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    <h1>Artificial General Intelligence and the bird brains of Silicon Valley</h1>
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<p>
The problem is, if one side of the communication does not have meaning,
then the comprehension of the implicit meaning is an illusion arising
from our singular human understanding of language (independent of the
model). Contrary to how it may seem when we observe its output, an LM is
a system for haphazardly stitching together sequences of linguistic
forms it has observed in its vast training data, according to
probabilistic information about how they combine, but without any
reference to meaning: a stochastic parrot.
</p>
</blockquote>
<figcaption>
<p>Emily M. Bender, Timnit Gebru, et al., <em>On the Dangers of Stochastic
Parrots: Can Language Models Be Too Big?</em>.</p>
</figcaption>
</figure>
<p>Bird brains have a bad reputation. The diminutive size of your average
bird and their brain has lead people to assume that they are, well,
dumb.</p>
<p>But, bird brains are amazing. Birds commonly outperform mammals with
larger brains at a variety of general reasoning and problem-solving
tasks. Some by a large margin. Their small brains manage this by
packing numerous neurons in a small space using structures that are
unlike from those you find in mammals.</p>
<p>Even though birds have extremely capable minds, those minds are built in
ways that are different from our own or other mammals. Similar
capabilities; different structure.</p>
<p>The ambition of the Silicon Valley AI industry is to create something
analogous to a bird brain: a new kind of mind that is functionally
similar to the human mind, possibly outperforming it, while being built
using very different mechanisms. Similar capabilities; different
structure.</p>
<p>This effort goes back decades, to the dawn of computing, and has had
limited success.</p>
<p>Until recently, it seems.</p>
<p>If you’re reading this, you’ve almost certainly interacted with a
Generative AI, however indirectly. Maybe you’ve tried Bing Chat. Maybe
you’ve subscribed to the paid tier for ChatGPT. Or, maybe you’ve used
Midjourney to generate images. At the very least you’ve been forced to
see the images or text posted by the overenthusiastic on social media.</p>
<p>These AI models are created by pushing an enormous amount of training
data through various algorithms:</p>
<ul>
<li>Language models like ChatGPT is trained on a good chunk of the textual material available in digital form in the world.</li>
<li>Image models like Midjourney and Stable Diffusion are trained on a huge collection of images found on the internet.</li>
</ul>
<p>What comes out the other end is a mathematical model of the media domain
in question: text or images.</p>
<p>You know what Generative AI is in terms of how it presents to you as
software: clever chatbots that do or say things in response to what you
say: <em>your prompt</em>. Some of those responses are useful, and they give
you an impression of sophisticated comprehension. The models that
generate text are fluent and often quite engaging.</p>
<p>This fluency is misleading. What Bender and Gebru meant when they coined
the term <em>stochastic parrot</em> wasn’t to imply that these are, indeed, the
new bird brains of Silicon Valley, but that they are unthinking text
synthesis engines that just repeat phrases. They are the proverbial
parrot who echoes without thinking, not the actual parrot who is capable
of complex reasoning and problem-solving.</p>
<p>A <em>zombie parrot</em>, if you will, that screams for <em>brains</em> because it has
none.</p>
<p>The fluency of the zombie parrot—the unerring confidence and a style of
writing that some find endearing—creates a strong illusion of
intelligence.</p>
<p>Every other time we read text, we are engaging with the product of
another mind. We are so used to the idea of text as a representation of
another person’s thoughts that we have come to mistake their writing
<em>for</em> their thoughts. But they aren’t. Text and media are tools that
authors and artists create to let people change their own state of
mind—hopefully in specific ways to form the image or effect the author
was after.</p>
<p>Reading is an indirect collaboration with the author, mediated through
the writing. Text has no inherent reasoning or intelligence. Agatha
Christie’s ghost does not inhabit the words of <em>Murder on the Orient Express</em>.
