A Neural Network Writes About Networks

And Other Marvelously Meta Matters

For over a month now, I’ve been writing about the rising seas of AI-generated media, the profusion of AI-generated art, and the ethical quandaries involved. So, it was only a matter of time before I stumbled onto the topic of AI prose generators like ChatGPT.

Except, I didn’t stumble.

I was pushed.

Pushed by several folks familiar with my interest in AIs and then by the sudden onslaught of headlines heralding the overnight sensation known at OpenAI’s superchatbot. Didn’t I warn you about the coming deluge? The water is rising fast now.

Anyway, I spent a little time today playing with the world’s latest chatbot darling. Here’s some of what I found.

Explain Yourself!

Not feeling very original, I started by asking the chatbot about itself, and here is its self-portrait:

ChatGPT is a natural language processing (NLP) tool that uses a pre-trained language model to generate responses to user input in a conversational manner. The tool takes in user input, processes it using the language model, and generates a response based on the context and keywords in the input. The generated response is then displayed to the user, allowing for a back-and-forth conversation.

Well, that’s pretty prosaic but also nicely succinct. In fact, it’s written in better English than you’ll see in much of today’s blogosphere, both macro and micro.

Alrighty then. So, I wanted to go a bit deeper and asked it a simple question: “Is ChatGPT based on a neural network?” Here’s what it had to say:

Yes, ChatGPT is a variant of the GPT-3 language model, which is based on a neural network architecture. GPT-3 uses a type of network known as a transformer, which is specifically designed for natural language processing tasks. The model is trained using a combination of supervised learning, in which it is provided with large amounts of labeled data to learn from, and unsupervised learning, in which it is allowed to discover patterns in the data on its own. This allows it to generate human-like text that is often difficult to distinguish from text written by a person.

The Network Writing About Networks

But what, you may wonder, is a neural network? Glad you asked. ChatGPT has the answer to that one as well:

Neural networks are a type of machine learning algorithm that is designed to recognize patterns in data. They are called “neural networks” because they are inspired by the way the human brain works, with individual neurons working together to process information. In a neural network, there are many interconnected processing nodes, each of which performs a simple calculation on the data it receives. These calculations are designed to be similar to the way that neurons in the brain process information. The network as a whole is able to learn and adapt based on the data it receives, allowing it to improve its performance over time. This makes neural networks a powerful tool for tasks like image and speech recognition, natural language processing, and more.

The Difference Between Systems and Networks

Then I asked ChatGPT about the difference between systems and networks. I was genuinely curious about how it would parse those differences since I’ve found the distinction challenging myself. It did a find job laying things out clearly and at considerable length:

In a general sense, networks and systems are both collections of interconnected components that work together to achieve a common goal. However, there are some key differences between the two.

Networks are typically characterized by their interconnectedness and the flow of information or resources between nodes in the network. Networks can be thought of as a web of connections that facilitate the transfer of information or resources. Examples of networks in the natural world include neural networks in the brain, social networks, and ecological networks.

On the other hand, systems are characterized by their organization and the relationship between components that work together to achieve a common goal. Systems can be thought of as a set of components that are organized and interact with each other to perform a specific function or process. Examples of systems in the natural world include the human body, ecosystems, and weather patterns.

In summary, the key differences between networks and systems are the focus on interconnectedness in networks and the focus on organization in systems. Networks are characterized by the flow of information or resources between nodes, while systems are characterized by the interaction and organization of components to achieve a specific goal.

Is the Universe a Network?

Honestly, I doubt I would have been able describe those distinctions that well in the space of four short paragraphs. Well done, superchatbot!

So, I took our discussion to the ultimate level, asking about whether the universe itself is a network. Being a network nerd, I found the answer inspiring.

The universe can be seen as a network because it is comprised of various interconnected systems and structures. For example, the solar system is a network of planets, moons, and other celestial bodies that are held together by gravity. Similarly, the galaxy is a network of stars, gas, and dust that are held together by the force of gravity.

Additionally, the universe is a network of interconnected physical forces and laws, such as gravity, electromagnetism, and the strong and weak nuclear forces. These forces and laws work together to govern the behavior of matter and energy in the universe.

