Large Language Models (LLMs) have revolutionised natural language processing, exhibiting impressive human-like capabilities. In particular, LLMs are capable of "lying", knowingly outputting false statements. Hence, it is of interest and importance to develop methods to detect when LLMs lie. Indeed, several authors trained classifiers to detect LLM lies based on their internal model activations. However, other researchers showed that these classifiers may fail to generalise, for example to negated statements.

David L. Craddock is the author of more than a dozen books about video games, including Break Out, about the history of Apple II games, and Rocket Jump, about the history of first-person shooters. "I tend to write a lot about games made in the '80s, '90s, and early '00s," Craddock says in Episode 481 of the Geek's Guide to the Galaxy podcast. "I love to write about creative people who had big ideas but very, very tight restrictions, and I think that from that comes some of the most enduring products--most enduring experiences--ever made." One of Craddock's most recent books is Stay Awhile and Listen: Book II, about the making of Blizzard's classic action RPG Diablo II. Craddock says this volume was a much bigger undertaking than Stay Awhile and Listen: Book I, about the original Diablo. "There was just so much more to juggle in terms of timeline, in terms of game," he says.

The COVID-19 pandemic has upended our lives and compelled governments, businesses and individuals to re-examine the ways of the past and reimagine the future. For India, the pandemic has also laid bare the several gaps in India's healthcare system and underscored the urgent need to improve health outcomes in the country. Digitization will play an integral role in plugging the prevailing gaps in India's healthcare ecosystem. In 2015, the government of India (GoI) launched the'Digital India' campaign with an aim to transform India into a leading economy of the future. In the last half a decade, the country has become the second-fastest digital adopter in the world--India has almost half a billion Internet users and more than 350 million smartphone users consuming 8.3 GB of data per month per subscriber.

What Ray Reiter has done has taken a set of ideas worked out by him and his collaborators over the last 11 years and recrystallized them into a sustained and consistent presentation. This is not a collection of those papers but a complete rewrite that avoids the usual repetition and notational inconsistency that one might expect. It makes one wish everyone as prolific as Reiter would copy his example--but because that's unlikely, we must be grateful for what he has given us. In case you haven't heard, Reiter and his crew, starting with the publication of Reiter (1991), breathed new life into the situation calculus (Mc-Carthy and Hayes 1969) that had gotten the reputation of being of limited expressiveness. The basic concept of the calculus is, of course, the situation, which we can think of as a state of affairs, that is, a complete specification of the truth values of all propositions (in a suitable logical language), although that's closer to McCarthy's and Hayes's traditional formulation than the analysis Reiter settles on (which I describe later).

The book might also supply points of interest, although not always dependable instruction, to social scientists, philosophers, and psychologists. Thornton describes his book as a research memorandum "in keeping with the technicolour spirit of our times" and also owns to "importing various devices from the pop-science genre" (Preface, pp. His pop-science offerings include "light relief through a concoction of dialogues, anecdotes, and other forms of non-scientific material" (Preface, p. 2) of which the more historical chapters make the best reading. Here, departures from strict accuracy are offset by the liveliness of Thornton's accounts of Kepler's work (chapter 3) and Turing's part in breaking the German wartime Enigma code (chapter 6). He is less successful with Hume's demonstration of the fallibility of the inductive process. The old philosophical concern was that theories inductively inferred from sampled facts cannot be guaranteed true for every single future fact that might be ...

Review of The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology If you are interested in writing a review, contact chandra@ cis.ohio-state.edu. AT question: Which one of the following doesn't belong with the rest? It is the only discipline in the list that is not under attack for being conceptually or methodologically confused. Objections to AI and computational cognitive science are myriad. Accordingly, there are many different reasons for these attacks. However, all of them come down to one simple observation: Humans seem a lot smarter than computers--not just smarter as in Einstein was smarter than I, or I am smarter than a chimpanzee, but more like I am smarter than a pencil sharpener. To many, computation seems like the wrong paradigm for studying the mind. All this is because of another truth: The computational paradigm is the best thing to come down the pike since the wheel. The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology, Jerry Fodor, Cambridge, Massachusetts, The MIT Press, 2000, 126 pages, $22.95. Jerry Fodor believes this latter claim. He says: [The computational theory of mind] is, in my view, by far the best theory of cognition that we've got; indeed, the only one we've got that's worth the bother of a serious discussion.… There is, in short, every reason to suppose that Computational Theory is part of the truth about cognition. It is a fascinating read. This dispute about quantity of truth is where the book gets its title. In 1997, Steven Pinker published an important book describing the current state of the art in cognitive science (see also Plotkin [1997]). Pinker's book is entitled How the Mind Works. In it, he describes how computationalism, psychological nativism (the idea that many of our concepts are innate), massive modularity (the idea that most mental processes occur within a domain-specific, encapsulated specialpurpose processor), and Darwinian adaptationism combine to form a robust (but nascent) theory of mind. Fodor, however, thinks that the mind doesn't work that way or, anyhow, not very much of the mind works that way. Fodor dubs the synthesis of computationalism, nativism, massive modularity, and adaptationism the new synthesis (p.

Human annotation of semantic interpretation tasks is a critical part of big data semantics, but it is based on an antiquated ideal of a single correct truth that needs to be similarly disrupted. We expose seven myths about human annotation, most of which derive from that antiquated ideal of truth, and dispel these myths with examples from our research. We propose a new theory of truth, crowd truth, that is based on the intuition that human interpretation is subjective, and that measuring annotations on the same objects of interpretation (in our examples, sentences) across a crowd will provide a useful representation of their subjectivity and the range of reasonable interpretations. In the past decade the amount of data and the scale of computation available has increased by a previously inconceivable amount. Computer science, and AI along with it, has moved solidly out of the realm of thought problems and into an empirical science.

One, enterprises have too many decisions to make. Two, it's difficult to find success with complex data integration. Those are the two main excuses I hear these days, as enterprises move to the cloud. Whatever the justification, the lack of attention to data integration is beginning to cause some real damage. Enterprises have so much coming at them that they don't think about every approach and technology that they need to think about.

It's illegal to use a legal name and BCCRSS document weblinks, aka the Truth, is shared by the first AI bot to attend Law School: The only Lawyer in History to promote Truth about the Legal Name Fraud.

This paper introduces conceptual relations that synthesize utilitarian and logical concepts, extending the logics of preference of Rescher. We define first, in the context of a possible worlds model, constraint-dependent measures that quantify the relative quality of alternative solutions of reasoning problems or the relative desirability of various policies in control, decision, and planning problems. We show that these measures may be interpreted as truth values in a multi valued logic and propose mechanisms for the representation of complex constraints as combinations of simpler restrictions. These extended logical operations permit also the combination and aggregation of goal-specific quality measures into global measures of utility. We identify also relations that represent differential preferences between alternative solutions and relate them to the previously defined desirability measures. Extending conventional modal logic formulations, we introduce structures for the representation of ignorance about the utility of alternative solutions. Finally, we examine relations between these concepts and similarity based semantic models of fuzzy logic.