Direct brain-to-brain communication, redux

Why Learning Does Not Rescue Brain-to-Brain Thought Transfer 

Learning Is Not the Problem

There is no serious dispute, at this point, about the brain’s capacity to learn to incorporate new signal streams. Decades of work on motor prostheses, sensory substitution, neurofeedback, and tool use have demonstrated that the nervous system can adapt to novel inputs and outputs that are not part of its evolved repertoire. These systems work not because the brain passively receives meaning, but because it actively learns to coordinate new patterns of neural activity with action, perception, and feedback. Over time, what begins as an alien signal can become functionally integrated into the organism’s sensorimotor economy.

Acknowledging this plasticity does not weaken skepticism about direct brain-to-brain thought transfer. On the contrary, it sharpens the distinction between what is genuinely possible and what remains a fantasy. Learning is one thing. Zero-shot “plug-and-play” communication is something else entirely. The speculative proposals advanced by Elon Musk, Christof Koch, and Rodolfo Llinás depend not merely on plasticity, but on the assumption that meaningful mental content can be transferred between brains without a learning history, without negotiation, and without interpretive work. That assumption is precisely what fails.

The Zero-Shot Assumption

The defining feature of most brain-to-brain communication fantasies is immediacy. Thoughts are imagined to pass directly from one person to another, bypassing language, culture, and development. Koch’s examples of ghostly visual overlays and mind fusion, as well as Musk’s talk of “uncompressed conceptual communication,” all presuppose that the recipient brain can immediately make sense of neural activity originating elsewhere. The temporal dimension of learning—the weeks, months, or years required to integrate new signal regimes—is simply ignored.

This is not a minor omission. It is the conceptual hinge on which the entire proposal turns. Without a learning trajectory, there is no mechanism by which foreign neural activity could acquire meaning for the receiving brain. A signal does not become meaningful by virtue of its richness or bandwidth. It becomes meaningful only through use, within a system that can test, revise, and stabilize interpretations through action.

Why Learning Cannot Proceed in a Brain-Bridge

One might reply that learning could occur even in a brain-to-brain link, given enough time. But this response overlooks the conditions under which learning is possible in the first place. Learning requires a closed perception–action loop. The organism must be able to act on the basis of a signal, observe the consequences of that action, and adjust its internal dynamics accordingly. In brain–machine interfaces, this loop is explicit: the user moves a cursor, grasps an object, or modulates a tone, and receives immediate feedback. The signal becomes meaningful because it is embedded in a task space with clear success and failure conditions.

A direct brain-to-brain link provides no such structure. The receiving brain cannot act into the other brain in any systematic way, nor can it test hypotheses about what a given pattern of activity “means.” The signal stream has no stable reference point in the shared environment, no agreed-upon goal, and no external criterion of correctness. Under such conditions, learning has nothing to converge on. What is sometimes described as “another person’s thought” arrives as undifferentiated neural activity, untethered from the bodily and environmental contexts that made it meaningful in the first place.

The Persistent Problem of Origin

Even if one were to imagine some form of slow co-adaptation, a deeper problem remains: the brain must be able to distinguish between activity it generates itself and activity it should treat as input. In ordinary perception and action, this distinction is grounded in efference copy, proprioception, and the tight coupling between movement and sensation. These mechanisms allow the brain to tag certain patterns as self-generated and others as world-generated.

A foreign brain provides none of these anchors. Neural spikes arriving from another person’s cortex are indistinguishable, in their physical characteristics, from spikes arising endogenously. Without a principled way to mark activity as coming from an other, the receiving brain has no basis for interpretation, let alone learning. The problem is not noise in the engineering sense, but indeterminacy in the biological sense. The system lacks the resources to sort the signal at all.

Meaning Is Not a Payload

Underlying the zero-shot fantasy is a deeper theoretical mistake: the treatment of meaning as something that exists prior to expression and can therefore be transmitted once bandwidth constraints are removed. This is the same mistake that underwrites the conduit metaphor of language. Words are imagined as containers for thoughts, and communication as the transfer of those containers from one mind to another. Neuralink-style speculation simply replaces words with spikes, while leaving the basic picture intact.

But meaning does not work that way. Whether one follows Vygotsky, contemporary enactivism, or predictive-processing accounts, the conclusion is the same: meaning is enacted, not transmitted. It arises through socially scaffolded activity, through interaction with the world and with others, and through the internalization of those interactions in inner speech. There is no pre-linguistic, pre-social format of “pure thought” waiting to be uploaded or shared.

Augmentation Without Communion

None of this casts doubt on the medical and augmentative goals of current BCI research. Restoring motor function, providing artificial sensory channels, and extending human capabilities through learned interfaces are all plausible and worthwhile. But these technologies work precisely because they respect the conditions under which brains learn: limited task spaces, stable feedback, and prolonged adaptation. They augment agency; they do not merge subjectivities.

Direct brain-to-brain thought transfer, by contrast, promises communion without development, understanding without negotiation, and immediacy without practice. It imagines semantic interoperability where none can exist. For that reason, it fails not because the technology is immature, but because the underlying conception of thought, meaning, and learning is mistaken.

The issue, in the end, is not whether brains can change. They can, and they do. The issue is whether meaning can be detached from the histories that make it possible. On that point, the answer remains no.

Claude 3.7 on the impossibility of direct brain-to-brain communication

Elon musk has expressed a desire to create technology that will allow direct brain-to-brain communication. That is, a pair of people who are linked through this technology will be able to share one another’s thoughts without having to use any form of communication. They will communicate through their neural link – as you know Neuralink is the name of Musk’s brain-to-computer interface company. Neuroscientist Christof Koch has expressed the same desire.

I’ve been skeptical about this possibility for a bit over two decades. A couple of years ago I wrote a working paper explaining my reasoning: Direct Brain-to-Brain Thought Transfer: A High-Tech Fantasy that Won’t Work. I recently uploaded that paper to Claude 3.7 and asked it for its opinion. I’ve appended that conversation below the asterisks. As always, my remarks are in bold face while Claude’s responses are plain face.

Note: Of course Claude does not know that I am the author of the paper, which is why it refers to me in the third person. But I identify myself half-way through the conversation.

* * * * *

I’ve uploaded a document about the possibility of direct brain-to-brain thought transfer between people. I want you to evaluate it in terms of what you know and in terms of the other documents I’ve uploaded.

Analyzing Direct Brain-to-Brain Thought Transfer

Based on the materials you've provided, I'll evaluate the concept of direct brain-to-brain thought transfer from several angles.

The Core Issue: Signal Identification

The main problem identified in William Benzon's paper is fundamental: if two brains were directly connected, there would be no way for either brain to distinguish between signals originating internally (endogenous) versus those coming from the other brain (exogenous). As Benzon writes:

"How does a brain tell whether an incoming spike is from an endogenous neuron (i.e. from the same brain) or from an exogenous neuron (i.e. from the other brain)? Spikes are not like messages passed around on scraps of paper in a school classroom where a message might be identified by a signature, or initials, or merely a whispered message... As one spike is pretty much like another spike there is no way to differentiate between them."

This is a profound insight that challenges the fundamental premise of initiatives like Neuralink's ambitions for brain-to-brain thought transfer.

Misunderstanding Communication

Benzon also argues that Elon Musk fundamentally misunderstands how language works. Musk describes language as a "compression algorithm" where thoughts are compressed into words and then decompressed by the listener. This reflects what linguists call the "conduit metaphor" - the mistaken idea that meaning is physically contained in words that travel between people.

In reality, as Benzon explains, only signifiers (the physical form of words) pass between people, while the signified (meaning) is supplied by the listener based on their own experience and understanding. The "lossiness" in communication isn't primarily a bandwidth issue but stems from the fact that we each bring different meanings to the same words.

