AI is infusing our daily lives. Is it an existential threat?

As AI is being subtly melded into our daily lives by the Google, Apple, and Microsoft clouds there is continuing debate over whether it is or Isn’t an existential threat. This debate has been brought to a head by the sudden and unexpected firing of Open AI’s chief executive Sam Altman and has starkly exposed the philosophical rift in the AI community between those who are more or less concerned about the potential risks of its untrammeled development. How many, if any, guardrails should be in place?. I have been on the techno-optimists side of this debate and passed on Marc Andreesen's rather extreme and overstated SubStack post titled "Why AI Will Save the World" in a June 23 MindBlog post. I was then completely horrified by his subsequent piece by this ideologist of the Silicon Valley elite titled "Fighting", which took a very positive view of Elon Musk challenging Mark Zuckerbert to a cage fight. I was going to write a piece on his dystopian future world, but two NYTimes pieces have done a better job at it than I could. They are Elizabeth Spiers "A Tech Overlord’s Horrifying, Silly Vision for Who Should Rule the World" and Ezra Klein's "The Chief Ideologist of the Silicon Valley Elite Has Some Strange Ideas".

Personalized chatbots and wearable AI devices for melding AI with our everyday lives

Personalized GPTs are here, but apparently not yet at the level of consumers like myself who don't want to do coding. I've been looking into making my own personal chatbot whose training data is 50 MB of my personal journal of about 20,000 pages. I would then like to query and chat with the resulting chatbot about events in my life since 1980. If anyone reading this has some advice, let me know!

Below are some URLs to my morning's reading:

These links are to articles from Kevin Roose, NYTimes tech columnist:  

Personalized A.I. Agents Are Here. Is the World Ready for Them?

Kevin Roose Hard Fork podcast (transcript available) on this and other AI topics 

And this article is on AI devices seeking to cut into smartphone dominance 

It points to two such devices: The Humane Ai Pin and The Rewind AI Pendant 

Using AI to find retinal biomarkers for patient sex that opthalmologists can be trained to see.

Delavari et al. do a demonstration of using AI to find aspects of human retina images that identify whether they are male or female retinas, a distinction that had not previously been accomplished by clinical opthalmologists. Here is their abstract:

We present a structured approach to combine explainability of artificial intelligence (AI) with the scientific method for scientific discovery. We demonstrate the utility of this approach in a proof-of-concept study where we uncover biomarkers from a convolutional neural network (CNN) model trained to classify patient sex in retinal images. This is a trait that is not currently recognized by diagnosticians in retinal images, yet, one successfully classified by CNNs. Our methodology consists of four phases: In Phase 1, CNN development, we train a visual geometry group (VGG) model to recognize patient sex in retinal images. In Phase 2, Inspiration, we review visualizations obtained from post hoc interpretability tools to make observations, and articulate exploratory hypotheses. Here, we listed 14 hypotheses retinal sex differences. In Phase 3, Exploration, we test all exploratory hypotheses on an independent dataset. Out of 14 exploratory hypotheses, nine revealed significant differences. In Phase 4, Verification, we re-tested the nine flagged hypotheses on a new dataset. Five were verified, revealing (i) significantly greater length, (ii) more nodes, and (iii) more branches of retinal vasculature, (iv) greater retinal area covered by the vessels in the superior temporal quadrant, and (v) darker peripapillary region in male eyes. Finally, we trained a group of ophthalmologists (⁠N=26⁠) to recognize the novel retinal features for sex classification. While their pretraining performance was not different from chance level or the performance of a nonexpert group (⁠N=31⁠), after training, their performance increased significantly (⁠p<0.001⁠, d=2.63⁠). These findings showcase the potential for retinal biomarker discovery through CNN applications, with the added utility of empowering medical practitioners with new diagnostic capabilities to enhance their clinical toolkit.

Fluid Fogs and Fixed Flows

For the MindBlog readers who may have have noted my  "This is the New 'Real World' post and followed me down the rabbit hole of Ventkatesh Rao’s narrative worlds, I pass on the following abstracting  of his 9/23/2023  installment, titled “Fluid Fogs and Fixed Flows,”  which reduces its length by half. I have done this mainly for my own use, to facilitate my future recall of his ideas:

Worlds and Real World

To briefly recap last week’s essay, I’m using world and real world in the sense of last week’s essay: A world is a coherent, immersive, totalizing subjectivity you can inhabit, as a sort of cognitive indoors. The real world is the corresponding outdoors — the messy union of the dozen or so most consequential worlds in existence at any given time.

The process by which the real world emerges, as a negotiation among worlds, is one that makes it qualitatively different. In brief, regular worlds are finite games, while the real world is the infinite game.

Weirdness, Fog, and Unnarratability

The relationship between weirdness, brain fog, and unnarratability is something like the relationship between a crisis, the panic it induces, and the solvability of the crisis.

World-brain fog affects those in a given world. Real-world-brain fog affects everybody. For us individual sentient elements of these world-brains, this fog manifests as the spectacle of history becoming incoherent.

Fog vs. Flow

The opposite of brain fog is flow. When thoughts flow easily, clearly, and tastefully, from one idea to the right next idea...Where the one-step-at-a-time ethos I identified earlier in this series as the essence of never-ending stories is not just all you can live by, it’s all you need to live by.

To be clear, I’m not saying fog is bad and flow is good. That would be like saying clear weather is good and storms are bad. This is just a pair of opposed subjective cognitive conditions. Setting aside valuative judgments, my claim is that the real-world-brain is in a state of fog rather than flow right now.

To say that the real world is suffering from world-brain fog is to say that the infinite game is in narrative trouble, regardless of the prevailing calculus of winners and losers in the constituent finite games. The question of winners and losers is distinct from the question of whether the game can be continued. The real world being foggy means it is hard to see how to continue the game, whether you’re currently winning or losing any specific game.

Okay, so that’s the big thesis here: history feels far more unnarratable than it needs to, because the real world is suffering from world-brain fog. If we can get rid of this fog, we’ll still have to deal with the objective realities of the permaweird, but that might be an easier task.

Individual Fogs

To think through the idea of a foggy real-world brain, it’s useful to think about the more familiar individual-brain phenomenon.

I’ll use myself as an example to analyze these factors...Looking back 10 years at my 2013 archives, 2023’s output of words feels like a congealed sludge by comparison. ..The sludginess of 2023 seems to afflict all words being produced by everybody.

In the last couple of years, this god-like situation awareness of the broad currents of my own thought has become dissipated, fragmented, and yes, foggy. I often forget obvious connections I’ve made in the past, or worse, fail to make them in the first place. Sometimes I forget entire trails of thought I’ve gone down, over multiple essays. Sometimes I clumsily re-think thoughts I’ve previously thought in more elegant ways. There is no sense of a longer compounding journey unfolding over years and millions of words. Instead, there is a sense of a random walk comprising individual short outings of a few thousand words. When the fog is at its worst, the 2 million words seem like so much rubble.

So my individual brain fog in the sense of such missed connections and missed opportunities for emergence is bad for the kind of thinking and writing I do. The fog/flow pair is neutral, but for certain kinds of activity, such as thinking and writing in hypertext-augmented ways, fog is very bad. Just like literal fog is very bad for ships trying to navigate treacherous waters.

The largest fraction of the value of writing online in a densely internally linked way³ lies in the larger structures of thought that emerge. I’ve previously jokingly referred to my personal instance of this as the “extended ribbonfarm blogamatic universe,” but more seriously and generally, we might call these personal protocol narratives. It’s a particular way of being in the infinite game, one step at a time, that’s available to writers. Anyone who writes primarily online, with a lot of nonlinear internal hyperlinking, has a personal protocol narrative by default. Traditional writers usually don’t, unless they work extra hard for it⁴ (something I'm too lazy to do, which makes me think in a non-internet world, I wouldn’t be a writer).

This superpower is the reason people like me eschew traditional media and book-writing and largely stick to blogs, microblogs, and newsletters. Not only is the emergent landscape the bulk of the value, it is the main enabling factor in its own creation. I can write in certain ways because I have this evolving canvas doing most of the work. If this emergent landscape of thought starts to disappear, the whole thing falls apart.

