K AI QMA
Introduction
The Football is Fixed Network have been working for three decades on creating systems thinking approaches to the analysis of the sector, the markets, the corruptions, the hierarchical machinations, the evolution of oversight & regulation, the integration of components, the utilisation of white hat hacking to gather hidden information, the critical impacts of cause & effect, cybernetics, the psychology of human behaviouralism, the appropriate inversion of disinformation offered by mainstream media & their talking heads, the apportioning of criminalised constructs plus the analytics relating to military strategy & turf wars between organised crime groups (locally, nationally & globally).
By addressing these factors individually and how they interact in complex (and often unexpected) ways, we have been slowly but surely creating a very powerful tool K AI QMA.
In more recent times we have been trialing the resultant composite both specifically (in the underground global betting markets through our broker) and holistically (in the progression of systemic hyperrealities).
Analytical Approaches
K AI QMA incorporates an ever-evolving continuum of analytical approaches.
We began in the mid-nineties with a machine learning-based use of neural networks which mimicked the manner in which neurons in the human brain communicate. Neurons are aggregated into layers to create different transformations on inputs that flow through the network to produce outputs. Via the repeated training up of neural networks using bespoke back propagation techniques, these networks are able to undertake predictive modelling, particularly accurately in corrupted sectors and markets..
Excitingly, these networks learn from experience (just like most humans) and we are able to develop robust conclusions from disparate and, to the untrained eye, entirely unrelated sets of data, knowledge and information.
This is a key advance - incorporating hard data in numerical form with non-subjective fuzzier nodes e.g. the role of corruptions (both systemic and semi-systemic).
The resultant neural networks have been a part of the market making process in global betting markets over recent years..
The key threshold that must be achieved in this long term project is kaizen - where the neural networks demonstrate a self-generated state of continuous improvement over time. In effect, they think our futures on our behalf.
K AI QMA is unique and bespoke.
In many ways, Artificial Intelligence as applied to the football industry is actually nothing more than deep machine learning even though the best examples might well be very deep. We agree with many others that something cannot be termed human-level Artificial General Intelligence until it possesses consciousness and the median date for that utopia currently stands around 2035.
Or never.
Both the security people in the Football is Fixed Network and myself have colossal confidence in K AI QMA.
Let's look at some of the foundations of this confidence.
Firstly, having 30 years of bespoke databases developed in real time is a significant advantage. The monitoring of the contortions in football and the betting markets (as such progressions were happening) have allowed a hologram of the realities to be pictured as and when they occurred.
This is a huge advantage as otherwise it would be impossible to 'look back' at these advancements from where we now stand.
So, for example, the oscillation in recent years in corruption in the Premier League between systemic and semi-systemic matrices is extraordinarily difficult to reconstruct with hindsight as some of the fuzzier inputs (see below) are not able to be reconstructed efficiently or accurately. These repetitive bifurcations have to be modeled in real time as the immediate is chaotic.
Secondly, the longevity of our research (neural networks were one of the key reasons why our Network based ourselves in Bucuresti from 2004) enables experimentation to be undertaken as the sport evolves. Hyperparameters were discovered and utilised from 2004 onwards and a large amount of experimentation was streamlined using stochastic neural networks and other Bayesian realities.
Thirdly, the pot-calling-kettle reaction of OpenAI boss Sam Altman to DeepSeek allegedly using distillation of his products to train up the Chinese open source alternative when Altman stole the data of others to train up his own product doesn't apply with K AI QMA as all distillation was / is internal and, moreover, proprietary.
Nobody has our unique databases.
Fourthly, we swiftly adapted and extrapolated the work of David Orrell to develop our Quantum Market Analytics that has now been fully assimilated within K AI QMA.
Fifthly, a critical input to creating something akin to K AI QMA is to have extensive talents in modeling closed systems. It should not be a surprise to anyone that Llang Wenfeng (the founder of DeepSeek) initially trained up his network to aid his High-Flyer hedge fund.
Football betting markets share structural similarities with international financial markets in that there are a finite number of inputs to the future oscillations in market price. This isn't the time nor place to fully explore these inputs but both of these infrastructures are closed systems. In football, for example, the price of oil, currency oscillations, trade tariffs and political changes are of no consequence to the sport (or are so peripheral as to be virtually irrelevant).
