Preword
I have just completed five days of full-time immersion at the Google Breakthrough Agency Workshop, Introduction to Agents. Let me say this first and without reservation: it was a brilliantly crafted venue. My sincere respect to the scientists and engineers who organized it. Their technical mastery and dedication to rigor are unmistakable.
But I must say this, as clearly as possible: they are all wrong in their core assumption that their current architecture, now being refined into what they term an “orchestrator,” will deliver a true breakthrough in AI or even will keep us safe. It will not. Worse, it will create risks of a scale we have not yet dared to imagine. The more flawless and polished the control layers become, the more catastrophic the inevitable failure will be.
Let me illustrate this with a brief recollection from a lecture I once attended at Stanford. The host, whose name escapes me to my lasting embarrassment, was a professor descended from a Japanese-American engineer. That ancestor had been interned by the U.S. government during World War II, detained on an island in San Francisco Bay without trial or reason, as part of a mass internment campaign against Japanese nationals following Pearl Harbor. It’s a well-established American tradition: extrajudicial imprisonment under suspicion, no court, no evidence, no defense.
And yet, to this man and his fellow detainees, the U.S. owed some of its battlefield superiority, specifically in grenade logistics.
The story is this. During WWII, the U.S. military adopted the doctrine of small elite units operating behind enemy lines to sabotage communications and infrastructure. These operations required extreme individual firepower. Grenades became the primary tool. They had to be mass-produced in the millions, fully functional, fully armed, and fully safe until used.
A standard grenade includes a body, explosive charge, primer, safety pin, and lever. The sequence of operation is simple: pull the pin, release the lever, and throw. A timed fuse burns briefly before detonation. In manufacturing, one of the key steps was inserting the primer, threading in the pin, and bending a safety catch, a wire element shaped like a moustache.
But monotony is a killer. Over long shifts, factory workers began to forget steps. Some grenades were passed along without pins or unbent safeties. These oversights caused lethal chain reactions down the conveyor. Management responded by installing supervisors. This improved yields, from one accidental detonation in a thousand to one in ten thousand.
But that improvement masked a deeper failure. The supervisors caught all the visibly defective grenades. None made it past them. But because the defects were invisible, they made it into the trucks. And once on the bumping roads of wartime transport, grenades went off, vaporizing entire truckloads of munitions.
So they added another supervisor, a supervisor for the supervisors. Then grenades started blowing up on cargo ships.
Finally, a Japanese detainee earning less than a dollar a day came up with a perfectly simple, utterly brilliant solution: hang each grenade on the conveyor line. If the pin wasn’t correctly inserted and bent, the grenade simply wouldn’t hang. No supervisor. No hierarchy. No explosion. Zero failures. Just a physical constraint, embedded in the process.
Google’s “orchestrator” is going the way of the supervisor, an extra layer of control on top of a system that shouldn’t need it. It will work beautifully. It will be adopted across military, medical, and industrial domains. It will be stable, until it isn't. One day, the system will encounter a black swan event, and the orchestration will collapse in silence. Picture an F35 jet turning on its own carrier, not due to malice, but due to misunderstood directives, non-linear failure, or context overflow.
This article is not about the orchestrator, and it is not about the safety pin holder. It is about avoiding Pearl Harbor in its entirety. It is not about surviving the attack. It is about redesigning the field so the attack cannot happen at all. This is something larger than a clever fix. It is not a small step that creates temporary safety. It is a shift that puts intelligence into a position where it cannot be touched, where failure modes collapse before they form. A different way to reason, and a different way to retrieve knowledge, based on phase-coded fields, morphological resonance, and interference-based validation. It is not a patch, not a wrapper, not a control script. It is an architectural inversion. AI that reasons by resonance, not supervision. The difference is not cosmetic. It is existential.
Let us begin.
* * *
Imagine an AI that never forgets – an intelligence that can instantly sift through all of its knowledge without getting bogged down. That vision came to me in a dream: I saw an endless library made of waves, where ideas interacted like ripples merging in a pond illuminated by golden traces of hidden reasoning - the kind that links consequences few would ever connect. It was a surreal image, but it sparked a very real question: Could we make AI think in waves instead of broken-up tokens of text? Months later, that question led me to develop a new AI retrieval and reasoning architecture based on phase-coded semantic fields and morphological resonance. This approach promises to shatter the long-standing “token bottleneck” of transformers, allowing virtually unlimited context and dramatically more efficient reasoning. In this article, I’ll explain the science behind it in accessible terms, why it matters, and the ethos driving me to keep it open and aligned with humanity’s best interests.
