This paper presents three established theories of human decision-making and describes how they can be integrated to provide a model of purposive human action. Taking seriously the idea of language as action the model is then applied to the conversational user interfaces. Theory based AI research has had a hard time recently and the aim here is to revitalise interest in understanding what LLMs are actually doing other than running poorly understood machine learning routines over all the data the relevant Big Tech company can hoover up. When a raspberry pi computer for under 50USD is up to 400 times faster than the first commercial Cray super computer~\cite{crayVpi}, Big Tech can get really close to having an infinite number of monkeys typing at random and producing text, some of which will make sense. By understanding where ChatGPT's apparent intelligence comes from, perhaps we can perform the magic with fewer resources and at the same time gain some understanding about our relationship with our world.

Roger C. Schank, a scientist who made influential contributions to the field of artificial intelligence and then, as an academic, author and entrepreneur, focused on how people learn, died on Jan. 29. He was 76. His wife, Annie Schank, said the cause was heart failure. Dr. Schank's research combined linguistics, cognitive science and computing. In a 1995 essay, he described the common theme of his varied projects in academics and business as "trying to understand the nature of the human mind" and "building models of the human mind on the computer." In the late 1960s and '70s, Dr. Schank developed ideas for how to represent in symbols for a computer simple concepts — like people and places, objects and events, cause-and-effect relationships — that humans describe with words. His model was called "conceptual dependency theory."

In recent years, natural language processing (NLP) has become a part of our everyday lives. Smartphones now come equipped with NLP-powered voice assistants that interpret and understand human speech in order to provide relevant responses to user queries. NLP also helps translation apps break down communication barriers by analyzing input in one language and transforming it into another language. Even word processors rely on NLP to check the grammar, logic, and syntax of written input. And NLP is now an integral part of customer service; it's used to guide people to the right representative through verbal commands. Yet, few people actually understand how NLP plays a role in making them possible.

The answers people give when asked to 'think of the unexpected' for everyday event scenarios appear to be more expected than unexpected. There are expected unexpected outcomes that closely adhere to the given information in a scenario, based on familiar disruptions and common plan-failures. There are also unexpected unexpected outcomes that are more inventive, that depart from given information, adding new concepts/actions. However, people seem to tend to conceive of the unexpected as the former more than the latter. Study 1 tests these proposals by analysing the object-concepts people mention in their reports of the unexpected and the agreement between their answers. Study 2 shows that object-choices are weakly influenced by recency, the order of sentences in the scenario. The implications of these results for ideas in philosophy, psychology and computing is discussed

In March 2018, my collaborator Neus Lorenzo and I had the privilege of hosting a symposium and a workshop at the annual Mobile Learning Week, an event co-sponsored by UNESCO and the International Telecommunication Union (ITU). UNESCO is the educational, scientific, and cultural organization of the United Nations. The ITU, also a UN agency, coordinates telecommunications, spectrum allocations, and policy positions on information and communication services which, it seems, the world no longer knows how to live without. The event made for an amazing week, during which we were able to interact with some of the smartest people from all over the world on subjects connected to all kinds of learning in so many different cultural contexts, but all associated with the major question of mobility. A subtheme that ran through many of the interventions, including our own, was Artificial Intelligence (AI).

This overview of the Yale Artificial Intelligence Project serves as an introduction to Scientific Datalink's microfiche publication of Yale AI Technical Reports Researchers develop new ideas and plant them in programs. The programs are cultivated, hybridized, nurtured. The weaker ideas die out. The stronger ideas are grafted onto new stock and serve as the basis of hearty new strains. At Yale, there has been a traditional summer seminar series at which graduate students present their unprepossessing theories to the vocal and critical review of their colleagues.

Via G. Galilei 5, 21027 Ispra (VA), Italy The period since the last conference in this series has been characterized by the explosive expansion of AI out of the confines of institutions of basic research like university departments into the worlds of industry, business, and government (a development I had long expected). But it seems to me that there are plenty-perhaps an overabundance-of other occasions, other conferences, other workshops, and the like, at which the applications of AI would appropriately be considered. In fact, it is ironic-though perhaps it may be understandable-that precisely now, when the outside world has discovered and started showing its appreciation of AI and its potential, there is a widespread malaise among research workers in the field about the health of their subject. This malaise has to do not only with logistic issues such as the drain of very good people from research into applications, or some of the gross inadequacies of structural and funding support by governments. It has to do also with the very heart and methodology of the subject.

In fact, there are enough opinions for four men. That is, the views advanced are contradictory. I agree with one of the A fifth answer is also advanced, but is immediately withdrawn. Roger Schanks, and disagree with the other three. Schank hoped that his article would start a debate on As & hank points out, this is unsatisfactory because it leads the issues he raised.

The other articles in the NL chapter of the Handbook include a historical sketch of machine translation from one language to another, which was the subject of the very earliest ideas about processing language with computers; technical articles on some of the grammars and parsing techniques that AI researchers have used in their programs; and an article on text generation, the creation of sentences by the program. Finally, there are several articles describing the NL programs themselves: the early systems of the 1960s and the major research projects of the last decade, including Wilks'S machine translation system, Winograd's SHRDLU, Woods's LUNAR, Schank's MARGIE, SAM, and PAM, and Hendrix's LIFER. Two other chapters of the Handbook are especially relevant to NL research. Speech understanding research attempts to build computer interfaces that understand spoken language. In the 197Os, speech and natural language understanding research were often closely linked.

Department of Coqmter Scieme, Carnegie-Melloll Ulziversity, Pittsburgh, Penmylvania 15213 One, is that most of these people make essentially no distinction between computers, broadly defined, and artificial intelligence-probably for very good reason. As far as they're concerned, there is no difference; they're just worried about the impact of very capable, smart computers. Enthusiasm and exaggerated expectations were very much in evidence. The computer seems to be a mythic emblem for a bright, high-tech future that is going to make our lives so much easier. But it was interesting to hear the subjects that people were interested in.