A useful definition of AGI (artificial general intelligence)

The term, I believe, was coined sometime in the 1990s because, by then, work in AI had concentrated on narrow application domains of one sort of another (e.g. chess). So, artificial general intelligence (AIG) is not narrow. It is broad. Here's a defintion supplied by Tom Davidson about a year ago:

By AGI, I mean computer program(s) that can perform virtually any cognitive task as well as any human, for no more money than it would cost for a human to do it. The field of AI is largely understood to have begun in Dartmouth in 1956, and since its inception one of its central aims has been to develop AGI.

OK. I note that the scope is very broad, which is no surprise to me.

But what does it mean? Yes, I know what the words mean and all that. But "any cognitive task"? Would you can to enumerate them? If not, then just how are we to work with that phrase?

One reason why GPT-3 cannot be scaled up to AGI

I found this remark by David Piepgrass (a software developer) buried deep in a discussion at Astral Codex Ten:

I think people misunderstand GPT in general, because to humans, words have meanings, so we hear GPT speak words and we think it's intelligent. I think the biggest GPT3 is only intelligent in the same sense as a human's lingustic subsystem, and in that respect it's a superintelligence: so far beyond any human that we mistake it for having general intelligence. But I'm pretty sure GPT3 doesn't have *mental models*, so there are a great many questions it'll never be able to answer no matter how far it is scaled up (except if it's already seen an answer that it can repeat.)

Yes. Though I note, that if you believe semantics to be an aspect of the linguistic system (as I do), then GPT-3 doesn’t really cover language. But, in this context, that’s a quibble.

Here’s the important point: When Gary Marcus and others say GPT-3 lacks meaning, that’s what they’re talking about, no mental model. That’s what was going on in with symbolic AI back in the Jurassic era, the construction of a mental model in some propositional form. It’s not at all obvious just how one can couple neural nets with symbolic propositional models, but it has to be done somehow.

For some hints, see these posts:

• Think of GPT-3 as System 1. Now augment it with a symbolic System 2. [Dual-System, Neuro-Symbolic Reasoning]
• Geoffrey Hinton says deep learning will do everything. I’m not sure what he means, but I offer some pointers. Version 2.

Strange bird on the Hudson?

These bleeding-edge AI thinkers have little faith in human progress and seem to fear their own shadows

I suspect, though, if you put the question to them, they’d deny it. “Are you crazy! Of course we believe in human progress.” Don’t believe them. It’s not that I think they’re being deceptive. Rather, I think they’re deceived about the implications of their ideas.

Just what bleeding-edge AI thinkers am I talking about? Scott Summers has a long and tangled post called, Biological Anchors: A Trick That Might Or Might Not Work. He says the post is “trying to review and summarize Eliezer Yudkowksy's recent dialogues on AI safety.”[1] Those people and others like them.

Let me explain

At the heart of those discussions is a long report in which Ajeya Cotra attempts to estimate when human-level AI would finally see the light of day – I’ve not read it myself, but I’ve read quite a lot about it. They assume that human-level AI is inevitable, fear that it might go rogue and turn on us, and are very worried that there is little we can do to stop this from happening – though, I note, some of them are sitting on funding they’d like to give out to researchers who want to try.

From my point of view that means that they’ve given up on humanity and have all but ceded the future to computers. As you may know, I take a different view of these matters. I believe that, over the long haul, humankind as evolved ever more powerful systems of thought and that we are currently in the process of doing it again. This is not the place to offer even a short synopsis how this has happened beyond noting that it involves reflective abstraction (as discussed by Jean Piaget) and the recursive elaboration of new systems of thought over old so that the processes of old systems become objects operated on by the newer systems.[2] Computers and the idea(s) of computation are central to the current evolutionary ramp.

Computers didn’t come out of nowhere. They don’t grow on trees. We invented them. We had to conceived, design, construct, and operate them. Time and again. Computers allow us to explore ideas in ways that would be impossible without them. Even the idea of computation, without being linked to any specific computational activity, has proven enormously fruitful.

I doubt that these AI thinkers would find anything surprising or even interesting in that paragraph. But they take it for granted. They shouldn’t do that, not if they care about the future.

The process of developing new computational regimes requires us to develop new ideas. We have done and are doing that. Surely if we are to develop artificial systems with (near) human-level conceptual capabilities, will that not force us, give us the means, to think more deeply about ourselves? Other fields are developing new ideas as well, perhaps even fundamentally new ideas. Those ideas in our heads, in our collective culture, that’s where progress lies. They are the matrix in which computation, the ideas and the technologies, exists.