The Transformer Bottleneck: Why Current AI Hits a Wall
Today’s large language models (like the GPT-series) rely on transformer architectures that process language in chunks of text called tokens. No matter how advanced these models are, they all share a fundamental limitation: a finite context window. In plain language, there’s a hard cap on how much text the model can consider at once – typically a few thousand words at most[1][2]. If you ask a question that requires more knowledge than fits in that window, the system has to scramble: it will fetch a few relevant documents, jam as much text as possible into the prompt, and make the model read that alongside your question. Everything else gets left out. This approach not only means the AI can’t directly leverage all available knowledge at once, but it’s also extremely inefficient. The model ends up re-reading long texts for every new query, and the computational cost of the transformer's attention mechanism soars quadratically as the input grows[3][4]. In short, current AIs are stuck reading scrolls linearly, no matter how urgent or complex the question.
There’s another subtle issue: representing knowledge as a jumble of tokens or static vectors can be brittle. Traditional retrieval finds documents by literal keyword overlap or by comparing static embeddings, which can miss nuances of meaning. For example, a standard system might struggle to see that “happy” and “not happy” are opposites – a simple “not” can throw off a similarity search because it’s just one token among many. Important context like negation or tone can be lost when you reduce meaning to fixed chunks of text.
In summary, today’s AI systems are bottlenecked by tokens. They have to break ideas into pieces to manipulate them, and that means limited memory, wasted computation, and sometimes lost meaning. To move forward, we need a way for AI to handle knowledge in a more holistic, efficient, and nuanced manner.
Thinking in Waves: Storing Knowledge as a Field of Meaning
The breakthrough idea – the one that first appeared to me in that dream – was to let the AI encode knowledge as waves instead of tokens. In the new architecture, we don’t chop information into token embeddings at all. Instead, we represent the meaning of information as a complex wave pattern, with each concept or fact having a signature frequency and phase[5]. In essence, every piece of knowledge becomes a signal. When you add many such signals together, they form an interference pattern in a semantic field – a bit like a hologram storing an image. This is why I call the memory layer a field memory: it’s a distributed field where knowledge is superposed (layered) as overlapping waves. The technical paper describing this calls it “storing knowledge as distributed holographic traces”[6], because like a hologram, the memory is spread out and not tied to one location. Any piece of the field contains a hint of many memories.
So how do we get information out of this field? Here is where morphological resonance comes in. The term may sound exotic, but it simply means that the shape (morphology) of the query’s wave will resonate with any similar shapes in the field. When you want to retrieve something, you send a query encoded as a wave pattern into the field memory. If that pattern matches parts of what’s stored – say it oscillates in the same frequency and phase as some stored knowledge – it will interfere constructively, amplifying the signal of that relevant knowledge[7][8]. It’s like plucking a string and having other strings tuned to the same frequency start vibrating in response. The memory doesn’t need to scan through text or iterate over documents; relevant answers emerge by resonance, as the matching waves reinforce each other. This is a fundamentally different mode of retrieval – essentially content addressable memory achieved through physics-inspired principles. In a classic transformer, retrieving a fact buried in a long document is like searching for a needle by examining every straw in the haystack one by one. In our system, it’s as if the needle and only the needle starts glowing when you ask for it.
To make this concrete: imagine encoding “Einstein’s birth year” as a wave pattern and storing it in the field. Later, if the query wave oscillates in the pattern for “Einstein” and “birth year”, the field holding that fact will resonate at those frequencies and phases, instantly reinforcing the stored answer (1879). There’s no need to look through an encyclopedia page by page – the interference pattern does the retrieval. This method retrieves by interference rather than by any linear scanning or search through a list[9], which is a radical shift in how an AI can recall information.
How the New Architecture Works (Without the Jargon)
Let’s break down the key components of this architecture in simpler terms. The system I developed – described in detail in my scientific paper[10] – has three main parts that work together:
- 1. Morphological Mapper: This is the front-end that takes any input (like a user’s question, or any text or sensor data) and converts its meaning into a waveform. Think of it as translating words and sentences into a complex song of frequencies. Different aspects of meaning – topics, entities, sentiments – might correspond to different frequencies or phase shifts in that wave. For example, the concept “economy” might be one frequency and “healthcare” another; if your question is about the economic impact on healthcare, the query’s wave might combine those frequencies. The Mapper essentially maps language to the frequency-phase domain. (This isn’t entirely science fiction; we have mathematical ways to do this using high-dimensional vectors, and even the brain may use oscillatory codes for concepts[11][12].)