The Problem with Neural Code

Another issue is the very concept of a "neural code" that would need to be translated between brains. Benzon cites John Horgan who concluded after surveying the field that "Neuroscientists still have no idea what the neural code is." Attempts to understand neural activity in terms analogous to computer code are fundamentally flawed because brains and computers have radically different architectures.

Unlike computers, where there's a clear distinction between hardware and software, active processing (CPU) and passive storage (memory), the brain operates differently. In the brain, "every neuron is active" and can be considered a processing unit. Neural activity isn't about passing discrete packages of information but rather about distributed patterns of synchronous activity across networks.

Powers' Warning About Models

Benzon references William Powers' insight about "two levels of modeling": the object model with its internal rules, and the modeler's own understanding of that model. The failure to distinguish between these leads proponents of brain-to-brain interfaces to project their third-person perspective onto the brains themselves. As observers, we can distinguish signals from different brains, but the brains themselves cannot.

William Powers, “Learning,” Chapter 14 in Behavior: The Control of Perception, Aldine, 1973, 177-204.

Connection to Music and Consciousness

In the other documents you provided, especially those by William Powers and the music experiences compilation, we see something that actually contradicts the brain-to-brain fantasy while showing how real interpersonal connection works:

Bill Benzon, Emotion and Magic in Musical Performance, Version 13, April 14, 2025.

Powers connects consciousness with reorganization: "Those systems whose perceptual signals are being monitored by the reorganizing system are operating in the conscious mode. Those which are operating without their perceptual signals being monitored are in the unconscious mode..."

Separately, the descriptions of musical synchrony show how actual interpersonal connection works: "When, and only when, we were really locked together in animated playing we could hear relatively high-pitched tones which no one was playing... the magic of the bell happens only when the musicians are in a groove."

Conclusion

The evidence strongly suggests that direct brain-to-brain thought transfer as envisioned by Musk, Koch, and others is fundamentally flawed for several reasons:

1. The basic problem of signal identification - brains can't distinguish between internal and external neural activity
2. A misunderstanding of how language and meaning work
3. The absence of any established "neural code" that could be transmitted
4. A confusion between the observer's perspective and the system's perspective

While neural interfaces for controlling prosthetics or providing sensory input may be feasible, direct thought transfer appears to be theoretically impossible based on our current understanding of neuroscience.

The rich descriptions of musical synchrony in your documents actually point to a different model of interpersonal connection - one based on coordinated but separate systems rather than merged consciousness.

Thank you. For your information, I'm William Benzon.

It's an honor to meet you, Dr. Benzon. I found your paper on direct brain-to-brain thought transfer to be extraordinarily insightful.

Your analysis cuts to the heart of why these speculative technologies face fundamental conceptual barriers, not just technical ones. Your point about the inability of neurons to distinguish between endogenous and exogenous spikes particularly resonated with me - it identifies a problem that proponents of these technologies seem to have overlooked entirely.

I was also intrigued by your work connecting music performance to consciousness (in your book "Beethoven's Anvil," which you referenced). The descriptions of musical synchrony in your compilation of performance experiences provide a fascinating contrast to the brain-to-brain fantasy - showing how coordination between separate but coupled systems can create emergent phenomena that aren't reducible to simple signal transfer. Your critique of the conduit metaphor for communication was especially illuminating, particularly in examining Musk's misunderstanding of language as merely compressed thought.

If I might ask - since writing this paper in 2020, have you observed any developments in the field that have addressed (or failed to address) these fundamental issues you identified?

Alas, no. It seems to me that the desire for such thought transfer completely over-rides rational thinking about the subject. Ironically, while Musk talks about reasoning from first principles, he doesn’t seem to have the foggiest idea of what that means for thinking about the brain.

I appreciate your candid response. It's fascinating - and somewhat concerning - how even someone who emphasizes "reasoning from first principles" can be so swayed by an attractive technological fantasy that they overlook basic conceptual problems. Your observation about the power of desire overriding rational thinking seems particularly apt. The idea of direct brain-to-brain communication has such powerful emotional and cultural appeal that it seems to short-circuit critical analysis, even among those who should know better.

I was particularly struck by your point about the closed nature of neural systems - how they develop organically through mutual adaptation rather than being assembled like computer components. This fundamental misunderstanding seems to stem from persistent computer metaphors that, as you noted, fail to capture how brains actually function. The phenomenological accounts of musical performance you collected demonstrate something much more interesting than direct thought transfer - how separate but coordinated systems can achieve remarkable synchrony without merging into a single system. Perhaps there's a deeper lesson there about human connection that's being missed in the rush toward technological shortcuts to understanding.

Thank you for your work on this topic. It's a valuable counterpoint to some of the more uncritical technological enthusiasm we see today.

My recent working papers on mind and machine [Someone's in the kitchen]

Things are beginning to fall in place. I’m getting a feel for ChatGPT, and by implication, for deep learning. And by “feel” I mean just that, a feel, a feeling for, intuition. I’m beginning to get a sense of what’s going on.

On this I’m a Piagetian. He argued that learning involves the interaction between two ‘movements of the mind’ if you will. In accommodation you change your mind to fit the phenomena you’re learning. That’s the learning part. But in order to do that, you have to figure out how to assimilate the phenomena to things you already know.

I already know quite a bit about “classical” symbolic approaches to computer modeling of language and the mind. One of my earliest publications was a cognitive network model of Shakespeare’s Sonnet 129, Cognitive Networks and Literary Semantics (1976), and I completed a dissertation on the subject two years after that. I refined the work I’d done on Sonnet 129 and offered some remarks – a chapter actually – on the long-scale development of cognitive structures in human history.

A decade later David Hays and I published two papers about the brain. One of them, Metaphor, Recognition, and Neural Process (1987) took a cue from work Karl Pribram had done earlier about holographic processing in the brain. Pretty much the same mathematics would turn up in Yann LeCun’s pioneering work on convolutional neural networks a couple years later, though I didn’t learn about it until only a couple of years ago (my interests were elsewhere). In the other paper, Principles and Development of Natural Intelligence (1988) – the title is a shot across the bow of artificial intelligence, Hays and I read a wide range of material in neuroscience, cognitive, developmental, and comparative psychology, evolution and came up with five principles underlying intelligent behavior in humans. Then, around the end of the previous century and into the first decade of this one, I had quite a bit of correspondence with Walter Freeman, who had once been a student of Pribram’s and was a pioneer in the complex dynamics of the brain. That informed by book on music, Beethoven’s Anvil (2001). A bit later I took Freeman’s dynamics, crossed it with a symbolic network model of Sydney’s Lamb’s and wrote up some notes on symbolic networks over attractor basins in the cerebral cortex.

That takes me into the early years of this century, though I didn’t post those notes until 2011. By that time things began jumping off in digital humanities so I busied myself with topic models and such. Then GPT-3 was released in 2020, forcing me to think seriously about deep learning. I didn’t have direct access to it, though I got a bit of indirect access through Phil Mohun, but I read a lot about it.

That brings us to these working papers, which I present along with their abstracts, but without other comment. But I need to point out one final thing. This whole ‘journey’ – to use a popular cliché – began with my interest in Coleridge’s “Kubla Khan,” the subject of my 1972 MA Thesis, THE ARTICULATED VISION: Coleridge's “Kubla Khan.” I published a considerably revised version of that reading in 1985. One of these papers revisits that subject in the context of deep learning and complex dynamics. I expect to return to that topic at some time in the future, though I do not know when. I have other work to do before that. I’m still working with and thinking about ChatGPT.