No chaos there.
The Cosmology of our Universe is also a closed system represented by the laws of physics and defining occurrences such as.the inflationary period or the causes behind the cosmic microwave background radiation or the birth of dark matter or etc.
As an aside, there are also cutting edge cosmological inputs affecting markets but that is a research project about which we are not willing to comment currently.
The upshot is that closed systems are easier to model than other infrastructures!
Sixthly, it is critical to avoid irrelevant inputs in deep machine learning. The general rule is that everything that is subjective should not form a part of the learning processes. Our structure has produced two valid outliers to this generalisation - i) historical inputs related to, for example, forensic psychology or the monitoring of the body language of corrupt participants may be made robust by aggregating across the views of members of the Football is Fixed Network; ii) some fuzzy inputs have been made performative by incorporation into configurative structures.
Seventhly, the vast majority of current attempts to use deep machine learning to predict future football betting market realities and match outcomes exist in the box labelled 'rubbish in, rubbish out'. It will take decades for these constructs to be able to mimic the success of K AI QMA which will, of course, itself have evolved over that time period. Just as the universe is expanding at an ever faster rate, K AI QMA will extend its lead over competitors into the future. The event horizon has already been passed.
Eighthly, the modelling of carefully mined data and constructs over a 30 year period produces a continuum of holograms of the evolving hyperrealities of the football sector.
The continuum of knickpoints, breakpoints, self-similarities, bifurcations, quantum leaps, structural reformations in market and sector constructs markedly enhance the resultant deep model.
This evolution has parallels with the physical reality of spacetime where slices of reality exist going backwards in time all the way to the Big Bang but may only have been truly witnessed if one was around at, say, baryogenesis or the onset of cosmic acceleration.
Ninethly, being a direct action cellular network, we have inputs that simply don't exist elsewhere - for example, we have copious amounts of data and evidences from white hat hacking, from private detective work, and via intercepts and information / transcripts privately made available to us as we are perceived (correctly) as a trustworthy body.
Tenthly, deep machine learning is being integrated into every aspect of our Quantum Market Analytics from the decisions about what data to collect to the integration and interpretation of such data. Additionally, some events are far more interesting than others in that they reveal significantly more valuable information. Our use of generative deep machine learning allows more complex questions to be asked and these questions stretch further into the future enhancing the performativity of the neural network.
K AI QMA evolves via unexpected occurrences.
K AI QMA models uncertainty.
Business Structure
Unlike DeepSeek, Football is Fixed have absolutely no intention of making K AI QMA open source.
Furthermore, we have no desire to licence the product to any entity.
The club we are liaising with on a contractual basis understand that this, for the moment, exists as merely a high level consultative arrangement.
One of our lawyers who markedly helped in the creation of K AI QMA is the late and great David McNeight - a mathematician who became one of Europe's leading patent lawyers.
Never ever let go of your data was one of his mantras.
The training up of K AI QMA with three decades worth of bespoke data is one of our unique selling points - nobody has this data and nobody can create what we have created (see The Lizard for the exception that proves this rule).
As a direct action network, we bring a very unique perception of the realities at play. The Football is Fixed Network are not an insider looking out nor are we an outsider looking in but, rather, we are a freethinking construct that has 'liaised' with insiders (often without permission) from all power loci in football throughout the development of K AI QMA.
Our focus is only partially on remuneration as K AI QMA is merely another development in the saving of as much integrity in football as is possible in a late capitalism template.
Financially, we need lifelong enhanced security, the ability to continue sousveillance in real time and a decent level of living. Nothing more, nothing less. Any excess profits will be regenerated for social projects.
Although we have signed a contract with a leading club, we were not willing in the negotiation process to fully couple ourselves with this entity. The construct is mutually beneficial in that the club has access to new inputs that help address corruptions perpetrated against their interests, for example, while we are able to more fully enhance our very beautiful K AI QMA.
We are about to begin the beguine of approaching and interviewing other entities that have expressed an interest in K AI QMA.
That deal will be with a club that isn't linked to organised crime, or a legitimate market maker or with a regulatory body with teeth.