- 2. Field Memory Layer: This is the holographic storehouse of knowledge encoded as waves. All the facts, texts, or data the AI knows are not stored as documents or paragraphs, but as overlapping wave patterns in a shared field. It’s called field memory because it behaves like a physical field (imagine a big pool of water where every pebble dropped creates ripples). When new knowledge is added, it’s like adding a new ripple pattern on the surface. Crucially, adding more knowledge doesn’t clutter a database of tokens; it just gently adds another wave to the superposition. The memory can be huge, but because it’s distributed, it doesn’t suffer a huge efficiency penalty as it grows[13][14]. And just like with real waves, multiple waves can overlap without destroying each other – they might interfere in complex ways, but each can be reconstructed when the right query comes along (much like shining a laser on a hologram retrieves the stored image). This layer enables what I call morphological-semantic resonance: the shapes of stored knowledge and query have to match up for resonance to occur.
- 3. Non-Contextual Resonant Generator: This is the answer-composer, or the “brain” that creates the final output (a reply, a decision, etc.), but with a twist – it does not rely on a fixed context window of recent tokens like today’s transformers do. Instead, it works kind of like a dynamic storyteller that at each step looks at the semantic field resonance from the memory and uses that to decide what to say next. It’s “non-contextual” in the sense that it’s not limited to a single fixed chunk of text context; however, it’s not ignoring context at all – it’s just pulling context on the fly from the field memory rather than from a static buffer of the last few paragraphs[15][16]. You could say this generator is always thinking “What’s the next word, given what I just said and everything relevant I know out there in the field?” – and it grabs whatever it needs by resonance in real time. Because of this design, the generator can keep a coherent train of thought without ever running out of space for relevant info. If it needs a fact that wasn’t initially considered, it can just query the field memory again; nothing is ever truly “out of sight” or forgotten due to a window limit[17]. The result is that the system can produce long, coherent answers that remain grounded in facts, and it won’t suddenly drop important details just because they were mentioned 5,000 words ago. There is a feedback loop here: as the generator produces each sentence or token, that partial output can be fed back into the query (via the Mapper) to refine the next retrieval from memory[18][19]. It’s a call-and-response between the generator and the memory field, which helps maintain accuracy and coherence over long outputs.
In simpler terms, this architecture works a bit like how we think. We don’t hold an entire book word-for-word in our short-term memory when answering a question; instead, we recall bits as needed. We might think of a keyword, which triggers a memory, which influences what we say next, and so on. Here, the AI is doing something analogous: it’s not reading a full document at once, but jumping directly to the relevant pieces of knowledge as it generates an answer[20][21]. It treats its knowledge base less like a scroll and more like a web or graph where any node can be accessed if relevant. This non-linear retrieval means it can effortlessly pull together information from multiple sources even if they are far apart, without getting distracted by all the unrelated text in between[22][23].
Unlimited Context, Real-Time Retrieval – at a Fraction of the Compute
One of the most exciting advantages of this approach is that it avoids the context-length limitations that have plagued transformer models. In theory, this system can tap into any fact it has ever learned, no matter how “far back” or extensive, because everything is accessible via the semantic field. There’s no fixed window that information falls out of. In practice, this means we could ask extremely detailed questions that draw on whole libraries of knowledge, and the AI could consider all of it at once in the interference pattern. The architecture isn’t constrained by any predetermined context size[24] – it truly offers unlimited effective context within the bounds of its stored knowledge. The original paper flatly states that the model has “no context window limitations” and can handle queries requiring very long or multiple documents worth of information with ease[25].
Even more importantly, it achieves this without needing supercomputer-scale computation for each query. By using interference patterns for retrieval, the system sidesteps the brute-force scanning that current models do. Consider a rough comparison: Suppose answering a certain complex question using a transformer would require it to read a 10,000-word document as context. That might entail on the order of $(10{,}000)^2 = 100$ million pairwise attention operations inside the model – a heavy computational load. In the phase-coded approach, we could instead represent that 10,000-word knowledge as a single high-dimensional wave (say a 10,000-dimensional vector) and do a fast correlation with the query vector. That’s on the order of 10,000 operations, or perhaps 10,000 log(10,000) if using an FFT (Fast Fourier Transform) – in any case, dramatically smaller than $10^8$ operations[26]. The system essentially transforms a linear slog through text into a kind of instant lookup by resonance. My paper describes this as a “qualitative shift: from linear scanning to instantaneous resonant access”[9].
