Tech
Robot regret: New research helps robots make safer decisions around humans
Imagine for a moment that you’re in an auto factory. A robot and a human are working next to each other on the production line. The robot is busy rapidly assembling car doors while the human runs quality control, inspecting the doors for damage and making sure they come together as they should.
Robots and humans can make formidable teams in manufacturing, health care and numerous other industries. While the robot might be quicker and more effective at monotonous, repetitive tasks like assembling large auto parts, the person can excel at certain tasks that are more complex or require more dexterity.
But there can be a dark side to these robot-human interactions. People are prone to making mistakes and acting unpredictably, which can create unexpected situations that robots aren’t prepared to handle. The results can be tragic.
New and emerging research could change the way robots handle the uncertainty that comes hand-in-hand with human interactions. Morteza Lahijanian, an associate professor in CU Boulder’s Ann and H.J. Smead Department of Aerospace Engineering Sciences, develops processes that let robots make safer decisions around humans while still trying to complete their tasks efficiently.
In a new study presented at the International Joint Conference on Artificial Intelligence in August 2025, Lahijanian and graduate students Karan Muvvala and Qi Heng Ho devised new algorithms that help robots create the best possible outcomes from their actions in situations that carry some uncertainty and risk.
“How do we go from very structured environments where there is no human, where the robots are doing everything by themselves, to unstructured environments where there are a lot of uncertainties and other agents?” Lahijanian asked.
“If you’re a robot, you have to be able to interact with others. You have to put yourself out there and take a risk and see what happens. But how do you make that decision, and how much risk do you want to tolerate?”
Similar to humans, robots have mental models that they use to make decisions. When working with a human, a robot will try to predict the person’s actions and respond accordingly. The robot is optimized for completing a task—assembling an auto part, for example—but ideally, it will also take other factors into consideration.
In the new study, the research team drew upon game theory, a mathematical concept that originated in economics, to develop the new algorithms for robots. Game theory analyzes how companies, governments and individuals make decisions in a system where other “players” are also making choices that affect the ultimate outcome.
In robotics, game theory conceptualizes a robot as being one of numerous players in a game that it’s trying to win. For a robot, “winning” is completing a task successfully—but winning is never guaranteed when there’s a human in the mix, and keeping the human safe is also a top priority.
So instead of trying to guarantee a robot will always win, the researchers proposed the concept of a robot finding an “admissible strategy.” Using such a strategy, a robot will accomplish as much of its task as possible while also minimizing any harm, including to a human.
“In choosing a strategy, you don’t want the robot to seem very adversarial,” said Lahijanian. “In order to give that softness to the robot, we look at the notion of regret. Is the robot going to regret its action in the future? And in optimizing for the best action at the moment, you try to take an action that you won’t regret.”
Let’s go back to the auto factory where the robot and human are working side-by-side. If the person makes mistakes or is not cooperative, using the researchers’ algorithms, a robot could take matters into its own hands. If the person is making mistakes, the robot will try to fix these without endangering the person. But if that doesn’t work, the robot could, for example, pick up what it’s working on and take it to a safer area to finish its task.
Much like a chess champion who thinks several turns ahead about an opponent’s possible moves, a robot will try to anticipate what a person will do and stay several steps ahead of them, Lahijanian said.
But the goal is not to attempt the impossible and perfectly predict a person’s actions. Instead, the goal is to create robots that put people’s safety first.
“If you want to have collaboration between a human and a robot, the robot has to adjust itself to the human. We don’t want humans to adjust themselves to the robot,” he said. “You can have a human who is a novice and doesn’t know what they’re doing, or you can have a human who is an expert. But as a robot, you don’t know which kind of human you’re going to get. So you need to have a strategy for all possible cases.”
And when robots can work safely alongside humans, they can enhance people’s lives and provide real and tangible benefits to society.
As more industries embrace robots and artificial intelligence, there are many lingering questions about what AI will ultimately be capable of doing, whether it will be able to take over the jobs that people have historically done, and what that could mean for humanity. But there are upsides to robots being able to take on certain types of jobs. They could work in fields with labor shortages, such as health care for older populations, and physically challenging jobs that may take a toll on workers’ health.
