Tech
AI systems are great at tests. But how do they perform in real life?
Earlier this month, when OpenAI released its latest flagship artificial intelligence (AI) system, GPT-5, the company said it was “much smarter across the board” than earlier models. Backing up the claim were high scores on a range of benchmark tests assessing domains such as software coding, mathematics and health care.
Benchmark tests like these have become the standard way we assess AI systems—but they don’t tell us much about the actual performance and effects of these systems in the real world.
What would be a better way to measure AI models? A group of AI researchers and metrologists—experts in the science of measurement—recently outlined a way forward.
Metrology is important here because we need ways of not only ensuring the reliability of the AI systems we may increasingly depend upon, but also some measure of their broader economic, cultural, and societal impact.
Measuring safety
We count on metrology to ensure the tools, products, services, and processes we use are reliable.
Take something close to my heart as a biomedical ethicist—health AI. In health care, AI promises to improve diagnoses and patient monitoring, make medicine more personalized and help prevent diseases, as well as handle some administrative tasks.
These promises will only be realized if we can be sure health AI is safe and effective, and that means finding reliable ways to measure it.
We already have well-established systems for measuring the safety and effectiveness of drugs and medical devices, for example. But this is not yet the case for AI—not in health care, or in other domains such as education, employment, law enforcement, insurance, and biometrics.
Test results and real effects
At present, most evaluation of state-of-the-art AI systems relies on benchmarks. These are tests that aim to assess AI systems based on their outputs.
They might answer questions about how often a system’s responses are accurate or relevant, or how they compare to responses from a human expert.
There are literally hundreds of AI benchmarks, covering a wide range of knowledge domains.
However, benchmark performance tells us little about the effect these models will have in real-world settings. For this, we need to consider the context in which a system is deployed.
The problem with benchmarks
Benchmarks have become very important to commercial AI developers to show off product performance and attract funding.
For example, in April this year a young startup called Cognition AI posted impressive results on a software engineering benchmark. Soon after, the company raised US$175 million (A$270 million) in funding in a deal that valued it at US$2 billion (A$3.1 billion).
Benchmarks have also been gamed. Meta seems to have adjusted some versions of its Llama-4 model to optimize its score on a prominent chatbot-ranking site. After OpenAI’s o3 model scored highly on the FrontierMath benchmark, it came out that the company had had access to the dataset behind the benchmark, raising questions about the result.
The overall risk here is known as Goodhart’s law, after British economist Charles Goodhart: “When a measure becomes a target, it ceases to be a good measure.”
In the words of Rumman Chowdhury, who has helped shape the development of the field of algorithmic ethics, placing too much importance on metrics can lead to “manipulation, gaming, and a myopic focus on short-term qualities and inadequate consideration of long-term consequences”.
Beyond benchmarks
So if not benchmarks, then what? Let’s return to the example of health AI. The first benchmarks for evaluating the usefulness of large language models (LLMs) in health care made use of medical licensing exams. These are used to assess the competence and safety of doctors before they’re allowed to practice in particular jurisdictions.
State-of-the-art models now achieve near-perfect scores on such benchmarks. However, these have been widely criticized for not adequately reflecting the complexity and diversity of real-world clinical practice.
In response, a new generation of “holistic” frameworks have been developed to evaluate these models across more diverse and realistic tasks. For health applications, the most sophisticated is the MedHELM evaluation framework, which includes 35 benchmarks across five categories of clinical tasks, from decision-making and note-taking to communication and research.
What better testing would look like
More holistic evaluation frameworks such as MedHELM aim to avoid these pitfalls. They have been designed to reflect the actual demands of a particular field of practice.
However, these frameworks still fall short of accounting for the ways humans interact with AI system in the real world. And they don’t even begin to come to terms with their impacts on the broader economic, cultural, and societal contexts in which they operate.
For this we will need a whole new evaluation ecosystem. It will need to draw on expertise from academia, industry, and civil society with the aim of developing rigorous and reproducible ways to evaluate AI systems.