And while hypertext is a powerful brain-augmentation technology, it can’t defend against all cognitive afflictions. In particular, brain fog is deadly. It weakens your ability to make new internal links, and as a result makes the connected landscape less connected, and therefore both less useful, and less usable. Brain fog drives a vicious cycle of degeneration towards a more primitive textuality. At some point, I might have no technical advantage at all over book-writing cavemen or even typewriter-wielding Neanderthals.

Entangled Fogs

Some technologies are simply foggier than others…mail newsletter platforms are much foggier than blogs…blogs simply want to create rich internal linking..I use an order of magnitude fewer links in newsletters than in blog posts. I know this because I still retain stronger gestalt memories of my blog archives than my newsletter archives.

Biology and technology conspire to create brain fog in messy ways. When I got Covid a year ago, and experienced a few months of a more biological style brain fog, writing in my peculiar way felt insanely difficult, and what writing I was able to do was much more disconnected than my norm…much of brain fog can and should be attributed to factors in the environment. Just as your panic at being caught in a fire isn’t entirely in your head — there is actually a fire — brain fog isn’t all in your head: you’re in a foggy condition. You’re in an unnarratable world. The stories that you want to tell, and are used to telling, are suddenly less tellable.

This is where the entanglement with world-brain fog comes in.

Accounting for age, medium, and Covid-type effects, I think there remains a large unexplained factor in every case, though the fraction varies….I think there is something going on at the cultural, societal level, that makes it vastly harder to remember the gist of large bodies of information…But if I am right, unnarratability and world-brain fog should affect everybody, regardless of age and occupation, and I think I see signs everywhere that this is the case.

Fixed and Fluid Logics of Caring

Now we can ask the question. What does it mean for a world, specifically the real world to experience something analogous to what I just described at the individual level? What is world-brain fog? …And since there is nobody “there” to experience it, how does it manifest in the lives of us individuals who are like the neurons of the world brains we inhabit?

We’ve already seen one element of what it feels like. A sense that there’s more fogginess than you can attribute to your own circumstances…Here’s another: it’s hard to decide what to care about. Logics of caring are in fact essential in creating flow out of fog. The world is always complex. What you care about is what determines how simple it is for you. How you pick what to care about is your logic of caring.

…you might want a locus of care that is both stable, and world-like. This disposition is what I’m calling fixed logic of care…People with fixed logics of care love to talk about values, because that’s where the fixedness manifests explicitly.

…you might want a locus of care that follows the liveliest streams of events in the world. …You want to be where the action is, not where your values point. This disposition is a fluid logic of care.

fixed/fluid not the same as conservative/liberal,traditional/progressive, winning/losing

It might seem like I’ve set up an argument that admits no world-scale flow at all for either fixed or fluid logics of caring. This is incorrect. A few well-defined groups sneak through this sieve of constraints and appear to be experiencing world-scale flow. All of them operate by fixed logics of caring, but also have an additional factor in common: they rest atop what I call interposing intelligences.

Interposing Intelligences

The first well-defined group that seems to have retained a sense of world-scale flow is economists…anyone for whom the the global economy is the primary sense-making lens on reality…it’s all just been a game of watching various numbers go up and down in a curiously ahistorical mirror world. In that mirror world, there has been no Great Weirding.

There’s a reason for this. The economy offers one of the few remaining world-scale fixed logics of caring. To care through that logic about anything in the world at all is to care about it in economic terms. There’s even a term for this operation of bringing a matter into the fixed logic of care: “pricing it in.” To the economist-mind, economics is the primary phenomenological ground of the world. Things don’t become real until they become economically real. Intentions don’t become real until they become revealed preferences. Narratives don’t become real until they show up in indicators.

Now this is interesting. Economics seems to function in modernity as a better religion than actual religions. It allows you to have a sense of inhabiting the world rather than a besieged, paranoid corner of it. It allows you to care about the world in a fixed way, while still keeping up reasonably with its fluid, dynamic, changing nature. What it cannot accommodate, it can confidently treat as noise.

Unlike the changeless, distant gods or Gods of traditional religions, the God of economics is a live intelligence, doing all the fluid thinking so you can care in fixed ways. And it’s obvious why it’s able to do that. The economy is, after all, the closest thing to a live, planet-scale artificial intelligence.A different way to think about this helps generalize from this one data point. Economics provides a fixed logic of caring despite a complex, Permaweird world because it rests atop a vast, interposing⁵ intelligence that processes or tunes out most of the weirdness. A kind of intelligence that religion once embodied in a slower, less weird era. A Turing-complete pre-processing/post-processing layer, mediating your relationship to reality. I’m using the term interposing intelligence rather than container or matrix because the mediation has a more open and leaky topology. It allows you to compute with reality data more easily, but doesn’t necessarily seal you off in a bubble reality. Interposing intelligences are more like slippers than spacesuits; more like gardens than arcologies.

The cryptoeconomy is another obvious example, with blockchains playing the role of the interposing intelligence.

A third world is the world of machine learning, which is a rather on-the-nose kind of interposing intelligence layer. … There is a new world of people being born, whose relationship to reality is beginning to be entirely mediated by the interposing intelligence of machine learning.  

A fourth world is perhaps the oldest: the global institutional landscape peopled by careerists devoted to individual institutions. It’s not as obvious as in the case of the economy, but the institutional world (which its critics often refer to as the global Deep State) and its inhabitants (whom critics tend to refer to uniformly as “bureaucrats”) is in fact a world-scale computer that sustains a fixed logic of caring within itself. Shorn of the conspiratorial fantasies of critics, deep state is not a bad term for it.

Is there a way to hold on to a fixed logic of caring, without retreating from the world, and without resting on top of an interposing intelligence? I don’t think this is possible anymore.

Find Fluidity

The problem with everybody switching to fixed logics of caring is that it doesn’t solve the fogginess of the real world. In fact, even if all dozen or so consequential worlds that make up the real world were to harden into de facto fixed-logics-of-caring worlds that individually found flow within, you would still not be free of the fog in the real world. Combating fog in the real world requires at least a fraction of humanity operating by fluid logics of caring.

to want a fluid logic of care is to want “a locus of care that follows the liveliest streams of events in the world. …it used to work well until about 2015,

You could care about tech, for example. What was good for tech was good for the world, and vice versa. But unlike economics, tech does not offer a fixed logic for how to care.
Cosmopolitan globalism was another. Pre-wokism social justice was a third. Following basic scientific advances was a fourth.

But all these examples have “failed” in a certain way since 2015. You can still operate by them, but you will get lost in fog and lose all sense of flow. As a result, all these example worlds have succumbed to sclerotic fixed logics imported from adjacent domains. Technology is increasingly applied investment economics. Cosmopolitan globalism and social justice are now both applied Deep Statisms. No doubt other once-fluid logics of caring will get “compiled,” as it were, to fixed logics of caring running atop interposing intelligence layers.

So is there a way to retain a fluid logic of caring?
Reality — and this time I mean material reality — does indeed have a liberal bias in a rather precise
sense: it requires fluid logics of caring to de-fog. A logic of caring that follows the action instead of being driven by values.

No combination of fixed logics of caring will do the trick. Nor will operating atop a fixed interposing intelligence layer.

Multiple Interposing Intelligences

My big takeaway from the analysis so far is this: there is no way to retain flow in the world today without augmenting your intelligence in some way. This is evident even in my personal, rather primitive case of using hypertext as an integral element of my writing and sensemaking.

This is why all known examples of worlds in flow today rest atop powerful interposing intelligence layers that mediate relations to reality: the economy, blockchains, AI itself, and institutions. But the inescapable cost of this seems to be that fluid logics of caring become fixed, and our sense of the real world, as opposed to our favored individual ones, becomes vulnerable to fog.

To retain fluidity, you must retain an unmediated connection to reality. But the unaugmented brain is clearly not enough for that connection to be tractable to manage.