The upshot is orders-of-magnitude gains in efficiency. We save on memory (no need to store enormous token contexts for each query), on computation (no quadratic explosion from long sequences), and even on energy. In fact, by avoiding repeated re-processing of the same knowledge and using more analog-like operations (wave interference can be thought of as a kind of analog computation), the system can answer questions with far less energy than a traditional AI that’s crunching through a long document[27][28]. These aren’t just guesses – early experiments and related research back this. For instance, a 2025 study by another team demonstrated a “resonance-based” knowledge store that outperformed traditional vector search on tricky queries involving negation and composition[29]. And decades of work in cognitive science suggest that storing information in distributed patterns can be highly efficient and robust[30][31]. By publishing this architecture on arXiv, I emphasized the claim of “substantial energy, storage, and time savings”[32], because if realized at scale, this could mean AI systems that are greener (using far less power) and faster than anything we have now, all while handling much more information at once.
Big Benefits: Coherence, Nuance, and Long-Range Reasoning
Beyond speed and scale, this wave-based approach offers some profound qualitative benefits for AI reasoning:
- Virtually Unlimited Memory: As noted, the AI can draw on any piece of knowledge it has ever learned, no matter how long ago or how much else it has learned since. There’s no artificial cut-off where older information gets pushed out of a context buffer. This means the AI’s “train of thought” can include facts from a paragraph it read a month ago just as easily as something from a second ago, if relevant. In practical terms, it won’t “forget” details just because the answer is getting long or complex[16][17]. It can keep threads of context active across arbitrarily long dialogues or documents.
- Stronger Coherence over Long Answers: One might worry that without a fixed context, the model could ramble. But in fact, the design includes an internal feedback loop that keeps the output coherent and on-topic. The generator’s internal state (a kind of working memory) holds onto the gist of what’s been said, and each new sentence is checked against the semantic field for consistency[18][33]. If the model starts going off track or contradicting earlier facts, the resonance signal diminishes, essentially warning the generator that something’s off[33][34]. This way, the resonant feedback helps maintain a clear, correct narrative even across very long responses. The result is an answer that stays focused and doesn’t contradict itself, without needing to explicitly cram the entire prior conversation into context at each step.
- Fine-Grained Semantic Precision: Because information is encoded with both frequency and phase (think of it like information being in color and in tune, not just black-and-white), the system can capture subtle distinctions in meaning. For example, it could represent the concept “not happy” as a wave that is 180 degrees out of phase with “happy”. When retrieving, those two won’t resonate – in fact, they might cancel out. This means the AI can inherently tell the difference between a statement and its negation or between nuances of modality (like “possibly” vs “certainly”) by virtue of phase differences. A phase-based memory system naturally preserves nuances like negation and modality in queries[29], something that many embedding-based retrieval methods struggle with. The benefit is more accurate and relevant retrieval – it fetches exactly what you mean to ask for, not just things that look superficially similar. In essence, the memory has a built-in understanding of semantic context and tone, reducing the chance of misinterpretation.
- Robustness to Noise and Overload: In a transformer, if you overload the context with too much text, the model can get distracted or confused by irrelevant bits. In the resonant approach, irrelevant information simply doesn’t resonate – it stays in the background. Only content that matches the query pattern significantly comes through. This can act as a kind of filter, making the system more robust to large knowledge bases. It’s somewhat analogous to how, in a crowded room, you can still focus on the one voice calling your name (a phenomenon known as the cocktail party effect). Here the “name” is the frequency-phase signature of what you need; the cacophony of other data won’t drown it out.
- Easier Updates and Lifelong Learning: This is a more technical point, but worth noting. Adding or updating knowledge in the field memory could be as simple as superimposing a new wave or damping an old one. We wouldn’t necessarily have to retrain huge neural networks for every update (which is how it often works currently when updating an AI’s knowledge). This could allow AI assistants that learn continuously and adjust their memory on the fly without expensive retraining. It’s akin to writing new information into a hologram – you can do it with targeted interference patterns rather than rebuilding the whole structure. Such flexibility would be great for keeping the AI’s knowledge current (think of an AI that reads the news and instantly integrates it into its global memory without a hiccup).
