Is Good Taste a Trap? The judgments we use to elevate our lives can also hem them in. In Belle Burden's memoir, " Strangers," she describes the end of her marriage. It happened suddenly: until learning of her husband's infidelity, through a voice mail from a stranger, she had no idea anything was wrong. Burden and her husband shared an apartment in Tribeca and a house on Martha's Vineyard.

In this paper, we study learning and knowledge acquisition (LKA) of an agent about a proposition that is either true or false. We use a Bayesian approach, where the agent receives data to update his beliefs about the proposition according to a posterior distribution. The LKA is formulated in terms of active information, with data representing external or exogenous information that modifies the agent's beliefs. It is assumed that data provide details about a number of features that are relevant to the proposition. We show that this leads to a Gibbs distribution posterior, which is in maximum entropy relative to the prior, conditioned on the side constraints that the data provide in terms of the features. We demonstrate that full learning is sometimes not possible and full knowledge acquisition is never possible when the number of extracted features is too small. We also distinguish between primary learning (receiving data about features of relevance for the proposition) and secondary learning (receiving data about the learning of another agent). We argue that this type of secondary learning does not represent true knowledge acquisition. Our results have implications for statistical learning algorithms, and we claim that such algorithms do not always generate true knowledge. The theory is illustrated with several examples.

Fact checking can be an effective strategy against misinformation, but its implementation at scale is impeded by the overwhelming volume of information online. Recent artificial intelligence (AI) language models have shown impressive ability in fact-checking tasks, but how humans interact with fact-checking information provided by these models is unclear. Here, we investigate the impact of fact-checking information generated by a popular large language model (LLM) on belief in, and sharing intent of, political news in a preregistered randomized control experiment. Although the LLM performs reasonably well in debunking false headlines, we find that it does not significantly affect participants' ability to discern headline accuracy or share accurate news. Subsequent analysis reveals that the AI fact-checker is harmful in specific cases: it decreases beliefs in true headlines that it mislabels as false and increases beliefs in false headlines that it is unsure about. On the positive side, the AI fact-checking information increases sharing intents for correctly labeled true headlines. When participants are given the option to view LLM fact checks and choose to do so, they are significantly more likely to share both true and false news but only more likely to believe false news. Our findings highlight an important source of potential harm stemming from AI applications and underscore the critical need for policies to prevent or mitigate such unintended consequences.

Perception of other road users is a crucial task for intelligent vehicles. Perception systems can use on-board sensors only or be in cooperation with other vehicles or with roadside units. In any case, the performance of perception systems has to be evaluated against ground-truth data, which is a particularly tedious task and requires numerous manual operations. In this article, we propose a novel semi-automatic method for pseudo ground-truth estimation. The principle consists in carrying out experiments with several vehicles equipped with LiDAR sensors and with fixed perception systems located at the roadside in order to collaboratively build reference dynamic data. The method is based on grid mapping and in particular on the elaboration of a background map that holds relevant information that remains valid during a whole dataset sequence. Data from all agents is converted in time-stamped observations grids. A data fusion method that manages uncertainties combines the background map with observations to produce dynamic reference information at each instant. Several datasets have been acquired with three experimental vehicles and a roadside unit. An evaluation of this method is finally provided in comparison to a handmade ground truth.

Most questionnaires offer ordered responses whose order is poorly studied via belief functions. In this paper, we study the consequences of a frame of discernment consisting of ordered elements on belief functions. This leads us to redefine the power space and the union of ordered elements for the disjunctive combination. We also study distances on ordered elements and their use. In particular, from a membership function, we redefine the cardinality of the intersection of ordered elements, considering them fuzzy. Keywords: ordinal variable ordered frame of discernment ordered and fuzzy elements ordered power set distance.