Why fear your own creations?

The men and women who created atomic weaponry feared their own creations. They had good reason to. The science was there; they knew that, once the engineering had been done, that the bombs would be horribly powerful. They were proven correct in Japan in 1945.

The situation with computing is quite different. We have yet to create human-level general AI, and yet these thinkers who are trying to bring it about, they’re already afraid of what will happen if they are successful. They fear the shadows they cast ahead of themselves as they walk.

Why?

Notes

[1] My recent post, What is AI alignment about?, contains a small snippet near the end of those conversations.

[2] This blog post isn’t the place to set forth these ideas, which the late David Hays and I have developed over the course of years. I’ve blogged extensively about these ideas. See, in particular, posts with these labels:

• cultural ranks https://new-savanna.blogspot.com/search/label/cultural%20ranks
• tech evol https://new-savanna.blogspot.com/search/label/tech%20evol

Note that not all of those posts will be specifically about the ideas that Hays and I have developed. Many are about ideas that I feel resonate with our ideas.

For a more systematic guide to those ideas, see, Mind-Culture Coevolution: Major Transitions in the Development of Human Culture and Society
, New Savanna, July 4, 2020, https://new-savanna.blogspot.com/2014/08/mind-culture-coevolution-major.html.

Here is a downloadable PDF, https://www.academia.edu/37815917/Mind-Culture_Coevolution_Major_Transitions_in_the_Development_of_Human_Culture_and_Society.

If you want to go straight to the Singularity, see my post, Redefining the Coming Singularity – It’s not what you think, New Savanna, May 16, 2017, https://new-savanna.blogspot.com/2014/09/redefining-coming-singularity-its-not.html.

That post is also available as a downloadable PDF, with some additional material, https://www.academia.edu/8847096/Redefining_the_Coming_Singularity_It_s_not_what_you_think.

Brian Cantwell Smith, Effing the ineffable: What AI teaches us about what can and cannot be said

Abstract:

A classical story takes the world to consist of objects exemplifying properties and standing in relations. Machine learning and other recent developments in AI support a different view: the world is stupefyingly rich and detailed, far more than can be captured in any finite representation. Representing the world in terms of objects, properties, and relations results from coarse-graining or abstracting over much richer underlying representations which carry more information than can readily be expressed in words. Or at least: more information than can be expressed in words according to classical theories of what words can mean and refer to.

Questions arise. What is out there, and how can we characterize it? What can words mean and refer to? Is the content of human language limited in the ways that classical theories assume? If I report that I laughed, and you grin in response, what has been communicated—and how?

Registration:

Smith starts discussing his notion of registration at about 20:54. He frames it in terms of the long-standing philosophical debate between realism – the world is out there independent of us – and constructivism – "objects and properties are human constructs. Each of those captures something true of the world. See the discussion of Miriam Yevick's work in, Showdown at the AI Corral, or: What kinds of mental structures are constructible by current ML/neural-net methods? [& Miriam Yevick 1975].

About Brian Cantwell Smith:

Brian Cantwell Smith came to Toronto as dean of the Faculty of Information in 2003, after positions at Xerox PARC, Stanford, University of Indiana, and Duke University. He was a founder of the Stanford Center for the Study of Language and Information, first president of Computer Professionals for Social Responsibility, and president of the Society for Philosophy and Psychology.

Smith’s research focuses on the foundations of computation and artificial intelligence. In the 1980s he developed the world’s first reflective programming language (3Lisp). He is the author of On the Origin of Objects and The Promise of Artificial Intelligence: Reckoning and Judgment (MIT Press, 1996 and 2019).

The psychedelic iris

Rodney Brooks has been making predictions: Concerning AI, “We’re still back in phlogiston land…”

Back on January 1, 2018 Rodney Brooks issued fairly specific predictions in three areas: 1) self-driving cars, 2) Artificial Intelligence, machine learning, and robotics, and 3) progress in the space industry. There are over a dozen predictions in each of those three areas. Brooks has updated those predictions each year since and plans to do so until 2050. You can find the most recent update, for 1.1.22, here: https://rodneybrooks.com/predictions-scorecard-2022-january-01/.

I’m not going to reprise any of those specific updates here, but I’d like to copy over some of his commentary for that second area, Artificial Intelligence, machine learning, and robotics.

Where’s the next big thing?