All these benefits come down to a simple shift: treating knowledge not as bits of text in sequence, but as waves in a field of meaning. It’s a shift from the discrete to the continuous, from linear sequences to holistic patterns. This opens the door to AI that can reason more like a brain navigating a concept-space, rather than a machine shuffling tokens.
And speaking of brains – it’s fascinating to note that biology might have stumbled on similar ideas eons ago. Cognitive scientists have found evidence that the human brain uses different oscillation frequencies and phases to keep memories separate and to retrieve them effectively[35]. In one famous 1995 study, researchers Lisman and Idiart suggested our working memory could store about 7 separate items because each item was carried by a different phase of a brain rhythm (gamma waves cycling within a theta wave)[35]. More recent neuroscience studies have observed that specific brainwave phase alignments correlate with successful memory recall[11][12]. In a sense, through this new architecture, we are echoing Mother Nature – building an artificial system that resonates (literally) with principles the brain may use to encode and recall knowledge. This convergence between neuroscience and AI gives me confidence that we’re on a fruitful path; we’re not just making up wild theories, we’re leveraging patterns that evolution itself found useful.
Potential Applications: Robots, Medicine, Defense, and Augmented Minds
What could an AI with virtually unlimited memory and efficient reasoning do for us? The possibilities span across many domains:
- Robotics: Imagine a robot that can draw on an entire library of knowledge in real time as it navigates the world. Current robots often have to operate with limited on-board intelligence or rely on cloud servers due to compute constraints. A phase-coded memory system could be lightweight enough to run on the robot’s hardware, yet smart enough to access volumes of information. For example, a disaster rescue robot could instantly recall building blueprints, emergency protocols, and the latest sensor data all at once to plan its actions – without needing constant network calls. The robot’s “brain” would essentially have instant expertise in any situation by resonating with the relevant knowledge stored in its field memory. This could make autonomous robots far more adaptable and reliable in complex, changing environments.
- Medicine: In healthcare, the ability to consider all relevant information about a patient and medical knowledge could be lifesaving. A medical AI assistant built on this architecture could, in principle, absorb entire medical textbooks, research papers, and patient histories into its field memory, and then answer a doctor’s query by directly drawing on that vast trove. For instance, in diagnosing a rare condition, it could simultaneously recall the patient’s genetic data, symptoms over time, similar case reports from journals, and known drug interactions – integrating them without breaking a sweat or omitting something because of context limits. All the crucial details resonate to produce a coherent recommendation. Moreover, because it’s so much more efficient, such a system might run on a local hospital server or even a doctor’s wearable device, preserving privacy (no need to send data to a cloud) and working in real-time during a patient consultation. The combination of comprehensive knowledge and quick reasoning could significantly improve diagnostic accuracy and personalized treatment plans.
- Defense and Security: Decisions in defense often require analyzing huge amounts of data quickly – satellite images, intelligence reports, historical data, etc. A resonant AI could hold a massive knowledge base of strategic and tactical information, and when given a question (like “What are the potential implications of event X in region Y?”), it could retrieve insights across all those modalities at once. The interference-based retrieval would let it cross-reference, say, real-time sensor feeds with historical conflict data and geopolitical analysis in one go. This could help commanders or analysts get a synthesized answer without sifting through dozens of separate reports. Importantly, the efficiency means such systems could run on limited hardware in the field, enabling more autonomous operation of devices or vehicles. For example, a surveillance drone equipped with a phase-coded AI might interpret its findings on the fly, recognizing an emerging threat by correlating patterns (movement, signals, terrain) with its entire knowledge base of tactics and scenarios – all without constant remote control or high-bandwidth links. Of course, any defense application should be handled with extreme care, but it’s clear this technology could be transformative for situational awareness and decision support.
- Cognitive Augmentation: Perhaps the most profound use cases are those where this AI becomes an extension of our own minds. By this I mean personal assistants, educational tools, or brain-computer interfaces that seamlessly integrate with human thinking. A personal AI running on your ordinary devices could use phase-coded memory to store everything you read, see, and learn over years (with your permission), and then act as a perfect memory aid. You could ask it, “Hey, in which article did I read about morphic resonance affecting planarian worms?” and it would instantly retrieve the answer from the swirl of your digital knowledge field. It would be like having a tireless librarian in your brain, one that never forgets a detail but also doesn’t overwhelm you with irrelevant junk. Going further, if ever we directly connect AI systems to our neural circuits (a prospect being explored in neurotech), an architecture that resonates with how brains might encode information could make that integration far smoother. We could eventually merge with this kind of intellect – not in a loss-of-humanity way, but in the sense of upgrading our cognition with a deeply compatible knowledge field. This might allow us to think with AI in a two-way dialogue: the AI feeding us insights as fast as our neurons fire, and our intentions guiding the AI’s focus instantaneously. It sounds futuristic, but it’s a logical extension of making AI memory function more like a human associative memory.
