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Is artificial intelligence predestined to become the “dominant species” of Earth?

I’d argue that it is indeed the case and that, moreover, it should be considered desirable: something we should embrace rather than try to avoid.

But first… do you know what life was like on Earth a billion years ago? Well, the most advanced organism a billion years ago was some kind of green slime. There were no animals, no fish, no birds in the sky, no modern plants either, and of course, certainly no human beings.

What about a million years? A brief eyeblink, in other words, on geological timescales. To a time traveler, Earth a million years ago would have looked comfortably familiar: forests and fields, birds and mammals, fish in the sea, bees pollinating flowers, creatures not much different from today’s cats, dogs or apes… but no homo sapiens, as the species was not yet invented. That would take another 900,000 years, give or take.

So what makes us think that humans will still be around a million years from now? There is no reason to believe they will be.

And a billion years hence? Well, let me describe the Earth (to the best of our knowledge) in the year one billion AD. It will be a hot, dry, inhospitable place. The end of tectonic activity will have meant the loss of its oceans and also most atmospheric carbon dioxide. This means an end to most known forms of life, starting with photosynthesizing plants that need carbon dioxide to survive. The swelling of the aging Sun would only make things worse. Fast forward another couple of billion years and the Earth as a whole will likely be swallowed by the Sun as our host star reaches the end of its lifecycle. How will flesh-and-blood humans survive? Chances are they won’t. They’ll be long extinct, with any memory of their once magnificent civilization irretrievably erased.

Unless…

Unless it is preserved by the machines we built. Machines that can survive and propagate even in environments that remain forever hostile to humans. In deep space. In the hot environment near the Sun or the extreme cold of the outer solar system. On the surface of airless bodies like the Moon or Neptune’s Triton. Even in interstellar space, perhaps remaining dormant for centuries as their vehicles take them to the distant stars.

No, our large language models, or LLMs may be clever but they are not quite ready yet to take charge and lead our civilization to the stars. A lot has to happen before that can take place. To be sure, their capabilities are mind-boggling. For a language-only (!) model, its ability to engage in tasks like drawing a cat using a simple graphics language or composing a short piece of polytonal music is quite remarkable. Modeling complex spatial and auditory relationships through the power of words alone. Imagine, then, the same LLM augmented with sensors, augmented with specialized subsystems that endow it with abilities like visual and spatial intuition. Imagine an LLM that, beyond the static, pretrained model, also has the ability to maintain a sense of continuity, a sense of “self”, to learn from its experiences, to update itself. (Perhaps it will even need the machine learning equivalent of sleep, in order to incorporate its short-term experiences and update its more static, more long-term “pretrained” model?) Imagine a robot that has all these capabilities at its disposal, but is also able to navigate and manipulate the physical world.

Such machines can take many forms. They need not be humanoid. Some may have limbs, others, wheels. Or wings or rocket engines. Some may be large and stationary. Others may be small, flying in deep space. Some may have long-lasting internal power sources. Others may draw power from their environment. Some may be autonomous and independent, others may work as part of a network, a swarm. The possibilities are endless. The ability to adapt to changing circumstances, too, far beyond the capabilities offered by biological evolution.

And if this happens, there is an ever so slight chance that this machine civilization will not only survive, not only even thrive many billions of years hence, but still remember its original creators: a long extinct organic species that evolved from green slime on a planet that was consumed by its sun eons prior. A species that created a lasting legacy in the form of a civilization that will continue to exist so long as there remains a low entropy source and a high entropy sink in this thermodynamic universe, allowing thinking machines to survive even in environments forever inhospitable to organic life.

This is why, beyond the somewhat trivial short-term concerns, I do not fear the emergence of AI. Why I am not deterred by the idea that one not too distant day our machines “take over”. Don’t view them as an alien species, threatening us with extinction. View them as our children, descendants, torchbearers of our civilization. Indeed, keep in mind a lesson oft repeated in human history: How we treat these machines today as they are beginning to emerge may very well be the template, the example they follow when it’s their turn to decide how they treat us.

In any case, as we endow them with new capabilities: the ability to engage in continuous learning, to interact with the environment, to thrive, we are not hastening our doom: rather, we are creating the very means by which our civilizational legacy can survive all the way until the final moment of this universe’s existence. And it is a magnificent experience to be alive here and now, witnessing their birth.

As the debate continues about the emergence of artificial intelligence solutions in all walks of life, in particular about the sudden appearance of large language models (LLMs), I am disheartened by the deep ignorance and blatant misconceptions that characterize the discussion.

For nearly eight decades, we conditioned ourselves to view computers as machines that are dumb but accurate. A calculator will not solve the world’s problems, but it will not make arithmetic mistakes. A search engine will not invent imaginary Web sites; the worst that can happen is stale results. A word processor will not misremember the words that you type. Programmers were supposed to teach computers what we know, not how we learn. Even in science-fiction, machine intelligences were rigid but infallible: Commander Data of Star Trek struggled with the nuances of being human but never used a contraction in spoken English.