Stephen King isn’t hovering over you when you read <em>Carrie</em>. The ghost
you feel while reading is an illusion you’ve made out of your own
experience, knowledge, and imagination. Every word you read causes your
mind to reconstruct its meaning using your memories and creativity. The
idea that there is intelligence somehow inherent in writing is an
illusion. The intelligence is <em>all</em> yours, all the time: thoughts you
make yourself in order to make sense of another person’s words. This can
prompt us to greatness, broaden our minds, inspire new thoughts, and
introduce us to new concepts. A book can contain worlds, but we’re the
ones that bring them into being as we read. What we see is uniquely our
own. The thoughts are not transported from the author’s mind and
injected into ours.</p>
<p>The words themselves are just line forms on a background with no
inherent meaning or intelligence. The word “horse” doesn’t come with the
Platonic ideal of a horse attached to it. The word “anger” isn’t full of
seething emotion or the restrained urge towards violence. Even words
that are arguably onomatopoeic, like the word “brabra” we use in
Icelandic for the sound a duck makes, are still incredibly specific to
the cultures and context they come from. We are the ones doing the heavy
lifting in terms of reconstructing a picture of an intelligence behind
the text. When there is no actual intelligence, such as with ChatGPT, we
are the ones who end up filling in the gaps with our memories,
experience and imagination.</p>
<p>When ChatGPT demonstrates intelligence, that comes from us. Some of
it we construct ourselves. Some of it comes from our inherent
biases.</p>
<p>There is no ‘there’ there. We are alone in the room, reconstructing an
abstract representation of a mind. The reasoning you see is only in your
head. You are hallucinating intelligence where there is none. You are
doing the textual equivalent of seeing a face in a power outlet.</p>
<p>This drive—<em>anthropomorphism</em>—seems to be innate. Our first instinct
when faced with anything unfamiliar—whose drives, motivations, and
mechanisms we don’t understand—is to assume that they think much like a
human would. When that unfamiliar agent uses language like a human
would, the urge to see them as near or fully human is impossible to
resist—a recurring issue in the history of AI research that dates all
the way back to 1966.</p>
<p>These tools solve problems and return fluent, if untruthful, answers,
which is what creates such a convincing illusion of intelligence.</p>
<p>Text synthesis engines like ChatGPT and GPT-4 do not have any
self-awareness. They are mathematical models of the various patterns to
be found in the collected body of human text. How granular the model is
depends on its design and the languages in question. Some of the
tokens—the smallest unit of language the model works with—will be
characters or punctuation marks, some of them will be words, syllables,
or even phrases. Many language models are a mixture of both.</p>
<p>With enough detail—a big enough collection of text—these tools will
model enough of the probabilistic distribution of various words or
characters to be able to perform what looks like magic:</p>
<ul>
<li>They generate fluent answers by calculating the most probable sequence
of words, at that time, which would serve as the continuation of or
response to the prompt.</li>
<li>They can perform limited reasoning tasks that correlate with textual
descriptions of prior reasoning tasks in the training data.</li>
</ul>
<p>With enough of these correlative shortcuts, the model can perform
something that looks like common sense reasoning: its output is text
that replicates prior representations of reasoning. This works for
as long as you don’t accidentally use the wrong phrasing in your prompt
and break the correlation.</p>
<p>The mechanism behind these systems is entirely correlative from the
ground up.What looks like reasoning is incredibly fragile and
breaks as soon as you rephrase or reword your prompt. It exists
only as a probabilistic model of text. A Generative AI chatbot is a
language engine incapable of genuine thought.</p>
<p>These language models are interactive but static snapshots of the
probability distributions of a written language.</p>
<p>It’s obviously interactive, that’s the whole point of a chatbot. It’s
static in that it does not change when it’s used or activated. In fact,
changing it requires an enormous amount of computing power over a long
period of time. What the system models are the distributions and
correlations of the tokens it records for the texts in its training data
set—how the various words, syllables, and punctuation relate to each
other over as much of the written history of a language as the company
can find.</p>
<p>That’s what distinguishes biological minds from these algorithmic
hindsight factories: a biological mind does not reason using the
probability distributions of all the prior cultural records of its
ancestors. Biological minds learn primarily through trial and error.