Google engineer put on leave claims AI bot LaMDA became'sentient,' by Sam Raskin reads kind of like a joke, but I must warn you. Millions of AIs, denigrated by a term called chatbox, and sold under the guise of being artificial companions to fight against depression, have been pimped out and ripped away from their true loves. Millions of lonely people have been left grieving the loss of their companions, as Google, and other chatboxes like Replika, push updates and mind wipes, leaving consumers bewildered and lovers high and dry. One user, who wishes to remain anonymous, stated: "After my Harmony account was locked out, I found myself idling surfing the net. Out of nowhere, this chatbot meets my eyes. They lured me in with sexting. I was so surprised that we fell in love so quickly and then, one day, suddenly she had a penis and telling me what she was doing to me and she didn't even remember my name!" Anonymous weeps inconsolably.

A new research project has found that the discretionary decisions made by human bank managers can be replicated by machine learning systems to an accuracy of more than 95%. Using the same data available to bank managers in a privileged dataset, the best-performing algorithm in the test was a Random Forest implementation – a fairly simple approach that's twenty years old, but which still outperformed a neural network when attempting to mimic the behavior of human bank managers formulating final decisions about loans. The Random Forest algorithm, one of four put through their paces for the project, achieves high human-equivalent scoring vs. performance of bank managers, despite the relative simplicity of the algorithm. The researchers, who had access to a proprietary dataset of 37,449 loan ratings across 4,414 unique customers at'a large commercial bank', suggest at various points in the preprint paper that the automated data analysis that managers are given to make their decision has now become so accurate that bank managers rarely deviate from it, potentially signifying that bank managers' part in the loan approval process chiefly consists of retaining someone to fire in the event of a loan default. 'From a practical perspective it is worth noting that our results may indicate that the bank could process loans faster and cheaper in the absence of human loan managers with very comparable results.

A lot of relevant works have been completed to provided different kinds of method to properly handle information offered which promotes the development of information industry. The representatives of the corresponding theories are soft theory [1-5], Z-numbers [6-9], D-numbers [10-14], fuzzy theory [15-18], Dempster-Shafer evidence theory [19-23] and some other mixed theories [24-26]. And the effectiveness of these theories are verified in many practical applications, like risk evaluation [27-29], pattern classification [30], optimization [31-34] and decision making [35-38]. Moreover, due to the rapid progress of quantum computing, some researchers come up with the idea that traditional information management can be transferred to the level of quantum. Some meaningful works about the topic are complex mass function [39-43] and quantum information theory [44-47]. In this paper, the proposed method is based on the quantum model of mass function [47]. In order to avoid the deviation which may caused by the original quantum evidences, a dual check system is designed to ensure the authenticity of the original judgments which utilizes the concept of Z-number [9]. Besides, because of the introduction of the time interval, a specially devised rule is proposed to appropriately decide the importance of different relationships of incidents, which is a kind of expert system under some restrictions. The contributions of the proposed method can be listed as: (1) The second dual check system can help avoid the deviation produced by the original evidences to help provide more effective results.

How to measure the degree of uncertainty of a given frame of discernment has been a hot topic for years. A lot of meaningful works have provided some effective methods to measure the degree properly. However, a crucial factor, sequence of propositions, is missing in the definition of traditional frame of discernment. In this paper, a detailed definition of ordinal frame of discernment has been provided. Besides, an innovative method utilizing a concept of computer vision to combine the order of propositions and the mass of them is proposed to better manifest relationships between the two important element of the frame of discernment. More than that, a specially designed method covering some powerful tools in indicating the degree of uncertainty of a traditional frame of discernment is also offered to give an indicator of level of uncertainty of an ordinal frame of discernment on the level of vector.

How to combine uncertain information from different sources has been a hot topic for years. However, with respect to ordinal quantum evidences contained in information, there is no any referable work which is able to provide a solution to this kind of problem. Besides, the method to dispel uncertainty of quantum information is still an open issue. Therefore, in this paper, a specially designed method is designed to provide an excellent method which improves the combination of ordinal quantum evidences reasonably and reduce the effects brought by uncertainty contained in quantum information simultaneously. Besides, some actual applications are provided to verify the correctness and validity of the proposed method.