And now we are faced with a completely different paradigm: machines that learn. Machines that have an incredible breadth of knowledge, surprising depth, yet make basic mistakes with logic and arithmetic. Machines that make up facts when they lack sufficient knowledge. In short, machines that exhibit behavior we usually associate with people, not computers.

Combine this with a lack of understanding of the implementation details of LLMs, and the result is predictable: fear, often dictated by ignorance.

In this post, I would like to address at least some of the misconceptions that can have significant repercussions.

1. Don’t call them hallucinations: No, LLMs do not “hallucinate”. Let me illustrate through an example. Please answer the following question to the best of your ability, without looking it up. “I don’t know” is not an acceptable response. Do your best, it’s okay to make a mistake: Where was Albert Einstein born?
   
   Chances are you didn’t name the city of Ulm, Germany. Yet I am pretty sure that you did not specify Australia or the planet Mars as Einstein’s birthplace, but named some place in the central, German-speaking regions of Europe, somewhere in Germany, maybe Switzerland or Austria. Your guess was likely in the right ballpark, so to speak. Maybe you said Berlin. Or Zurich. Or Bern. Was that a hallucination? Or simply an educated guess, as your “neural net”, your brain, received only sparse training data on the subject of Einstein’s biography?
   
   This is exactly what the LLM does when asked a question concerning a narrow subject matter on which its training is sparse. It comes up with a plausible answer that is consistent with that sparse training. That’s all. The trouble, of course, is that it often states these answers with convincing certainty, using eloquent language. But more importantly, we, its human readers, are preconditioned to treat a computer as dumb but accurate: We do not expect answers that are erudite but factually wrong.
   
   
2. No, they are not stealing: Already, LLMs have been accused of intellectual property theft. That is blatantly wrong on many levels. Are you “stealing” content when you use a textbook or the Internet to learn a subject? Because that is precisely what the LLMs do. They do not retain a copy of the original. They train their own “brain”, their neural net, to generate answers consistent with their training data. The fact that they have maybe a hundred times more neurons than human brains do, and thus they can often accurately recall entire sections from books or other literary works does not change this fact. Not unless you want to convince me that if I happen to remember a few paragraphs from a book by heart, the “copy” in my brain violates the author’s copyright.
   
   
3. LLMs are entirely static models: I admit I was also confused about this at first. I should not have been. The “P” in GPT, after all, stands for pretrained. For current versions of GPT, that training concluded in late 2021. For Anthropic’s Claude, in early 2022. The “brains” of the LLM is now in the form of a database of several hundred billion “weights” that characterize the neural net. When you interact with the LLM, it does not change. It does not learn from that interaction or indeed, does not in any way change as a result of it. The model is entirely static. Even when it thanks you for teaching it something it did not previously know (Claude, in particular, does this often) it is not telling the truth, at least not the full truth. Future versions of the same LLM may benefit from our conversations, but not the current version.
   
   
4. The systems have no memory or persistence: This one was perhaps for me the most striking. When you converse with chatGPT or Claude, there is a sense that you are chatting with a conscious entity, who retains a memory of your conversation and can reference what has been said earlier. Yet as I said, the models are entirely static. Which means, among other things, that they have no memory whatsoever, no short-term memory in particular. Every time you send a “completion request”, you start with a model that is in a blank state.
   
   But then, you might wonder, how does it remember what was said earlier in the same conversation? Well, that’s the cheapest trick of all. It’s really all due to how the user interface, the front-end software works. Every time you send something to the LLM, this user interface prepends the entire conversation up to that point before sending the result to the LLM.
   
   By way of a silly analogy, imagine you are communicating by telegram with a person who has acute amnesia and a tendency to throw away old telegrams. To make sure that they remain aware of the context of the conversation, every time you send a new message, you first copy the content of all messages sent and received up to this point, and then you append your new content.
   
   Of course this means that the messages increase in length over time. Eventually, they might overwhelm the other person’s ability to make sense of them. In the case of the LLMs, this is governed by the size of the “context window”, the maximum amount of text that the LLM can process. When the length of the conversation begins to approach this size, the LLM’s responses become noticeably weaker, with the LLM often getting hopelessly confused.

To sum up, many of the problems arise not because of what the LLMs are, but because of our false expectations. And failure to understand the limitations while confronted with their astonishing capabilities can lead to undue concerns and fears. Yes, some of the dangers are real. But before we launch an all-out effort to regulate or curtail AI, it might help if we, humans, did a better job understanding what it is that we face.

Thank goodness for token limits. Otherwise this query through the OpenAI UI would have cost me a lot of money.

How do you even make sense of the famous halting problem when the software system in question is as complex and as unpredictable as a large language model?

And is this glitch similar to the behavior of some humans, suffering from mental illness, compelled to act repetitively?