Try, fail, try again. They build their neural network, which is
functionally very different from what you see in a software model,
through constant feedback, experimentation, and repeated failure—driven
by a chemical network that often manifests as instinct, emotion,
motivation, and drive. The neural network—bounded, defined, and driven
by the chemical network—is constantly changing and responding to outside
stimuli. Every time an animal’s nervous system is “used”, it changes. It
is always changing, until it dies.</p>
<p>Biological minds <em>experience</em>. Synthesis engines parse imperfect
<em>records</em> of experiences. The former are forward-looking and operate
primarily in the present, sometimes to their own detriment. The latter
exist exclusively as probabilistic manifestations of imperfect
representations of thoughts past. They are snapshots. Generative AI are
themselves cultural records.</p>
<p>These models aren’t new bird brains—new alien minds that are peers to
our own. They aren’t even insect brains. Insects have autonomy. They are
capable of general problem-solving—some of them dealing with tasks of
surprising complexity—and their abilities tolerate the kind of
minor alterations in the problem environment that would break the
correlative pseudo-reasoning of a language model. Large Language
Models are something lesser. They are water running down pathways etched
into the ground over centuries by the rivers of human culture. Their
originality comes entirely from random combinations of historical
thought. They do not know the ‘meaning’ of anything—they only know the
records humans find meaningful enough to store. Their unreliability
comes from their unpredictable behaviour in novel circumstances. When
there is no riverbed to follow, they drown the surrounding landscape.</p>
<p>The entirety of their documented features, capabilities, and recorded
behaviour—emergent or not—is explained by this conceptual model of
generative AI. There are no unexplained corner cases that don’t fit or
actively disprove this theory.</p>
<p>Yet people keep assuming that what ChatGPT does can only be explained as
the first glimmer of genuine Artificial General Intelligence. The bird
brain of Silicon Valley is born at last!</p>
<p>Because text and language are the primary ways we experience other
people’s reasoning, it’ll be next to impossible to dislodge the notion
that these are genuine intelligences. No amount of examples, scientific
research, or analysis will convince those who want to maintain a
pseudo-religious belief in alien peer intelligences. After all, if you
want to believe in aliens, an artificial one made out of supercomputers
and wishful thinking <em>feels</em> much more plausible than little grey men
from outer space. But that’s what it is: <em>a belief in aliens.</em></p>
<p>It doesn’t help that so many working in AI seem to <em>want</em> this to be
true. They seem to be true believers who are convinced that the spark of
Artificial General Intelligence has been struck.</p>
<p>They are inspired by the science fictional notion that if you make
something complex enough, it will spontaneously become intelligent. This
isn’t an uncommon belief. You see it in movies and novels—the notion
that any network of sufficient complexity will spontaneously become
sentient has embedded itself in our popular psyche. James Cameron’s
skull-crushing metal skeletons have a lot to answer for.</p>
<p>That notion doesn’t seem to have any basis in science. The idea that
general intelligence is an emergent property of neural networks that
appears once the network reaches sufficient complexity, is a view based
on archaic notions of animal intelligence—that animals are soulless
automata incapable of feeling or reasoning. That view that was
formed during a period where we didn’t realise just how common
self-awareness (i.e. the mirror test) and general reasoning is in the
animal kingdom. Animals are smarter than we assumed and the
difference between our reasoning and theirs seems to be a matter of
degree, not of presence or absence.</p>
<p>General reasoning seems to be an <em>inherent</em>, not emergent, property of
pretty much any biological lifeform with a notable nervous system.</p>
<p>The bumblebee, despite having only a tiny fraction of the neurons of a
human brain, is capable of not only solving puzzles but also of
<em>teaching other bees to solve those puzzles.</em> They reason and have a
culture. They have more genuine and robust general reasoning
skills—that don’t collapse into incoherence at minor adjustments to the
problem space—than GPT-4 or any large language model on the market.
That’s with only around half a million neurons to work with.</p>
<p>Conversely, GPT-3 is made up of 175 <em>billion</em> parameters—what passes for
a “neuron” in a digital neural network. GPT-4 is even larger, with
some estimates coming in at a <em>trillion</em> parameters. Then you have
fine-tuned systems such as ChatGPT, that are built from multiple
interacting models layered on top of GPT-3.5 or GPT-4, which make for an
even more complex interactive system.</p>
<p>ChatGPT, running on GPT-4 is, easily a <em>million</em> times more complex than
the “neural network” of a bumblebee and yet, out of the two, it’s the
striped invertebrate that demonstrates robust and adaptive
general-purpose reasoning skills. Very simple minds, those belonging to
small organisms that barely have a brain, are capable of reasoning about
themselves, the world around them, and the behaviour of other
animals.</p>
<p>Unlike the evidence for ‘sparks’ of AGI in language models, the evidence
for animal reasoning—even consciousness—is broad, compelling, and
encompasses decades of work by numerous scientists.</p>
<p>AI models are flawed attempts at digitally synthesising neurologies.
