Though humans seem to be remarkable learners, arguments in cognitive science and philosophy of mind have long maintained that learning something fundamentally new is impossible. Specifically, Jerry Fodor's arguments for radical concept nativism hold that most, if not all, concepts are innate and that what many call concept learning never actually leads to the acquisition of new concepts. These arguments have deeply affected cognitive science, and many believe that the counterarguments to radical concept nativism have been either unsuccessful or only apply to a narrow class of concepts. This paper first reviews the features and limitations of prior arguments. We then identify three critical points - related to issues of expressive power, conceptual structure, and concept possession - at which the arguments in favor of radical concept nativism diverge from describing actual human cognition. We use ideas from computer science and information theory to formalize the relevant ideas in ways that are arguably more scientifically productive. We conclude that, as a result, there is an important sense in which people do indeed learn new concepts.

The Mixture-of-Experts (MoE) architecture has demonstrated significant advantages in the era of Large Language Models (LLMs), offering enhanced capabilities with reduced inference costs. However, deploying MoE-based LLMs on memoryconstrained edge devices remains challenging due to their substantial memory requirements. While existing expertoffloading methods alleviate the memory requirements, they often incur significant expert-loading costs or compromise model accuracy. We present HOBBIT, a mixed precision expert offloading system to enable flexible and efficient MoE inference. Our key insight is that dynamically replacing less critical cache-miss experts with low precision versions can substantially reduce expert-loading latency while preserving model accuracy. HOBBIT introduces three innovative techniques that map the natural hierarchy of MoE computation: (1) a token-level dynamic expert loading mechanism, (2) a layer-level adaptive expert prefetching technique, and (3) a sequence-level multidimensional expert caching policy. These innovations fully leverage the benefits of mixedprecision expert inference. By implementing HOBBIT on top of the renowned LLM inference framework Llama.cpp, we evaluate its performance across different edge devices with representative MoE models. The results demonstrate that HOBBIT achieves up to a 9.93x speedup in decoding compared to state-of-the-art MoE offloading systems.

As a teenager, Veronika Megler was intent on becoming a statistician. She signed up for a computer science course at Melbourne University, reasoning it would assist her chosen career. "I think there were four women in a class of about 220 people, and it was pretty misogynistic," she recalls. Megler had already built her own PC, buying the motherboard, chips, capacitors and diodes from an electronics shop in Melbourne. "In the store they'd say'tell your boyfriend we don't have these'," she recalls.

Embracer, the mega game publisher that's been snapping up new properties left and right, has made a deal to acquire the intellectual property catalogue and worldwide rights to various JRR Tolkien-related media and merch. To be precise, it will own the rights to "motion pictures, video games, board games, merchandising, theme parks and stage productions" based on the The Lord of the Rings trilogy and The Hobbit if the deal pushes through. It will also own the rights tied to any future literary work related to LOTR and The Hobbit that's authorized by the Tolkien Estate. This isn't the first Tolkien-related purchase Embracer has made: Back in 2021, it bought the board game publisher Asmodee, which has published over a dozen LOTR board games over the past 20 years. And if the acquisition goes through, Embracer will work with Amazon on The Lord of the Rings: Rings of Power series that will start streaming on September 2nd.

Life has been busy for the stars of "Sherlock" since the series premiered in 2010, with Steven Moffat and Mark Gatiss applying new London style and contemporary quirks to Arthur Conan Doyle's famous consulting detective. Its fourth season -- there have been breaks -- begins Sunday on PBS' "Masterpiece: Mystery!" Martin Freeman, the series' Dr. John Watson, has gone from a guy you might have seen on the British version of "The Office" or in "The Hitchhiker's Guide to the Galaxy" to playing Bilbo Baggins in three "Hobbit" movies and the hapless Lester Nygaard in the first season of FX's "Fargo," and hosting "Saturday Night Live." Benedict Cumberbatch, its Sherlock (also in the "Hobbit" movies, as the voice of Smaug) has, among other things, played Khan in "Star Trek Into Darkness," the title role in "Doctor Strange," codebreaker Alan Turing in "The Imitation Game" and Richard III in BBC's "The Hollow Crown" Shakespeare cycle; sung "Comfortably Numb" with Pink Floyd's David Gilmour at the Royal Albert Hall; and has become something of an international, official hot guy. Conan Doyle wrote 60 Holmes stories, but the world has deemed that insufficient, and many other hands have filled out the tale. Holmes is a useful mix of specific qualities and scant details -- an attitude, occupation and method as much as a full-fleshed, full-fledged character, and so familiar that even some characters not called Sherlock Holmes, like Hugh Laurie's Dr. House on "House," are recognizably him.