Furthermore, the universe is a network of living organisms and ecosystems that are interconnected through the cycles of matter and energy. For example, plants and animals rely on each other for food and oxygen, and their waste products are used by other organisms in the ecosystem.

Overall, the universe can be seen as a vast, interconnected network of systems, structures, forces, laws, and living organisms that work together to create and sustain the complex and dynamic nature of the universe.

Well, okay, so ChatGPT might get a little metaphysical here. For example, is there really such a thing as network of laws? Well, maybe. All in all, though, superchatbot whipped up a Carl-Sagan-esque answer about a topic as large as the universe in just a few seconds.

Poetic Justice

Like many other people, I was blown away by OpenAI’s superchatbot. I mean, yeah, it did some weird things. For example, it told me a pretty off-color joke about Joe Biden and a downright surreal one about Donald Trump. The bot may not be quite ready for its own comedy special yet.

And, it sometimes contradicted itself in weird ways, one minute claiming “I am in a good headspace and am able to appreciate the present moment” and the next “I do not have the ability to experience emotions.”

But still, it was able to do many other fascinating things, from writing love poetry to God in the manner of John Donne to providing a nice little book review of Moby-Dick.

Honestly, circa 2022-23, it appears we writers may soon be redundant, or at least practicing our craft with much greater humility. And it’s not just us, either. I also played a bit with OpenAI’s computer programming tool. Just by describing what I wanted the program to do, I got the codebot to write up several bits of a Python code, from a simple dictionary to a function that identifies whether or not a number is prime.

So, the good(ish) news is that we writers and artists will not be lonely in the unemployment line. Developers will be right there alongside us. Poetic justice, I suppose. In fact, I asked ChatGPT to write a poem on the topic, so I’m going to give it the last rather chillingly optimistic word:

In a world of endless possibility

Where machines can do the work of many

The jobs that once belonged to us

Are now at risk of obsolescence

Gone are the days of endless code

When writing meant pouring out your soul

Now AI can do it faster and better

Leaving writers out in the cold

And as for artists, once so revered

Their skills no longer needed

As AI can create with ease

Leaving them to wonder and grieve

But fear not, dear human friends

For though our jobs may disappear

We will find new ways to thrive

In a world transformed by AI.

The Rising Seas of AI-Generated Media

We are about to be awash in AI-generated media, and our society may have a tough time surviving it.

Generated by Stable Diffusion. The prompt was “Dali tsunami”

Our feet are already wet, of course. The bots inhabit Twitter like so many virtual lice. And chatbots are helpfully annoying visitors on corporate websites the world over. Meanwhile, algorithms have been honing their scribbler skills on the virtual Grub Street of the Internet for a while now.

But soon, and by soon I mean within months, we will be hip deep in AI-generated content and wondering how high the tide is going to get.

My guess is high, baby. Very high indeed.

What Are We Really Talking Here?

Techopedia defines generative AI as a “broad label that’s used to describe any type of artificial intelligence that uses unsupervised learning algorithms to create new digital images, video, audio, text or code.” In short, it’s all about AI-generated media.

Generated by Stable Diffusion. Prompt was “network”

I think that label will ultimately prove too restrictive, but let’s start there. So far, most of the hype is indeed around media, especially image creation and automated writing, with music and video not being far behind.

But we’ll get to that.

For now it’s enough to say that generative AI works by learning from, and being “inspired by,” the dynamic global reticulum that is the Internet.

But generative AI also applies to things like computer code. And, by and by, it’ll start generating atoms in addition to bits and bytes. For example, why couldn’t generative AI be applied to 3D printing? Why not car and clothing design? Why not, even, the creation of new biological systems?

The Money Generator

First, let’s follow the money. So how much dough is going into generative AI these days?

Answer: how much you got, angels and VCs?

Generated by Stable Diffusion. Prompt “printing press printing money”

For example, a start-up called Stability AI, which created the increasingly popular Stable Diffusion image-generating algorithm, was recently injected with a whopping $101 million round of investment capital. The company is now valued at a billion bucks.