How do you resolve this paradox?

I think the trick is to inhabit more than one interposing intelligence layer. If you’re only an economist or only a deep-state institutionalist, you’ll retreat to a fixed logic of caring; a terminal derp.

But if you’re both, the possibility of fluid logics of caring remains, because the two interposing varieties of intelligence are not compatible. Sometimes they will be in conflict when they try to mediate your presence in the world. And in that conflict, you will find just enough of an unmediated connection to reality to continue caring about the world in a fluid way, without becoming overwhelmed by complexity.

A specific example of this is thinking about holding the stock of a company you work for. Both economic and institutional logics of caring apply, but neither provides a complete answer to the question of how much of the stock to hold, and when to sell. The two fixed answers will likely be incompatible, so you’ll need a fluid logic to integrate them. If you’re in the public sector, voting on taxes creates a similar tension.

I listed 4 world-scale interposing intelligences earlier, and each pairing seems to work well. Cryptoeconomics and traditional economics seem caught in a dialectic of discovering each other’s fundamental flaws and destablizing each other. Machine learning and blockchains seem headed for a collision via zero-knowledge proof technologies. Institutionalism and blockchains seem headed for a collision via smart contract technology. Institutionalism and economics have been the locus of the familiar Left/Right tension across the world.

I’ll let you work out the other combinations, but if you’ve tried thinking about the world through any two of the available interposing intelligences, you’ll realize how difficult it is. Difficult, but it’s possible. And at least in my case, the more I practice, the better I get at this (I try to straddle all four of the ones I’ve listed).

Why does this work? Why does it serve to “continue the game” in infinite game terms? One way to think about it is to think about life in terms of step-by-step decisions.

If you live within a traditional world that does not supply an interposing intelligence layer at all, you will mostly not have any decision-support at all that can keep up. Your decisions outside your shrinking world will be random and chaotic. Your instinct will be to restrict scope until all decisions are within the scope of your logic of caring, whether fluid or fixed.

If you live atop a single interposing intelligence, you will always have meaningful decision-support within a fixed logic of caring. You’ll have a take on everything, nad feel in flow within your world, but have a sense of the “real world” you share with others being in a state of insane chaos. It would all make sense and flow beautifully if only those others stopped being stupid and joined your world.

But if you live atop more than one interposing intelligence, you will have to choose at every step whether to tap into one of the available fixed logics of caring (picking a side), or improvising your own choice. In the latter case, your thinking will leak through and connect to reality in an unmediated way. If you’re able to do this consistently, you will likely experience increasing amounts of flow, and also beat back the fogginess of the real world, not just your own world.

And this notion of straddling a sort of plate-tectonics of multiple interposing intelligences, with gaps, faultlines and inconsistencies, is the reason the resulting narrative is a kind of protocol narrative. The narrative of the real world emerges out of an interoperable network of world narratives. And through the conflicts between worlds, the infinite game keeps renewing itself.

But it takes a critical mass of humans operating by fluid logics of caring for this to happen. And until that critical mass is reached, the real world will remain foggy for everybody. And trying to be in that minority will be a thankless and stressful task, immersed in fog.
But then again, public service has never been an easy calling.

AI, a boon for science and a disaster for creatives

The Sept. 16 issue of the Economist has two excellent articles: How artificial intelligence can revolutionise science and How scientists are using artificial intelligence. I pass on here some edited clips from the first of these articles. I also want to point to much less benign commentary on how AI is moving toward threatening the livelihoods of creators of music, art, and literature: The Internet Is About to Get Much Worse.  

Could AI turbocharge scientific progress and lead to a golden age of discovery?

Some believe that AI can turbocharge scientific progress and lead to a golden age of discovery...Such claims provide a useful counterbalance to fears about large-scale unemployment and killer robots.

Many previous technologies have been falsely hailed as panaceas. The electric telegraph was lauded in the 1850s as a herald of world peace, as were aircraft in the 1900s; pundits in the 1990s said the internet would reduce inequality and eradicate nationalism...but there have been several periods in history when new approaches and new tools did indeed help bring about bursts of world-changing scientific discovery and innovation.

In the 17th century microscopes and telescopes opened up new vistas of discovery and encouraged researchers to favour their own observations over the received wisdom of antiquity, while the introduction of scientific journals gave them new ways to share and publicise their findings. The result was rapid progress in astronomy, physics and other fields, and new inventions from the pendulum clock to the steam engine—the prime mover of the Industrial Revolution.

Then, starting in the late 19th century, the establishment of research laboratories, which brought together ideas, people and materials on an industrial scale, gave rise to further innovations such as artificial fertiliser, pharmaceuticals and the transistor, the building block of the computer..the journal and the laboratory went further still: they altered scientific practice itself and unlocked more powerful means of making discoveries, by allowing people and ideas to mingle in new ways and on a larger scale. AI, too, has the potential to set off such a transformation.

Two areas in particular look promising. The first is “literature-based discovery” (LBD), which involves analysing existing scientific literature, using ChatGPT-style language analysis, to look for new hypotheses, connections or ideas that humans may have missed. LBD is showing promise in identifying new experiments to try—and even suggesting potential research collaborators.

The second area is “robot scientists”, also known as “self-driving labs”. These are robotic systems that use AI to form new hypotheses, based on analysis of existing data and literature, and then test those hypotheses by performing hundreds or thousands of experiments, in fields including systems biology and materials science. Unlike human scientists, robots are less attached to previous results, less driven by bias—and, crucially, easy to replicate.

In 1665, during a period of rapid scientific progress, Robert Hooke, an English polymath, described the advent of new scientific instruments such as the microscope and telescope as “the adding of artificial organs to the natural”. They let researchers explore previously inaccessible realms and discover things in new ways, “with prodigious benefit to all sorts of useful knowledge”. For Hooke’s modern-day successors, the adding of artificial intelligence to the scientific toolkit is poised to do the same in the coming years—with similarly world-changing results.

Neuron–astrocyte networks might perform the core computations performed by AI transformer blocks

Fascinating ideas from Kozachkov et al. Their text contains primers on Astrocyte biology and the transformers found in AI Generative Pre-trained Transformers such as ChatGPT.  

Significance

Transformers have become the default choice of neural architecture for many machine learning applications. Their success across multiple domains such as language, vision, and speech raises the question: How can one build Transformers using biological computational units? At the same time, in the glial community, there is gradually accumulating evidence that astrocytes, formerly believed to be passive house-keeping cells in the brain, in fact play an important role in the brain’s information processing and computation. In this work we hypothesize that neuron–astrocyte networks can naturally implement the core computation performed by the Transformer block in AI. The omnipresence of astrocytes in almost any brain area may explain the success of Transformers across a diverse set of information domains and computational tasks.

Abstract

Glial cells account for between 50% and 90% of all human brain cells, and serve a variety of important developmental, structural, and metabolic functions. Recent experimental efforts suggest that astrocytes, a type of glial cell, are also directly involved in core cognitive processes such as learning and memory. While it is well established that astrocytes and neurons are connected to one another in feedback loops across many timescales and spatial scales, there is a gap in understanding the computational role of neuron–astrocyte interactions. To help bridge this gap, we draw on recent advances in AI and astrocyte imaging technology. In particular, we show that neuron–astrocyte networks can naturally perform the core computation of a Transformer, a particularly successful type of AI architecture. In doing so, we provide a concrete, normative, and experimentally testable account of neuron–astrocyte communication. Because Transformers are so successful across a wide variety of task domains, such as language, vision, and audition, our analysis may help explain the ubiquity, flexibility, and power of the brain’s neuron–astrocyte networks.

What is intelligence?

I want to share a message sent by Clint Sprott to the Chaos and Complexity seminar group at the University of Wisconsin which he started many years ago - it is currently engaged in a discussion of intelligence:

I asked ChatGPT to define intelligence and whether it is intelligent:

User
How would you define intelligence?
ChatGPT

Defining intelligence is a complex and multifaceted task, as it encompasses a wide range of abilities and capacities exhibited by living organisms and artificial systems. Intelligence can be broadly described as the ability to acquire and apply knowledge, learn from experiences, reason, solve problems, adapt to new situations, and exhibit complex cognitive functions.