And now that we have reached the stage where we are totally confident that K AI QMA is robust enough through self-generated kaizen, we will secrete K AI QMA in house for our proprietary purposes.
We refuse to engage with organised crime groups on any level whatsoever other than mockery, destabilisation and exposition.
Applications To The Football Sector
There are far too many manners in which K AI QMA may be used in the football industry to be listed here.
But it is obvious where some of the enhancements exist for our partners (current and future) - clubs will outperform rivals via the outputs of K AI QMA and market makers will price and trade markets more efficiently leading to a reduction in hedging, for example, and regulatory bodies will be able to detect and act on corruptions more swiftly and more proactively.
In effect, K AI QMA turns the corruptions from an insidious distortion of a beautiful sport into a competitive advantage at the expense of those undertaking criminal behaviours.
We have often repeated the hyperreality that once you attach a market onto a sport, the sport is terminally corrupted with the market taking coercive control. What K AI QMA proves is that to understand this new Sport 2.0, you have to understand the corrupting markets that are leeched onto it.
Corruption is never exhilarating but the eradication of corruption most certainly is.
Simplistic machine learning is being proffered by organised crime to tilt legitimacy in matters like referee selection, market obfuscation, fixture list distortions, tournament construct biases and a whole host of other aspects that have no place in a sport of integrity.
K AI QMA solves such naive constructs facile with ease.
Cartels will be fragmented, insider trading will be monitored, outed and terminated, organised crime will be cornered in constructs of their own making.
K AI QMA twists the flux.
We toy with the zeitgeist of corruption (the gap between the fans' perception of reality and the actuality of the criminalised construct).
It is hardly rocket science for K AI QMA to 'solve' corruption events like last season's Premier League match between Crystal Palace and Brentford as individuals across the spectrum were actively involved in an entirely rigged event - nothing was left to chance.
Where K AI QMA excels is in more complex corruption constructs where different power groups seek to control the match outcome from different vantage points - such events share parallels with a poker table where Entity A is trading on control of the clients of one football agent, Entity B knows that the visiting team will be taking performance enhancing substances, Entity C sees the insider trading from one power base, Entity D has coerced a goalkeeper to perform appropriately while Entity E controls the referee and the VAR official. And, also, Entity E is interacting with Entity A.
K AI QMA solves these events.
We are already in conversations with the British state over how K AI QMA might enhance Great Britain PLC.
Football is a huge soft power earner for Britain and the future performativity to the state is being demolished by the short term smash and grab of organised crime.
We have been reliably informed that the deeper levels of state markedly prefer a more robust strategic template existent into the future rather than a private equity-type structure where mafia extract all of the value plus the soul of integrity from the sport and stash it offshore away from prying eyes.
Over time, Britain gains most from a legitimacy in the sport and gains least from a mafia matrix.
The Lizard
Football is Fixed is by no means the only player in this three-dimensional chess match.
Terry Steans, the former head of global investigations at FIFA, introduced us to an individual who undertook a Ph.D in Artificial Intelligence at the Massachusetts Institute of Technology and who, rather than tread the well worn path to Silicon Valley, elected to work instead for a South East Asian market maker.
And, excluding K AI QMA, the pinnacle of this pyramid of enhanced deep machine learning (by a considerable distance) is occupied by Tony Bloom, the chairman of Brighton & Hove Albion. Not only has Bloom successfully modeled the global betting markets, he also recently sacked all scouts at the club once he had trained up his transfer market sabermetrics model to self-generating progressive levels of performativity.
Football is Fixed cannot compete with this latter transfer market application yet nor with the level of power engagement achieved by state entities like Manchester City in their transfer activities.
That is a project further down the line.
But we are able to markedly enhance performance in relation to betting markets and the reading of structural realities at both clubs - in Bloom's case to take more sophisticated control of the betting markets and to further enhance his exponentially inflating legacy operation and, in Manchester City's case, to protect themselves against corruptions undermining the success of the club institutionally and on the field of play, as a couple of examples.
Consciousness
Andrei Linde: "I cannot imagine a consistent theory of everything that ignores consciousness."
Our current focus to evolve K AI QMA to commercial completion is on consciousness.