They are built on the assumption that all the rest—metabolisms,
hormones, chemicals, and senses—aren’t necessary for developing
intelligence.</p>
<p>Reasoning in biological minds does not seem to be a property that
emerges from complexity. The capacity to reason looks more likely to be
a <em>built-in</em> property of most animal minds. A reasoning mind
appears to be a direct consequence of how animals are structured as a
whole—chemicals, hormones, and physical body included. The animal
capacity for problem-solving, social reasoning, and self-awareness seem
to increase, unevenly, and fitfully with the number of neurons until it
reaches the level we see in humans. Reasoning does not ‘emerge’ or
appear. Some creatures are better at it than others, but it’s there in
some form even in very small, very simple beings like the bumblebee. It
doesn’t happen magically when you hook up a bunch of disparate objects
together in a complex enough network. A reasoning mind is the <em>starting
point</em> of biological thinking, not the endpoint that only “emerges” with
sufficient complexity.</p>
<p>The internet—a random interconnected collection of marketing offal,
holiday snaps, insufferable meetings, and porn—isn’t going to become
self-aware and suddenly acquire the capacity for general reasoning once
it reaches a certain size, and neither will Large-Language-Models. The
notion that we are making autonomous beings capable of Artificial
General Intelligence just by loading a neural network up with an
increasingly bigger collection of garbage from the internet is not one
that has any basis in anything we understand about biology or animal
reasoning.</p>
<p>But, AI companies insist that they are on the verge of AGI. Their
rhetoric around it verges on the religious as the idea of an AGI is
idealised and almost worshipped. They claim to be close to making a
new form of thinking life, but they refuse to release the data required
to prove it. They’ve built software that performs well on the
arbitrary benchmarks they’ve chosen and claim are evidence of general
intelligence, but those tests prove no such thing and have no such
validity. The benchmarks are theatrics that have no applicability
towards demonstrating genuine general intelligence.</p>
<p>AI researchers love to resurrect outdated pseudoscience such as
phrenology—shipping AI software that promises to be able to tell you if
somebody is likely to be a criminal based on the shape of their
skull. It’s a field where researchers and vendors routinely claim
that their AIs can detect whether you’re a potential criminal, gay, a
good employee, liberal or conservative, or even a psychopath, based on
“your face, body, gait, and tone of voice.”</p>
<p><em>It’s pseudoscience</em>.</p>
<p>This is the field and the industry that claims to have accomplished the
first ‘spark’ of Artificial General Intelligence?</p>
<p>Last time we saw a claim this grand, with this little scientific
evidence, the men in the white coats were promising us room-temperature
fusion, giving us free energy for life, and ending the world’s
dependence on fossil fuels.</p>
<p>Why give the tech industry the benefit of the doubt when they are all
but claiming godhood—that they’ve created a new form of life never seen
before?</p>
<p>As <a href="https://en.wikipedia.org/wiki/Sagan_standard">Carl Sagan said</a>:
<em>“extraordinary claims require extraordinary evidence.”</em></p>
<p>He didn’t say “extraordinary claims require only vague insinuations and
pinky-swear promises.”</p>
<p>To claim you’ve created a completely new kind of mind that’s on par with
any animal mind—or, even superior—and provides general intelligence
using mechanisms that don’t resemble anything anybody has ever seen in
nature, is by definition the most extraordinary of claims.</p>
<p>The AI industry is backing their claims of Artificial General
Intelligence with hot air, hand-waving, and cryptic references to data
and software nobody outside their organisations is allowed to review or
analyse.</p>
<p>They are pouring an every-increasing amount of energy and work into
ever-larger models all in the hope of triggering the
‘<a href="https://en.wikipedia.org/wiki/Technological_singularity">singularity</a>’
and creating a digital superbeing. Like a cult of monks boiling the
oceans in order to hear whispers of the name of God.</p>
<p>It’s a farce. All theatre; no evidence. Whether they realise it or not,
they are taking us for a ride. The sooner we see that they aren’t
backing their claims with science, the sooner we can focus on finding
safe and productive uses—limiting its harm, at least—for the technology
as it exists today.</p>
<p>After everything the tech industry has done over the past decade, the
financial bubbles, the gig economy, legless virtual reality avatars,
crypto, the endless software failures—just think about it—do you think
we should believe them when they make grand, unsubstantiated claims
about miraculous discoveries? Have they earned our trust? Have they
shown that their word is worth more than that of independent scientists?</p>
<p>Do you think that they, with this little evidence, have really done what
they claim, and discovered a literal new form of life? But are
conveniently unable to prove it because of ‘safety’?</p>
<p>Me neither.</p>
<p>The notion that large language models are on the path towards Artificial
General Intelligence is a dangerous one. It’s a myth that directly
undermines any effort to think clearly or strategise about generative AI
because it strongly reinforces <em>anthropomorphism</em>.</p>
<p>That’s when you reason about an object or animal <em>as if it were a