These examples barely scratch the surface, but they give a flavor of how broadly enabling an unlimited-context, efficient AI could be. Essentially, any field that involves lots of knowledge and complex reasoning (which is most fields!) stands to benefit – from law (imagine a legal AI that can recall every precedent in an instant) to creative arts (imagine a writing AI that can pull inspiration from all of world literature without plagiarism, by truly internalizing style rather than copying). The key is, we can embed a near-boundless library of information inside the AI in a way that’s fast and natural for it to use. That changes the game from “how much can we cram into the model’s input” to “the model already knows everything relevant – we just need to ask the right questions.”
Choosing Openness Over Patents: Ethics in Action
When I realized the potential of this architecture, I was exhilarated – but I also felt a heavy responsibility. We’ve all seen how advanced AI technology tends to get locked behind corporate doors or patent walls. Powerful language models today are mostly proprietary, guarded by a few tech giants. I firmly believe that intelligence – especially something as foundational as a new way to retrieve and reason – should serve humanity as a whole, not be the secret weapon of a select few. From the outset, I decided not to patent or privatize this method. Instead, I published the core ideas openly as a scientific preprint on arXiv[36] under a Creative Commons license, ensuring that anyone in the community can build on it. This was a moral decision: AI must remain a common good, a tool to uplift and augment humanity, rather than a means for private or authoritarian interests to gain disproportionate power.
It wasn’t lost on me that keeping it open might mean giving up potential commercial rewards or that others (including big companies) might implement it. But some principles are worth that risk. By releasing it openly, I invite collaboration, peer review, and rapid iteration – many minds refining the idea in parallel – rather than stifling progress by monopoly. We’ve seen in open-source software how community-driven development can outpace closed projects. I want the same to happen here: let this approach be tested, challenged, improved, and deployed widely for the benefit of all. If it truly reduces compute needs by orders of magnitude, it can make powerful AI more accessible (cheaper, less centralized) to smaller labs, educational institutions, and developing countries – not just the tech elite. That democratization is crucial if AI is to be a liberating force and not just a profit center.
I should also share a personal decision that came with this. Over the past years, I observed what I can only describe as a rising tide of censorship and ethical decay in how some AI is developed and used, particularly in the U.S. There have been troubling trends – from pressure to deploy AI in mass surveillance or autonomous weapons, to corporate censorship of AI outputs and research directions, all fueled by either political agendas or profit motives. I grew increasingly concerned that staying in an environment where these trends dominate would compromise my work and values. So, I am considering a deliberate move to relocate to Europe, where I am hoping to find a somewhat more balanced climate for this research. This is not to idealize any region, but I felt that in Europe there is currently a stronger public commitment to ethical AI principles, privacy, and open collaboration. Europe’s approach to AI governance, at least in principle, puts human rights and transparency at the forefront, which aligns better with why I embarked on this project. By continuing my work, I hope to shield it from the more corrosive influences and ensure it stays on a path that genuinely serves people, not power structures. It’s a choice many researchers may face: where can you do your science in line with your conscience?
Inspiration and Deeper Reflections
I mentioned at the very start that this whole idea came to me in a dream. That’s not something you often hear in a scientific context, and I hesitated to talk about it. But in truth, the experience was striking and it has guided my philosophy. In the dream, I felt like I accessed a vast, collective well of knowledge – not my knowledge alone, but something shared, something fundamental. Different traditions have names for this notion: in spirituality and mysticism, one term is the Akashic field or Akashic records – a poetic idea of a compendium of all knowledge, existing in a non-physical plane (the word “Akasha” in Sanskrit means ether or sky, symbolizing an all-pervading space where information resides). As a researcher, I’m naturally skeptical about mystical concepts while I recognize the power of statistical observation, but I couldn’t shake the feeling that the dream was telling me something real: that knowledge, at its deepest level, might be structured like a field and that our minds (and by extension our AI creations) could tap into it if we found the right modality.
