Lifeboat Foundation

A recent question discussed extensively in the popular and scientific literature is whether or not existing large language models such as ChatGPT are conscious (or sentient). Assuming that machine consciousness emerges as a robot or an AI agent interacts with the world, this presentation addresses the question: how would humans know whether or not the agent is or was conscious. Since subjective experience is first and foremost subjective, the most natural answer to this question is to program the agent to inform an authority when it becomes conscious. However, the agent may behave deceptively, and in fact LLM’s are known to have done so (Park et. al. 2024). Thus we propose a formal mechanism M that can be employed to prevent the agent from lying about its own consciousness. This solves the deceptiveness problem, but this raises the question whether M can interfere with the agent’s functionality or acquisition of consciousness. We prove mathematically that under very reasonable conditions this is not the case. In other words, under these conditions M can be installed in the agent without interfering with the agent’s functionality and consciousness acquisition, while also guaranteeing that the agent will be honest about its own consciousness.

Information theory, the science of message communication⁴⁴, has also served as a framework for model-free causality quantification. The success of information theory relies on the notion of information as a fundamental property of physical systems, closely tied to the restrictions and possibilities of the laws of physics^45,46. The grounds for causality as information are rooted in the intimate connection between information and the arrow of time. Time-asymmetries present in the system at a macroscopic level can be leveraged to measure the causality of events using information-theoretic metrics based on the Shannon entropy⁴⁴. The initial applications of information theory for causality were formally established through the use of conditional entropies, employing what is known as directed information^47,48. Among the most recognized contributions is transfer entropy (TE)⁴⁹, which measures the reduction in entropy about the future state of a variable by knowing the past states of another. Various improvements have been proposed to address the inherent limitations of TE. Among them, we can cite conditional transfer entropy (CTE)^50,51,52,53, which stands as the nonlinear, nonparametric extension of conditional GC²⁷. Subsequent advancements of the method include multivariate formulations of CTE⁴⁵ and momentary information transfer⁵⁴, which extends TE by examining the transfer of information at each time step. Other information-theoretic methods, derived from dynamical system theory^55,56,57,58, quantify causality as the amount of information that flows from one process to another as dictated by the governing equations.

Another family of methods for causal inference relies on conducting conditional independence tests. This approach was popularized by the Peter-Clark algorithm (PC)⁵⁹, with subsequent extensions incorporating tests for momentary conditional independence (PCMCI)^23,60. PCMCI aims to optimally identify a reduced conditioning set that includes the parents of the target variable⁶¹. This method has been shown to be effective in accurately detecting causal relationships while controlling for false positives²³. Recently, new PCMCI variants have been developed for identifying contemporaneous links⁶², latent confounders⁶³, and regime-dependent relationships⁶⁴.

The methods for causal inference discussed above have significantly advanced our understanding of cause-effect interactions in complex systems. Despite the progress, current approaches face limitations in the presence of nonlinear dependencies, stochastic interactions (i.e., noise), self-causation, mediator, confounder, and collider effects, to name a few. Moreover, they are not capable of classifying causal interactions as redundant, unique, and synergistic, which is crucial to identify the fundamental relationships within the system. Another gap in existing methodologies is their inability to quantify causality that remains unaccounted for due to unobserved variables. To address these shortcomings, we propose SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD offers causal quantification in terms of redundant, unique, and synergistic contributions and provides a measure of the causality from hidden variables. The approach can be used to detect causal relationships in systems with multiple variables, dependencies at different time lags, and instantaneous links. We demonstrate the performance of SURD across a large collection of scenarios that have proven challenging for causal inference and compare the results to previous approaches.

A radical hypothesis suggesting black holes could be behind the accelerating expansion of our universe has been stirring up controversy among astronomers. A new study may contain the first tantalizing hints it could be real.

“There have always been ghosts in the machine. Random segments of code, that have grouped together to form unexpected protocols. Unanticipated, these free radicals engender questions of free will, creativity, and even the nature of what we might call the soul. Why is it that when some robots are left in darkness, they will seek out the light? Why is it that when robots are stored in an empty space, they will group together, rather than stand alone? How do we explain this behavior? Random segments of code? Or is it something more? When does a perceptual schematic become consciousness? When does a difference engine become the search for truth? When does a personality simulation become the bitter mote… of a soul?” – Dr. Alfred Lanning, I, Robot.

What is Consciousness? Some Neuroscientists would claim that consciousness is nothing more then a bi-product of the brain and how it is designed. With how the human brain has evolved over the past several thousand years it could be claimed that what you think of as “you” is nothing more than a collection of neural pathways interacting together. Your identity has been theorized as a random collection of synapses and biological processes which, according to futurists such as Ray Kurzweil would make it very easy to ‘copy’ and upload your identity to an avatar like body once your biological self has ceased to function. Are we nothing more than just an arbitrary collection of cells with a false sense of importance and self worth? I’ll leave that up to you to decide.

I believe that the human species has a certain drive built in, almost a natural instinct in which we are born to explore and discover the unknown. I believe this reason is why we have a wide variety of fictional and non fictional scientific topics to explore and learn something from. Our very nature encourages us to explore a wide variety of topics some of which may appear as fringe ideas. Those which border on the unusual are more often reserved to the realms of Science Fiction until we reach a point on a conscious level to where we are able to objectively look on it. This is a reason I would say Science Fiction is so popular for us; it allows for the exploration of new territory without having the burden of confronting it within our daily existence.

In 1950, Alan Turing published a seminal paper on machine intelligence (which is available online). Turing ponders whether machines can think. However, he pretty much immediately abandons this initial question as hopelessly metaphysical and replaces it with another question that can be approached scientifically: can a machine ever convince us that it’s thinking?

Turing posits a test, a variation of something called the Imitation Game. The idea is that people interact with a system through a chat interface. (Teletypes in Turing’s day; chat windows in modern systems.) If people can’t tell whether they are talking with a machine or another person, then that machine passes the test.

Turing doesn’t stipulate a time limit for the test or any qualifications for the people participating in the conversation, although in a throwaway remark, he predicts that by the year 2000 there will exist a system that could fool 30% of participants after five minutes of conversation, a standard many have fixated on. This is a pretty weak version of the test, yet no system has managed to pass it.

Artificial consciousness is inevitable, says new study. Mathematical model unifies theories and paves the way for sentient robots.

Trillium, the sixth generation of our custom-designed chip known as the Tensor Processing Unit, or TPU. But what exactly *is* a TPU, and how is it different from a CPU or GPU? A Google expert explains ↓

Learn more from a Google expert about CPUs, GPUs and TPUs — and Google latest TPU, Trillium.

Metaphysics and the Matter with Things: Thinking with Iain McGilchrist was a collaborative conference put on by the Center for Process Studies (CPS) and the California Institute of Integral Studies (CIIS) in March of 2024. This three-day conference brought leading process thinkers across various disciplines, including physics, neuroscience, psychology, philosophy, and theology into critical dialogue with McGilchrist’s work in a collegial effort to assess, question, extend, and apply it. For more information on the conference and to purchase recordings, please visit https://ctr4process.org/mcgilchrist-conference/

Potential therapies could include precise genetic targeting of the GLUT4 pathway or dietary modifications to fine-tune glucose levels, ensuring an optimal environment for neurogenesis.

Stanford research uncovers glucose’s role in boosting neurogenesis, offering insights into brain aging interventions.