Meanwhile other image generators such as DALL-E 2 and Midjourney have already acquired millions of users.

But investors are not just hot for image generators. Jasper, a generative writing company that’s just a year old (and one that plagues me with ads on Facebook) recently raised $125 million in venture capital and has a $1.5 billion valuation.

Investing in these technologies is so hot that a Gen AI Market Map from Sequoia recently went viral. The wealth wave rises and everyone wants to catch it.

Running the Gamut

Although image and prose (usually with an eye toward marketing) are the hot tickets in generative AI for now, they are just the proverbial tip of the iceberg. Indeed, it appears that Stability AI, for one, has much grander plans beyond images.

Generated by Stable Diffusion. Prompt was “color gamut”

The New York Times reports that the company’s soon-to-be massive investments in AI hardware will “allow the company to expand beyond A.I.-generated images into video, audio and other formats, as well as make it easy for users around the world to operate their own, localized versions of its algorithms.”

Think about that a second. Video. So people will be able to ask generative AI to quickly create a video of anything they can imagine.

Fake Film Flim-Flams

Who knows where this leads? I suppose soon we’ll be seeing “secret” tapes of the Kennedy assassination, purported “spy video” of the Trump/Putin bromance, and conspiracy-supporting flicks “starring” a computer-generated Joe Biden.

Generated by Stable Diffusion. Prompt was “human shakes hands with extraterrestrial”

We can only imagine the kind of crap that will turn up on YouTube and social media. Seems likely that one of the things that generative AI will generate is a whole new slew of conspiracists who come to the party armed with the latest videos of Biden handing over Hunter’s laptop to the pedophiliac aliens who wiped Hilary’s emails to ensure that Obama’s birth place couldn’t be traced back to the socialist Venusians who are behind the great global warming scam.

Even leaving political insanity aside, however, what happens to the film and television industries? How long until supercomputers are cranking out new Netflix series at the rate of one per minute?

Maybe movies get personalized. For example, you tell some generative AI to create a brand new Die Hard movie in which a virtual you plays the Bruce Willis role and, presto, out pops your afternoon’s entertainment. Yippee ki yay, motherfucker!

So, AI-generated media on steroids. On an exponential growth curve!

Play that Fakey Music

Then there are the sound tracks to go with those AI-gen movies. The Recording Industry Association of America (RIAA) is already gearing up for these battles. Here’s a snippet of what it submitted to the Office of the U.S. Trade Representative.

Generated by Stable Diffusion. Prompt was “music”

There are online services that, purportedly using artificial intelligence (AI), extract, or rather, copy, the vocals, instrumentals, or some portion of the instrumentals (a music stem) from a sound recording, and/or generate, master or remix a recording to be very similar to or almost as good as reference tracks by selected, well known sound recording artists.

To the extent these services, or their partners, are training their AI models using our members’ music, that use is unauthorized and infringes our members’ rights by making unauthorized copies of our members’ works. In any event, the files these services disseminate are either unauthorized copies or unauthorized derivative works of our members’ music.

That’s an interesting argument that will probably be tried by all creative industries. That is, just training your AI based on Internet copies of musical works violates copyright even if you have no intention of directly using that work in a commercial project. I imagine the same argument could be applied to any copyrighted work. Who know what this will mean for “synthetic media,” as some are calling.

Of course, there are plenty of uncopyrighted works AI can be trained on, but keeping copyrighted stuff from being used for machine learning programs could put a sizeable dent in the quality of generative AI products.

So, it won’t only be media that’s generated. Imagine the blizzard of lawsuits until it’s all worked out.

Stay tuned.

Revenge of the Code

AI can code these days. Often impressively so. I suppose it’d be ironic if a lot of software developers were put out of work by intelligent software, but that’s the direction we seem headed.

Consider the performance of DeepMind’s AlphaCode, an AI designed to solve challenging coding problems. The team that designed it had it compete with human coders to solve 10 challenges on Codeforces, a platform hosting coding contests.

Generated by Stable Diffusion. The prompt was “Vinge singularity”

Prof. John Naughton writing in The Guardian describes the contest and summarizes, “The impressive thing about the design of the Codeforces competitions is that it’s not possible to solve problems through shortcuts, such as duplicating solutions seen before or trying out every potentially related algorithm. To do well, you have to be creative.”