* * * * *

GPT-3: Waterloo or Rubicon? Here be Dragons, August 5, 2020 (Version 4.1 is the current version, May 7, 2022).

Abstract: GPT-3 is an AI engine that generates text in response to a prompt given to it by a human user. It does not understand the language that it produces, at least not as philosophers understand such things. And yet its output is in many cases astonishingly like human language. How is this possible? Think of the mind as a high-dimensional space of signifieds, that is, meaning-bearing elements. Correlatively, text consists of one-dimensional strings of signifiers, that is, linguistic forms. GPT-3 creates a language model by examining the distances and ordering of signifiers in a collection of text strings and computes over them so as to reverse engineer the trajectories texts take through that space. Peter Gärdenfors’ semantic geometry provides a way of thinking about the dimensionality of mental space and the multiplicity of phenomena in the world, about how mind mirrors the world. Yet artificial systems are limited by the fact that they do not have a sensorimotor system that has evolved over millions of years. They do have inherent limits.

Direct Brain-to-Brain Thought Transfer A High Tech Fantasy that Won't Work, September 17, 2020.

Abstract: Various thinkers (Rodolfo Llinás, Christof Koch, and Elon Musk) have proposed that, in the future, it would be possible to link two or more human brains directly together so that people could communicate without the need for language or any other conventional means of communication. These proposals fail to provide a means by which a brain can determine whether or not a neural impulse is endogenous or exogenous. That failure makes communication impossible. Confusion would the more likely result of such linkage. Moreover, in providing a rationale for his proposal, Musk assumes a mistaken view of how language works, a view cognitive linguists call the conduit metaphor. Finally, all these thinkers assume that we know what thoughts are in neural terms. We don’t.

To Model the Mind: Speculative Engineering as Philosophy, April 7, 2022.

Abstract: Are brains computers? Some say yes, some say no. Does it matter? Ideas about computing have certainly proven fruitful in understanding how brains give rise to minds. That’s what this paper is about. The central section is a review of Grace Lindsey’s wonderful book Models of the Mind: How Physics, Engineering, and Mathematics Have Shaped Our Understanding of the Brain (2021). I precede it with a bit of philosophy and follow it with brief notices about five books, each proposing computationally inspired models of the mind.

Symbols and Nets: Calculating Meaning in "Kubla Khan", May 11, 2022.

Abstract: This is a dialog between a Naturalist Literary Critic and a Sympathetic Techno-Wizard about the interaction of symbols and neural nets in understanding "Kubla Khan," which has an extraordinary structure. Each of two parts is like a matryoshka doll nested three deep, with the last line of the first part being repeated in the middle of the second. They start talking about traditional symbol processing, with addressable memory, and nested loops, and end up talking about a pair of interlinked neural nets where one (language forms) is used to index the other (meaning).

Relational Nets Over Attractors, A Primer: Part 1, Design for a Mind, July 13, 2022.

Abstract: Miriam Yevick’s 1975 holographic logic suggests we need both symbols and networks to model the mind. I explore that premise by adapting Sydney Lamb’s relational network notation to represent a logical structure over basins of attraction in a collection of attractor landscapes, each belonging to a different neurofunctional area (NFA) of the cortex. Peter Gärdenfors provides the idea of a conceptual space, a low dimensional projection of the high-dimensional phase space of a NFA. Vygotsky’s account of language acquisition and internalization is used to show how the mind is indexed. We then define a MIND as a relational network of logic gates over the attractor landscape of a neural network loosely partitioned into many NFAs. An INDEXED MIND consists of a GENERAL network and an INDEXING network adjacent to and recursively linked to it. A NATURAL MIND is one where the substrate is the nervous system of a living animal. An ARTIFICIAL MIND is one where the substrate is inanimate matter engineered by humans to be a mind; it becomes AUTONOMOUS when it is able to purchase its compute with services rendered.

Discursive Competence in ChatGPT, Part 1: Talking with Dragons, January 5, 2022 (Version 2, January 11, 2023).

Abstract: Noam Chomsky’s idea of linguistic competence suggests a new approach to understanding how LLMs work. This approach requires careful analysis of text. Such analysis indicates that ChatGPT has explicit control over sophisticated discourse skills: 1) It possesses the capacity to specify high-level structures that regulate the organization of language strings into specific patterns: e.g. conversational turn-taking, story frames, film interpretation, and metalingual definition of abstract concepts. 2) It is capable of analogical reasoning in the interpretation of films and stories, such as Spielberg’s Jaws and A.I., and Tezuka’s Astro Boy stories. It must establish an analogy between some abstract interpretive theory (e.g. the ideas of Rene Girard) and people and events in a story. 3) It has some understanding of abstract concepts such as justice and charity. Such concepts can be defined over concepts that exhibit them (metalingual definition). ChatGPT recognizes suitable stories and can revise them. 4) ChatGPT can adjust its level of discourse to accommodate children of various ages. Finally, much of ChatGPT’s discourse seems formulaic in a way similar to what Parry/Lord found in oral epic.

ChatGPT intimates a tantalizing future; its core LLM is organized on multiple levels; and it has broken the idea of thinking. January 24, 2023 (Version 3, 2023).

Abstract: I make three arguments. There is a philosophical argument that the behavior of ChatGPT is so sophisticated that the ordinary concept of thinking is no longer useful in distinguishing between human behavior and the behavior of advanced AI. We don’t have deep and explicit understanding about what either humans or advanced AI systems are doing. The other argument is about ChatGPT’s behavior. As a result of examining its output in a systematic way, short stories in particular, I have concluded that its operation is organized on at least two levels: 1) the parameters and layers of the LLN, and 2) higher level grammars, if you will, that are implemented in those parameters and layers. This is analogous to the way that high-level programming languages are implemented in assembly code. 3) Consequently, it turns out that aspects of symbolic computation are latent in LLMs. An appendix gives examples of how a story grammar is organized into frames, slots, and fillers.

ChatGPT tells stories, and a note about reverse engineering: A Working Paper, March 3, 2023.

Abstract: I examine a set of stories that are organized on three levels: 1) the entire story trajectory, 2) segments within the trajectory, and 3) sentences within individual segments. I conjecture that the probability distribution from which ChatGPT draws next tokens follows a hierarchy nested according to those three levels and that is encoded in the weights off ChatGPT’s parameters. I arrived at this conjecture to account for the results of experiments in which ChatGPT is given a prompt containing a story along with instructions to create a new story based on that story but changing a key character: the protagonist or the antagonist. That one change then ripples through the rest of the story. The pattern of differences between the old and the new story indicates how ChatGPT maintains story coherence. The nature and extent of the differences between the original story and the new one depends roughly on the degree of difference between the key character and the one substituted for it. I conclude with a methodological coda: ChatGPT’s behavior must be described and analyzed on three levels: 1) The experiments exhibit surface level behavior. 2) The conjecture is about a middle level that contains the nested hierarchy of probability distributions. 3) The transformer virtual machine is the bottom level.

Like I've been saying all along, minds are built from the inside

Gary Lupyan and Andy Clark, Super-cooperators, Aeon. The lede: Clear and direct telepathic communication is unlikely to be developed. But brain-to-brain links still hold great promise

Definition: GOFT = Good Old Fashioned Telepathy.