Work on this has already begun. There are methods for evaluating the real-world impact of AI systems in the contexts in which they’re deployed—things like red-teaming (where testers deliberately try to produce unwanted outputs from the system) and field testing (where a system is tested in real-world environments). The next step is to refine and systematize these methods, so that what actually counts can be reliably measured.
If AI delivers even a fraction of the transformation it’s hyped to bring, we need a measurement science that safeguards the interests of all of us, not just the tech elite.
More information:
Reva Schwartz et al, Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI’s Real World Effects, arXiv (2025). DOI: 10.48550/arxiv.2505.18893
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Tech
I’ve Tried Every Digital Notebook. Here Are the Best Ones on Sale
I love a digital notebook. I write about them all year long here at WIRED, and it’s not often my favorites go on sale. (Or for any to go on sale, besides Amazon’s own sale events.) But this year, multiple digital notebooks I love are on sale for the biggest sale event of the year.
If you’ve thought about getting one of these for yourself, there’s truly no better moment. From reMarkable’s on-sale bundles to Kobo’s deals, you can shop five of the best digital notebooks we’ve ever tried right now at a lower price than you might find until next year. They’re a handy device just about everyone can enjoy, whether you want to digitally annotate your books or write out your grocery list without using a piece of paper.
Looking for more great sales to shop? Don’t miss our guides to the Best Amazon Device and Kindle Deals, Best Laptop Deals, the Absolute Best Cyber Monday Deals, and our liveblog.
Update Dec. 1: We updated prices, links, and deals, and added the Rocketbook Fusion Plus notebook.
The Best Digital Notebook Deals
Some of the best digital notebooks we’ve tried come from reMarkable, and one of reMarkable’s models always seems to reign supreme over our digital notebooks guide. While the Paper Pro Move is the newest model, the reMarkable Paper Pro that launched in September 2024 is my current all-around favorite. It’s not only powerful with tons of tools and an easy interface, but packs a color screen for colorful notes. It also has a gentle front light so that you can use it in darker environments. You can get the bundles on sale right now, so combine one of reMarkable’s markers and folio covers with a Paper Pro to get $50 off.
The best discount from reMarkable is actually for its older device and our previous top pick, the reMarkable 2. It doesn’t have a color screen or the front light, but you’ll get the reMarkable’s great software and options for accessories like the Keyboard Folio to use it like a laptop. The reMarkable 2 bundles are also on sale, so add on your favorite folio of choice on reMarkable’s website to get $70 off.
The Kobo Libra Colour is my favorite all-around e-reader with its color screen and page turner buttons, but you can add on a stylus to have it double as a digital notebook. It’s one of the more affordable options, and it’s a smaller screen than the rest of these, but I especially love that you can use the stylus to doodle on the books you’re reading (something you can’t do with the Kindle Scribe). It’s $30 off on Kobo’s site for Cyber Monday.
The second-generation Kindle Scribe isn’t the best digital notebook, but the long battery life (12 weeks!!) and convenient starting point of it being a Kindle I could already be reading on makes it a great go-to for casual notetakers and doodlers. It’s a good choice for Kindle and Amazon users, and there are new models due out this winter, but they likely won’t be as cheap as this one. (Especially since some of those new models will have color!)
If you like the idea of getting a Kobo e-reader that doubles as a digital notebook, you can go for more of a classic size with the larger Elipsa 2E. This one comes with the stylus, so you won’t have to add it on, and it’s $50 off.
The Rocketbook Fusion Plus digital planner and notebook is for those who don’t want to charge their notebook or give up on the whole “paper” experience. Take notes with the included, erasable Pilot Frixion Pen, scan photos of the pages into the app, and erase the whole thing with the damp microfiber cloth (also included). Fusion Plus is on its steepest discount of recent memory, and comes templates that range from monthly and weekly pages to project management and meeting notes.
Power up with unlimited access to WIRED. Get best-in-class reporting and exclusive subscriber content that’s too important to ignore. Subscribe Today.
