Developmental AI is a bootstrapping approach where embodied AIs start with innate competences and learn by interacting with the world. They develop abilities in small steps along a bio-inspired trajectory. However, developmental AIs have not yet reached the abilities of young children. In contrast, mainstream approaches for creating AIs have led to valuable AI systems and impressive feats. These approaches include deep learning and generative approaches (e.g., large language models) and manually constructed symbolic approaches. Manually constructed AIs are brittle even in circumscribed domains. Generative AIs are helpful on average, but they can make strange mistakes and not notice them. They sometimes lack common sense and social alignment. This position paper lays out prospects, gaps, and challenges for augmenting AI mainstream approaches with developmental AI. The ambition is to create data-rich experientially based foundation models and human-compatible, resilient, and trustworthy AIs. This research aims to produce AIs that learn to communicate, establish common ground, read critically, consider the provenance of information, test hypotheses, and collaborate. A virtuous multidisciplinary research cycle has led to developmental AIs with capabilities for multimodal perception, object recognition, and manipulation. Computational models for hierarchical planning, abstraction discovery, curiosity, and language acquisition exist but need to be adapted to an embodied learning approach. They need to bridge competence gaps involving nonverbal communication, speech, reading, and writing. Aspirationally, developmental AIs would learn, share what they learn, and collaborate to achieve high standards. The approach would make the creation of AIs more democratic, enabling more people to train, test, build on, and replicate AIs.

The underlying idea is to get real interoperation among those Information Systems in order to enlarge the benefits that users can get from the Web by increasing machines' processable tasks. Although agent technology and Web Services technology have been developed in separate ways, there exists a recent work (Greenwood and M.Lyell, 2007) which tries to consolidate their approaches into a common specification describing how to seamlessly interconnect FIPA compliant agent systems (FIPA, 2005) with W3C compliant Web Services. The purpose of specifying an infrastructure for integrating these two technologies is to provide a common means of allowing each to discover and invoke instances of the other. Considering the previous approach, in the rest of this paper we will only concentrate on aspects of inter-agent communication. In general, communication among agents is based on the interchange of messages, which in this context are also known as communication acts.

One relevant aspect in the development of the Semantic Web framework is the achievement of a real inter-agents communication capability at the semantic level. The agents should be able to communicate and understand each other using standard communication protocols freely, that is, without needing a laborious a priori preparation, before the communication takes place. For that setting we present in this paper a proposal that promotes to describe standard communication protocols using Semantic Web technology (specifically, OWL-DL and SWRL). Those protocols are constituted by communication acts. In our proposal those communication acts are described as terms that belong to a communication acts ontology, that we have developed, called CommOnt. The intended semantics associated to the communication acts in the ontology is expressed through social commitments that are formalized as fluents in the Event Calculus. In summary, OWL-DL reasoners and rule engines help in our proposal for reasoning about protocols. We define some comparison relationships (dealing with notions of equivalence and specialization) between protocols used by agents from different systems.

Over the last years, there has been a change of perspective concerning the management of information systems, since they are no longer isolated and need to communicate with others. However, from a semantic point of view, real communication is difficult to achieve due to the heterogeneity of the systems. We present a proposal which, considering information systems are represented by software agents, provides a framework that favours a semantic communication among them, overcoming the heterogeneity of their agent communication languages. The main components of the framework are a suite of ontologies -- conceptualizing communication acts -- that will be used for generating the communication conversion, and an Event Calculus interpretation of the communications, which will be used for formalizing the notion of a satisfactory conversion. Moreover, we present a motivating example in order to complete the explanation of the whole picture.

Sen. Josh Hawley sat down with Fox News Digital for a wide-ranging interview about his new book, "Manhood: The Masculine Virtues America Needs." Senator Josh Hawley, R-Mo., is very concerned about the power of Artificial Intelligence to "manipulate Americans and the "facts" they are given from the technology on a daily basis. "I'm worried about AI's power to manipulate our attention, to manipulate our opinions and to manipulate the information that we're given," he told Fox News Digital in a recent in-person interview. The Missouri senator, the ranking member on the Senate Judiciary Subcommittee on Privacy, Technology and the Law, continued, "Already you can see these generative AI systems – these large language models – that are trained on all the information on the internet." AI WILL BE THE POLITICAL LEFT'S'SINGLE GREATEST WEAPON' AGAINST RELIGIOUS FAITH AND TRUTH, SAYS EXPERT He added, "You can train them on your own.

The Bureau of Multiversal Arbitration is an unusual workplace. Maude Fletcher's alright, though she needs to learn how to turn off caps lock in the company chat. But trying to deal with Byron G Snodgrass is like handling an energetic poodle, and Phil is a bit stiff. Byron G Snodgrass is an energetic poodle. A peace lily, I think.

It is well known that argumentation can usefully be analysed as a distinct, if complex, type of speech act. Speech acts that form a part of argumentative discourse, and in particular, of argumentative dialogue, can be seen as anchors for the establishment of inferences between propositions in the domain of discourse. Most often, the speech acts that directly give rise to inference are implicit, but can be drawn out in analysis by consideration of the type of dialogue game being played. AI approaches to argumentation often focus solely on such inferences as the means by which persuasion can be effected – but this is in contrast with psychological and rhetorical models which have long recognised the role played by extra-logical features of the dialogical context. These ‘peripheral’ cues can not only affect persuasive effect of the logical, ‘central’ argumentation, but can override and dominate it. This paper presents a theory which allows both central and peripheral aspects of argumentation to be represented in a coherent analytical account based on the sequences of speech acts which constitute dialogues.