Intelligence is not limited to a single dimension but rather exists along a spectrum, manifesting in various forms and levels across different species and individuals. It includes both cognitive abilities, such as logical reasoning, memory, language, and spatial awareness, as well as emotional intelligence, social skills, creativity, and adaptability.

It's essential to recognize that intelligence is not solely determined by traditional measures like IQ tests. Intelligence is a dynamic and context-dependent trait that can manifest differently in different environments and cultural contexts. Moreover, intelligence can also be seen in non-biological entities, such as artificial intelligence, where it refers to the ability of machines to perform tasks that typically require human intelligence.

Overall, intelligence is a complex and evolving concept, and our understanding of it continues to evolve as we explore and study the diverse manifestations of intelligence in both living beings and intelligent systems.

User
Are you intelligent?
ChatGPT

As an AI language model, I don't possess consciousness, self-awareness, or subjective experiences, which are crucial components of human intelligence. However, I have been trained on vast amounts of data and have been designed to perform specific tasks, such as understanding and generating human-like text based on the patterns in the data I was trained on.

While AI models like me can process information quickly, analyze patterns, and provide useful responses, it's essential to understand that this is a form of artificial intelligence, and it differs significantly from human intelligence in terms of awareness, emotions, and consciousness.

In essence, I am a tool created by humans to assist with various tasks, but I do not possess genuine intelligence or consciousness as humans do. My responses are based solely on patterns in the data I've been trained on and the algorithms that govern my functioning.
 

The evolution of transhuman forms - a MindBlog paragraph edited by GPT implementations

In this post I am documenting the final result of passing a paragraph I wrote on future human evolution through a number of different GPT (generative pre-trained transformers). This is to archive the results of this exercise for myself, and in case it might be of interest to a few MindBlog readers. The bots were prompted by a request to re-write or edit the paragraph to make it more accessible to a reader with a college education.

The bottom line is that the edited versions with the most depth and coherence were obtained using Open AI’s ChatGPT-4.  Bard.google.com, Google Workspace Labs, and Microsoft’s Bing, respectively, provided increasingly less useful versions.   Here is my original paragraph:

The linguistic capabilities of generative pre-trained transformers based on silicon  - the same capabilities that have enabled human civilization  - will soon exceed those of humans. We have brought our understanding of what human beings as they now exist are about to a plateau level at which further increments of understanding will become increasingly small. The cream has been skimmed, the best cherries have been picked, and it seems appropriate to turn our  attention now to studying the evolution of trans human forms utilizing LLMs that are better able to manage themselves and the planet.  The action is in the the pretraining P of GPT, as the models move beyond their initial human management to the stage of evolving themselves, hopefully into benign versions of Darwin’s  “forms most beautiful and most wonderful.” What is the point of humans other than being self replicating machines that evolve into further forms, hopefully before they extinguish themselves by destroying the ecological environment required for their sustenance? Can they evolve into trans human minds (or mind, singular)?

Here is the result of including some of the edits suggested by ChatGPT4’s regenerated response, a  vers. 3 of the paragraph above:

The linguistic capabilities of generative pre-trained transformers based on silicon - the same capabilities that have enabled human civilization - will soon surpass those of humans.

Our understanding of human nature as it currently exists has reached a plateau level, at which further increments of understanding are becoming increasingly small. The cream has been skimmed, the best cherries have been picked. Consequently it seems the right time to shift our focus toward studying the evolution of 'transhuman' forms. These are beings that could potentially leverage advanced language models (LLMs) to better self-regulate themselves and manage the planet.

The real crux of the matter lies in the pretraining phase of these transformers (symbolized by 'P' in GPT). This is when the models are initially developed and fine-tuned. As they evolve beyond their human programming the optimistic vision is that they might transform into benign versions of what Charles Darwin called “forms most beautiful and most wonderful.”

From a philosophical perspective, one might ask: What is the ultimate purpose of human beings, if not to serve as self-replicating entities that evolve into more advanced forms - hopefully before they extinguish themselves by destroying the ecological environment required for their sustenance? Is it possible for humans to evolve into a collective of transhuman minds or even a singular, transcendent mind?  These are questions worth exploring as we stand on the brink of an AI-enhanced future.

Managing the risks of AI in pragmatic ways.

I want to pass on the final paragraphs of a recent commentary by Venkatesh Rao on the tragedy of the Titan submersible, which was a consequence of Stockton Rush, the CEO of OceanGate Expeditions, taking a number of design risks to reduce costs and increase profits. The bulk of Rao's piece deals with issues in the design of potentially dangerous new technologies, and the final paragraphs deal with managing the risks of artificial intelligence in pragmatic ways.

...AI risk, understood as something very similar to ordinary kinds of engineering risk (such as the risk of submersibles imploding), is an important matter, but lurid theological conceptions of AI risk and “alignment” are a not-even-wrong basis for managing it. The Titan affair, as an object lesson in traditional risk-management, offers many good lessons for how to manage real AI risks in pragmatic ways.

But there’s another point, a novel one, that is present in the case of AI that I don’t think has ever been present in technological leaps of the past.

AI is different from other technologies in that it alters the felt balance between knowledge and incomprehension that shapes our individual and collective risk-taking in the world.

AIs are already very good at embodying knowledge, and better at explaining many complex matters than most humans. But they are not yet very good at embodying doubt and incomprehension. They structurally lack epistemic humility and the ability to act on a consciousness of ignorance in justifiably arbitrary ways (ie on the basis of untheorized conservative decision principles backed by a track record). This is something bureaucratic standards bodies do very well. It is something that “software bureaucracies” (such as RLFH — reinforcement learning with human feedback) don’t do very well at all. The much demonized (by the entrepreneurial class) risk-aversion of bureaucrats is also a kind of ex-officio epistemic humility that is an essential ingredient of technology ecosystems.

On the flip side, AI itself is currently a technology full of incomprehensibilities. We understand the low-level mechanics of graph weights, gradient descents, backpropagation, and matrix multiplications. We do not understand how that low-level machinery produces the emergent outcomes it does. Our incomprehensions about AI are comparable to our incomprehensions about our own minds. This makes them extremely well-suited (impedance matched) to being bolted onto our minds as cognitive prosthetics that feel very comfortable, increase our confidence about what we think we know, and turn into extensions of ourselves (this is not exactly surprising, given that they are trained on human-generated data).

As with submersibles, we are at an alchemical state of understanding with AIs, but because of the nature of the technology itself, we might develop a prosthetic overconfidence in our state of indirect knowledge about the world, via AI.

AI might turn all humans who use it into Stockton Rushes.

The risk that AIs might destroy us is in science-fictional ways is overblown, but the risk that they might tempt us into generalized epistemic overconfidence, and systematically blind us to our incomprehensions, leading us to hurt ourselves in complex ways, is probably not sufficiently recognized.

Already, masses of programmers are relying on AIs like Github Copilot, and acting with a level of confidence in generated code that is likely not justified. AI-augmented programmers, even if sober and cautious as unaugmented individuals, might be taking Stockton-Rush type risks due to the false confidence induced by their tools. I don’t know that this is true, but the reports I see about people being 10x more productive and taking pleasure in programming again strike me as warning signs. I suspect there might be premature aestheticization going on here.

And I suspect it will take a few AI-powered Titan-like tragedies for us to wise-up and do something about it.

One way to think about this risk is by analogy to WMDs. Most people think nuclear weapons when they hear the phrase, but perhaps the most destructive WMD in the world is cheap and highly effective small arms, which have made conflicts far deadlier in the last century, and killed way more humans in aggregate than nuclear weapons.

You do not need to worry about a single AI going “AGI” and bring God-like catastrophes of malice or indifference down upon us. We lack the “nuclear science” to make that sort of thing happen. But you do need to be worried about millions of ordinary humans, drawn into drunken overconfidence by AI tools, wreaking the kind of havoc small arms do.

