Approximately 2 TCF (Trillion Cubic Feet) of acid gas are projected to be injected into the Surmeh formation. The recommended injection rate is 180 MMSCFD (Million standard cubic feet per day), based on acid gas content and gas in place of the source of injection. The tight nature of the Surmeh formation limits injectivity, with a maximum achievable rate of 7 MMSCFD for permeability of 1 mD (millidarcy). However, higher porosity (12%) and permeability of 100 mD enable more efficient injection without fracturing the formation. This study provides valuable insights into the feasibility of AGI in saline aquifers, emphasizing reservoir characterization, geomechanics, caprock integrity, and rock properties. The findings contribute to the implementation of environmentally sustainable acid gas disposal at offshore reservoirs.

Healthy human subjects develop spontaneous CD8+ T cell responses to melanocyte antigens (MA) expressed by normal melanocytes, such as Tyrosinase, MAGE-A3, Melan/Mart-1, gp100, and NY-ESO-1. This natural autoimmunity directed against melanocytes might confer protection against the development of malignant melanoma (MM), where MA are present as overexpressed tumor-associated antigens. Consistent with this notion we report here that functional T cell reactivity to MA was found to be diminished in untreated MM patients: while 57.5% of healthy controls (n=40) exhibited high-frequency MA-specific T cell reactivity ex vivo, such was detected in only 12% of the untreated MM patients (n=24). Three lines of evidence suggest that the MA-reactive T cells present in healthy subjects undergo exhaustion once MM establishes itself. First, only the MA-specific T cell reactivity was affected in the MM patients; that to third party recall antigens was not. Second, in these patients, the residual MA-specific T cells, unlike third party antigen reactive T cells, were functionally impaired, showing a diminished per cell IFN-γ productivity. Third, successful immunotherapy with AGI-101H melanoma vaccine restored natural CD8+ T cell autoimmunity to MA in 85% of the vaccinated patients (n= 27). The role of natural T cell autoimmunity to tumor-associated MA is discussed based on discrete levels of T cell activation thresholds.

Artificial general intelligence (AGI) progression metrics indicate AGI will occur within decades. No proof exists that AGI will benefit humans and not harm or eliminate humans. I propose a set of logically distinct conceptual components that are necessary and sufficient to 1) ensure various AGI scenarios will not harm humanity and 2) robustly align AGI and human values and goals. By systematically addressing pathways to malevolent AI we can induce the methods/axioms required to redress them. Distributed ledger technology (DLT, ‘blockchain’) is integral to this proposal, e.g. ‘smart contracts’ are necessary to address evolution of AI that will be too fast for human monitoring and intervention. The proposed axioms: 1) Access to technology by market license. 2) Transparent ethics embodied in DLT. 3) Morality encrypted via DLT. 4) Behavior control structure with values at roots. 5) Individual bar-code identification of critical components. 6) Configuration Item (from business continuity/disaster recovery planning). 7) Identity verification secured via DLT. 8) ‘Smart’ automated contracts based on DLT. 9) Decentralized applications - AI software modules encrypted via DLT. 10) Audit trail of component usage stored via DLT. 11) Social ostracism (denial of resources) augmented by DLT petitions. 12) Game theory and mechanism design.

Methods are currently lacking to prove artificial general intelligence (AGI) safety. An AGI ‘hard takeoff’ is possible, in which first generation AGI1 rapidly triggers a succession of more powerful AGIn that differ dramatically in their computational capabilities (AGIn≪AGIn+1). No proof exists that AGI will benefit humans or of a sound value-alignment method. Numerous paths toward human extinction or subjugation have been identified. We suggest that probabilistic proof methods are the fundamental paradigm for proving safety and value-alignment between disparately powerful autonomous agents. Interactive proof systems (IPS) describe mathematical communication protocols wherein a Verifier queries a computationally more powerful Prover and reduces the probability of the Prover deceiving the Verifier to any specified low probability (e.g., 2-100). IPS procedures can test AGI behavior control systems that incorporate hard-coded ethics or value-learning methods. Mapping the axioms and transformation rules of a behavior control system to a finite set of prime numbers allows validation of ‘safe’ behavior via IPS number-theoretic methods. Many other representations are needed for proving various AGI properties. Multi-prover IPS, program-checking IPS, and probabilistically checkable proofs further extend the paradigm. In toto, IPS provides a way to reduce AGIn↔AGIn+1 interaction hazards to an acceptably low level.

In this paper we present a review of recent developments in AI towards the possibility of an artificial intelligence equals that of human intelligence. AI technology has always shown a stepwise increase in its capacity and complexity. The last step took place several years ago, with the increased progress in deep neural network technology. Each such step goes hand in hand with our understanding of ourselves and our understanding of human cognition. Indeed, AI was always about the question of understanding human nature. AI percolates into our lives, changing our environment. We believe that the next few steps in AI technology, and in our understanding of human behavior, will bring about much more powerful machines, flexible enough to resemble human behavior. In this context, there are two research fields: Artificial Social Intelligence (ASI) and General Artificial Intelligence (AGI). The authors also allude to one of the main challenges for AI, embodied cognition, and explain how it can viewed as an opportunity for further progress in AI research.

Humans have a unique cognitive ability known as relational abstraction, which allows us to identify logical rules and patterns beyond basic perceptual characteristics. This ability represents a key difference between how humans and other animals learn and interact with the world. With current large language models rivaling human intelligence in many regards, we investigated whether relational abstraction was an emergent chat completion capacity in various models. We find that despite their impressive language processing skills, all tested language models failed the relational match-to-sample (RMTS) test, a benchmark for assessing relational abstraction. These results challenge the assumption that advanced language skills inherently confer the capacity for complex relational reasoning. The paper highlights the need for a broader evaluation of AI cognitive abilities, emphasizing that language proficiency alone may not be indicative of certain higher-order cognitive processes thought to be supported by language.