Tech
Artificial tendons give muscle-powered robots a boost
Our muscles are nature’s actuators. The sinewy tissue is what generates the forces that make our bodies move. In recent years, engineers have used real muscle tissue to actuate “biohybrid robots” made from both living tissue and synthetic parts. By pairing lab-grown muscles with synthetic skeletons, researchers are engineering a menagerie of muscle-powered crawlers, walkers, swimmers, and grippers.
But for the most part, these designs are limited in the amount of motion and power they can produce. Now, MIT engineers are aiming to give bio-bots a power lift with artificial tendons.
In a study appearing today in the journal Advanced Science, the researchers developed artificial tendons made from tough and flexible hydrogel. They attached the rubber band-like tendons to either end of a small piece of lab-grown muscle, forming a “muscle-tendon unit.” Then they connected the ends of each artificial tendon to the fingers of a robotic gripper.
When they stimulated the central muscle to contract, the tendons pulled the gripper’s fingers together. The robot pinched its fingers together three times faster, and with 30 times greater force, compared with the same design without the connecting tendons.
The researchers envision the new muscle-tendon unit can be fit to a wide range of biohybrid robot designs, much like a universal engineering element.
“We are introducing artificial tendons as interchangeable connectors between muscle actuators and robotic skeletons,” says lead author Ritu Raman, an assistant professor of mechanical engineering (MechE) at MIT. “Such modularity could make it easier to design a wide range of robotic applications, from microscale surgical tools to adaptive, autonomous exploratory machines.”
The study’s MIT co-authors include graduate students Nicolas Castro, Maheera Bawa, Bastien Aymon, Sonika Kohli, and Angel Bu; undergraduate Annika Marschner; postdoc Ronald Heisser; alumni Sarah J. Wu ’19, SM ’21, PhD ’24 and Laura Rosado ’22, SM ’25; and MechE professors Martin Culpepper and Xuanhe Zhao.
Muscle’s gains
Raman and her colleagues at MIT are at the forefront of biohybrid robotics, a relatively new field that has emerged in the last decade. They focus on combining synthetic, structural robotic parts with living muscle tissue as natural actuators.
“Most actuators that engineers typically work with are really hard to make small,” Raman says. “Past a certain size, the basic physics doesn’t work. The nice thing about muscle is, each cell is an independent actuator that generates force and produces motion. So you could, in principle, make robots that are really small.”
Muscle actuators also come with other advantages, which Raman’s team has already demonstrated: The tissue can grow stronger as it works out, and can naturally heal when injured. For these reasons, Raman and others envision that muscly droids could one day be sent out to explore environments that are too remote or dangerous for humans. Such muscle-bound bots could build up their strength for unforeseen traverses or heal themselves when help is unavailable. Biohybrid bots could also serve as small, surgical assistants that perform delicate, microscale procedures inside the body.
All these future scenarios are motivating Raman and others to find ways to pair living muscles with synthetic skeletons. Designs to date have involved growing a band of muscle and attaching either end to a synthetic skeleton, similar to looping a rubber band around two posts. When the muscle is stimulated to contract, it can pull the parts of a skeleton together to generate a desired motion.
But Raman says this method produces a lot of wasted muscle that is used to attach the tissue to the skeleton rather than to make it move. And that connection isn’t always secure. Muscle is quite soft compared with skeletal structures, and the difference can cause muscle to tear or detach. What’s more, it is often only the contractions in the central part of the muscle that end up doing any work — an amount that’s relatively small and generates little force.
“We thought, how do we stop wasting muscle material, make it more modular so it can attach to anything, and make it work more efficiently?” Raman says. “The solution the body has come up with is to have tendons that are halfway in stiffness between muscle and bone, that allow you to bridge this mechanical mismatch between soft muscle and rigid skeleton. They’re like thin cables that wrap around joints efficiently.”
“Smartly connected”
In their new work, Raman and her colleagues designed artificial tendons to connect natural muscle tissue with a synthetic gripper skeleton. Their material of choice was hydrogel — a squishy yet sturdy polymer-based gel. Raman obtained hydrogel samples from her colleague and co-author Xuanhe Zhao, who has pioneered the development of hydrogels at MIT. Zhao’s group has derived recipes for hydrogels of varying toughness and stretch that can stick to many surfaces, including synthetic and biological materials.
