The cost of information processing in physical systems calls for a trade-off between performance and energetic expenditure. Here we formulate and study a computation-dissipation bottleneck in mesoscopic systems used as input-output devices. Using both real datasets and synthetic tasks, we show how non-equilibrium leads to enhanced performance. Our framework sheds light on a crucial compromise between information compression, input-output computation and dynamic irreversibility induced by non-reciprocal interactions.

More than 100 of the world's leading experts in Artificial Intelligence (AI) and ethics have signed up to be part of a new journal created by a North East professor. University of Sunderland's Pro-Vice-Chancellor John MacIntyre launches'AI and Ethics' this month alongside his co-Editor-in-Chief, Professor Larry Medsker of George Washington University in the US, and Rachel Moriarty, Publishing Editor at Springer. Five years in the making, the journal has attracted around 100 of the world's leading thinkers and practitioners in this field of study to be part of its editorial board and aims to promote informed debate and discussion of the ethical, regulatory and policy implications that arise from the development of AI. Professor MacIntyre said: "Our objective is to be useful to a wide range of audiences – the academic and scientific community, the commercial and product development community, users of AI, those developing governance and regulatory frameworks for AI, and the public. We want to provide an outlet to publish high-quality work and making it available to be used by those audiences."

Machine learning has been at the forefront of the movement for free and open access to research. For example, in 2001 the Editorial Board of the Machine Learning Journal resigned en masse to form a new zero-cost open access journal, the Journal of Machine Learning Research (JMLR). "…journals should principally serve the needs of the intellectual community, in particular by providing the immediate and universal access to journal articles that modern technology supports, and doing so at a cost that excludes no one." In addition to JMLR, virtually all of the major machine learning outlets including NIPS, ICML, ICLR, COLT, UAI, and AISTATS make no charge for access to or publication of papers. In the light of this, and the recent announcement by Nature Publishing Group of a new closed-access journal, "Nature Machine Intelligence", the following list of researchers hereby state that they will not submit to, review, or edit for this new journal.

NEW YORK, NY - JUNE 16: Director of Facebook AI Research Yann LeCun attends the 2016 Wired Business Conference on June 16, 2016 in New York City. Renowned artificial intelligence (AI) experts from almost all of the tech giants are planning to boycott a new journal from Nature Publishing Group, which is widely regarded as one of the most influential science publishers in the world. Nature's new Machine Intelligence Journal is due to be published for the first time in January 2019. Nature said it will cover the "best research from across the field of artificial intelligence" but it will also be a closed access journal, and this has angered many in the AI community who want to see AI research openly available to everyone. Over 2,000 people -- including more than 75 from Google, 25 from Microsoft, 23 from DeepMind, 16 from Facebook, and 11 from Amazon -- have pledged to "not submit to, review, or edit for this new journal". They each signed a statement from Oregon State University's Professor Thomas Dietterich that was published on Monday.

Held to the same high editorial standards as Radiology, Radiology: Artificial Intelligence, a new RSNA journal to be launched in early 2019, will highlight the emerging applications of machine learning and artificial intelligence in the field of imaging across multiple disciplines. Radiology: Artificial Intelligence will be published bi-monthly and available exclusively online. RSNA members will receive a complimentary subscription to the new journal as a member benefit. Institutions can receive free access through December 31, 2019 by registering at rsna.org/freetrial. Original research and editorial submissions to Radiology: Artificial Intelligence will be accepted in the coming months.

Budding authors face a minefield when it comes to publishing their work. For a large fee, as much as $3,000, they can make their work available to anyone who wants to read it. Or they can avoid the fee and have readers pay the publisher instead. Often it is libraries that foot this bill through expensive annual subscriptions. This is not the lot of wannabe fiction writers, it's the business of academic publishing.

Early next year, the Springer Nature publishing group will launch a new subscription journal devoted to artificial intelligence. Like its other journals, Nature will impose a pay wall and restrict access to paying customers--a move that isn't going over well with AI researchers, who say a for-profit subscription journal is not what the field needs right now. Scheduled for launch in January 2019, the new journal will be called Nature Machine Intelligence, and it'll be the 53rd journal to bear the illustrious Nature name. The new online-only journal, headed by editor-in-chief Liesbeth Venema (previously a physics editor at Nature), will cover the "best research from across the field of artificial intelligence," and will include research and perspectives from the "fast-moving" fields of AI, machine learning, and robotics. But if a petition organized by Tom Dietterich from the International Machine Learning Society and a computer scientist at Oregon University is any indication, the new journal won't include content from a sizable portion of the AI research community.

If scientific journals don't make their articles available to everyone, we want nothing to do with them. That's the gist of a petition signed by more than 2,000 artificial intelligence researchers publicized in a tweet on Saturday. Specifically, the scientists are boycotting a recently-announced journal, Nature Machine Intelligence, because it would trap the articles published there behind a paywall. Artificial intelligence research should be transparent and open to the community at large, argues Tom Dietterich of Oregon State University, the machine learning researcher who began the boycott, according to Retraction Watch. Reminder: science publishing is a business. Many journals, especially the most reputable ones (which include those from the Nature Publishing Group), require payment from anyone who wants to read a full article.

More than two thousand researchers have signed a petition to boycott a new Nature journal over the fact it will be available only by subscription. The new journal -- Nature Machine Intelligence, about machine learning -- plans to charge readers for access, unlike most other journals in the field. The researchers who signed the petition have pledged not to submit their work to the new journal, and will decline to review or edit papers for it, as well. Most journals published by Nature Publishing Group are available only by subscription -- but that doesn't work for the machine learning community, the signatories argue: In contrast, we would welcome new zero-cost open access journals and conferences in artificial intelligence and machine learning. The petition was released on Saturday around 2000 UTC time, according to coordinator Tom Dietterich of Oregon State University; by Tuesday morning, it had 2160 signatures.

Nature, one of the most prestigious scientific journals in the world, has just announce plans to create a Machine Intelligence imprint, and researchers are not happy. The field has been doing fine with open-access journals -- why clog it up with the paid-access model everyone has been trying to escape for decades? Over two thousand have signed a statement saying they won't publish in it. Academic publishing is a tumult right now, with open-access journals and proponents thereof battling with the old-guard prestige of the likes of Science and Nature -- along with the fees from jealous keepers such as Elsevier and Springer. Meanwhile sites like Sci-Hub have worked to liberate the data held by paid journals, illegally of course, and become indispensable in the process.