I want to pass on the following text of a Marc Adreeessen substack post. This is because my usual extracting of small clips of text to give a sense of the whole just wasn't doing its job. I think that all of this sane article should be read by anyone interested in AI. Andreessen thinks that AI is quite possibly the most important – and best – thing our civilization has ever created, and that the development and proliferation of AI – far from a risk that we should fear – is a moral obligation that we have to ourselves, to our children, and to our future. I would urge you to have patience with the article's arguments against the conventional wisdom regarding the dangers of AI, even if they seem jarring and overstated to you.

Why AI Will Save The World

The era of Artificial Intelligence is here, and boy are people freaking out.

Fortunately, I am here to bring the good news: AI will not destroy the world, and in fact may save it.

First, a short description of what AI is: The application of mathematics and software code to teach computers how to understand, synthesize, and generate knowledge in ways similar to how people do it. AI is a computer program like any other – it runs, takes input, processes, and generates output. AI’s output is useful across a wide range of fields, ranging from coding to medicine to law to the creative arts. It is owned by people and controlled by people, like any other technology.

A shorter description of what AI isn’t: Killer software and robots that will spring to life and decide to murder the human race or otherwise ruin everything, like you see in the movies.

An even shorter description of what AI could be: A way to make everything we care about better.

Why AI Can Make Everything We Care About Better

The most validated core conclusion of social science across many decades and thousands of studies is that human intelligence makes a very broad range of life outcomes better. Smarter people have better outcomes in almost every domain of activity: academic achievement, job performance, occupational status, income, creativity, physical health, longevity, learning new skills, managing complex tasks, leadership, entrepreneurial success, conflict resolution, reading comprehension, financial decision making, understanding others’ perspectives, creative arts, parenting outcomes, and life satisfaction.

Further, human intelligence is the lever that we have used for millennia to create the world we live in today: science, technology, math, physics, chemistry, medicine, energy, construction, transportation, communication, art, music, culture, philosophy, ethics, morality. Without the application of intelligence on all these domains, we would all still be living in mud huts, scratching out a meager existence of subsistence farming. Instead we have used our intelligence to raise our standard of living on the order of 10,000X over the last 4,000 years.

What AI offers us is the opportunity to profoundly augment human intelligence to make all of these outcomes of intelligence – and many others, from the creation of new medicines to ways to solve climate change to technologies to reach the stars – much, much better from here.

AI augmentation of human intelligence has already started – AI is already around us in the form of computer control systems of many kinds, is now rapidly escalating with AI Large Language Models like ChatGPT, and will accelerate very quickly from here – if we let it.

In our new era of AI:

• Every child will have an AI tutor that is infinitely patient, infinitely compassionate, infinitely knowledgeable, infinitely helpful. The AI tutor will be by each child’s side every step of their development, helping them maximize their potential with the machine version of infinite love.

• Every person will have an AI assistant/coach/mentor/trainer/advisor/therapist that is infinitely patient, infinitely compassionate, infinitely knowledgeable, and infinitely helpful. The AI assistant will be present through all of life’s opportunities and challenges, maximizing every person’s outcomes.

• Every scientist will have an AI assistant/collaborator/partner that will greatly expand their scope of scientific research and achievement. Every artist, every engineer, every businessperson, every doctor, every caregiver will have the same in their worlds.

• Every leader of people – CEO, government official, nonprofit president, athletic coach, teacher – will have the same. The magnification effects of better decisions by leaders across the people they lead are enormous, so this intelligence augmentation may be the most important of all.

• Productivity growth throughout the economy will accelerate dramatically, driving economic growth, creation of new industries, creation of new jobs, and wage growth, and resulting in a new era of heightened material prosperity across the planet.

• Scientific breakthroughs and new technologies and medicines will dramatically expand, as AI helps us further decode the laws of nature and harvest them for our benefit.

• The creative arts will enter a golden age, as AI-augmented artists, musicians, writers, and filmmakers gain the ability to realize their visions far faster and at greater scale than ever before.

• I even think AI is going to improve warfare, when it has to happen, by reducing wartime death rates dramatically. Every war is characterized by terrible decisions made under intense pressure and with sharply limited information by very limited human leaders. Now, military commanders and political leaders will have AI advisors that will help them make much better strategic and tactical decisions, minimizing risk, error, and unnecessary bloodshed.

• In short, anything that people do with their natural intelligence today can be done much better with AI, and we will be able to take on new challenges that have been impossible to tackle without AI, from curing all diseases to achieving interstellar travel.

• And this isn’t just about intelligence! Perhaps the most underestimated quality of AI is how humanizing it can be. AI art gives people who otherwise lack technical skills the freedom to create and share their artistic ideas. Talking to an empathetic AI friend really does improve their ability to handle adversity. And AI medical chatbots are already more empathetic than their human counterparts. Rather than making the world harsher and more mechanistic, infinitely patient and sympathetic AI will make the world warmer and nicer.

The stakes here are high. The opportunities are profound. AI is quite possibly the most important – and best – thing our civilization has ever created, certainly on par with electricity and microchips, and probably beyond those.

The development and proliferation of AI – far from a risk that we should fear – is a moral obligation that we have to ourselves, to our children, and to our future.

We should be living in a much better world with AI, and now we can.

So Why The Panic?

In contrast to this positive view, the public conversation about AI is presently shot through with hysterical fear and paranoia.

We hear claims that AI will variously kill us all, ruin our society, take all our jobs, cause crippling inequality, and enable bad people to do awful things.

What explains this divergence in potential outcomes from near utopia to horrifying dystopia?

Historically, every new technology that matters, from electric lighting to automobiles to radio to the Internet, has sparked a moral panic – a social contagion that convinces people the new technology is going to destroy the world, or society, or both. The fine folks at Pessimists Archive have documented these technology-driven moral panics over the decades; their history makes the pattern vividly clear. It turns out this present panic is not even the first for AI.

Now, it is certainly the case that many new technologies have led to bad outcomes – often the same technologies that have been otherwise enormously beneficial to our welfare. So it’s not that the mere existence of a moral panic means there is nothing to be concerned about.

But a moral panic is by its very nature irrational – it takes what may be a legitimate concern and inflates it into a level of hysteria that ironically makes it harder to confront actually serious concerns.

And wow do we have a full-blown moral panic about AI right now.

This moral panic is already being used as a motivating force by a variety of actors to demand policy action – new AI restrictions, regulations, and laws. These actors, who are making extremely dramatic public statements about the dangers of AI – feeding on and further inflaming moral panic – all present themselves as selfless champions of the public good.

But are they?

And are they right or wrong?

The Baptists And Bootleggers Of AI

Economists have observed a longstanding pattern in reform movements of this kind. The actors within movements like these fall into two categories – “Baptists” and “Bootleggers” – drawing on the historical example of the prohibition of alcohol in the United States in the 1920’s:

• “Baptists” are the true believer social reformers who legitimately feel – deeply and emotionally, if not rationally – that new restrictions, regulations, and laws are required to prevent societal disaster.
  
  For alcohol prohibition, these actors were often literally devout Christians who felt that alcohol was destroying the moral fabric of society.
  
  For AI risk, these actors are true believers that AI presents one or another existential risks – strap them to a polygraph, they really mean it.

• “Bootleggers” are the self-interested opportunists who stand to financially profit by the imposition of new restrictions, regulations, and laws that insulate them from competitors.
  
  For alcohol prohibition, these were the literal bootleggers who made a fortune selling illicit alcohol to Americans when legitimate alcohol sales were banned.
  
  For AI risk, these are CEOs who stand to make more money if regulatory barriers are erected that form a cartel of government-blessed AI vendors protected from new startup and open source competition – the software version of “too big to fail” banks.

A cynic would suggest that some of the apparent Baptists are also Bootleggers – specifically the ones paid to attack AI by their universities, think tanks, activist groups, and media outlets. If you are paid a salary or receive grants to foster AI panic…you are probably a Bootlegger.