Back in 2018 I predicted that “the next big thing”, to replace Deep Learning, as the go to hot topic in AI would arrive somewhere between 2023 and 2027. I was convinced of this as there has always been a next big thing in AI. Neural networks have been the next big thing three times already. But others have had their shot at that title too, including (in no particular order) Bayesian inference, reinforcement learning, the primal sketch, shape from shading, frames, constraint programming, heuristic search, etc.

We are starting to get close to my window for the next big thing. Are there any candidates? I must admit that so far they all seem to be derivatives of deep learning in one way or another. If that is all we get I will be terribly disappointed, and probably have to give myself a bad grade on this prediction.

So far the things that I see bubbling around and getting people excited are transformers, foundation models, and unsupervised learning.

Concerning transformers:

These language models are over interpreted by people as understanding what they are spitting out, especially when the press writes stories where they have cherry picked responses. But they come with incredible problems, including copyright violations, intellectual theft of code, and even outright life threatening danger when they find their way into consumer products. Tech companies have a real problem in rushing some of these systems to market.

Continuing on:

Foundation models are large trained models that start out as a basis for tuning particular applications. There has been some self important announcements with a sort of me too feel (“Hey, I produced a foundation model too!!”), which don’t amount to much of an intellectual contribution. If this turns out to be the next big thing I am going to have to rip off my mask of equanimity and revert to my natural state of being a grumpy old man.

Unsupervised learning is an idea that has been around for a long time. Not a big intellectual jump to want to get it into deep learning–may be a hard technical problem, but not an intellectual breakthrough this time around.

The problem with AI

I have often stated that I think the field of AI, despite the great practical successes recently of Deep Learning, is probably a few hundred years away from where most people think it is. We’re still back in phlogiston land, not having yet figured out the elements, including oxygen.

Read that again and think about it. Does he really mean that? Why would he say such a thing? Is he nuts?

Let us assume that he’s correct. Given how impressive some current AI demonstrations are, can we not take Brooks’s view as implying that we have learned, or at least have the potential to learn, about ourselves and our own capacities? [Yeah, I know, that needs some unpacking. Maybe later.]

After he goes through his 14 specific predictions, Brooks reminds us of his bona fides:

AI, Robotics, and Machine Learning are areas that I have a real personal investment in. I wrote a terrible Masters thesis on ML back in 1977. I joined the Stanford AI Lab later that year, then the MIT AI Lab four years later, and became director of that lab in 1997, merging it with LCS (Lab for Computer Science) to form MIT CSAIL in 2003, the largest lab at MIT, still today. I have founded six AI and robotics companies. After 45 years in the academic and industry trenches can I be unbiased? Probably not.

I know that many who disagree with me will dismiss me for all that experience that I have. Perhaps those who agree with me should also dismiss me for the same reason!!

That last paragraph is interesting. Why would someone dismiss him for all his experience? He really knows this stuff, no? How can anyone look at this area without being biased in some way? Doesn’t naivete impose its own biases?

As you know, I’m of the belief that we’re in transition from one intellectual era to another. To which era does AI, robotics, and machine learning belong, the old one or the new. Maybe it straddles both. Maybe AI and robotics are old, machine learning new. Or maybe the perceptron is old, transformers new? Are we talking phlogiston or oxygen? How do you tell?

He goes on to state:

My current belief is that it all gets back to the symbol grounding problem, and even more deeply to adopting a computational approach to AI, Robotics, and ML (and I expect almost no one will agree with that latter claim).

Color me sympathetic to that last claim, that the computational approach is problematic. I’ve written a post on Brooks’s views: Has the computer metaphor for the mind run out of steam? New Savanna, June 19, 2019, https://new-savanna.blogspot.com/2019/06/has-computer-metaphor-for-mind-run-out.html.

He concludes by mentioning Brian Cantwell Smith, The Promise of Artificial Intelligence.

In this book Smith introduces the idea of registration, as a maintained relationship between an object outside of us and what goes on inside our head (and he would have it also in a classical computer) despite changes in perception and even context.

I’ve not read the book, but I’ve read reviews. I believe Smith introduces a distinction between reckoning and judgement. Reckoning is what computers do, but only humans are capable of judgement, at least so far. Intelligence requires judgement. I think we do need a fairly specific term for what it is that AI systems do. I kind of like “reckoning”. Note: Smith talks about registration in the video I've embedded here.

Agency in machines and humans

Those who fear that AIs will go rogue (e.g. the alignment folks) seem to think that agency comes with human-level intelligence. For all I know, they may have theorized just how it comes about. But that’s not how it works in humans.