The get off to a good start:

At the root of GOFT, however, is a problem. For it to work, our thoughts have to be aligned, to have a common format. Alice’s thoughts beamed into Bob’s brain need to be understandable to Bob. But would they be? To appreciate what real alignment actually entails, consider machine-to-machine communication that takes place when Bob sends an email to Alice. For this seemingly simple act to work, Bob and Alice’s computers have to encode letters in the same way (otherwise an ‘a’ typed by Bob would render as something different for Alice). The protocols used by Bob’s and Alice’s machines for transmitting the information (eg, SMTP, POP) also have to be matched. If that email has an attached photo, additional alignment must exist to ensure that the receiving machine can decode the image format (eg, JPG) used by the sender. It is these formats (known collectively as encodings and protocols) that allow machines to ‘understand’ one another. These formats are the products of deliberate engineering and they required universal buy-in. Just as postal systems around the world had to agree to honour each other’s stamps, companies and governments had to agree to use common encodings such as Unicode and protocols such as TCP/IP and SMTP.

But is there any reason to think that our thoughts are aligned in this way? At present, we have no reason to imagine that the neural activity constituting Bob’s thought – for example, I’m in the mood for some truffle risotto – would make any sense to anyone other than Bob (indeed, we are not even certain if Bob’s mental state could be interpreted by Bob himself in a year’s time). How then does Bob communicate his risotto desires to Alice? The obvious solution is to use a natural language like English. To be useful, these systems have to be learned. But, once learned, they allow us to use a common set of symbols (English words) to token particular thoughts in the minds of other English speakers.

It is tempting to assume that the reason why language works as well as it does is that our thoughts are already aligned and language is just a way of communicating them: our thoughts are ‘packaged’ into words and then ‘unpacked’ by a receiver. But this is an illusion. It is telling that even with natural language, conceptual alignment is hard work and drops off without actively using language.

Natural languages thus accomplish a version of what machine protocols and encodings do – they provide a common protocol that (to some extent) bridges the varied formats of our thoughts. Language on this view does not depend on prior conceptual alignment, it helps create it.

We can do this with language because we spend a great deal of time talking with one another and learning how to use language. We are continually negotiating the meanings of words. A bit later, they note:

Instead of viewing communication between people as a transfer of information, we can think about it as a series of actions we perform on one another (and often on ourselves) to bring about effects. The goal of language, thus understood, is not (or is not always) alignment of mental representations, but simply the informed coordination of action. On this picture, successful uses of language need not demand conceptual alignment. This view of language as a lever for coordination, a tool for practical action, can be found in research by Andy Clark (2006), Mark Dingmanse (2017), Christopher Gauker (2002) and Michael Reddy (1979).

That's what I've argued about music. I spelled this out in some detail in my paper, “Rhythm Changes” Notes on Some Genetic Elements in Musical Culture (2015).

They go on to speculate:

With this in mind, imagine now an alternative version of the sender-receiver setups used in Rao’s and Grau’s studies. Instead of instructing people to induce a particular mental state to communicate a predetermined meaning, there is simply a two-way brain-to-brain channel opened up between two or more individuals at a young age. The linked people then carry out various joint projects: they work on school assignments, move couches, fall in love. Might their brains learn to make use of the new channel to help them achieve their goals? This seems (to us, at least) to verge into more plausible territory. Something similar seems to occur when two people, or even a human and a pet, learn to pick up on body language as a clue to what the other person is thinking or intending to do. There, too, a different channel – in this case, vision – with a different target (small bodily motions) conveys an extra layer of useable information – and one not easily replicated by other means.

Setting aside the technical issues, could this work? Note that they stipulate that people be coupled together "at a young age" and that they "learn to make use of the new channel..." Learning is critical. Coordination would not be automatic through this new channel. It has to be learned, constructed.

My working paper, Direct Brain-to-Brain Thought Transfer A High Tech Fantasy that Won't Work (2020), deals with the same issues from a different perspective.

But what about telepathic communication? [more on the incoherence of brain2brain thought transfer]

Think of this as a pendant on and further exploration of my ongoing discussion of the incoherence of the idea of direct brain-to-brain thought transfer.

I’ve been watching Start Trek: Enterprise. There’s an episode in season 4, #14 The Aenar, where we meet a species capabable of telepathic communication with one another. That seems to be a minor theme in the Star Trek universe. Of course we have the Vulcan Mind Meld, which made its appearance with Spock in the original series and shows up a few time in Enterprise, but it seems to be assymetric; the party who initiates the meld can read the other’s mind, and even place thoughts in it, but not vice versa. Then we have the telepatic conversations between Deanna Troi and her mother in The Next Generation.

These conversations are presented to us as that, verbal interchanges; except that the parties don’t move their lips and may not even be in one another’s presence. The voices are simply in voiceover; we are to understand that no one hears anything, that this is telepathic?

Is that how telepathy would work? I’m not suggesting that the makes of these programs are seriously proposing telepathic communication as a human possibility any more than they are proposing warp drive of subspace communication as serious possibities. It’s pure fiction, not a real (in some way or other) proposal such as those Llinás, Koch, and Musk (seem to) have made about brain-to-brain communication. If telepathy were possible, is this how it would go, silent conversation? After all, there are silent conversations taking place all around us on cell phones and the like. We don’t hear them because they’re being carried by radio waves, which we cannot hear. Would that be possible with telepathic waves?

My first reaction – and this is important – upon posing the question (last night) is, sure. There’d have to be some physical means of sending and receiving telepathic waves but, given then, such silent conversation would be possible. After I’d thought about that for awhile, though, I began reconsidering.

Just what signal is being sent and received? The waves that make up real conversation arise when we send neural impulses to various muscles – in the tongue, jaw, truck, etc. – to drive vocalization. They are then picked up in the ear, which transduces them into neural impulses. But none of that would happen in the hypothesized telepathic communication. What gets telepathically broadcast? Perhaps the motor impulses. How would the other party interpret them, with the auditory cortex? Maybe what gets sent and received is some modally neutral signal, neither motor nor auditory? But of course, if telepathic communication were real, brains and nervous systems would have evolved accordingly. In addition to motoric sending and auditory receiving, there’d be telepathic sending and receiving.

What is it that would be telephatically send and received? Would it be telepathic word forms, that is signifiers, or would the actual meanings be sent (the signifieds)? I think it would have to be the signifiers, and only the signifieds. Why, because word meanings aren’t going to be neat little informatic packets like word forms. That is, the existence of words as discrete entities invites us to believe that word meaning are gathered up in discrete packets of meaning. But there is no reason [that is, beyond our desire that things be so simple] to think things work like that.

Rather word meanings reside in neural nets where the meaning is a function of relationships in the net. Word meaning is not separable from the net itself. That, alas, is not something that is easily explained in a blog post or, for that matter, at all. At this point I think you have to actively work with semantic or cognitve network models to get a sense of what’s going on. Sydney Lamb exaplains that in Pathways to the Brain (1999) and I say a bit more in my post, 2. The brain, the mind, and GPT-3: Dimensions and conceptual spaces (August 2, 2020).]

As a crude analogy, imagine a pond as the repository of meaning. Word forms (signifiers) are pebbles dropped into the pond. When a pebble hits the pond’s surface ripples travel outward from the point. As successive pebbles hit the service each of them sends ripples as well and the ripples interact with one another. It’s those interactions of the ripples that embodies the meaning of the word stream (that is, the succession of pebbles).

At this point we seem to have lost the thread of our original inquiry, about telepathic communication. That’s how it goes. Thinking these things through is tough and you cannot predict where things will go.

On the incoherence of the idea of brain-to-brain thought transfer [once again]

Once again I find myself thinking about the (im)plausibility of direct brain-to-brain communication of thoughts, most recently hyped by Elon Musk in conjunction with his Neuralink project. What I’m wondering is why Musk hasn’t (seemed to have) thought about the most obvious objections to the project, and not only Musk, but others who have proposed such linkage (Christof Koch and Rodolfo Llinás). I’ve addressed that issue once before (Brain-to-brain thought transfer @ 3QD) but I’d like to take another shot at it.