The problem with the Bootleggers is that they win. The Baptists are naive ideologues, the Bootleggers are cynical operators, and so the result of reform movements like these is often that the Bootleggers get what they want – regulatory capture, insulation from competition, the formation of a cartel – and the Baptists are left wondering where their drive for social improvement went so wrong.

We just lived through a stunning example of this – banking reform after the 2008 global financial crisis. The Baptists told us that we needed new laws and regulations to break up the “too big to fail” banks to prevent such a crisis from ever happening again. So Congress passed the Dodd-Frank Act of 2010, which was marketed as satisfying the Baptists’ goal, but in reality was coopted by the Bootleggers – the big banks. The result is that the same banks that were “too big to fail” in 2008 are much, much larger now.

So in practice, even when the Baptists are genuine – and even when the Baptists are right – they are used as cover by manipulative and venal Bootleggers to benefit themselves. 

And this is what is happening in the drive for AI regulation right now.

However, it isn’t sufficient to simply identify the actors and impugn their motives. We should consider the arguments of both the Baptists and the Bootleggers on their merits.

AI Risk #1: Will AI Kill Us All?

The first and original AI doomer risk is that AI will decide to literally kill humanity.

The fear that technology of our own creation will rise up and destroy us is deeply coded into our culture. The Greeks expressed this fear in the Prometheus Myth – Prometheus brought the destructive power of fire, and more generally technology (“techne”), to man, for which Prometheus was condemned to perpetual torture by the gods. Later, Mary Shelley gave us moderns our own version of this myth in her novel Frankenstein, or, The Modern Prometheus, in which we develop the technology for eternal life, which then rises up and seeks to destroy us. And of course, no AI panic newspaper story is complete without a still image of a gleaming red-eyed killer robot from James Cameron’s Terminator films.

The presumed evolutionary purpose of this mythology is to motivate us to seriously consider potential risks of new technologies – fire, after all, can indeed be used to burn down entire cities. But just as fire was also the foundation of modern civilization as used to keep us warm and safe in a cold and hostile world, this mythology ignores the far greater upside of most – all? – new technologies, and in practice inflames destructive emotion rather than reasoned analysis. Just because premodern man freaked out like this doesn’t mean we have to; we can apply rationality instead.

My view is that the idea that AI will decide to literally kill humanity is a profound category error. AI is not a living being that has been primed by billions of years of evolution to participate in the battle for the survival of the fittest, as animals are, and as we are. It is math – code – computers, built by people, owned by people, used by people, controlled by people. The idea that it will at some point develop a mind of its own and decide that it has motivations that lead it to try to kill us is a superstitious handwave.

In short, AI doesn’t want, it doesn’t have goals, it doesn’t want to kill you, because it’s not alive. And AI is a machine – is not going to come alive any more than your toaster will.

Now, obviously, there are true believers in killer AI – Baptists – who are gaining a suddenly stratospheric amount of media coverage for their terrifying warnings, some of whom claim to have been studying the topic for decades and say they are now scared out of their minds by what they have learned. Some of these true believers are even actual innovators of the technology. These actors are arguing for a variety of bizarre and extreme restrictions on AI ranging from a ban on AI development, all the way up to military airstrikes on datacenters and nuclear war. They argue that because people like me cannot rule out future catastrophic consequences of AI, that we must assume a precautionary stance that may require large amounts of physical violence and death in order to prevent potential existential risk.

My response is that their position is non-scientific – What is the testable hypothesis? What would falsify the hypothesis? How do we know when we are getting into a danger zone? These questions go mainly unanswered apart from “You can’t prove it won’t happen!” In fact, these Baptists’ position is so non-scientific and so extreme – a conspiracy theory about math and code – and is already calling for physical violence, that I will do something I would normally not do and question their motives as well.

Specifically, I think three things are going on:

First, recall that John Von Neumann responded to Robert Oppenheimer’s famous hand-wringing about his role creating nuclear weapons – which helped end World War II and prevent World War III – with, “Some people confess guilt to claim credit for the sin.” What is the most dramatic way one can claim credit for the importance of one’s work without sounding overtly boastful? This explains the mismatch between the words and actions of the Baptists who are actually building and funding AI – watch their actions, not their words. (Truman was harsher after meeting with Oppenheimer: “Don’t let that crybaby in here again.”)

Second, some of the Baptists are actually Bootleggers. There is a whole profession of “AI safety expert”, “AI ethicist”, “AI risk researcher”. They are paid to be doomers, and their statements should be processed appropriately.

Third, California is justifiably famous for our many thousands of cults, from EST to the Peoples Temple, from Heaven’s Gate to the Manson Family. Many, although not all, of these cults are harmless, and maybe even serve a purpose for alienated people who find homes in them. But some are very dangerous indeed, and cults have a notoriously hard time straddling the line that ultimately leads to violence and death.

And the reality, which is obvious to everyone in the Bay Area but probably not outside of it, is that “AI risk” has developed into a cult, which has suddenly emerged into the daylight of global press attention and the public conversation. This cult has pulled in not just fringe characters, but also some actual industry experts and a not small number of wealthy donors – including, until recently, Sam Bankman-Fried. And it’s developed a full panoply of cult behaviors and beliefs.

This cult is why there are a set of AI risk doomers who sound so extreme – it’s not that they actually have secret knowledge that make their extremism logical, it’s that they’ve whipped themselves into a frenzy and really are…extremely extreme.

It turns out that this type of cult isn’t new – there is a longstanding Western tradition of millenarianism, which generates apocalypse cults. The AI risk cult has all the hallmarks of a millenarian apocalypse cult. From Wikipedia, with additions by me:

“Millenarianism is the belief by a group or movement [AI risk doomers] in a coming fundamental transformation of society [the arrival of AI], after which all things will be changed [AI utopia, dystopia, and/or end of the world]. Only dramatic events [AI bans, airstrikes on datacenters, nuclear strikes on unregulated AI] are seen as able to change the world [prevent AI] and the change is anticipated to be brought about, or survived, by a group of the devout and dedicated. In most millenarian scenarios, the disaster or battle to come [AI apocalypse, or its prevention] will be followed by a new, purified world [AI bans] in which the believers will be rewarded [or at least acknowledged to have been correct all along].”

This apocalypse cult pattern is so obvious that I am surprised more people don’t see it.

Don’t get me wrong, cults are fun to hear about, their written material is often creative and fascinating, and their members are engaging at dinner parties and on TV. But their extreme beliefs should not determine the future of laws and society – obviously not.

AI Risk #2: Will AI Ruin Our Society?

The second widely mooted AI risk is that AI will ruin our society, by generating outputs that will be so “harmful”, to use the nomenclature of this kind of doomer, as to cause profound damage to humanity, even if we’re not literally killed.

Short version: If the murder robots don’t get us, the hate speech and misinformation will.

This is a relatively recent doomer concern that branched off from and somewhat took over the “AI risk” movement that I described above. In fact, the terminology of AI risk recently changed from “AI safety” – the term used by people who are worried that AI would literally kill us – to “AI alignment” – the term used by people who are worried about societal “harms”. The original AI safety people are frustrated by this shift, although they don’t know how to put it back in the box – they now advocate that the actual AI risk topic be renamed “AI notkilleveryoneism”, which has not yet been widely adopted but is at least clear.

The tipoff to the nature of the AI societal risk claim is its own term, “AI alignment”. Alignment with what? Human values. Whose human values? Ah, that’s where things get tricky.

As it happens, I have had a front row seat to an analogous situation – the social media “trust and safety” wars. As is now obvious, social media services have been under massive pressure from governments and activists to ban, restrict, censor, and otherwise suppress a wide range of content for many years. And the same concerns of “hate speech” (and its mathematical counterpart, “algorithmic bias”) and “misinformation” are being directly transferred from the social media context to the new frontier of “AI alignment”. 

My big learnings from the social media wars are:

On the one hand, there is no absolutist free speech position. First, every country, including the United States, makes at least some content illegal. Second, there are certain kinds of content, like child pornography and incitements to real world violence, that are nearly universally agreed to be off limits – legal or not – by virtually every society. So any technological platform that facilitates or generates content – speech – is going to have some restrictions.

