Google has updated its Bard AI chatbot, so you can use it to parse YouTube videos. In its most recent experiment update log, the company has announced it has expanded the capabilities of Bard's YouTube extension, so when it's enabled, the generative AI can "understand some video content." For example, Google said you'd be able to ask Bard how many eggs were used in a video for an olive oil cake recipe. Bard first gained the ability to pull data from YouTube in September after an update integrated it with other Google products, including Docs, Maps, Lens, Flights and Hotels. Google claims people have been asking for "deeper engagement with YouTube videos" -- but I guess that entails watching them less.

The fundamental nature of Elgot iteration has been extensively explored by Bloom and Esik in the form of iteration theories, while Kleene iteration became extremely popular as an integral part of (untyped) formalisms, such as automata theory, regular expressions and Kleene algebra. Here, we establish a formal connection between Elgot iteration and Kleene iteration in the form of Elgot monads and Kleene monads, respectively. We also introduce a novel class of while-monads, which like Kleene monads admit a relatively simple description in algebraic terms. Like Elgot monads, while-monads cover a large variety of models that meaningfully support while-loops, but may fail the Kleene algebra laws, or even fail to support a Kleen iteration operator altogether.

In this paper, we consider a problem in which distributively extracted features are used for performing inference in wireless networks. We elaborate on our proposed architecture, which we herein refer to as "in-network learning", provide a suitable loss function and discuss its optimization using neural networks. We compare its performance with both Federated- and Split learning; and show that this architecture offers both better accuracy and bandwidth savings.

IDAHO FALLS, Idaho, Dec. 5, 2019 – A powerful new supercomputer arrived this week at Idaho National Laboratory's Collaborative Computing Center. The machine has the power to run complex modeling and simulation applications, which are essential to developing next-generation nuclear technologies. Named after a central Idaho mountain range, Sawtooth arrives in December and will be available to users early next year. That is the highest ranking reached by an INL supercomputer. Of 102 new systems added to the list in the past six months, only three were faster than Sawtooth.

In this book, we consider various many-valued logics: standard, linear, hyperbolic, parabolic, non-Archimedean, p-adic, interval, neutrosophic, etc. We survey also results which show the tree different proof-theoretic frameworks for many-valued logics, e.g. frameworks of the following deductive calculi: Hilbert's style, sequent, and hypersequent. We present a general way that allows to construct systematically analytic calculi for a large family of non-Archimedean many-valued logics: hyperrational-valued, hyperreal-valued, and p-adic valued logics characterized by a special format of semantics with an appropriate rejection of Archimedes' axiom. These logics are built as different extensions of standard many-valued logics (namely, Lukasiewicz's, Goedel's, Product, and Post's logics). The informal sense of Archimedes' axiom is that anything can be measured by a ruler. Also logical multiple-validity without Archimedes' axiom consists in that the set of truth values is infinite and it is not well-founded and well-ordered. On the base of non-Archimedean valued logics, we construct non-Archimedean valued interval neutrosophic logic INL by which we can describe neutrality phenomena.