The holiday shopping season reaches its peak when Cyber Monday deals roll around. Sales are often better than on Black Friday, but with every retailer promising the best Cyber Monday discounts, it can be tricky to work out what deals to snag and when to pass. Luckily, we have done the hard work for you. WIRED reviewers try countless gadgets, tools, and digital delights of all kinds every week, and we have developed smart shopping tips and tricks to weed out fake discounts and bring you the real deals. We can say with confidence that these are the absolute best Black Friday and Cyber Monday deals you're going to find this weekend. You will find regular updates as products go out of stock and prices change, and we'll keep scouring to find more deals worth grabbing. Updated November 27, 2022: We've added deals on umbrellas, mobile accessories, and more. We've also updated pricing and availability. We test products year-round and handpicked these deals. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide throughout the Cyber Monday weekend. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Just like upgrading the bed you sleep on, few things will improve your life like a good chair. If you park your body in front of a desk all day, it's a good idea to give the humble chair more attention. Some of our favorites are on sale right now. See our Best Office Chairs guide for more. Our top office chair pick is a bit cheaper right now. We've tested more than 35 office chairs in the past year and this is the one to get based on comfort, quality, adjustability, and price. At this price, you'd be hard-pressed to find a cheaper way to get seven points of adjustment. The only thing we don't like is that pet hair tends to cling, so keep a lint roller handy. The Zeph looks wonderful--there are dozens of color customizations--but the only adjustment you can make is to raise the seat up or down. You might think that would make for an uncomfortable chair, but it doesn't. This lack of adjustability is by design as the Zeph is designed to mold to your body. We do suggest you get the seat pad and arms, which add a smidge more comfort, though it does jack the price up a bit. The Zeph is compact, making it a great option for smaller spaces.

TL;DR: As of Nov. 27, you can get the Mymanu CLIK S Translation Earbuds(opens in a new tab) for just $99 instead of $220 -- that's a 55% discount. Adventure beyond the tourist stops during your next international vacation. Skip the clunky translation dictionary and the unreliable browser translator and start wearing a live translator in your ear(opens in a new tab). Mymanu CLIK S is an award-winning pair of translation earbuds that could help you experience your next holiday destination in a new way. You can also jam out to your favorite tunes or listen to a podcast with these HD earbuds.

Researchers at Kyoto University and Nagoya University in Japan have recently devised a new, automatic approach for designing robots that could simultaneously improve their shape, structure, movements, and controller components. This approach, presented in a paper published in Artificial Life and Robotics, draws inspiration from the evolution of vertebrates, the broad category of animals that possess a backbone or spinal column, which includes mammals, reptiles, birds, amphibians, and fishes. "The automatic robot design is a completely novel research project for the Matsuno Lab, the laboratory led by Fumitoshi Matsuno, and this is the first paper published for this project," Ryosuke Koike, one of the researchers who carried out the study, told TechXplore. "Its primary objective was to design a good-performing robot for a given task. Since there are innumerable possible combinations of robot morphologies and controllers, it is impossible to reach the best robot by manual human exploration. Therefore, we realized that it is necessary to establish a method for automatically designing robots using computers."

