Governance frameworks should address the prospect of AI systems that cannot be safely tested.

Background. Neuroendocrine differentiation has been demonstrated by immunohistochemical preparations in many cases of acinar type prostatic adenocarcinoma (CAP). Some studies have suggested that this may indicate an adverse prognosis. Methods. Tissue samples from 38 consecutive patients with clinical Stage II (AJCC) CAP who underwent radical retropubic prostatectomy (RRP) were studied after made antichromogranin (ChA) and neuron-specific enolase (NSE). All followed for at least 4 years...

Abstract We analyze the expected behavior of an advanced artificial agent with a learned goal planning in unknown environment. Given few assumptions, we argue that it will encounter fundamental ambiguity data about its goal. For example, if provide large reward to indicate something world is satisfactory us, may hypothesize what satisfied us was sending itself; no observation can refute that. Then this lead intervene whatever protocol set up for discuss analogous failure mode approximate...

Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the long-term goal of obtaining guarantee apply in every context, we consider estimating context-dependent bound probability violating given specification. Such risk evaluation need be performed at run-time provide guardrail against dangerous actions an AI. Noting different plausible hypotheses about world could produce very outcomes, and because do not know...

Objective To determine whether the quantification of certain neuroendocrine and proliferative markers would help in prognostic evaluation prostatic adenocarcinomas obtained during transurethral resection prostate (TURP). Materials methods Samples from two groups patients with cancer were examined. One group comprised 23 patients, whom 12 stage IV 11 III, all Gleason scores ≥7; this was designated as high‐grade, high‐stage (HGHS). The second 10 consecutive T1a adenocarcinoma ≤6, low‐grade,...

General intelligence, the ability to solve arbitrary solvable problems, is supposed by many be artificially constructible. Narrow a given particularly difficult problem, has seen impressive recent development. Notable examples include self-driving cars, Go engines, image classifiers, and translators. Artificial Intelligence (AGI) presents dangers that narrow intelligence does not: if something smarter than us across every domain were indifferent our concerns, it would an existential threat...

The near-ultraviolet π*←π absorption system of weakly bound complexes formed between tropolone (TrOH) and formic acid (FA) under cryogenic free-jet expansion conditions has been interrogated by exploiting a variety fluorescence-based laser-spectroscopic probes, with synergistic quantum-chemical calculations built upon diverse model chemistries being enlisted to unravel the structural dynamical properties pertinent ground [X̃1A'] excited [Ã1A'π*π] electronic states. For binary TrOH ⋅ FA...

Reinforcement Learning agents are expected to eventually perform well. Typically, this takes the form of a guarantee about asymptotic behavior an algorithm given some assumptions environment. We present for policy whose value approaches optimal with probability 1 in all computable probabilistic environments, provided agent has bounded horizon. This is known as strong optimality, and it was previously unknown whether possible be strongly asymptotically class environments. Our agent,...

Gaussian processes (GPs) produce good probabilistic models of functions, but most GP kernels require $O((n+m)n^2)$ time, where $n$ is the number data points and $m$ predictive locations. We present a new kernel that allows for process regression in $O((n+m)\log(n+m))$ time. Our "binary tree" places all on leaves binary tree, with depending only depth deepest common ancestor. can store resulting matrix $O(n)$ space $O(n \log n)$ as sum sparse rank-one matrices, approximately invert Sparse...

Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the long-term goal of obtaining guarantee apply in every context, we consider estimating context-dependent bound probability violating given specification. Such risk evaluation need be performed at run-time provide guardrail against dangerous actions an AI. Noting different plausible hypotheses about world could produce very outcomes, and because do not know...

In reinforcement learning, if the agent's reward differs from designers' true utility, even only rarely, state distribution resulting policy can be very bad, in theory and practice. When RL policies would devolve into undesired behavior, a common countermeasure is KL regularization to trusted ("Don't do anything I wouldn't do"). All current cutting-edge language models are agents that KL-regularized "base policy" purely predictive. Unfortunately, we demonstrate when this base Bayesian...

Algorithmic Information Theory has inspired intractable constructions of general intelligence (AGI), and undiscovered tractable approximations are likely feasible. Reinforcement Learning (RL), the dominant paradigm by which an agent might learn to solve arbitrary solvable problems, gives a dangerous incentive: gain "power" in order intervene provision their own reward. We review arguments that generally intelligent algorithmic-information-theoretic reinforcement learners such as Hutter's...

If we could define the set of all bad outcomes, hard-code an agent which avoids them; however, in sufficiently complex environments, this is infeasible. We do not know any general-purpose approaches literature to avoiding novel failure modes. Motivated by this, idealized Bayesian reinforcement learner follows a policy that maximizes worst-case expected reward over world-models. call pessimistic, since it optimizes assuming worst case. A scalar parameter tunes agent's pessimism changing size...

You have accessJournal of UrologyDiscussed Poster, Monday, May 10, 2004, 1:00 - 5:00 pm1 Apr 20041101: Fully Human Anti-PSMA Monoclonal Antibodies for Prostate Cancer Therapy Dangshe Ma, Jason P. Gardner, Christine E. Hopf, Michael Cohen, Gerald Donovan, Norbert Schuelke, and William C. Olson MaDangshe Ma More articles by this author , GardnerJason Gardner HopfChristine Hopf CohenMichael Cohen DonovanGerald Donovan SchuelkeNorbert Schuelke OlsonWilliam View All Author...

General intelligence, the ability to solve arbitrary solvable problems, is supposed by many be artificially constructible. Narrow a given particularly difficult problem, has seen impressive recent development. Notable examples include self-driving cars, Go engines, image classifiers, and translators. Artificial Intelligence (AGI) presents dangers that narrow intelligence does not: if something smarter than us across every domain were indifferent our concerns, it would an existential threat...

Reinforcement Learning agents are expected to eventually perform well. Typically, this takes the form of a guarantee about asymptotic behavior an algorithm given some assumptions environment. We present for policy whose value approaches optimal with probability 1 in all computable probabilistic environments, provided agent has bounded horizon. This is known as strong optimality, and it was previously unknown whether possible be strongly asymptotically class environments. Our agent,...