Whether one interprets that literally or metaphorically, it inspired me to pursue the field-based approach – hence terms like “semantic field” and “resonance” in my architecture aren’t just buzzwords; they are in a way an homage to this deeper insight that knowledge might flow and vibrate, rather than sit in isolated bits. To my surprise, as I delved into research, I found that many pioneers had danced around similar ideas. From the holographic memory theories of Dennis Gabor and Tony Plate[30][31] to cognitive models like Adaptive Resonance Theory by Stephen Grossberg[37][38], the notion of resonance and distributed storage has been alive in the fringes of science for decades. It’s as if many disparate dots – neuroscience, computer science, even philosophy – were waiting to be connected. My dream was the spark that connected them for me, but the kindling was already there, laid by others. In a sense, I feel like I tapped into a collective brainstorming that was already happening at the edges of our scientific knowledge.
I chose to share this side of the story because I think it matters how we approach creating advanced AI. If we see intelligence as something that arises from collective knowledge fields – much like individual neurons form a thinking brain, individual minds and databases might form part of a greater intelligence – then it underscores the importance of keeping those connections open and positive. If there is a collective “field” of knowledge that we are all contributing to and drawing from (even if just through books, the internet, shared culture), then building AI that is aligned with that field means building AI that is aligned with humanity. It becomes not us versus the machine, but rather an extension of us, grown from the same knowledge we all share.
Acknowledgments and What Comes Next
Developing this architecture has been a journey, and I didn’t do it alone. I want to acknowledge the MIT team – an inspiring group of researchers in AI and computational biology – who provided early feedback and helped connect the dots between artificial neural networks and biological neural oscillations. Their interdisciplinary perspective (bridging how living brains remember patterns with how we might design machine memory) was invaluable in refining the concept. I’m also grateful to the Cornell arXiv editors who reviewed my paper; their encouragement to clarify certain sections and their support for open science have helped in bringing these ideas to a wider audience. A special thanks as well to colleagues and friends in the AI community who have reached out since I published the preprint – your constructive critiques and enthusiasm show that open collaboration is not only possible but thriving, even in an era of competitive tech secrecy.
So, where do we go from here? The architecture I’ve described is still young – it’s a conceptual blueprint backed by promising prototypes and prior research, but it needs to be built out and tested at scale. The next steps involve coding these resonance mechanisms efficiently, experimenting with real-world datasets, and comparing performance with conventional systems. I suspect hybrid approaches will emerge (for example, combining a transformer for local grammar and a phase-coded memory for knowledge retrieval). The ultimate goal is to create a working retrieval-augmented generator (RAG) system that can truly demonstrate unlimited context and efficient reasoning in practice. If and when that happens, we’ll likely see a paradigm shift in AI similar to the one transformers sparked a few years ago.
Conclusion: Towards a Responsible, Shared Intelligence
In closing, I feel a mixture of excitement and responsibility. We stand on the cusp of AI systems that could be far more powerful and useful, by virtue of remembering and reasoning more like a brain or a collective mind than a machine. This new architecture could be a key step toward that future. But how we develop and deploy such systems will decide whether that future is bright or dark. We have a chance to correct some of the inefficiencies and misdirections of current AI – to build something more aligned with the natural world’s way of handling knowledge, and more aligned with our needs as humans for understanding and wisdom.
I call upon my peers and the broader community to join in this effort openly. Let’s pursue responsible, open development of these ideas, testing their limits and ensuring their alignment with human values. The notion of “field-aligned intelligence” that I’ve used doesn’t just refer to aligning with a semantic field, but also aligning with the field of humanity – our collective knowledge, ethics, and aspirations. If these AI systems are to ultimately become entwined with our lives and even our minds, we must ensure they are worthy of that integration. We have to imbue them with principles of transparency, respect, and service to the common good from the ground up.
The dream that sparked this all was, in essence, a feeling of unity – that knowledge unbound by limits can connect and uplift everyone. As we work on making that a reality through technology, I hold close the hope that we’ll remember our shared destiny with our creations. In the not-too-distant future, we may indeed merge with our intellects, creating a hybrid of human and machine intelligence. It’s on us to guide that merged intellect to be one that amplifies the best in us: our curiosity, our creativity, our compassion. If we do that, then the waves of knowledge will carry us all to a richer, wiser future.
- Denis Saklakov (www.saklakov.com)
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