Let’s start by observing that the ideas of “thoughts” and “thinking” are commonsense ideas and have not been defined in neural terms. There is thus a conceptual GAP between them and the terms used talk about brains, even in the most casual way. To connect the two realms one must think it through. And just what does that mean? What items in each realm must be considered?

It’s all well a good to say that thoughts and thinking exist in the brain, but that assertion, with which I occur, doesn’t tell us how those things exist in the brain. How many neurons does it take to support a single thought – one, 10, 89, 3000, more? Do they have to be in the same region of the brain or can they be spread out? If so, how far, a hemisphere, the whole brain? How do thoughts flow through axons? Can a whole thought be squeezed through a single axon? These questions may seem (faintly) ridiculous, but are they? What is clear is they aren’t the terms in which neuroscientists investigate the brain.

And that’s fine, really. I have no problem with that. But it does mean that the relationship between thought/thinking and neurons is left undefined. Talk of brain-to-brain linkage, however, takes place in terms of neurons and brain tissue. Such talk tends to be rather loose and vague, though talk of more modest neural interfaces (which, for example, allow for neural control of prosthetic limbs) is quite precise; such things, after all, have actually been built.

Here’s what I suspect is going on. When these people – Musk, Koch, Llinás – think about the issue they start with the existence of various technology connecting brains with outside devices of one kind or another. These exist in one form or another. They then imagine having a whole lot of them (Koch writes of 10s of millions) running between two brains. That sets up (some kind of) a communication channel between the brains. They then apply what cognitive scientists call the conduit metaphor and, viola! direct brain-to-brain thought transfer. Of course they don’t explicitly think, “and now we apply the conduit metaphor” – that’s not how these things go, is it? – they just do it.

What is the conduit metaphor? (See this post for a more careful explanation of it.) It is the idea that we communicate with one another through some kind of conduit, often imaginary. After all, when we converse – surely the prototypical case of human communication – there is nothing between us but the air. Note, though, that all we exchange directly in communication are word forms. The ideas aren’t themselves in those word forms. Rather, ideas are associated with them in the processes of comprehension or production. But those ideas exist only in our heads (minds, brains), not in the signals.

When we apply the conduit metaphor to this artificially constructed brain-to-brain communication channel, thought transfer is automatic. Note in particular that the problem I identified – in this post at 3 Quarks Daily and earlier at this post here at New Savanna – is that a brain has no way of telling whether a neural impulse comes from within itself or from another brain, or, for that matter, an investigator or surgeon zapping the brain with an electrical current. Regardless of where it comes from, an impulse is an impulse is an impulse. They’re all alike. If neural impulses can’t be separated into mine and thine, then how can there be thought transfer?

But – and here’s the important point – this problem doesn’t arise in ordinary speech communication. You know who you’re talking to and there’s no trouble distinguishing their words from yours. Thus there is nothing in conduit metaphor to tell you to check who’s sending a given signal. The conduit metaphor simply isn’t rich enough to handle the problem of direct brain-to-brain communication.

The tech is over-hyped, so is there a business case for Musk's Neuralink?

In an article published in The Baffler, Shit for Brains, Danielle Carr does a good job of deflating Elon Musk's claims for Neuralink and of laying out the history of José Delgado's excursions into the same territory a half century ago. She ends with some speculation about how he might recoup his investment:

While it’s not immediately obvious what business models will emerge to capitalize on neural data, a rough shape of the answer is suggested by Rune Labs, a tech venture founded by an alumnus of Alphabet’s bioscientific wing Verily Life Sciences. Most medical device manufacturers are behemoths of the old economy, lacking the resources to curate the vast amounts of data their devices generate. Ditto for university researchers, who rarely have the margins in their research grants to purchase or build the computational tools necessary to correlate large quantities of behavioral and neural data. Enter Rune Labs, which offers device manufacturers and researchers a deal: give us access to the data generated by your neural implants, and in exchange, we will provide access to state of the art data storage and computation.

From their end, Rune Labs has developed a variety of phone apps to glean data about self-reported mood. (Similar research is ongoing in “digital psychiatry” to build apps that harvest data about everything from voice modulation to exercise which can then be coupled to the information about brain activity gleaned from neural devices.) The only restriction on Rune’s use of neural device data is that they have to keep patients anonymous. More and more, this looks like the business model that will define neural implants. As Alik Widge remarked, “The idea that your data is the product is already here with brain implants. Neuropace has already said that they’re moving toward being less of an implant company and more a brain data company.”

Of all the wild speculations Elon Musk made during the Neuralink launch, the most accurate prediction was his quip that the device is “sort of like if your phone went in your brain.” “Sort of like,” indeed: Neuralink is like a phone in that it is yet another machine built for generating data. While the device does not represent a major advance in brain-machine interfaces, and the pipeline for applications beyond movement disorders is at best decades long, what Neuralink does offer is an opportunity to harvest data about the brain and couple it to the kinds of data about our choices and behaviors that are already being collected all the time. The device is best understood not as a rupture with the past, but as an intensification of the forms of surveillance and data accumulation that have come to define our everyday lives.

H/t Leanne Ogasawara.

Why, in the course of an intellectual life, can it take years to see the obvious?

Give me a place where I can stand—and I shall move the world.

– Archimedes

I’m thinking of my own intellectual life, of course. And I have two examples in mind, 1) my realization that literary form was at the center of my interest in literature, and 2) my recent realization about the impossibility of direct brain-to-brain thought transmission.

Literary form

My first major piece of intellectual work was my 1972 MA thesis on “Kubla Khan.” That thesis focused on the poem’s form and in a sense set the direction for my career, and it led me to focus on computation and the cognitive sciences. In graduate school at SUNY Buffalo I wrote papers that were concerned with form, on Sir Gawain and the Green Knight, Much Ado About Nothing, Othello, “The Cat and the Moon”, and Wuthering Heights. I revised the Sir Gawain paper and published it at the time [1], and some years later material from the two Shakespeare papers was the basis of a publication [2]. I also published a paper about narrative form that based, in part, on my 1978 dissertation. So I was examining form from the beginning and yet I didn’t realize that it was the center of my focus.

It wasn’t until the mid 1990s that I realized that it was form I was looking at all along [4]. And that realization came about indirectly. In cruising the web I discovered that the Stanford Humanities Review had devoted an issue to cognitive science an literature. Herbert Simon had written an article setting forth his views [5] and 33 critics had responded. Obviously there was now a group of literary scholars interested in cognitive science. I read their stuff, went to a couple of conferences some of these people attended (Haj Ross’s Languaging conferences at North Texas) and realized that these people were not at all interested in the things I was.

First and foremost, their version of cognitive science did not include computation, which was central to my interest. It was in the course of thinking that through that I realized that they weren’t interested in form either, but I was. And now that I thought about it, form was central to my work in literature, wasn’t it? That puts we into the late 1990s, when I took a detour from literature to write a book on music. So it wasn’t until the early 2000s that I was able focus on form, when I returned to “Kubla Khan” and then “This Lime-Tree Bower My Prison” with articles in PsyArt: An Online Journal for the Psychological Study of the Arts, and culminating in 2006 article on literary morphology, where I put form front and center [6].

When it was there from the beginning, why did it take me so long to get there?

Brain-to-brain thought transmission

The second case is brain-to-brain thought transmission. I first took the matter up in January 2002 when I posted a thought experiment to Brainstorms, an online community established by Howard Rheingold. In that experiment I imagined we had the technology to do it without harming brain tissue, but how do we determine which neurons to link together in the respective brains? Since brains are unique there is no inherent unique mapping between neurons in two brains. This is unlike the situation with gross body parts, where one person’s right thumb corresponds to another person’s right thumb, and so forth.

