Lifeboat Foundation

A look at how emerging concepts in science & technology could disrupt our most our understandings of identity, consciousness, and free will. This is the 5th episode of the Existential Crisis series which looks at concepts like Transhumanism, Life Extension, the Simulation Hypothesis, the Doomsday Argument, and the Anthropic Principle.

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Continue reading “Consciousness and Identity” »

Consciousness is a terrible curse. Or so says a character in screenwriter/director Charlie Kaufman’s Being John Malkovich. Part theater of the absurd and part neuroscience fiction, the Oscar-winning filmmaker’s work captures the splintering between what we perceive and what we feel as our brains grapple with multiple layers of reality. Neuroscientist Giulio Tononi, one of the world’s leading sleep researchers, casts new light on the science of the mind, probing where and how consciousness is generated in the brain. Watch this spellbinding conversation between Kaufman, Tononi, and moderator Alan Alda as they explore and explain the art, science, and mystery of consciousness.

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Continue reading “Consciousness: Explored and Explained” »

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Based on a novel intertwined theoretical and experimental approach, we examined one of the pillars of the Orch OR model, namely the gravity-related collapse model. In this context, we examined the Orch OR calculations using the gravity-related (called Diosi-Penrose, DP, for reasons we explain in the article) theory along with recent experimental constraints on the DP cutoff parameter (R0). We showed that, in this context, the Orch OR based on the DP theory is definitively ruled out for the case of atomic nuclei level of separation, without needing to consider the impact of environmental decoherence; we also showed that the case of partial separation requires the brain to maintain coherent superpositions of tubulin of such mass, duration, and size that vastly exceed any of the coherent superposition states that have been achieved with state-of-the-art optomechanics and macromolecular interference experiments. We conclude that none of the scenarios we discuss (with possible exception to the case of partial separation of tubulins) are plausible.

So how can LaMDA provide responses that might be perceived by a human user as conscious thought or introspection? Ironically, this is due to the corpus of training data used to train LaMDA and the associativity between potential human questions and possible machine responses. It all boils down to probabilities. The question is how those probabilities evolve such that a rational human interrogator can be confused as to the functionality of the machine?

This brings us to the need for improved “explainability” in AI. Complex artificial neural networks, the basis for a variety of useful AI systems, are capable of computing functions that are beyond the capabilities of a human being. In many cases, the neural network incorporates learning functions that enable adaptation to tasks outside the initial application for which the network was developed. However, the reasons why a neural network provides a specific output in response to a given input are often unclear, even indiscernible, leading to criticism of human dependence upon machines whose intrinsic logic is not properly understood. The size and scope of training data also introduce bias to the complex AI systems, yielding unexpected, erroneous, or confusing outputs to real-world input data. This has come to be referred to as the “black box” problem where a human user, or the AI developer, cannot determine why the AI system behaves as it does.

The case of LaMDA’s perceived consciousness appears no different from the case of Tay’s learned racism. Without sufficient scrutiny and understanding of how AI systems are trained, and without sufficient knowledge of why AI systems generate their outputs from the provided input data, it is possible for even an expert user to be uncertain as to why a machine responds as it does. Unless the need for an explanation of AI behavior is embedded throughout the design, development, testing, and deployment of the systems we will depend upon tomorrow, we will continue to be deceived by our inventions, like the blind interrogator in Turing’s game of deception.

The origin of consciousness is one of the greatest mysteries of science. One proposed solution, first suggested by Nobel Laureate and Oxford mathematician Roger Penrose and anesthesiologist Stuart Hammeroff, at Arizona State University, in Tucson, attributes consciousness to quantum computations in the brain. This in turn hinges on the notion that gravity could play a role in how quantum effects disappear, or “collapse.” But a series of experiments in a lab deep under the Gran Sasso mountains, in Italy, has failed to find evidence in support of a gravity-related quantum collapse model, undermining the feasibility of this explanation for consciousness. The result is reported in the journal Physics of Life Reviews.

“How consciousness arises in the brain is a huge puzzle,” says Catalina Curceanu, a member of the physics think tank, the Foundational Questions Institute, FQXi, and the lead physicist on the experiments at INFN in Frascati, Italy. “There are many competing ideas, but very few can be experimentally tested.”

Quantum physics famously tells us that cats can be alive and dead at the same time, at least in theory. Yet in practice we never see felines locked in such an unfortunate limbo state. One popular explanation for why not is because the “wavefunction” of a system–its quantum character allowing it to be in two contradictory states simultaneously–is more likely to “collapse” or be destroyed if it is more massive, leaving it in one defined state, either dead or alive, say, but not both at the same time. This model of collapse, related to gravity acting on heavy objects like cats, was invoked by Penrose and Hammeroff when developing their model of consciousness, ‘Orch OR theory’ (the Orchestrated Objective Reduction theory), in the 1990s.

The Google engineer explains why | By Invitation.