact of what’s called ‘Artificial Intelligence’ and allied fields (such as supposedly autonomous robots).
Till now, crypto, in all its fetidness, has escaped comment on these virtual pages. Well, ‘needs must’ as the old saying goes: the increasingly loud chorus of people – some of whom are well-intentioned techies – either singing the praises of crypto, NFTs, web3 etc. or, offering a lukewarm response to the threat this poses of, ‘something good might happen’ compels me to put fingers to keyboard.
More precisely though, this thread on Twitter provided the framework for a proper response:
In contrast to Marco’s clear declaration, which reflects my own view, there was this thread, in which Anil Dash presented the idea that, even though there are lots of ‘bad people’ involved in the space, there are also good people and these good people are trying to carve out a good space (for, in this case, artists using NFTs):
One of the most common maladies of our age is a belief – more appropriate in children than adults – that the problem with societies is the presence of ‘bad people’ who, being bad, spend their time, like villians in a Bond movie, imagining bad things to do and not as a function or emergent property of the society itself that enables these bad people. Normally, I’d insert a bit about the materialist analysis of capitalism but I’ll let that go for a later post.
There’s a wealthofevidence that the entire point of the crypto space is the realization of libertarian fantasies – the removal of constraints that protect those who’re considered weak or foolish (no need for deposit guarantees, now there are smart contracts!).
The presence of people with good intentions in this space (whether as software developers, activists or what have you) only serves to provide visual cover for the grift; indeed, this is how grifts function: earnest people are required. You, an earnest person hoping to do good things, suppress your knowledge of all the problems – the fundamental, baked into the cake problems – thinking that your niceness is a tactic for change.
This is an abdication of, as Hannah Arendt put it, your duty to think.
It’s time for us to abandon niceness for a solid and consistent application of principle, openly state who our adversaries are and vigorously resist their propaganda.
The first step is to stop fooling ourselves that our niceness is a tactic.
This blog is dedicated to analyzing the field of ‘artificial intelligence’ specifically, and the tech industry generally from a materialist perspective.
By materialist perspective, I mean a point of view informed, in large measure by historical materialism but also, by a deep analysis of the nuts and bolts – the supply chains, extractive industrial activity, data centers and actually existing capabilities undergirding what the tech industry presents as magic.
There is however, another layer operating which is captured by, among other disciplines, psychoanalytic theory.
In this series of video meditations, I consider aspects of the book. This is an ongoing project and I’ll be updating this post as new videos are added.
Computational Impacts 