On its first try, AlpaCode did pretty well. The folks at DeepMind write, “Overall, AlphaCode placed at approximately the level of the median competitor. Although far from winning competitions, this result represents a substantial leap in AI problem-solving capabilities and we hope that our results will inspire the competitive programming community.”

To me, a very amateurish duffer in Python, this is both impressive and alarming. An AI that can reason out natural language instructions and then code creatively to solve problems? It’s kind of like a Turing test for programming, one that AlphaCode might well be on target to dominate in future iterations.

Naughton tries to reassure his readers, writing that “engineering is about building systems, not just about solving discrete puzzles,” but color me stunned.

With this, we seem to be one step closer to Vernor Vinge’s notion of the technological singularity, in case you needed another thing to keep you up at night.

Up and Atoms

Movies? Music? Code?

What’s next for generative AI once it finds its virtual footing?

Generated by Stable Diffusion. Prompt was “atoms”

Well, atoms are the natural next step.

Ask yourself: if generative AI can easily produce virtual images, why not sculptures via 3D printers? Indeed, why not innovative practical designs?

This is not a new idea. There is already something called generative design. Sculpteo.com describes, “Instead of starting to work on a design from scratch, with a generative design process, you tell the program what you need to accomplish, you set your design goals and mention all the parameters you can. No geometry is needed to start a project. The software will then deliver you hundreds or thousands of design options, the AI can also make an in-depth analysis of the design and establish which one is the most efficient one! This method is perfect to explore design possibilities to get an optimal part.”

Yup, perfect.

How About Bio?

Generated by Stable Diffusion. Prompt was “bioprinter”

Not long ago, I wrote a tongue-in-cheekish post about the singularity. An acquaintance of mine expressed alarm about the idea. When I asked what scared her most, she said, “If AI can alter DNA, I’d say the planet is doomed.”

That particular scenario had never occurred to me, but it’s easy enough to see her point. DNA is biological code. Why not create a generative AI that can design new life forms almost as easily as new images?

Generated by Stable Diffusion. Prompt was “live cells”

In fact, why stop at design? Why not 3D print the new critters? Again, this is a concept that already exists. As the article “3D Bioprinting with Live Cells” describes it, “Live cell printing, or 3D bioprinting, is an emerging technology that poses a revolutionary development for tissue engineering and regeneration. This bioprinting method involves the creation of a spatial arrangement of living cells and biologics into a functionalized tissue.”

The good news? Probably some fascinating new science, designer replacement organs on demand, and all the strange new machine-generated meat you can eat!

The bad news? Shudder. Let’s not go there today.

Mickey Mouse and the Age of Innovative AI

Although we’re calling this generative AI, the better term might be innovative AI. We are essentially contracting AI writers, artists and coders to do our bidding. Sure, they’re imitating, mixing and matching human-made media, but they are nonetheless “the talent” and will only get better at their jobs. We, on the other hand, are promoted to the positions of supercilious art directors, movie producers and, inevitably (yuck) critics.

Generated by Stable Diffusion. Prompt was “Tim Burton 3 people caught in whirlpool”

If the singularity ever actually happens, this emerging age of innovative AI will be seen as a critical milestone. It feels like a still rough draft of magic, and it may yet all turn out wonderfully.

But I find it hard not to foresee a Sorcerer’s Apprentice scenario. Remember in Fantasia, when Mickey Mouse harnesses the power of generative sorcery and winds up all wet and sucked down a whirlpool?

Unlike Mickey, we’ll have no sorcerer to save our sorry asses if we screw up the wizardry. This means that, on sum, we need to use these powerful technologies wisely. I hope we’re up to it. Forgive me if, given our recent experiences with everything from social media madness to games of nuclear chicken, I remain a bit skeptical on that front.

Feature image generated by Stable Diffusion. The prompt terms used were "Hokusai tsunami beach people," with Hokusai arguably being the greatest artist of tsunamis in human history. In other words, the AI imitated Hokusai's style and came up with this original piece.