This paper proposes a specific conceptualization of intelligence as computation. This conceptualization is intended to provide a unified view for all disciplines of intelligence research. Already, it unifies several conceptualizations currently under investigation, including physical, neural, embodied, morphological, and mechanical intelligences. To achieve this, the proposed conceptualization explains the differences among existing views by different computational paradigms, such as digital, analog, mechanical, or morphological computation. Viewing intelligence as a composition of computations from different paradigms, the challenges posed by previous conceptualizations are resolved. Intelligence is hypothesized as a multi-paradigmatic computation relying on specific computational principles. These principles distinguish intelligence from other, non-intelligent computations. The proposed conceptualization implies a multi-disciplinary research agenda that is intended to lead to unified science of intelligence.

It happened to us with Wolfram Alpha back in 2009. It happened with our Physics Project in 2020. I've been tracking neural net technology for a long time (about 43 years, actually). And even having watched developments in the past few years I find the performance of ChatGPT thoroughly remarkable. Finally, and suddenly, here's a system that can successfully generate text about almost anything--that's very comparable to what humans might write.

Interest in Artificial Intelligence is exploding, and for good reasons. Computers in cars, phone apps, and on the web can do amazing things that we simply could not do before 2012. This is an attempt to explain the current state of AI to a general audience without using mathematics, computer science, or neuroscience; discussions at these levels would focus on how AI works. Here I will discuss this at the level of Epistemology and will try to explain why it works. "Epistemology" sounds scary, but it really isn't. It's mostly scary because it is unknown; it is not taught in schools anymore.

A new computational paradigm has emerged in computer both Renault and Toyota have deployed online configuration science over the past few decades, which is exemplified by systems based on knowledge compilation). QBF solvers the use of SAT solvers to tackle problems in the complexity have been used in model checking, verification, debugging, class NP. Finally, function problem solvers have and engineering investment is made towards developing been used in model-based diagnosis, design debugging, highly efficient solvers for a prototypical problem CAD and bioinformatics. The cost of this investment is then on a variety of topics, including algorithms; descriptions amortized as these solvers are applied to a broader class of of implementations and/or evaluations of beyond NP problems via reductions (in contrast to developing dedicated solvers; their applications (including encodings); the complexity algorithms for each encountered problem). SAT solvers, classes they reach; and their connections to one for example, are now routinely used to solve problems in another.