On the other hand, the slippery slope is not a fallacy, it’s an inevitability. Once a framework for restricting even egregiously terrible content is in place – for example, for hate speech, a specific hurtful word, or for misinformation, obviously false claims like “the Pope is dead” – a shockingly broad range of government agencies and activist pressure groups and nongovernmental entities will kick into gear and demand ever greater levels of censorship and suppression of whatever speech they view as threatening to society and/or their own personal preferences. They will do this up to and including in ways that are nakedly felony crimes. This cycle in practice can run apparently forever, with the enthusiastic support of authoritarian hall monitors installed throughout our elite power structures. This has been cascading for a decade in social media and with only certain exceptions continues to get more fervent all the time.

And so this is the dynamic that has formed around “AI alignment” now. Its proponents claim the wisdom to engineer AI-generated speech and thought that are good for society, and to ban AI-generated speech and thoughts that are bad for society. Its opponents claim that the thought police are breathtakingly arrogant and presumptuous – and often outright criminal, at least in the US – and in fact are seeking to become a new kind of fused government-corporate-academic authoritarian speech dictatorship ripped straight from the pages of George Orwell’s 1984.

As the proponents of both “trust and safety” and “AI alignment” are clustered into the very narrow slice of the global population that characterizes the American coastal elites – which includes many of the people who work in and write about the tech industry – many of my readers will find yourselves primed to argue that dramatic restrictions on AI output are required to avoid destroying society. I will not attempt to talk you out of this now, I will simply state that this is the nature of the demand, and that most people in the world neither agree with your ideology nor want to see you win.

If you don’t agree with the prevailing niche morality that is being imposed on both social media and AI via ever-intensifying speech codes, you should also realize that the fight over what AI is allowed to say/generate will be even more important – by a lot – than the fight over social media censorship. AI is highly likely to be the control layer for everything in the world. How it is allowed to operate is going to matter perhaps more than anything else has ever mattered. You should be aware of how a small and isolated coterie of partisan social engineers are trying to determine that right now, under cover of the age-old claim that they are protecting you.

In short, don’t let the thought police suppress AI.

AI Risk #3: Will AI Take All Our Jobs?

The fear of job loss due variously to mechanization, automation, computerization, or AI has been a recurring panic for hundreds of years, since the original onset of machinery such as the mechanical loom. Even though every new major technology has led to more jobs at higher wages throughout history, each wave of this panic is accompanied by claims that “this time is different” – this is the time it will finally happen, this is the technology that will finally deliver the hammer blow to human labor. And yet, it never happens. 

We’ve been through two such technology-driven unemployment panic cycles in our recent past – the outsourcing panic of the 2000’s, and the automation panic of the 2010’s. Notwithstanding many talking heads, pundits, and even tech industry executives pounding the table throughout both decades that mass unemployment was near, by late 2019 – right before the onset of COVID – the world had more jobs at higher wages than ever in history.

Nevertheless this mistaken idea will not die.

And sure enough, it’s back.

This time, we finally have the technology that’s going to take all the jobs and render human workers superfluous – real AI. Surely this time history won’t repeat, and AI will cause mass unemployment – and not rapid economic, job, and wage growth – right?

No, that’s not going to happen – and in fact AI, if allowed to develop and proliferate throughout the economy, may cause the most dramatic and sustained economic boom of all time, with correspondingly record job and wage growth – the exact opposite of the fear. And here’s why.

The core mistake the automation-kills-jobs doomers keep making is called the Lump Of Labor Fallacy. This fallacy is the incorrect notion that there is a fixed amount of labor to be done in the economy at any given time, and either machines do it or people do it – and if machines do it, there will be no work for people to do.

The Lump Of Labor Fallacy flows naturally from naive intuition, but naive intuition here is wrong. When technology is applied to production, we get productivity growth – an increase in output generated by a reduction in inputs. The result is lower prices for goods and services. As prices for goods and services fall, we pay less for them, meaning that we now have extra spending power with which to buy other things. This increases demand in the economy, which drives the creation of new production – including new products and new industries – which then creates new jobs for the people who were replaced by machines in prior jobs. The result is a larger economy with higher material prosperity, more industries, more products, and more jobs.

But the good news doesn’t stop there. We also get higher wages. This is because, at the level of the individual worker, the marketplace sets compensation as a function of the marginal productivity of the worker. A worker in a technology-infused business will be more productive than a worker in a traditional business. The employer will either pay that worker more money as he is now more productive, or another employer will, purely out of self interest. The result is that technology introduced into an industry generally not only increases the number of jobs in the industry but also raises wages.

To summarize, technology empowers people to be more productive. This causes the prices for existing goods and services to fall, and for wages to rise. This in turn causes economic growth and job growth, while motivating the creation of new jobs and new industries. If a market economy is allowed to function normally and if technology is allowed to be introduced freely, this is a perpetual upward cycle that never ends. For, as Milton Friedman observed, “Human wants and needs are endless” – we always want more than we have. A technology-infused market economy is the way we get closer to delivering everything everyone could conceivably want, but never all the way there. And that is why technology doesn’t destroy jobs and never will.

These are such mindblowing ideas for people who have not been exposed to them that it may take you some time to wrap your head around them. But I swear I’m not making them up – in fact you can read all about them in standard economics textbooks. I recommend the chapter The Curse of Machinery in Henry Hazlitt’s Economics In One Lesson, and Frederic Bastiat’s satirical Candlemaker’s Petition to blot out the sun due to its unfair competition with the lighting industry, here modernized for our times.

But this time is different, you’re thinking. This time, with AI, we have the technology that can replace ALL human labor.

But, using the principles I described above, think of what it would mean for literally all existing human labor to be replaced by machines.

It would mean a takeoff rate of economic productivity growth that would be absolutely stratospheric, far beyond any historical precedent. Prices of existing goods and services would drop across the board to virtually zero. Consumer welfare would skyrocket. Consumer spending power would skyrocket. New demand in the economy would explode. Entrepreneurs would create dizzying arrays of new industries, products, and services, and employ as many people and AI as they could as fast as possible to meet all the new demand.

Suppose AI once again replaces that labor? The cycle would repeat, driving consumer welfare, economic growth, and job and wage growth even higher. It would be a straight spiral up to a material utopia that neither Adam Smith or Karl Marx ever dared dream of. 

We should be so lucky.

AI Risk #4: Will AI Lead To Crippling Inequality?

Speaking of Karl Marx, the concern about AI taking jobs segues directly into the next claimed AI risk, which is, OK, Marc, suppose AI does take all the jobs, either for bad or for good. Won’t that result in massive and crippling wealth inequality, as the owners of AI reap all the economic rewards and regular people get nothing?

As it happens, this was a central claim of Marxism, that the owners of the means of production – the bourgeoisie – would inevitably steal all societal wealth from the people who do the actual  work – the proletariat. This is another fallacy that simply will not die no matter how often it’s disproved by reality. But let’s drive a stake through its heart anyway.

The flaw in this theory is that, as the owner of a piece of technology, it’s not in your own interest to keep it to yourself – in fact the opposite, it’s in your own interest to sell it to as many customers as possible. The largest market in the world for any product is the entire world, all 8 billion of us. And so in reality, every new technology – even ones that start by selling to the rarefied air of high-paying big companies or wealthy consumers – rapidly proliferates until it’s in the hands of the largest possible mass market, ultimately everyone on the planet.

The classic example of this was Elon Musk’s so-called “secret plan” – which he naturally published openly – for Tesla in 2006:

Step 1, Build [expensive] sports car

Step 2, Use that money to build an affordable car

Step 3, Use that money to build an even more affordable car

…which is of course exactly what he’s done, becoming the richest man in the world as a result.

That last point is key. Would Elon be even richer if he only sold cars to rich people today? No. Would he be even richer than that if he only made cars for himself? Of course not. No, he maximizes his own profit by selling to the largest possible market, the world.