Lahijanian also believes that, when they’re used correctly, robots and AI can enhance human talents and expand what we’re capable of doing.
“Human-robot collaboration is about combining complementary strengths: humans contribute intelligence, judgment, and flexibility, while robots offer precision, strength, and reliability,” he said.
“Together, they can achieve more than either could alone, safely and efficiently.”
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Robot regret: New research helps robots make safer decisions around humans (2025, August 28)
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Tech
Mom’s Microwaved Coffee Won’t Stand a Chance With This Ember Smart Mug Deal
The Ember Smart Mug 2 is niche, but it has a loyal following. Even though we think there are better mug warmers on the market, Ember is like Apple AirPods or Kleenex. People want what they want. Right now, for Mother’s Day, the Ember Smart Mug 2 is on sale for just under $100, a 30 percent discount and a match of the very best price we’ve tracked. You can save at Amazon, Best Buy, and the manufacturer’s website.
This smart mug is probably overkill. It has a smartphone app that notifies you when your coffee reaches the ideal temperature, and its onboard light also provides a visual indicator that your brew is ready. It intelligently adjusts power usage to keep your drink warm when you’re nearby, and turns off when you’re not around. The self-heating mug is on sale in a few variations—10 or 14 ounces, in blue, white, black, and purple.
The mug offers up to 80 minutes of powered heating time, or you can pop it on the included charging coaster to keep the battery going all day. And you don’t need the smartphone app unless you want to precisely dictate your coffee temperature—the mug defaults to 135 degrees Fahrenheit without your specific input.
Our main gripe is that this proprietary warming system is not dishwasher safe. You need to hand-wash each component, and ensure you do so carefully, because the items are not cheap to replace. But if Mom has been putzing around the house drinking perpetually microwaved coffee, perhaps an upgrade is in order. We have additional recommendations in our guide to the Best Coffee Warmers. You may also want to check our related stories on the Best Espresso Machines, Best Coffee Machines, and Best Pod Coffee Makers.
Tech
AI-Designed Drugs by a DeepMind Spinoff Are Headed to Human Trials
Google DeepMind’s AlphaFold has already revolutionized scientists’ understanding of proteins. Now, the ability of the platform to design safe and effective drugs is about to be put to the test.
Isomorphic Labs, the UK-based biotech spinoff of Google DeepMind, will soon begin human trials of drugs designed by its Nobel Prize–winning AI technology. “We’re gearing up to go into the clinic,” Isomorphic Labs president Max Jaderberg said on April 16 at WIRED Health in London. “It’s going to be a very exciting moment as we go into clinical trials and start seeing the efficacy of these molecules.”
Jaderberg did not elaborate on the timeline, but it’s later than the company had planned to initiate human studies. Last year, CEO Demis Hassabis said it would have AI-designed drugs in clinical trials by the end of 2025.
Isomorphic Labs was founded in 2021 as a spinoff from Alphabet’s AI research subsidiary, Google DeepMind. The company uses DeepMind’s AlphaFold, a groundbreaking AI platform that predicts protein structures, for drug discovery.
Built from 20 different amino acids, proteins are essential for all living organisms. Long strings of amino acids link together and fold up to make a protein’s three-dimensional structure, which dictates the protein’s function. Researchers had tried to predict protein structures since the 1970s, but this was a painstaking process given the astronomically high number of possible shapes a protein chain can take.
That changed in 2020, when DeepMind’s Hassabis and John Jumper presented stunning results from AlphaFold 2, which uses deep-learning techniques. A year later, the company released an open-source version of AlphaFold available to anyone.
In 2024, DeepMind and Isomorphic Labs released AlphaFold 3, which advanced scientists’ understanding of proteins even further. It moved beyond modeling proteins in isolation to predicting other important molecules, such as DNA and RNA, and their interactions with proteins.
“This is exactly what you need for drug discovery: You need to see how a small molecule is going to bind to a drug, how strongly, and also what else it might bind to,” Hassabis told WIRED at the time.
