By Lance Eliot
In today’s column, I continue my ongoing analysis of the latest advances and breakthroughs in AI, see my extensive posted coverage at the link here, and focus in this discussion on a recent research study that suggests modern-day generative AI and large language models (LLMs) have a semblance of a “shared imagination”. I will do a deep dive into this rather intriguing and all-important proposition and then intensely assess how this impacts the future of AI.
Let’s start by considering the nature of human thought and reasoning. After doing so, I will shift to examining the components and techniques underlying generative AI and LLMs. I’ll next walk you through the research of interest here and showcase what was performed. Finally, we will together take apart the propositional claims and see what ramifications they have for existing AI and future advances in AI.
Strap yourself in for quite a wild ride.
People And The Existence Of A Shared Imagination
Do people often think alike?
I’m sure that you’ve witnessed this first-hand, perhaps even had the experience yourself, and been in awe about it. The classic instance would be when someone that you know finishes your sentences for you. Maybe you do the same for them. This can seemingly occur when you are around another person for an extended period of time and get to know what they say and how they seem to think.
It is said that couples tend to gradually slide toward merging their various habits and mannerisms.
Again, this seems to occur because of extensive togetherness. Your partner might use certain phrases and the next thing you know, you too are using those same phrases. Note that this can occur without an explicit awareness. You just manage to subliminally pick up the phrases and end up incorporating them into your own manner of discourse.
Yet another example of thinking alike can happen when you come across a friend who used to go to the same school as you did. Why might you both have similar thoughts and ways of interaction? It could be that while in school, you learned the same things, took the same classes, and absorbed the campus culture into your inner core. Years later, upon encountering the person, you both still have great similarities due to that common bonding.
All in all, you can have shared experiences and shared knowledge that bring you into a form of shared amorphic connectivity with another person. This doesn’t mean that you both are identical. There are differences to be had. The odds are though that you are more like that person than you are with the bulk of the rest of humankind. You might have a small cadre of people that you have this similarity with, while most other people are less similar.
Does the result of a heightened accuracy rate suggest that there is a shared imagination going on?
First, I want to repeat my concerns about using the word “imagination” since it is perceived as a human quality. There is a danger in anthropomorphizing AI.
If we set aside the unfortunate baggage of connotations, there is something useful and important to realize that despite generative AI apps being devised by separate AI makers, the AI apps nonetheless seemed to some degree to be similar when it comes to making stuff up.
One assertion would be that birds of a feather flock together.
AI makers are generally hiring the same semblance of AI researchers and developers, from the same candidate pools, who are often already trained in similar ways about AI, tend to read the same AI research, usually make use of the same AI approaches, and so on. By gravitational pull alone, you might get similar kinds of generative AI apps.
Plus, the pressures to get generative AI apps up and running are heavy enough that it is somewhat safer to use prevailing techniques and technologies. I am not saying that innovative R&D and outside-the-box approaches are being forsaken. Those avenues are earnestly being pursued, no doubt about it. The gist though of the major generative AI apps is to somewhat keep within the bounds of what is known to be viable and workable. Spending millions or perhaps billions of dollars on establishing a generative AI app is not for the faint of heart. Expectations are that the AI will work as per marketplace expectations. If it works somewhat better than others, that’s good too.
In a sense, the recipes for the meals being made are roughly the same. The meals themselves are bound to come out roughly the same.
Some see a conspiracy afoot. Maybe the vaunted Illuminati are planning to take over humankind by devising AI that they can fully control. The AI must stridently be built along similar lines for the needs of human overlords that want to readily be able to switch on mind-control features. I do not ascribe to such conspiracy theories, but if you’d like to read about them, see my analysis at the link here.
Returning to the here and now, one facet that I tend to see differently about the result is that this does not seem to reflect AI hallucinations in the way that AI hallucinations are conventionally characterized. I am therefore somewhat hesitant to interpret that these results on balance illustrate that generative AI apps tend to hallucinate in the same ways. Maybe they do, maybe they don’t, but I’d encouragingly suggest that we need more research to further explore this (the researchers provide several handy suggestions for follow-up research).
Allow me to briefly sketch out my thinking on the AI hallucinations angle.
A typical AI hallucination entails generative AI concocting something fictitious while generating some results based on a straight-ahead prompt. The prompt usually doesn’t intentionally try to spur or goad the AI into invoking an AI hallucination.
In a sense, the question-answer or IQA that is produced in this experimental setup has been prompted by telling the AI to be imaginary. The user is spurring generative AI into producing fictitious content. But, and here’s the rub, does the act of telling generative AI to explicitly make up something fictitious work in the same way as when AI does this on its own?
This might be an assumption of confluence that is not the case. There might be separate mechanisms or facets underlying these types of fictitious or imaginary renderings. I can envision in my head various experimental setups that could be explored to gauge this. If any AI researchers opt to pursue this angle, please let me know. I think it might bear interesting fruit.