Humans are born with agency. Our capacity to exercise agency may be quite limited at birth, but it’s there. For that matter, animals possess agency as well. Whatever agency is, it doesn’t seem to inhere in intelligence.

And so forth.

Note: This comment in a rather long discussion confirms my suspicion.

What is AI alignment about?

There are people who believe that sooner or later artificially intelligent systems will have human level intelligence. Some of those believe this also fear that such systems might very well go rogue act in ways harmful to humans and perhaps even to humanity as a whole. That, more or less, is what is mean by the AI alignment problem. While I’ve known about this for some time, I don’t follow those discussions because I’m skeptical about the eventual development or, as the case may be, emergence of such systems.

But, on general principles, I do dip into those discussions from time to time. One of those discussions is taking place at Lesswrong, Late 2021 MIRI Conversations: AMA / Discussion. This particular discussion takes place at the end of a series of discussions that has been going on since late 2021. I’ve read a bits and pieces of those discussions, but no more. Anyhow, deep into this particular discussion Rob Bensinger compiled a list of “important questions people in the field seem to disagree a lot about”). I’m parking it here for future reference.

• Alignment
• How hard is alignment? What are the central obstacles? What kind of difficulty is it? (Is it hard like 'building a secure OS that works on the first try'? Hard like 'the engineering/logistics/implementation portion of the Manhattan Project'? Both? Some other option? Etc.)
• What alignment research directions are potentially useful, and what plans for developing aligned AGI [artificial general intelligence] have a chance of working?
• Deployment
• What should the first AGI systems be aligned to do?
• To what extent should we be thinking of "large disruptive act that upends the gameboard", versus "slow moderate roll-out of regulations and agreements across a few large actors"?
• Information spread
• How important is research closure and opsec for capabilities-synergistic ideas? (Now, later, in the endgame, etc.)
• Path to AGI
• Is AGI just "current SotA systems like GPT-3, but scaled up", or are we missing key insights?
• More broadly, what's the relationship between current approaches and AGI?
• How software- and/or hardware-bottlenecked are we on AGI?
• How compute- and/or data-efficient will AGI systems be?
• How far off is AGI? How possible is it to time future tech developments? How continuous is progress likely to be?
• How likely is it that AGI is in-paradigm for deep learning?
• If AGI comes from a new paradigm, how likely is it that it arises late in the paradigm (when the relevant approach is deployed at scale in large corporations) versus early (when a few fringe people are playing with the idea)?
• Should we expect warning shots? Would warning shots make a difference, and if so, would they be helpful or harmful?
• To what extent are there meaningfully different paths to AGI, versus just one path? How possible (and how desirable) is it to change which path humanity follows to get to AGI?
• Actors
• How likely is it that AGI is first developed by a large established org, versus a small startup-y org, versus an academic group, versus a government?
• How likely is it that governments play a role at all? What role would be desirable, if any? How tractable is it to try to get governments to play a good role (rather than a bad role), and/or to try to get governments to play a role at all (rather than no role)?

AIDS crew at work in Jersey City

Stop calling it the lizard brain [it's a misleading characterization of neuroanatomy and function]

Cesario, J., Johnson, D. J., & Eisthen, H. L. (2020). Your Brain Is Not an Onion With a Tiny Reptile Inside. Current Directions in Psychological Science, 29(3), 255–260. https://doi.org/10.1177/0963721420917687

Abstract:

A widespread misconception in much of psychology is that (a) as vertebrate animals evolved, “newer” brain structures were added over existing “older” brain structures, and (b) these newer, more complex structures endowed animals with newer and more complex psychological functions, behavioral flexibility, and language. This belief, although widely shared in introductory psychology textbooks, has long been discredited among neurobiologists and stands in contrast to the clear and unanimous agreement on these issues among those studying nervous-system evolution. We bring psychologists up to date on this issue by describing the more accurate model of neural evolution, and we provide examples of how this inaccurate view may have impeded progress in psychology. We urge psychologists to abandon this mistaken view of human brains.

From the article:

The final—and most important—problem with this mistaken view is the implication that anatomical evolution proceeds in the same fashion as geological strata, with new layers added over existing ones. Instead, much evolutionary change consists of transforming existing parts. Bats’ wings are not new appendages; their forelimbs were transformed into wings through several intermediate steps. In the same way, the cortex is not an evolutionary novelty unique to humans, primates, or mammals; all vertebrates possess structures evolutionarily related to our cortex (Fig. 1d). In fact, the cortex may even predate vertebrates (Dugas-Ford, Rowell, & Ragsdale, 2012; Tomer, Denes, Tessmar-Raible, & Arendt, 2010). Researchers studying the evolution of vertebrate brains do debate which parts of the forebrain correspond to which others across vertebrates, but all operate from the premise that all vertebrates possess the same basic brain—and forebrain—regions.