In short, everyone gets the thing – as we saw in the past with not just cars but also electricity, radio, computers, the Internet, mobile phones, and search engines. The makers of such technologies are highly motivated to drive down their prices until everyone on the planet can afford them. This is precisely what is already happening in AI – it’s why you can use state of the art generative AI not just at low cost but even for free today in the form of Microsoft Bing and Google Bard – and it is what will continue to happen. Not because such vendors are foolish or generous but precisely because they are greedy – they want to maximize the size of their market, which maximizes their profits.

So what happens is the opposite of technology driving centralization of wealth – individual customers of the technology, ultimately including everyone on the planet, are empowered instead, and capture most of the generated value. As with prior technologies, the companies that build AI – assuming they have to function in a free market – will compete furiously to make this happen.

Marx was wrong then, and he’s wrong now.

This is not to say that inequality is not an issue in our society. It is, it’s just not being driven by technology, it’s being driven by the reverse, by the sectors of the economy that are the most resistant to new technology, that have the most government intervention to prevent the adoption of new technology like AI – specifically housing, education, and health care. The actual risk of AI and inequality is not that AI will cause more inequality but rather that we will not allow AI to be used to reduce inequality.

AI Risk #5: Will AI Lead To Bad People Doing Bad Things?

So far I have explained why four of the five most often proposed risks of AI are not actually real – AI will not come to life and kill us, AI will not ruin our society, AI will not cause mass unemployment, and AI will not cause an ruinous increase in inequality. But now let’s address the fifth, the one I actually agree with: AI will make it easier for bad people to do bad things.

In some sense this is a tautology. Technology is a tool. Tools, starting with fire and rocks, can be used to do good things – cook food and build houses – and bad things – burn people and bludgeon people. Any technology can be used for good or bad. Fair enough. And AI will make it easier for criminals, terrorists, and hostile governments to do bad things, no question.

This causes some people to propose, well, in that case, let’s not take the risk, let’s ban AI now before this can happen. Unfortunately, AI is not some esoteric physical material that is hard to come by, like plutonium. It’s the opposite, it’s the easiest material in the world to come by – math and code.

The AI cat is obviously already out of the bag. You can learn how to build AI from thousands of free online courses, books, papers, and videos, and there are outstanding open source implementations proliferating by the day. AI is like air – it will be everywhere. The level of totalitarian oppression that would be required to arrest that would be so draconian – a world government monitoring and controlling all computers? jackbooted thugs in black helicopters seizing rogue GPUs? – that we would not have a society left to protect.

So instead, there are two very straightforward ways to address the risk of bad people doing bad things with AI, and these are precisely what we should focus on.

First, we have laws on the books to criminalize most of the bad things that anyone is going to do with AI. Hack into the Pentagon? That’s a crime. Steal money from a bank? That’s a crime. Create a bioweapon? That’s a crime. Commit a terrorist act? That’s a crime. We can simply focus on preventing those crimes when we can, and prosecuting them when we cannot. We don’t even need new laws – I’m not aware of a single actual bad use for AI that’s been proposed that’s not already illegal. And if a new bad use is identified, we ban that use. QED.

But you’ll notice what I slipped in there – I said we should focus first on preventing AI-assisted crimes before they happen – wouldn’t such prevention mean banning AI? Well, there’s another way to prevent such actions, and that’s by using AI as a defensive tool. The same capabilities that make AI dangerous in the hands of bad guys with bad goals make it powerful in the hands of good guys with good goals – specifically the good guys whose job it is to prevent bad things from happening.

For example, if you are worried about AI generating fake people and fake videos, the answer is to build new systems where people can verify themselves and real content via cryptographic signatures. Digital creation and alteration of both real and fake content was already here before AI; the answer is not to ban word processors and Photoshop – or AI – but to use technology to build a system that actually solves the problem.

And so, second, let’s mount major efforts to use AI for good, legitimate, defensive purposes. Let’s put AI to work in cyberdefense, in biological defense, in hunting terrorists, and in everything else that we do to keep ourselves, our communities, and our nation safe.

There are already many smart people in and out of government doing exactly this, of course – but if we apply all of the effort and brainpower that’s currently fixated on the futile prospect of banning AI to using AI to protect against bad people doing bad things, I think there’s no question a world infused with AI will be much safer than the world we live in today.

The Actual Risk Of Not Pursuing AI With Maximum Force And Speed

There is one final, and real, AI risk that is probably the scariest at all:

AI isn’t just being developed in the relatively free societies of the West, it is also being developed by the Communist Party of the People’s Republic of China.

China has a vastly different vision for AI than we do – they view it as a mechanism for authoritarian population control, full stop. They are not even being secretive about this, they are very clear about it, and they are already pursuing their agenda. And they do not intend to limit their AI strategy to China – they intend to proliferate it all across the world, everywhere they are powering 5G networks, everywhere they are loaning Belt And Road money, everywhere they are providing friendly consumer apps like Tiktok that serve as front ends to their centralized command and control AI.

The single greatest risk of AI is that China wins global AI dominance and we – the United States and the West – do not.

I propose a simple strategy for what to do about this – in fact, the same strategy President Ronald Reagan used to win the first Cold War with the Soviet Union.

“We win, they lose.”

Rather than allowing ungrounded panics around killer AI, “harmful” AI, job-destroying AI, and inequality-generating AI to put us on our back feet, we in the United States and the West should lean into AI as hard as we possibly can.

We should seek to win the race to global AI technological superiority and ensure that China does not.

In the process, we should drive AI into our economy and society as fast and hard as we possibly can, in order to maximize its gains for economic productivity and human potential.

This is the best way both to offset the real AI risks and to ensure that our way of life is not displaced by the much darker Chinese vision.

What Is To Be Done?

I propose a simple plan:

• Big AI companies should be allowed to build AI as fast and aggressively as they can – but not allowed to achieve regulatory capture, not allowed to establish a government-protect cartel that is insulated from market competition due to incorrect claims of AI risk. This will maximize the technological and societal payoff from the amazing capabilities of these companies, which are jewels of modern capitalism.

• Startup AI companies should be allowed to build AI as fast and aggressively as they can. They should neither confront government-granted protection of big companies, nor should they receive government assistance. They should simply be allowed to compete. If and as startups don’t succeed, their presence in the market will also continuously motivate big companies to be their best – our economies and societies win either way.

• Open source AI should be allowed to freely proliferate and compete with both big AI companies and startups. There should be no regulatory barriers to open source whatsoever. Even when open source does not beat companies, its widespread availability is a boon to students all over the world who want to learn how to build and use AI to become part of the technological future, and will ensure that AI is available to everyone who can benefit from it no matter who they are or how much money they have.

• To offset the risk of bad people doing bad things with AI, governments working in partnership with the private sector should vigorously engage in each area of potential risk to use AI to maximize society’s defensive capabilities. This shouldn’t be limited to AI-enabled risks but also more general problems such as malnutrition, disease, and climate. AI can be an incredibly powerful tool for solving problems, and we should embrace it as such.

• To prevent the risk of China achieving global AI dominance, we should use the full power of our private sector, our scientific establishment, and our governments in concert to drive American and Western AI to absolute global dominance, including ultimately inside China itself. We win, they lose.

And that is how we use AI to save the world.

It’s time to build.

Legends and Heroes

I close with two simple statements.

The development of AI started in the 1940’s, simultaneous with the invention of the computer. The first scientific paper on neural networks – the architecture of the AI we have today – was published in 1943. Entire generations of AI scientists over the last 80 years were born, went to school, worked, and in many cases passed away without seeing the payoff that we are receiving now. They are legends, every one.

Today, growing legions of engineers – many of whom are young and may have had grandparents or even great-grandparents involved in the creation of the ideas behind AI – are working to make AI a reality, against a wall of fear-mongering and doomerism that is attempting to paint them as reckless villains. I do not believe they are reckless or villains. They are heroes, every one. My firm and I are thrilled to back as many of them as we can, and we will stand alongside them and their work 100%.