The $430 Casio Moflin is a pointless but adorable robot you can't help but love. My friend's dog Wylie sits watching it, suspicious of its every move. Moflin is an AI companion robot made by the Japanese electronics manufacturer Casio. Via the companion app, I have chosen to name this one Puff, because--well--that is what it looks like. Wylie immediately clocks it as something to be wary of, a device that moves on its own but is not alive. Wylie barks, then runs out of the room, growling. When I found out Casio--yes, the same Casio that makes watches--had made an AI pet bot, I figured I was exactly the type of person it was made for.

We present an algorithm to sample stochastic differential equations conditioned on rather general constraints, including integral constraints, endpoint constraints, and stochastic integral constraints. The algorithm is a pathspace Metropolis-adjusted manifold sampling scheme, which samples stochastic paths on the submanifold of realizations that adhere to the conditioning constraint. We demonstrate the effectiveness of the algorithm by sampling a dynamical condensation phase transition, conditioning a random walk on a fixed Levy stochastic area, conditioning a stochastic nonlinear wave equation on high amplitude waves, and sampling a stochastic partial differential equation model of turbulent pipe flow conditioned on relaminarization events.

This paper presents a supervised method for detecting hallucinations in large language models. By analyzing token scores (logitis) across layers of the LLaMA model, we derive a small set, aiming to reduce overfitting, of features-including maximum, minimum, mean, standard deviation, and slope. We use logistic regression for classification and validate the model on the TruthfulQA and MMLU datasets. The results demonstrate significant performance gains, especially in zero-shot scenarios, highlighting the effectiveness and potential for generalization.

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Table 1: Measured comparison of three control strategies.

The application of Artificial Intelligence techniques to real-world problems has produced promising research results, but seldom has a system becoma a useful tool in its domain of expertise.

PUFF is now in routine use in Presbyterian Hospital, Pacific Medical Center (PMC), in San Francisco. The program produces a report, intended for patient records, that explains the clinical significance of measured quantitative test results and gives a diagnosis of the presence and severity of pulmonary disease in terms of the measured data, referral diagnosis, and patient history. "Rules", or statements of the form "IF condition THEN conclusion ", are used by the physiologist and the computer system to specify the system operation. The sequence of rules used to interpret the case also specifies a line of reasoning about the case, or the detailed explanation of the interpretation of the case. The use of rules for this type of knowledge based system is taken from the results of applied Artificial Intelligence research. In a 144 case prospective evaluation, there was a 91% overall rate of agreement between the rule based system diagnoses and the diagnoses of the designing physiologist; there was a 89% rate of agreement between the system diagnoses and diagnoses of a second independent physiologist.

Working in the EMYCIN environment, they were delighted to find that they could more easily distill their criteria for test interpretation by using the production rule formalism.

The success of MYCIN-like systems has demonstrated that for many diagnostic tasks expert behavior can be successfully captured in simple goaldirected production systems. However, even for this class of problems, difficulties have arisen with both the representation and control mechanisms. One such system, PUFF (Kunz et al., 1978), has established a creditable record in the domain of pulmonary function diagnosis. The representation problems in PUFF are manifest in a number of rules that have awkward premises and conclusions. The control problems are somewhat more severe. Physicians have criticized PUFF on the grounds that it asks questions that do not follow a logical line of reasoning and that it does not notice data that are atypical or erroneous for the determined diagnosis. In the CENTAUR sygtem, described in Chapter 23, an attempt was made to correct representational deficiencies by using prototypes (frames) to characterize some of the system's knowledge.

CENTAUR was designed in response to problems that occurred while using a purely rule-based system. The CENTAUR system offers an appropriate environment in which to experiment with knowledge representation issues such as determining what knowledge is most easily represented in rules and what is most easily represented in frames. In summary, much research remains to be done on this and associated knowledge representation issues. This present research is one attempt to make explicit the art of choosing the knowledge representation in AI by drawing comparisons between various approaches and by identifying the reasons for selecting one fundamental approach over another.