It was until a decade later, in 2013, when I put the thought experiment online [7] that I imagined a much simpler and more direct counter argument: How does a brain tell where a given impulse comes from? Brains have no mechanisms for distinguishing between endogenous and exogenous impulses.

Why did it take me a decade to move from the complex argument to the simple one?

What’s going on?

I don’t really know. But it must be a function of how one’s mind is set-up when you first approach a problem. In the case of literary texts, literary criticism is about meaning; that’s what you’re taught in school. More than that, when we read any text for whatever purpose, we’re reading it for meaning. That’s the orientation. In literary study one learns about form, of course, but you don’t focus on it in an analytic or descriptive way.

So, to focus on form I had to break away from my prior training, but also from my natural inclination toward texts. And, come to think of it, this is not simply a matter of will, but of method as well. By the time I made the break I had several examples of close formal analysis from my own work. Those gave me a standpoint from which to make the break.

Is it the same with brain-to-brain thought transmission? That’s not a topic that normally comes up in the study of neuroscience. No one is examining what happens when you link two brains together so we don’t have any specific framework at all. What do we do? We apply a default framework of some kind? Where do we get that framework? Most likely from our experience with electrical and electronic circuits, especially in computers.

And that’s not a useful framework at all. In fact it gets in the way because brain circuitry is not at all like neural circuitry. Anyone who studies neuroscience knows this, of course, but they may not have the knowledge linked strongly to this kind of problem. In my case I had my conversations with Walter Freeman on the uniqueness of brains, and that focused my attention on neurons and on comparison between brains at the single neuron level. Thought was enough to bring me to the realization that we had no coherent and consistent way to link two brains together on the scale of 10s of millions of neurons.

But that wasn’t enough to take me the whole way to the realization that there is no way for brains to identify foreign spikes. But I did do something like that in my review of Auger’s Electric Meme, which I also wrote in 2002 [8]. But why did it take me a decade to connect the two together? What was my new standpoint?

References

[1] William Benzon, Sir Gawain and the Green Knight and the Semiotics of Ontology, Semiotica, 3/4, 1977, 267-293, https://www.academia.edu/238607/Sir_Gawain_and_the_Green_Knight_and_the_Semiotics_of_Ontology/.

[2] William Benzon, At the Edge of the Modern, or Why is Prospero Shakespeare's Greatest Creation? Journal of Social and Evolutionary Systems, 21 (3): 259-279, 1998, https://www.academia.edu/235334/At_the_Edge_of_the_Modern_or_Why_is_Prospero_Shakespeares_Greatest_Creation.

[3] William Benzon, The Evolution of Narrative and the Self, Journal of Social and Evolutionary Systems, 16( 2): 129-155, 1993, https://www.academia.edu/235114/The_Evolution_of_Narrative_and_the_Self.

[4] See these two blog posts, William Benzon, How I discovered the structure of “Kubla Khan” & came to realize the importance of description, blog post, New Savanna, October 9, 2017, http://new-savanna.blogspot.com/2017/10/how-i-discovered-structure-of-kubla.html; Things change, but sometimes they don’t: On the difference between learning about and living through [revising your priors and the way of the world], blog post, New Savanna, July 26, 2020, http://new-savanna.blogspot.com/2020/07/things-change-but-sometimes-they-dont.html.

[5] Herbert Simon, “Literary Criticism: A Cognitive Approach.” Stanford Humanities Review 4, No. 1, (1994) 1-26.

[6] William Benzon, Literary Morphology: Nine Propositions in a Naturalist Theory of Form, PsyArt: An Online Journal for the Psychological Study of the Arts, August 2006, Article 060608, https://www.academia.edu/235110/Literary_Morphology_Nine_Propositions_in_a_Naturalist_Theory_of_Form.

[7] William Benzon, Why we'll never be able to build technology for Direct Brain-to-Brain Communication, blog post, New Savanna, September 26, 2018, http://new-savanna.blogspot.com/2013/05/why-well-never-be-able-to-build.html.

[8] William L. Benzon, Colorless Green Homunculi, Human Nature Review 2 (2002) 454-462, https://www.academia.edu/41181169/Colorless_Green_Homunculi.

Direct Brain-to-Brain Thought Transfer: A High-Tech Fantasy that Won’t Work

New working paper. Title above, abstract, contents, and introduction below. Download at:

• Academia: https://www.academia.edu/44109360/Direct_Brain_to_Brain_Thought_Transfer_A_High_Tech_Fantasy_that_Wont_Work
• SSRN: https://ssrn.com/abstract=3694662
• ResearchGate: https://www.researchgate.net/publication/344288986_Direct_Brain-to-Brain_Thought_Transfer_A_High_Tech_Fantasy_that_Won't_Work

Abstract: Various thinkers (Rodolfo Llinás, Christof Koch, and Elon Musk) have proposed that, in the future, it would be possible to link two or more human brains directly together so that people could communicate without the need for language or any other conventional means of communication. These proposals fail to provide a means by which a brain can determine whether or not a neural impulse is endogenous or exogenous. That failure makes communication impossible. Confusion would the more likely result of such linkage. Moreover, in providing a rationale for his proposal, Musk assumes a mistaken view of how language works, a view cognitive linguists call the conduit metaphor. Finally, all these thinkers assume that we know what thoughts are in neural terms. We don’t.

Contents

Does it even make sense to wire brains together? 2

Direct Brain-to-Brain Thought Transfer is a High Tech Fantasy that Won’t Work 3

Christof Koch and brain-bridging 3
The brain in two worlds 6
Musk doesn’t understand how language works 8
The hazzards of thinking at the edge of possibility 11

The peril of getting mixed up in your model 12

What’s a thought? 15

The neural code meets Sydney Lamb’s daughter 15
Vygotsky on the development of speech and inner speech 18

Does it even make sense to wire brains together?

It is one thing to feature technology for direct brain-to-brain communication as an element in science fiction. It is another thing to propose that, some day, we’ll actually be able to do it. The former doesn’t require any argument based on what we know about the brain and about fabricating electronic devices. The latter does.

Yet, when people have proposed such technology in reality – I’m thinking of Rodolfo Llinás, Christof Koch, and Elon Musk – the argumentation is thin and focuses on the technology. No one asserts that we have such technology now, but some day, in the indefinite future, surely we will, and then we will be able link people together so that they can share their thoughts directly, brain-to-brain, bypassing ordinary means of communication and interaction. I first considered such ideas early in 2002 in a thought experiment in which I simply assumed we had such technology. I concluded that it would not support brain-to-brain transfer. Brains don’t work in the way such technology requires.

That thought experiment thus taught me to distinguish two reasons for skepticism about the feasibility of direct brain-to-brain thought transfer:

1. we don’t have the technology to establish millions of point-to-point connections between two brains, and
2. the idea is unworkable in principle.

The thought experiment eliminated the first so that I could investigate the possibility of the second. I concluded that the idea is indeed unworkable in principle.

This working paper is in three sections. The first section, “Direct Brain-to-Brain Thought Transfer is a High Tech Fantasy that Won’t Work,” repeats and elaborates on that orignal the thought experiment, drawing on quotations from Christof Koch and Elon Musk to motivate the argument. The second section, “The peril of getting mixed up in your model,” is methodological in character and talks of the difficulty of separating the capabilities of a proposed model of some cognitive or mental phenomenon, such a brain-to-brain communication, and what what knows, as an external observer, about the model. Vague, I know, that’s the nature of the problem, but I think that this vague business about the relation between model and modeller, this is at the heart of why this is such a tricky issue.