We see consciousness as the ultimate prize, not to 'discover' it in the manner that Roger Penrose would approve but to fully develop a more primordial input to deep machine learning.
Modelling things that aren't yet understood is a feature of Cosmology.
Dark matter and consciousness exist as examples in this near vacuum.
We describe consciousness as subjective experience. So, as you feel like yourself at this moment in time, you are conscious whereas Deep Blue is not conscious. You are conscious when you are dreaming even with limited sensory inputs. There are three key elements to consciousness - how your brain processes information, why you feel subjective experiences and what physics determines whether an entity is conscious or not.
Simply, why should a particular arrangement of particles feel conscious?
God? The Multiverse? God & the Multiverse? Magic? Something else entirely?
And, what about the role of consciousness among indigenous cultures and the role of the sixth sense in 'gypsy' groups who, to some extent, have opted out of modernity and remain truer to a past that most have forgotten. And, more importantly, truer to the physics of that past and its interaction with our species.
After all, robins use quantum entanglement to navigate...
... while Elon Musk can't even get his self-driving cars to navigate autonomously.
Corruption & Deep Machine Learning
The most remarkable aspect of our cosmic history is that lifeless matter has become intelligent or, as Edward Robert Harrison would have it: "Hydrogen..., given enough time, turns into people."
Just as a singularity was the seed that led to our Universe so that term has come to define any intelligence explosion. The utilisation of deep machine learning and Artificial Intelligence has created the opportunities to develop universal intelligence in any sector if one is given access to all of the valuable / valid data.
K AI QMA is teleological and goal-orientated in that it both explains things in terms of goals rather than causes while also being explained more aptly by its effects rather than its causes. And then it inverts this logic to twist back on itself so that causes become effects and vice versa.
But what is intelligence?
The best definition I have heard historically is that intelligence is pattern recognition at speed, although intelligent people who research intelligence struggle to find a shared definition.
There are two basic classifications with intelligence, narrow and broad.
As an example of the former, let's assess how IBM's Deep Blue chess computer defeated Gary Kasparov in 1997. Deep Blue could only dethrone the world chess champion by using hardware and software to master the rules and strategies of chess. The game of chess is a perfect example of a closed system in that only the rules of the game and the two participants' moves matter to the outcome of the match.
A broader example of intelligence arrived in 2016 when the DeepMind AI system AlphaGo won a five match Go series against Lee Sedol, generally thought of as the world's leading player at the time. This was far more impressive than defeating Kasparov for several reasons.
Firstly, there are more possible Go positions than there are atoms in our Universe which means, for humans at least, it is pointless attempting to address all of the manners in which the game might play out.
Secondly, as Go depends consequently on intuition and creativity, AlphaGo showed mastery of these intelligent inputs.
Thirdly, human players depend on subconscious intuition to dovetail with their conscious reasoning - this is no longer solely a human attribute.
Football is Fixed have followed (to an extent) AlphaGo to create K AI QMA,
We have integrated the intuitive power of deep machine learning with the logical power of Artificial Intelligence to spot informative patterns in global betting markets.
Such human-machine collaboration constructs are also very valuable with research elsewhere into Cosmology and particle physics and financial investment and military strategies.
But, from the perspectives of most entities attempting to develop deep machine learning in football, the huge absence is an analysis of the extensive corruptions underpinning the sport.
Let's return to Kasparov.
What if Deep Blue was trained up to win at chess against all-comers but Kasparov had cheated (as Hans Niemann has more recently shown that cheating is a competitive advantage in chess)?
Kasparov could have introduced extra pieces at, say, Move 20 or he could have leveled up on the analytical continuum by using anal beads and software aka Niemann.
As Deep Blue wouldn't have known in advance which pieces would be placed where on the board at Move 20, such information could not be pre-incorporated strategically and competing against software and prompts would also reduce the competitive advantage of Deep Blue.
Corruption, in this case, would distort the hyperreality.
This is entirely analogous to football where corruptions (particular, semi-systemic and systemic) distort the infrastructures in the global markets and consequently the match outcomes.
If a team or market maker develops deep machine learning software to give an edge in the marketplace, the outcomes will always disappoint in corrupted games...