Artificial intelligence (AI), machine learning and digitalisation are key words that those tasked with improving business processes in the insurance sector will be well aware of – but these tools also present the opportunity to show end customers that the insurance industry values them. Read: Amazon's online insurance store – what does it mean for the industry? Read: There will be'winners and losers' in insurance now more than ever – Guidewire This was according to Andy Fairchild, advisor and non-executive director at various insurance-related firms and owner of consultancy Julyfourth Services. Speaking during an Insurance Times webinar entitled AI: A driving force for the future of insurance yesterday (24 November 2022), in association with Inawisdom, Fairchild explained: "[AI] is how we can show customers that we value collecting their data more than we currently do. "We must, as an industry, show customers how important that data is and how important data collection for the provision of an insurance product is." AI processing of customer data and the use of AI-enabled chatbots to respond to customer queries would improve the customer experience by speeding up often slow customer journeys, said Fairchild. But the collection of data behind these operations has to be improved too. Fairchild continued: "We can get that customer data from a person-to-person interaction or – increasingly – from a person-to-machine interaction and therein lies a big move for the industry." Fairchild added that the better collection and deployment of data to construct AI models could transform customers' interactions with the insurance sector from a "trudge process" into something that "they really value". AI and machine learning also have the potential to "revolutionise" the insurance sector in terms of risk selection and pricing if data collection improves, Fairchild added. Read: Brokers embrace cloud technologies to'maintain competitive edge' He explained: "The fundamentals of our industry are risk, risk selection, the terms that we underwrite that risk selection on and the price that we put on it." However, Sameer Deshpande, head of enterprise architecture at broker PIB Group, said that the insurance sector was lagging behind other areas of financial services in its use of artificial intelligence. Deshpande explained: "There are a number of areas where [insurance is] still behind the curve – [for example,] manual processes and document processing.

There's ion propulsion drones, and then this AI racing drone, developed by University of Zurich researchers. Human drone pilots were invited to the Robotics and Perception Group for a friendly race, with each one getting pit against various AI drones, starting with one using 36 tracking cameras. The camera is used to capture 400 fps of video, in which the AI drone uses in combination with four tracking markers. This footage is then sent to a vision and navigation system capable of translating it into flight commands. These are then sent to the drone in real-time over a wireless connection.

Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions. This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Model hyperparameters, performance measurements, run logs, model artifacts, data artifacts, etc., are all included in this. There are numerous approaches to implementing experiment logging. Spreadsheets are one option (no one uses them anymore!), or you can use GitHub to keep track of tests. Tracking machine learning experiments has always been a crucial step in ML development, but it used to be a labor-intensive, slow, and error-prone procedure. The market for contemporary experiment management and tracking solutions for machine learning has developed and increased over the past few years.

Abstract: Motivated by recent developments in optical switching and reconfigurable network design, we study dynamic binary search trees (BSTs) in the matching model. In the classical dynamic BST model, the cost of both link traversal and basic reconfiguration (rotation) is O(1). However, in the matching model, the BST is defined by two optical switches (that represent two matchings in an abstract way), and each switch (or matching) reconfiguration cost is α while a link traversal cost is still O(1). In this work, we propose Arithmetic BST (A-BST), a simple dynamic BST algorithm that is based on dynamic Shannon-Fano-Elias coding, and show that A-BST is statically optimal for sequences of length Ω(nαlogα) where n is the number of nodes (keys) in the tree. Abstract: The dynamic optimality conjecture, postulating the existence of an O(1)-competitive online algorithm for binary search trees (BSTs), is among the most fundamental open problems in dynamic data structures.

A number of commentators have argued in recent years that the media overemphasises negativity in its content. Answering this question is no trivial matter, as it requires a standard against which the media's coverage can be compared. That is, it is challenging to establish how negative or positive media content should be. What we can certainly determine instead is how the sentiment (positive or negative) and emotional undertones (such as fear, anger or joy) of news content compare with the same metrics at different points in time. This allows us to establish whether news media content is becoming more positive over time, more negative or pretty much staying the same.

A new deep learning method developed by a team of researchers at Chalmers can generate regulatory DNA sequences that control gene expression in a gene-specific manner. Despite significant sequence divergence from natural DNA, in vivo measurements reveal that 57% of the synthetic sequences with high expression levels outperform the expression levels of the correspondingly highly expressed natural controls. It will be possible to develop and produce vaccines, drugs against severe diseases, and alternative foods much more quickly and at a significantly lower cost with the help of this technology. The researchers investigated if de novo functional regulatory DNA, encompassing the whole gene regulatory structure and providing desired gene expression levels, can be produced simply from the information of natural regulatory sequences. The major goal is to directly correlate natural genomic data from Saccharomyces cerevisiae with the functional DNA regulatory sequence space, allowing for the controlled design of expression systems.