Neurobiologists do not debate whether any cortical regions are evolutionarily newer in some mammals than others. To be clear, even the prefrontal cortex, a region associated with reason and action planning, is not a uniquely human structure. Although there is debate concerning the relative size of the prefrontal cortex in humans compared with nonhuman animals (Passingham & Smaers, 2014; Sherwood, Bauernfeind, Bianchi, Raghanti, & Hof, 2012; Teffer & Semendeferi, 2012), all mammals have a prefrontal cortex.

The notion of layers added to existing structures across evolutionary time as species became more complex is simply incorrect. The misconception stems from the work of Paul MacLean, who in the 1940s began to study the brain region he called the limbic system (MacLean, 1949). MacLean later proposed that humans possess a triune brain consisting of three large divisions that evolved sequentially: The oldest, the “reptilian complex,” controls basic functions such as movement and breathing; next, the limbic system controls emotional responses; and finally, the cerebral cortex controls language and reasoning (MacLean, 1973). MacLean’s ideas were already understood to be incorrect by the time he published his 1990 book (see Reiner, 1990, for a critique of MacLean, 1990). Nevertheless, despite the mismatch with current understandings of vertebrate neurobiology, MacLean’s ideas remain popular in psychology.

Binding by synchrony

Is binding by synchrony back? https://t.co/rqymsxSxfd

— KordingLab 🦖 (@KordingLab) March 1, 2022

Steven Spielberg’s Jaws @3QD [plus further notes]

It’s time to finish my work on Steven Spielberg’s Jaws with a piece in 3 Quarks Daily:

Shark City Sacrifice: A Girardian reading of Steven Spielberg’s Jaws, https://3quarksdaily.com/3quarksdaily/2022/02/shark-city-sacrifice-a-girardian-reading-of-steven-spielbergs-jaws.html

As the title suggests, it is a revision of my original post – a different, tighter, ending, a sharp observation in the middle, and a revised order.

But am I finished? I like the 3QD piece. I think it’s better than my original post. Does it fully satisfy my curiosity, my interest, in Jaws? I don’t think so, but I have no specific plans to continuing working on the film. Maybe I will, maybe I won’t.

The problem: death in a pattern

What I’m seeding to understand is a pattern. Let’s say the pattern has three elements: 1) the film has two distinct parts (one set in Amity, the other on the ocean), 2) an obsessive shark hunter, 3) who is killed by a shark in the second part. For lack of a better term, let’s say that pattern is sacrifice, where sacrifice is understood as a kind of religious ritual. This is where Girard comes in, as he is a theorist of religious sacrifice. 

The problem, of course, is that there is no explicit religious ritual in Jaws. Rather, I’m reading it that way. Critics do this kind of thing all the time. What authorizes it? The pattern, no?

Jaws the film is also very much about death. Death came come as the natural end of a life well-lived; the body just wears out and one dies. But that’s not what happens in Jaws. The deaths in the first part are unexpected and brutal and happen in youth and middle age. They are “answered” by two deaths in the second part, both brutal. The shark kills Quint, Brody kills the shark. The pattern in the previous paragraph is organized around these deaths.

Quint shows up in the first part, abrasive, arrogant, but offering to kill the shark, for a price. He’s made his living hunting sharks, and everyone knows it. He’s identified with and obsessed by them. There is thus a coherence, an order, in the second part when the shark kills him and is, in turn, killed by the Chief. This is in stark contrast to the four deaths in the first part, which exhibit no human logic.

And so forth and so on. I could continue in this way and so once again work through the whole argument. I can see little point in doing that now. Perhaps I would fare better if I took some time to build some conceptual apparatus. But what would that apparatus be?

What of religion?

Here’s the original 1975 trailer for Jaws:

This is the opening voiceover:

There is a creature alive today, who has survived millions of years of evolution, without change, without passion, and without logic. It lives to kill, a mindless eating machine, it will attack, and devour, anything. It is as if God created the devil, and gave him, JAWS.