... and the vast majority of higher tier matches are corrupted in one way or another or another or another...
Football betting markets possess another matrix of manipulation - the outcomes to virtually all corrupted events are able to be inverted if the market structure evolves in certain ways.
This ensures that isolationism is a necessary part of a successful market strategy.
There are parallels here with the total idiocy of Eugene Fama's Efficient Market Hypothesis - all of the information is not in the price as the corruption attempts to secrete itself and constantly evolves and bifurcates to new temporary realities and strata.
Corruption (and insider trading) mean that criminalised entities can always find value in a market as such key inputs are not a foundational part of the pricing process.
And, even a more rounded tool like AlphaGo suffers from the same corruption deficit.
Let's take another glance at the transfer market - as every global footballer is a market in his or her own right, there is a vast panoply of markets for clubs, players' unions, agents and the criminal fringe to address / target.
At the summit of this database are the likes of Brighton's Tony Bloom who has created a bespoke deep machine learning piece of software to detect value globally. By avoiding corrupted cartels of agent-led matchfixers, he is able to internalise the entire analytical process from detection to analysis to purchase to performance to sale at a considerable profit to future financial opportunities.
Performative evolution...
At a similar level but due to financial might rather than analytical preeminence, Manchester City are able to search far and wide for the best prospects and the transfer fee has less resonance under state ownership. Indeed, performativity of the player rather than the market is the City goal.
In the gutter of the transfer market slither the cartels and the third party ownership slavery merchants. Here the goal is purely short term profit from vastly inflated transfer fees for moderate players which are actualised by mutually beneficial corruption structures within the transfer construct. The poverty of player selection and the inflation of transfer fee are then manipulated by controlled media output to create fake value on top of the basic talent level.
The only aims are to prolong the particular player farce as long as possible while maintaining leeched linkages on to clubs too.
This distortion of the transfer markets is a burnt earth policy and is totally corrupted.
In the holistic, Bloom's creation provides unexpected magic that was actually expected through the training process while organised crime templates yield expected tragic that was essentially unexpected (internally) as strategy forms no part of their process.
Longevity versus a pickpocketing...
This segmentation of the marketplace doesn't exist with the same resonance in the global betting markets.
Although the tiers of dark pool, SE Asian underground, private and public markets are individual strata in the infrastructure, they merge together in the same manner that futures and options and derivatives do in financial markets.
The musical chairs of the market makers means that every single market participant is impacted by corruption inputs - it is impossible to determine the outcome of a football match without a very decent hand or a five of a kind of corruption analytics and forensic psychology and white hat hacking and sousveillance and messengers in enemy camps.
Without a deep machine learning tool like K AI QMA,, the markets cannot be analysed as corruption in its variety of forms is the most impactful input.
Max Tegmark: "We've now arrived at an answer... about how tangible physical stuff can give rise to something that feels as intangible, abstract and ethereal as intelligence: it feels so non-physical because it's substrate-independent, taking on a life of its own that doesn't depend on or reflect the physical details. In short, computation is a pattern in the spacetime arrangement of particles, and it's not the particles but the pattern that really matters. Matter doesn't matter."
Risk Assessments & Scenario Analytics
The control of the inputs and the ongoing provision of viable parameters to allow K AI QMA to train itself up in all instances in perpetuity is the core of the operation but there needs to be other facets to the project.
In 2012, the collapse of a trading entity was not only very funny but it also cost Knight Capital $440 million dollars in forty-five minutes. This act of self-harming was caused by unverified trading software - greed yielding chaos (and all before half-time!).
Equally important is validation - the Flash Crash of 2010 was equivalently funny and was the result of expectations being violated via multiple automatic trading programs around the world misreading an outlier construct.
Related to this latter point (and particularly of relevance in corrupted football betting markets), does the system rely on assumptions that might not always hold? Plus there is the adjacent task of how to improve the system's take on uncertainty.
Enlightened human control of K AI QMA is also a necessity.
As is security.
When air gaps and Faraday cages may be circumvented then we have developed two defensive strategies to obliterate malicious operators.
One relates to the intricacies in the coding of the coding which creates impossibility for outsiders to achieve future kaizen.