Since its release, the AlphaFold platform has been able to predict the structure of virtually all the 200 million proteins known to researchers and has been used by more than 2 million people from 190 countries. The breakthrough earned Hassabis and Jumper the Nobel Prize for chemistry in 2024, with the Nobel committee noting that AlphaFold has enabled a number of scientific applications, including a better understanding of antibiotic resistance and the creation of images of enzymes that can decompose plastic.
Earlier this year, Isomorphic Labs announced an even more powerful tool, what it calls IsoDDE, its proprietary drug-design engine. In a technical paper, the company touts that the platform more than doubles the accuracy of AlphaFold 3.
The startup has formed partnerships with Eli Lilly and Novartis to work together on AI drug discovery and is also advancing its own “broad and exciting pipeline of new medicines” in oncology and immunology, Jaderberg said.
“The exciting thing about the molecules that we’re designing is because we have so much more of an understanding about how these molecules work, we’ve engineered them to be very, very potent,” Jaderberg told the audience at WIRED Health. “You can take them at a much lower dose, and they’ll have lower side effects, off target effects.”
Last year, Isomorphic appointed a chief medical officer and announced it had raised $600 million in its first funding round to gear up for clinical trials. Meanwhile, the company has been building a clinical development team. Its mission is to “solve all disease.”
“It’s a crazy mission,” Jaderberg said. “But we really mean it. We say it with a straight face, because we believe this should be possible.”
Tech
London Marathon runners get AI to go the extra mile | Computer Weekly
With huge crowds set to descend on London for the city’s iconic marathon this weekend, IT services provider Tata Consultancy Services (TCS), in partnership with Neurun, has launched a map-based tool powered by artificial intelligence (AI) to help participants and spectators navigate the event.
TCS RunConcierge is said to act as a “digital brain” for the London Marathon, bringing together official guidance, route support and course information in real time – a useful tool for this mass participation event, which saw more than 56,000 runners cross the finish line in 2025 and hundreds of thousands of spectators lining the 26.2-mile route.
Powered by Google Gemini, the platform is designed to deliver instant and reliable guidance for users, whether that be runners seeking information about start line logistics or the location of drinks stops – which will be very much needed with wall-to-wall sunshine forecast on the day – or supporters wishing to locate the best spot from which to cheer on participants or travel as quickly as possible between viewing points.
Users can see their current location on the map, ask for directions to key event destinations and access pre-loaded routes with direct links to Google Maps navigation. The tool also suggests personalised follow-up questions and features voice activation to enable hands-free use on the move. And with 60 languages supported, visitors from all over the world will be able to benefit from the event guidance.
For runners specifically, the immersive 3D map includes an elevation tracker, which could help them plan their strategy.
The partnership between TCS and Neurun is said to be built on a foundation of continuous innovation. New back-end capabilities include a self-serve admin portal that allows event organisers to manage RunConcierge independently, as well as a unique internal AI agent that tests the platform to help maintain content quality and identify improvements
Vinay Singhvi, head of UK and Ireland at Tata Consultancy Services, described the London Marathon as a monumental event, for which its goal is to use technology to make the experience as seamless and enjoyable as possible.
“Our partnership with Neurun allows us to innovate at pace, and the enhanced TCS RunConcierge is a prime example of how we are using AI to solve complex logistical challenges, providing runners and spectators with a trusted companion for the moments that matter most,” he said.
Neurun founder Cade Netscher said its partnership with TCS had been instrumental in developing the RunConcierge tool for the world’s most prestigious marathons, with previous successful deployments at the Sydney and New York City events.
“For London, we’ve integrated the latest AI advancements to create our most powerful and user-friendly version yet. We are excited to see how it helps thousands of people enjoy a more connected and stress-free marathon weekend,” he said.
Separately, in a demonstration of digital healthcare technology in action, TCS has created a digital twin of a para-athlete’s heart, which uses sensors and AI to monitor her heart during training sessions.
The para-athlete, Milly Pickles, is aiming to complete the London Marathon in under four-and-a-half hours next year, and is harnessing digital healthtech to reach her goal.
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