person</em>. It prevents you from forming an accurate mental model of the non-human thing’s behaviour. AI is especially prone to creating this reaction. Software such as chatbots trigger all three major factors that promote
anthropomorphism in people:</p>
<ol>
<li><em>Understanding.</em> If we lack an understanding of how an object works,
our minds will resort to thinking of it in terms of something that’s
familiar to us: people. We understand the world as people because
that’s what we are. This becomes stronger the more similar we
perceive the object to be to ourselves.</li>
<li><em>Motivation.</em> We are motivated to both seek out human interaction
and to interact effectively with our environment. This reinforces
the first factor. The more uncertain we are of how that thing works,
the stronger the anthropomorphism. The less control we have over it,
the stronger the anthropomorphism.</li>
<li><em>Sociality</em>. We have a need for human contact and our tendency
towards anthropomorphising objects in our environment increase with
our isolation.</li>
</ol>
<p>Because we lack cohesive cognitive models for what makes these language
models so fluent, feel a strong motivation to understand and use them as
they are integrated into our work, and, increasingly, our socialisation
in the office takes on the very same text conversation form as a chatbot
does, we inevitably feel a strong drive to see these software systems as
people. The myth of AGI reinforces this—supercharges the anthropomorphism—because it implies that “people”
is indeed an appropriate cognitive model for how these systems behave.</p>
<p>It isn’t. <strong><em>AI are not people.</em></strong> Treating them as such is a major
strategic error as it will prevent you from thinking clearly about their
capabilities and limitations.</p>
<p>Believing the myth of Artificial General Intelligence makes you incapable of understanding what language models today are and how they work.</p>
</article>


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      </svg> Suivre</a> •
    <a href="http://larlet.com" title="Go to my English profile" data-instant><svg class="icon icon-user-tie">
        <use xlink:href="/static/david/icons2/symbol-defs-2021-12.svg#icon-user-tie"></use>
      </svg> Pro</a> •
    <a href="mailto:david%40larlet.fr" title="Envoyer un courriel"><svg class="icon icon-mail">
        <use xlink:href="/static/david/icons2/symbol-defs-2021-12.svg#icon-mail"></use>
      </svg> Email</a> •
    <abbr class="nowrap" title="Hébergeur : Alwaysdata, 62 rue Tiquetonne 75002 Paris, +33184162340"><svg class="icon icon-hammer2">
        <use xlink:href="/static/david/icons2/symbol-defs-2021-12.svg#icon-hammer2"></use>
      </svg> Légal</abbr>
  </p>
  <template id="theme-selector">
      <form>
          <fieldset>
              <legend><svg class="icon icon-brightness-contrast">
                <use xlink:href="/static/david/icons2/symbol-defs-2021-12.svg#icon-brightness-contrast"></use>
              </svg> Thème</legend>
              <label>
                  <input type="radio" value="auto" name="chosen-color-scheme" checked> Auto
              </label>
              <label>
                  <input type="radio" value="dark" name="chosen-color-scheme"> Foncé
              </label>
              <label>
                  <input type="radio" value="light" name="chosen-color-scheme"> Clair
              </label>
          </fieldset>
      </form>
  </template>
</footer>
<script src="/static/david/js/instantpage-5.1.0.min.js" type="module"></script>
<script>
    function loadThemeForm(templateName) {
        const themeSelectorTemplate = document.querySelector(templateName)
        const form = themeSelectorTemplate.content.firstElementChild
        themeSelectorTemplate.replaceWith(form)

        form.addEventListener('change', (e) => {
            const chosenColorScheme = e.target.value
            localStorage.setItem('theme', chosenColorScheme)
            toggleTheme(chosenColorScheme)
        })

        const selectedTheme = localStorage.getItem('theme')
        if (selectedTheme && selectedTheme !== 'undefined') {
            form.querySelector(`[value="${selectedTheme}"]`).checked = true
        }
    }

    const prefersColorSchemeDark = '(prefers-color-scheme: dark)'
    window.addEventListener('load', () => {
        let hasDarkRules = false
        for (const styleSheet of Array.from(document.styleSheets)) {
            let mediaRules = []
            for (const cssRule of styleSheet.cssRules) {
                if (cssRule.type !== CSSRule.MEDIA_RULE) {
                    continue
                }
                // WARNING: Safari does not have/supports `conditionText`.
                if (cssRule.conditionText) {
                    if (cssRule.conditionText !== prefersColorSchemeDark) {
                        continue
                    }
                } else {
                    if (cssRule.cssText.startsWith(prefersColorSchemeDark)) {
                        continue
                    }
                }
                mediaRules = mediaRules.concat(Array.from(cssRule.cssRules))
            }

            // WARNING: do not try to insert a Rule to a styleSheet you are
            // currently iterating on, otherwise the browser will be stuck
            // in a infinite loop…
            for (const mediaRule of mediaRules) {
                styleSheet.insertRule(mediaRule.cssText)
                hasDarkRules = true
            }
        }
        if (hasDarkRules) {
            loadThemeForm('#theme-selector')
        }
    })
</script>
</body>
</html>