The other is pure military strategy - it is the policy of the Football is Fixed Network to always reverse hack any attempted intrusion unless it comes from a state.
And, on the subject of spies, we have messengers dotted around key loci in the football sector.
These entities provide our Network with massively valuable inputs regarding corruption.
Looking forward, these individuals may remain a robust option to us, require replacement by us, be turned by their handlers against us and need addressing as a double bluff / treble bluff route to enhancement or become peripheralised within their entity and no longer of any use to us.
As a Network, we also experience attempts at infiltration linked to the operations that we target. Usually we eliminate immediately but there are occasions (the Turing option) where longevity is allowed so that the infiltration might be played to our advantage.
This latter strategy has risks but the rewards are significant in that, in effect, you are providing false inputs to your enemies which results in vast under performance and chaos in their shoddy little worlds.
A sub-division of infiltration is honeypotting where beauty is the temptation but, in our collective experience, the honey is generally not so sweet.
Although Arianna is cute and is an exception to prove the rule...
We do, however, use an inversion of mechanised honeypotting where peripheral and largely irrelevant computer systems are sacrificed to encourage external attacks. The perpetrator believes that they are seeing stuff but they are only seeing the stuff we want them to see and, on top of that, we can monitor their attack procedures for inclusion in future military strategy attacks.
Game theorisation of power hierarchies allows us to detect any perturbations from the Nash equilibrium. This is obviously particularly relevant when observing organised crime and corruption and it is striking how swiftly evolution in the Nash equilibrium occurs in corrupted matrices.
This hyperreality further explains why K AI QMA being 'present' at the scene of the crime is so important in evaluation of corruption inputs to the marketplace.
Crime moves fast and, with some operations, seemingly randomly.
Without a full history of the evolution of corruption, an observer is restricted in the modeling that may be undertaken as such advances have virtually no viable inputs.
It is like looking at the cosmic microwave background radiation and detecting the patterns and the temperature variations and the polarisation and the multipoles that describe the Universe 13.8 billion years ago just after the Big Bang but you can look back no further than this plasma.
Anything that occurred prior to this time is invisible and cannot be used in future extrapolations.
Furthermore, everything that has happened since the Big Bang is also intellectually up for grabs.
But whereas cosmologists have the tool of physics, past corruptions can only be replicated on a hearsay anecdotal level.
Two brief examples of the analytical destabilisation caused by an inability to compile a realistic view of past happenings and past data would be:
a) John "The Clown" Colquhoun (a matchfixing crime operative) and Jonathan Moss (a corrupted lackey of a referee) forged their initial liaisons when they were both playing for Sunderland in 1992. The Clown has now hoovered all references to this onset of an organised crime corruption template with history being rewritten to suggest that their first interaction was 20 years later when Moss was promoted to the Select Group of PGMOL referees. We have two decades of transcripts and intercepts denied to all other observers that prove that this wasn't the case. These corruptions of spacetime cannot be reconstituted with hindsight.
b) The Financial Times: "For two years or more, the UK's Labour Force Survey (LFS) has been misfiring." This branch of the Office for National Statistics (ONS) has failed across the spectrum resulting in an impossibility of knowing whether employment is rising or falling from one quarter to the next - let alone how the labour market has evolved since the pandemic. It is estimated that it will take at least two further years to resolve the problems with LFS which means that the UK will not only have had a continual blind spot for 7 years but also that all future analyses will be flawed through a complete absence of pertinent data which could only be reconstituted via time travel. In effect, with extensive data gaps, you can't undertake forecasts because the reality is unknown. Consequently, the ONS is not fit for purpose and is undermining the UK's ability to govern itself.
Holistic data is everything.
Conclusion
Fyodor Dostoyevsky: "The mystery of human existence lies not in just staying alive, but in finding something to live for."
There are parallels between the state of the game in life and in football for our species, which is to be expected with humans at the core of both.
We find ourselves after cosmic evolution in the most stunningly beautiful Universe which has become alive and self-aware. The future potential for the Universe is beyond our dreams but extinction is equally likely. The outcome depends on humans alive today.
The same is true of football - beauty or death?
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We have no competitors.
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