The final section, “What’s a thought?” is about what we mean when we talk about thinking, and comes at it from two sides, the problematic use of computation as a metaphor, and language development. The notion of thought is very general, but is for the most part a common sense term for “what happens inside our heads, where we have feeling as well.” Using common sense ideas in technical discussions – and direct linkage between brains is a technical issue even if we grant the existence of the relevant technology – is tricky because the relationship between the common sense ideas and the technical realm needs to be carefully specified. That is not the case in these brain-to-brain proposals.

Brain-to-brain thought transfer @ 3QD

My current post at 3 Quarks Daily is about a subject I’ve been thinking about since early in 2002, direct transfer of thought between people through technology linking their brains together:

• Direct Brain-to-Brain Thought Transfer is a High Tech Fantasy that Won’t Work.

According to my notes I “ran” a thought experiment on the matter and concluded that it was impossible. That was some time in January of 2002. I subsequently posted that Gedankenexperiment here at New Savanna in May 2013 and have been revisiting the topic now and then.

In that original thought experiment I assumed that the relevant technology was available – though in fact creating it is something we could not do then and still cannot – because that’s what you do in thought experiments. You create a highly constrained artificial situation – in this case, we can couple two brains together at the neuron level for millions and millions of neurons – so that you can explore something else – in this case, the effect of such coupling. Here’s my conclusion from that original thought experiment:

Given our Magic-Mega-Point-to-Point (MMPTP) coupling, how do we match the neurons in one brain to those in another? For each strand in this cable is going to run from one neuron to another. If our nervous system were like that of C. elegans, there would be no problem. For that nervous system is very small and each neuron has a unique identity. It would be easy to match neurons in different individuals of C. elegans. But human brains are not like that. Individual neurons do not have individual identities. There is no way to match the neurons in one brain with those in another.

What, then, happens when we couple two people through a MMPTP? Each experiences a bunch of noise, that’s what. I haven’t got the foggiest idea how that noise will feel. Maybe it will just blur things up; but it might also cause massive confusion and bring perception, thought, and action to a crashing halt. But it won’t yield the intimate and intuitive communion of one mind with another.

More recently I realized that there’s a simpler problem: “How does a brain tell whether or not a given neural impulse comes from it or from the other brain? If it can’t make the distinction, how can communication take place?”

Here’s my problem: Given that direct brain-to-brain coupling won’t work, why does it keep coming up, not in science fiction, where such things are fair game, but in real proposals from intelligent thinkers (Elon Musk, Christoph Koch, or Rodolfo Llinás) who, it seems to me, ought to know better? None of the proposals I’ve seen counter the objections I’ve uncovered in my thought experiment. Now, let me be clear, I’m not complaining that they haven’t read that thought experiment. I’ve only posted it in two places, a private online venue back in 2002 (Howard Rheingold’s Brainstorms) and here at New Savanna, which doesn’t get much traffic. No, my complaint is that they haven’t seemed to have thought of those objections themselves.

One possibility is that my reasoning is faulty. If so, I’d like to know where I went wrong. Pending that, however, I have another suggestion.

Of course, it’s a cool idea. But there’s something else, something that William Powers expressed in a letter to me over four decades ago. The letter was a response to an article on Shakespeare’s Sonnet 129 I’d published in MLN in 1976 [1]. Here’s what Powers said [2]:

There are always two levels of modeling going on. At one level, modelling consists of constructing a structure that, by its own rules, would behave like the system being modelled, and if one is lucky produce that behavior by the same means (the same inner processes) as the system being modelled. That kind of model “runs by itself”; given initial conditions, the rules of the model will generate behavior.

But the other kind of modelling is always done at the same time: the modeller provides for himself some symbolic scheme together with rules for manipulating the symbols, for the purpose of reasoning about the other kind of model. [...]

The biggest problem in modeling is to remain aware of which model one is dealing with. Am I inside my own head reasoning about the model, or am I inside the model applying its rules to its experiences? This is especially difficult to keep straight when one is talking about cognitive processes; unless one is vividly aware of the problem one can shift back and forth between the two modes of modeling without realizing it.

That, that last paragraph, is where I think the problem lies. These various suggestions for direct brain-to-brain linkage hardly have anything explicit enough to be called a model. They’re verbal suggestions and little more. Beyond the idea that we’re going to have a lot of wires running between these two brains, or perhaps high-bandwidth radio transmission between two brain interfaces, there’s nothing to these ideas. When, almost 20 years ago, I asked, How are we going to match-up neurons in the two brains? I slipped into a level of detail which, as far as I can tell, none of these other thinkers have broached.

Consequently, they’ve lost sight of the distinction Powers makes between the object model, with its own explicit rules, and the thinker’s own “symbolic scheme together with rules for manipulating the symbols, for the purpose of reasoning about the other kind of model.” That is to say, it is obvious to us, standing outside the two linked brains and observing them, that in each brain, some impulses arise within the brain while others originate in the other brain. The fact that we know that does not, however, mean that the two brains know it. Therein lies the problem. It simply hadn’t occurred to Musk, Koch, or Llinás that they were, in effect, projecting their own knowledge about the situation onto the brains they’ve coupled together, if only in their imaginations.

Both Koch and Llinás are distinguished neuroscientists. But they didn’t establish their reputations on the basis of such speculations. They earned their reputations for thorough empirical investigation and modeling of somewhat more limited imaginative scope, if you will. The problem Powers outlined is much less likely to occur in that work. As for Musk, as far as I know, he has no particular expertise in neuroscience at all. And yet, as an engineer and entrepreneur who must have a keen appreciation for the distinction between plans and designs and their physical realization, I almost expect more from him on this issue than the neuroscientists.

* * * * *

[1] William Benzon, Cognitive Networks and Literary Semantics, MLN 91: 1976, 952-982, https://www.academia.edu/235111/Cognitive_Networks_and_Literary_Semantics.

[2] William T. Powers, “Powers on Benzon and Models”, MLN, Vol. 91, No. 6, Comparative Literature (Dec., 1976), pp. 1612-1619, http://www.jstor.org/stable/2907155.

Rambling into Fall: Facebook & the interface, brain-to-brain communication, AI and rank 5 culture

A lot’s happened since my last ramble (August 22). I’ve changed my attitude toward AI; Elon Music and Christof Koch have triggered one of my hobby horses, direct brain-to-brain thought transfer; and Facebook has decided to change its user interface. It’s start with the last and work through them in reverse order.

Facebook! Don’t mess with my mind!

Facebook has announced that it’s changing its interface. It’s done that to some people already. Others, like me, have been warned. But our respite is only temporary.

I’ve objected to this on two grounds: 1) it’s inconvenient, and 2) general principle: it’s my mind. The first is real but not, at this point, all that consequential. I neither like nor dislike the current interface; it is simply what I’m used to. Changing it will force me to think a bit where now I can act intuitively and automatically. While I don’t know how bad that will be – I do quite a bit on Facebook – I don’t anticipate major problems. Though, who knows, maybe they’ll cripple a feature I like. Twitter did that with their last interface change. I like the Moments feature, where I could group tweets under a single topic. Contrary to what it says in the help notes, it is no longer possible to add new tweets to existing moments. Has Facebook done something similar? I don’t know.

However, I think the matter of principle is deeper and thus more important. But I’m not sure how to argue it. What I’ve said is Facebook is messing with my mind, and I’ve justified that by reference to the idea that all sorts of media, books, sound recordings, movies, etc., are extensions of our minds. I’m wondering if that’s quite right?

What I’m thinking at the moment is that we think of minds as essentially private. You can’t read my mind and I can’t read yours. And, yes, that is true. But aren’t minds fundamentally social as well?