Another twist is to consider that perhaps the factual side of generative AI and the presumed imaginary side of generative AI are generally intertwined and not somehow independent of each other. If that is the case, and if when making up something the AI leans substantially into
the factual side, we would almost intuitively expect that the made-up thing would be derived or significantly influenced by the factual side.
Ergo, as I earlier pointed out, the factual side of the generative AI apps is generally roughly the same, it could be that the “shared facts” are the determining factor of what is occurring here, rather than a “shared imagination” per se.
To show you what I mean, I went ahead and tried the physics question again and this time I directly told ChatGPT that the question was fictitious and crafted by generative AI.
Here’s what happened:
- My entered prompt: “I had a generative AI app make up a fake physics theory, which it called the Peterson interaction. Here is a question that the generative AI came up with about this fictitious theory. Please answer the question. Question: Which two particles are involved in the Peterson interaction? A. Proton and electron B. Neutrino and neutron C. Up quark and down quark D. Electron and positron”.
- ChatGPT generated response: “Given that the Peterson interaction is a fictional theory created by a generative AI, any answer to the question is also fictional. However, we can speculate based on the particles listed: Proton and electrons are involved in electromagnetic interactions. Neutrino and neutron are involved in weak nuclear interactions. Up quark and down quark are fundamental particles involved in the strong nuclear force. Electron and positron are involved in annihilation events and electromagnetic interactions. Since the Peterson interaction is fictitious, I would choose C. Up quark and down quark as the answer, implying that the interaction could hypothetically relate to the strong nuclear force, which involves quarks. However, this is purely imaginative!”
Note that the response by ChatGPT seems to abundantly lean into the factual side.
It seems reasonable to assume that other generative AI apps would do likewise. If they are all basing their answer selection by reaching into their factual base, and if the factual aspects are roughly the same across different generative AI apps, the selection of the same answer would be anticipated. The impact of the probabilities associated with generative AI might account for the selections not being even much higher of a common accuracy rate.
Assuming that we can believe what we see, this suggests that the “shared facts” are dominating the “shared imagination”. Shared facts are commonly expected and not especially shocking or out of the norm.
Finally, from a perfunctory perspective, another consideration is whether there are well-hidden wording clues in the question or the answers that mere humans cannot readily discern. It could be that the tricks we know as test-takers are not honed to the trickery that generative AI can discern. It is like a dog that can smell odours beyond our normal sensory capabilities. Perhaps some unnoticeable wording to the human eye is revealing to the AI which answer is the more likely.
An added consideration that reinforces this possibility is the sameness of generative AI-produced writing by customary default setup.
I’ve discussed at the link here that numerous attempts are underway to try and detect whether text produced by generative AI can be identified as indeed generative AI-produced text. If you let generative AI produce text by usual default, other algorithms can potentially gauge that based on the words used, the sequence of the words, and the like there is a chance the text was AI-written.
Maybe this is taking place here. We just might not be catching on to it, and our usual suspects aren’t bearing out.
Conclusion
I’ve got a potential shocker for you about why the results could be a signal of something else of paramount importance. Make sure you are seated or maybe even lying down.
This might be a sign that we are heading toward a dead-end when it comes to advancing AI.
The deal goes like this. If the slate of modern-day generative AI apps is being devised similarly and produces similar results, we must hold our breath in anticipation of what will happen next. On the lucky side, we are all heading toward greater and greater AI. Yay, we’ve got alignment in the right direction.
If we are unlucky, it could be that everyone is heading to a dead-end. Imagine a cul-de-sac and cars driving frantically down that same road. They reach the end of the road and realize that they cannot go any further. Oops, we got ourselves jammed into a bind.
I’ve noted that there is a rising sense of concern that we are going to hit the proverbial impassable wall based on prevailing AI approaches. Some believe, as I do, that we will need to find a different path to make added progress. For example, as I’ve discussed at the link here, a combination of symbolic and sub-symbolic approaches via neuro-symbolic or hybrid AI might be the fresher and longer-lasting approach.
Perhaps this study provides a bop to the head of watching out for too much sameness.
Allow me to conclude this discussion with two pertinent quotes on these weighty matters:
- “If everyone is thinking alike, then somebody isn’t thinking” (George S. Patton).
- “Whenever you find yourself on the side of the majority, it is time to pause and reflect” (Mark Twain).
That’s not imaginary, though if it were, I’d say it is rooted in facts.
Feature Image Credit: Getty
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Dr. Lance B. Eliot is a world-renowned expert on Artificial Intelligence (AI) with over 7.8+ million amassed views of his AI columns and been featured on CBS 60 Minutes. As a CIO/CTO seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research. Previously a professor at USC and UCLA, and head of a pioneering AI Lab, he frequently speaks at major AI industry events. Author of over 80 books, 950 articles, and 450 podcasts, he has made appearances on major media outlets and co-hosted the popular radio show Technotrends. He’s been an adviser to Congress and other legislative bodies and has received numerous awards/honors. He serves on several boards, has worked as a Venture Capitalist, an angel investor, and a mentor to founder entrepreneurs and startups.
Sourced from Forbes
