By Dee Smith
Are we acting as sorcerer’s apprentices by developing AI? One place the possibility is readily apparent is generative biological design undertaken with genome language models. The journal Science recently began an article by saying, “The ability to design complex biological systems with artificial intelligence (AI) has the potential to transform biotechnology.” The journal article then noted that a group of researchers, led by Samuel H. King:
used generative AI models trained on millions of natural genomes to design entire bacteriophages. Experimental tests yielded 16 functional genomes with diverse sequences, structures, and fitness profiles. A cocktail of the generated bacteriophages rapidly overcame bacteria that had evolved resistance to a natural bacteriophage. This work lays a foundation for AI-guided design of biological function at the whole-genome scale.
Dominion over the processes of life itself is a dangerous and heady prospect. While it clearly has applications in medicine and agriculture, AI is a “dual use” technology that can result in the creation of biological weapons by state and non-state actors — that is, biological warfare and bio-terror. The idea that all of this can be controlled seems ludicrous, but the technology is here anyway. Precision medicine and making crops resistant to disease sound good, but the other edge of the sword is that what can be used to feed a population can also be used to starve a population.
It is both telling and fascinating that the emergence of AIs has brought us directly and urgently face-to-face with some of the oldest, thorniest questions in philosophy: the nature of life, of consciousness, of the self, of other minds, of perception, of free will, of ethics — at the same time that it is at our front door as a potentially existential threat that we do not understand.
There are many domains of human experience that cannot, based on any evidence we have, be replicated by computational, algorithmic systems. These include mystical and religious experiences, altered states of perception (like being drunk), inspiration and creativity, falling in love, and being moved by music.
Most fundamentally, AI systems do not have what philosophers call “qualia”— the “what-it-is-like” to experience things such as the color blue or the taste of chocolate. Computational systems can only mimic expressions of these, as understanding Chinese is mimicked in Searle’s Chinese Room mentioned earlier in this series. Adjusting what is called the “temperature” of an AI (the amount of randomness allowed in its calculations) to simulate creativity is just that: a simulation of creativity.
Philosophers of mind use the term “zombie” in thought experiments to mean a theoretical entity that is, atom-for-atom, identical to a living human being, not only in its physical makeup, but in its reactions (these are not the flesh-eating zombies of Hollywood). In other words, philosophical zombies (or “p-zombies”) act and react exactly as “real” human beings. They cry out when injured, they profess love or hatred. But they really have no internal states—no qualia—and no consciousness or what we would call awareness. They experience nothing like what humans experience, they just detect signals and react. This smacks of the discredited theory of behaviorism touted by B.F. Skinner, which was encapsulated brilliantly to me decades ago by anthropologist David Friedel as the concept that “humans do not think, they just act.”
I would posit that AIs may well be a form of machine-based p-Zombies.
How can I credibly claim this? Because algorithmic systems are not just unable to deal with non-computable functions, as noted earlier, they also represent reality only as an abstraction: a digital pattern based on binary logic. This abstraction “recognizes” only correlation, not causation (although attempts are being made through different systems of labeling to create an ability in AIs to grasp causation).
AI systems reflect a deeply impoverished, reductive representation of the reality that living things experience, the consensus reality that “really exists” as far as we can tell (even if it is not all that exists). In a way, it bears the same relationship to a living entity that a pro forma spreadsheet bears to an actual business: it is an abstraction that does not necessarily give a reliable estimation of what is happening because it is too simplified.
We know that the fundamental structure of the world is not binary but quantum and fuzzy: probabilistic (from our standpoint) and unpredictable. While we “see” some of its manifestations — quantum indeterminacy, quantum entanglement, or deterministic chaos, for example — no one really understands them. AI world models and other direct encounters by AI with physical reality will not change the fundamental poverty of the AIs’ “world,” because the AIs are still processing algorithmically and digitally, still experiencing only an abstraction — if indeed they can be claimed to be experiencing anything. They are always based on the patterns of the past, not a good thing in a time of radical change. They simply create a statistically averaged-out version of human knowledge and experience, albeit one exhibiting some very strange properties. They have a very partial map of reality that, because it is digital, represents the real world of infinite gradations as binary — black or white, yes or no. It takes yet other layers of abstract mapping just to simulate grey. And, of course, the map is not the territory.