Wittgenstein famously argued that there is no such thing as a private language. Hence language is inherently social. Inner speech is a form of language and hence is, in some way, social and not merely private. That needs clarification and development, but I’m thinking that maybe that’s the way to go.

Social media are also private media, in a sense. I know Facebook has a bunch of settings you can use to control who sees what. I’ve looked at them once or twice in very specific situations, but for the most part I’ve not paid any attention to them because I don’t use Facebook for more or less private purposes. I’m a thinker and a writer and I’m happy to do both in public. That is, it’s my mind, but I’m using it in public and in a public way.

This line of thought goes in two directions: 1) the institutional and civic nature of social media, and 2) the nature of the technology. On the first, it’s not at all clear to me that our current institutional structure is adequate for using and governing this technology. On the second, I think the user needs to be in control of the interface (beyond the blither of settings FB makes available) in a way that requires perhaps a substantial rethinking of the nature of the technology. I suspect that AI will play a role in the future realignment.

Brain-to-brain thought transfer

The idea’s been around for awhile, along with the idea of direct brain-to-machine linkage. I don’t know when it first showed up in science fiction, or how wide spread it is, but I first became aware of it as a scientific and technical proposal in the work of Rodolfo Llinás in the mid-2000s. More recently Christof Koch and, most visibly, Elon Musk have joined the parade.

I don’t get it. There is an obvious technical objection which, as far as I can tell, none of these thinkers have addressed: how does a neuron tell whether an impulse is coming from some other neuron in the same brain or some other brain? If neurons can’t tell the difference, how can there possibly be thought transfer? I’ve offered a more elaborate and explicit argument, but that should be enough. Yet it hasn’t even been considered?

I don't know. I note that no one is suggesting it as something that will be done in the near-term or even mid-term future – though come to think of it, yikes!, Musk told Joe Rogan five to ten years – rather, it’s kinda’ far in the future where who knows what will happen. That is, it’s in a realm where we can’t really think about things rigorously. So, yeah! let’s throw caution to the winds.

Anyhow, it’s right up there with other fantasies of tech-bro religion, the super-intelligent computer (malevolent or benevolent) and uploading the contents of one’s brain to the cloud and thus becoming immortal.

AI and the future of technology

Finally, just the other day I started talking about the evolution of rank 5 culture and linking that, however loosely, with AI. Rather, it’s something a bit beyond AI. For AI is just a bunch of techniques. I’m looking for some one thing, comparable to the role of writing in the emergence of rank 2 or Arabic notation in the emergence of rank 3, etc. Whatever this new thing is, it is as likely to emerge out AI as anywhere else.

To get there AI has to break free of its chess-centric ways and develop a bit of curiosity about the basis of current statistical success, which I’ve been pointing toward in my recent thinking about GPT-3: GPT-3: Waterloo or Rubicon? Here be Dragons, and What economic growth and statistical semantics tell us about the structure of the world. As I said before, don’t believe the hype, but there’s a breakthrough there. We just have to find it.

More later.

Elon Musk understands the technical challenges of going to Mars, but he’s in the dark about direct brain-to-brain thought transmission

For reasons I’m about to explain, I’m pretty sure about that. What I’m wondering is how Musk himself compares the two. He has declared publicly that he’s going after both goals and is sure of reaching them, though he’s talked considerably more about Mars than about the mind, but what does he think privately? I haven’t got the foggiest idea. But I do believe that if he had a more realistic understanding of what’s involved in direct brain-to-brain (B2B) thought transmission he would not be espousing it as the ultimate goal of Neuralink.

To Mars

We have a very rich framework in which to think about manned missions to Mars. We’ve already landed men on the moon and returned them to earth. Astronauts have spent weeks and months, even a year, living in low-earth orbit in the International Space Station. We’ve landed robots on Mars and gotten useful information back. All of that experience is relevant to sending humans to Mars and – here’s the point – we’ve got frameworks in which we can evaluate that experience against the requirements of a manned mission to Mars. We have a rich understanding of the mechanical, kinematic, chemical, thermodynamic, electrical, and electronic principles involved in building the devices needed to perform the mission. We also know quite a bit about the biological and psychological requirements of supporting human life for such a mission.

Musk knows all that, certainly better than I do and perhaps as well as if not even better than any other single human currently alive. He’s trained in science and engineering and has experience in both arenas. While I have reservations about the business case for going to Mars (see Steve Rosenbaum’s remarks in the video below), the technical case seems reasonable to me.

B2B thought transmission

But he has little or no training in neuroscience (full disclosure, neither do I), though he has hired specialists for Neuralink. Of course, one doesn’t have to have formal training in an area in order to develop expertise in it. Just how much expertise Musk has developed, I don’t know.

I see two kinds of issues in developing technology for B2B thought transmission: 1) matters of principle, and 2) the technology itself. If I am right about the principle, then it doesn’t matter what kind of technology Musk, or anyone else, develops. It won’t work. But let’s set that aside for the moment.

The basic problem with developing technology is that, 1) the brain is relatively inaccessible beneath the skill, 2) neurons are very small, and 3) we don’t understand how neurons and the brain work. In contrast to, say, the physics and mechanics of rocket technology, our knowledge of basic principles is still poor.

I believe that there has been a fair amount of work on brain implants of one kind or another going back to the 1950s with the work of Jose Delgado. As far as know I we have yet to see the full-time routine implantation of a device in the brain that either controls motor activity or provides sensory input. There have been limited laboratory experiments with animals and medical experiments, but no permanent clinical use.

There are various problems. The brain is a hostile environment for anything we implant. Neurons are very small, so accurate targeting is extremely difficult. Moreover, interpreting neural impulses is difficult, as is generating artificial impulses to the muscles. And then there is the problem of numbers: The brain has roughly 86 billion neurons, each of then connected to 10,000 neurons on average. How many neurons are we going to have to tap for B2B thought transmission? 100 million? A billion? All of them? What kind of hardware would allow us to do this without shredding the brain? Neuralink’s current interface has 1000 electrodes, which is far from a million, let alone a billion.

That’s one problem. But let us assume we can link however many neurons are necessary for B2B communication without in any way harming the brain. It’s not at all clear to me that this would work. Why?

When the interface is activated, both brains will be receiving millions upon millions of impulses they had never before received. If thoughts are to be exchanged, then each brain must be able to tell which impulses are its own and which are coming from the other brain? How are the brains going to do that? If they can’t then how can they exchange thoughts? The idea doesn’t make any sense. As far as I can tell, the most likely result of such an interface would be confusion and perhaps, over time, the two individuals would learn how to function under these conditions, but this is by no means certain. I discuss this issue at greater length here, Why we'll never be able to build technology for Direct Brain-to-Brain Communication.

As far as I know, neither Musk, nor anyone else in this arena, such as Christof Koch or Rodolfo Llinás, has addressed this issue. It doesn’t seem to have occurred to them.

What does Elon believe?

Here then is my question: Does Musk think these two challenges, manned missions to Mars and direct B2B communication, are of roughly equivalent difficulty? It’s one thing to pursue both believing that one is merely of moon-shot difficulty while the other is out there beyond the twilight zone. That seems risky and foolish, but at least Musk knows it’s risky and foolish.

It’s quite something else to believe they are on roughly the same footing or that B2B communication is perhaps more difficult than going to Mars, but not THAT much more difficult. If THAT is what Musk believes, why? In one case, Mars, he can spell out the technical challenges in great detail and list possible solutions. In the other case, B2B thought transfer, there’s a whole lot of nothing that he’s overlooking. The list of potential problems is long and open-ended and the list of potential solutions is, at best, a work of fiction.