To figure out how tough and stretchy artificial tendons should be in order to work in their gripper design, Raman’s team first modeled the design as a simple system of three types of springs, each representing the central muscle, the two connecting tendons, and the gripper skeleton. They assigned a certain stiffness to the muscle and skeleton, which were previously known, and used this to calculate the stiffness of the connecting tendons that would be required in order to move the gripper by a desired amount.
From this modeling, the team derived a recipe for hydrogel of a certain stiffness. Once the gel was made, the researchers carefully etched the gel into thin cables to form artificial tendons. They attached two tendons to either end of a small sample of muscle tissue, which they grew using lab-standard techniques. They then wrapped each tendon around a small post at the end of each finger of the robotic gripper — a skeleton design that was developed by MechE professor Martin Culpepper, an expert in designing and building precision machines.
When the team stimulated the muscle to contract, the tendons in turn pulled on the gripper to pinch its fingers together. Over multiple experiments, the researchers found that the muscle-tendon gripper worked three times faster and produced 30 times more force compared to when the gripper is actuated just with a band of muscle tissue (and without any artificial tendons). The new tendon-based design also was able to keep up this performance over 7,000 cycles, or muscle contractions.
Overall, Raman saw that the addition of artificial tendons increased the robot’s power-to-weight ratio by 11 times, meaning that the system required far less muscle to do just as much work.
“You just need a small piece of actuator that’s smartly connected to the skeleton,” Raman says. “Normally, if a muscle is really soft and attached to something with high resistance, it will just tear itself before moving anything. But if you attach it to something like a tendon that can resist tearing, it can really transmit its force through the tendon, and it can move a skeleton that it wouldn’t have been able to move otherwise.”
The team’s new muscle-tendon design successfully merges biology with robotics, says biomedical engineer Simone Schürle-Finke, associate professor of health sciences and technology at ETH Zürich.
“The tough-hydrogel tendons create a more physiological muscle–tendon–bone architecture, which greatly improves force transmission, durability, and modularity,” says Schürle-Finke, who was not involved with the study. “This moves the field toward biohybrid systems that can operate repeatably and eventually function outside the lab.”
With the new artificial tendons in place, Raman’s group is moving forward to develop other elements, such as skin-like protective casings, to enable muscle-powered robots in practical, real-world settings.
This research was supported, in part, by the U.S. Department of Defense Army Research Office, the MIT Research Support Committee, and the National Science Foundation.
Tech
The Best Cyber Monday Streaming Deals With a Convenient Roommate’s Email Address
HBO knows you’re bored and cold. It wants you to Max and chill with Noah Wyle in scrubs. The company offers some of the best Cyber Monday streaming deals with a ridiculously low-priced $3/month offer for basic HBO Max (it’s the version with ads and 2K streaming, but still, super-cheap). Disney Plus and Hulu deals are bundled up for $5/month. Apple TV wants back in your life for $6.
Of course, this deal is only meant for new customers. Not boring ol’ existing customers. If you already have basic HBO Max, you’re already paying $11 for the same service, and HBO would like you to keep doing that. Streaming apps are banking on you being complacent and happy in your streaming life. Maybe they’re even taking you for granted.
Sometimes you can get the current deal just by threatening to cancel, or actually canceling, your account. Suddenly, you’re an exciting new customer again! Another method is by using an alternate email account (perhaps your spouse’s or roommate’s?) and alternate payment information as a new customer. If you do use a burner email (you did not hear this from me), check in on your favorite app’s terms of service to make sure you’re not in violation by re-enrolling with different emails. I’ll also issue the caveat that you lose all your viewing data and tailored suggestions if you sign up anew.
But times and wallets are tight! And $3 HBO Max sounds pretty good. After all, every middle-aged American man needs to rewatch The Wire once every five years or so—assuming he’s not the kind of middle-aged man who rewatches The Sopranos instead. Here are the current best streaming deals for Cyber Monday 2025.
Devon Maloney; ARCHIVE ID: 546772
Regular price: $80
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