I don’t recall God being mentioned in the film at all. How’d God make it into the trailer? I suppose it’s nothing more than a conventionalized gesture, but still, it’s there. What of the reference to “millions of years of evolution”? The reference is secular, but it evokes a context stretching far beyond the small town of Amity. The shark, those JAWS, is not merely a hungry animal. It is a force of Nature.

Spielberg is pushing beyond the bounds of naturalistic realism, as he would do two years later in Close Encounters of the Third Kind (1977), and then again in Raiders of the Lost Ark (1981). Why? What need does he thereby satisfy? Whatever it is, that’s what underlies the sacrificial plot in Jaws.

Red iris

Being the Ricardos is wonderful in its virtuoso juggling of recollection, life, and fiction [Media Notes 68]

With Nicole Kidman as Lucille Ball and Javier Bardem as Desi Arnez, Being the Ricardos (2021) does a delightful job of telescoping events though three time scales. As the film opens we see and hear three writers from the TV show, I Love Lucy. I would guess that the present for those writers is sometime in the last quarter of the previous century, though no date is specified. They are telling as about past events, in particular, events during one week in 1953.

Then we shift back in time to Sunday evening of that week. Lucy and Desi are at home. They argue about his infidelities and then begin to engage in make-up sex. They stop when they hear Walter Winchell (a very influential purveyor of gossip) announce, at the end of his television broadcast, that “the most popular actress in America” is a Communist. He’s talking about Lucy. Back in 1953 such an accusation could have career-ending consequences.

Then we shift to the next day, with these words appearing on the screen:

MONDAY
Table Read

The cast, writers, director, and show-runner are seated around a table, chatting about Winchell’s announcement. Lucy and Desi are not there. They’re in a meeting with executives having the same conversation. Will they still have a show for taping on Friday night? Back to the table read; Lucy and Desi enter; Desi explains that Winchell got it wrong; they sit down.

They begin reading through the script. She starts critiquing the script. Chatting. We see that shot as it will appear in the show, in black and white. More chatting and critiquing, Kidman’s Lucille Ball voice is different from her Lucy Arnez voice.

Those writers reappear in their interviews, telling us how Lucy and Desi met. We shift further into the past and see their initial courtship. Arnez sings a tender ballad in Spanish. (Bardem does this beautifully.) They dance in a crowded nightclub. At one point he observes that she’s “kinetically gifted,” and important observation about what will become her comedic technique.

Back to the table read. Bill (the actor who portrays Fred Mertz in the TV show) and Desi talk about the accusation hanging over Lucy’s head. Desi and Lucy talk, about Winchell, about infidelity.

TUESDAY
Blocking Rehearsal

And so the movie goes, flowing between the writers in the present, rehearsal, shots from the show (in black and white) events during that week, back into the early days of Lucy’s and Desi’s relationship.

Lucy and Desi drop a bomb: Lucy’s pregnant.

WEDNESDAY
Camera Blocking

Continuing as before, interweaving the past, rehearsals (the show itself), artistry and tradecraft, crisis management.

Lucy and Desi tell the executives that they do not want to hide her pregnancy from the audience, though that has been done, but instead want to work it into the show. The executives do not like this. Remember, 1953.

THURSDAY
Run-Throughs

What interests me is that the film-makers – Aaron Sorkin wrote and directed – expect us to follow these events, to assemble them in our minds into a coherent story.

On Wednesday Desi had sent a telegram to the Chairman of Phillip Morris, which sponsored I Love Lucy, asking him how he wanted Lucy’s pregnancy handled. His reply came back: “Don’t fuck with the Cuban.”

Of course we dip into the deep past as well, ever advancing on the moment Lucy leaves movies for I Love Lucy.

Now we hear the band playing the “I Love Lucy” theme off camera and then, five or six seconds later:

FRIDAY
Show Night

It all comes together.* There’s no mystery about the commie-scandal collapsing. We all know that I Love Lucy continued for seven more years.

But just how these various streams come together, well, that’s the magic of film, no? Hollywood, speaking metaphorically and generically, had to learn how to do such things. Equally, we, the audience, had to learn as well. What cues do they offer us and what do we have to know about the world, and about movies, to pick up on them and assemble them into a coherent narrative.

This IS, after all, a film ABOUT film, and yet it doesn’t seem META, it doesn’t feel intellectual or self-conscious. It feels natural. 

How did we arrive here? 

Follow the word “home.”

* * * * *

*Note: This is the only time that the action moves from one day (in 1953) to the next BEFORE we are cued by labels on the screen. Something similar happens in Sir Gawain and the Green Knight, a medieval epic.