Latest news with #robots
Bloomberg
2 hours ago
- Sport
- Bloomberg
Humanoid Robots Play Soccer Poorly in Chinese Exhibition Match
They looked like tipsy 7-year-olds stumbling about the soccer pitch. But the game that unfolded at an industrial zone in Beijing was a breakthrough for humanoid robots and the artificial intelligence that powered them through a 5-3 match on Saturday. Clad in black and purple jerseys with individual player numbers, diminutive humanoids faced off for two 10-minute halves, their movements controlled not by gesticulating coaches on the sidelines but by built-in algorithms.
CTV News
13 hours ago
- General
- CTV News
How to make cleaning much easier
Cleaning tasks can take a lot of effort in ways that can be hard on your joints and muscles. Fortunately, there are now robots that can do the vacuuming for you, mops that save you the trouble of filling up a heavy bucket of water, and sponges that cut down on the scrubbing needed to get pots grime-free. In fact, Consumer Reports experts have tested a wide variety of tools and products and have plenty of advice about making cleaning painless. Here's what to try to make a variety of cleaning tasks easier. Keep helpful tools handy A long-handled grabber can be useful for picking up items on the floor without having to bend down. Microfibre dusters with extendable handles will allow you to dust the top of your ceiling fan blades or objects on high shelves without needing a step stool. Keep your balance Whatever household chores you have ahead of you, protect yourself from slips and trips. Getting regular exercise, including strength training, can not only help you stay up to the challenge of various tasks but also help you improve your balance and avoid falls. It's also wise to wear sturdy shoes with nonskid soles, light the space you'll be cleaning brightly, and keep high-traffic areas free of tripping hazards such as stray cords, loose rugs, and clutter. Make mopping a cinch If mopping feels like an ordeal, consider an electric steam mop. With these, hot water from the mop's tank creates steam, which gets floors clean with less effort. Use the right scrubbers A good sponge can significantly cut down on scrubbing. For dishes, our evaluators liked the Skura Style Skrubby Sponge for its flexibility and ability to easily dispatch tough grime. For grimy grout and food spills on stovetops, try a melamine foam sponge (aka a Magic Eraser). These abrasive sponges can take on tough stains and scum. Outsource your vacuuming While an upright or canister vacuum is still a necessity for deep cleaning (particularly for rugs and carpeting), a robotic vacuum can do lighter floor cleanups for you. Many can be programmed to sweep at the same time every day. By Catherine Roberts, Consumer Reports Senior Health & Food Reporter
The Independent
a day ago
- Science
- The Independent
Robots made from unlikely new material
Scientists at the University of Bristol have developed robots using rice paper, a material commonly found in Vietnamese spring rolls. This rice paper offers a biodegradable, non-toxic, and edible alternative to silicon, which is typically used in soft robotics. The research aims to make soft robotics experimentation more accessible and sustainable, allowing for innovation from home. Potential applications for these rice paper robots include agricultural reseeding, reforestation in difficult areas, and culinary uses. This breakthrough contributes to the advancing field of soft robotics, which holds promise for transforming areas like biomedicine, nuclear decommissioning, and space exploration.
Globe and Mail
a day ago
- Business
- Globe and Mail
AITX Creates Times Square Buzz with Bold Promotion of Its Security Robots
A Little Mischief in Manhattan Designed to Draw Eyes to Its Breakthrough Security Technology Detroit, Michigan--(Newsfile Corp. - June 27, 2025) - On Friday, June 27, Times Square will get a surprise guest appearance from a missing dog, a wanted robot, and a curious crowd. Artificial Intelligence Technology Solutions, Inc. (OTC Pink: AITX) (the "Company") , the Company behind some of the most advanced AI-based autonomous security robots on the market, is taking its message straight to the streets of New York with a bold visibility campaign designed to stop pedestrians, spark conversation, and draw attention from curious onlookers and the press. Artist's depiction of the future of security, AITX's HERO and RADDOG LE2, casually blending in with 400,000 tourists in Times Square. To view an enhanced version of this graphic, please visit: AITX CEO Steve Reinharz is expected to go live from Times Square at 12:30 PM ET on Friday, June 27 via X (formerly Twitter). The livestream will offer a firsthand look at the campaign's impact, share insights into the Company's approach to public engagement, and maybe even track down a missing robot or two. The campaign features missing dog posters that don't bark and wanted signs for a robot that hasn't technically done anything wrong. It's all part of AITX's creative push to spotlight its robotic security solutions, including RADDOG ™ LE2 and the recently revealed humanoid known as HERO ™ (Humanoid Enforcement & Response Officer). There's no booth, no handouts, and no one dressed as a mascot. Just curiosity, a few QR codes, and a growing number of people asking what AITX is really up to. For those looking to understand the full scope of the Company's ambitions, the AITX Company Profile is now available for download. "We're always looking for creative ways to earn attention without spending millions," said Steve Reinharz, CEO/CTO and founder of AITX. "This campaign is about sparking curiosity, starting conversations, and pulling new eyeballs into our world of autonomous security. Sometimes the smartest move is to surprise people, make them look twice, and let word of mouth do the rest." The Company expects the campaign to generate strong visibility among both the general public and media outlets, with social sharing and word of mouth doing much of the heavy lifting. Both HERO and RADDOG LE2 are currently in development, with availability anticipated later in 2025. This marks their first unofficial public appearance, offering a glimpse at what the Company believes will become headline-making additions to the evolving world of security automation. To explore AITX's full vision and progress, the Company invites investors and media to download the AITX Company Profile. New Yorkers, keep your eyes open. If you spot a robotic dog or a humanoid robot wandering through Times Square, snap a photo, scan the QR code, and help us track them down. HERO and RADDOG LE2 have gone missing, and we could use a few million eyes to find them. AITX, through its primary subsidiary, Robotic Assistance Devices, Inc. (RAD), is redefining the nearly $50 billion (US) security and guarding services industry 1 through its broad lineup of innovative, AI-driven Solutions-as-a-Service business model. RAD solutions are specifically designed to provide cost savings to businesses of between 35%-80% when compared to the industry's existing and costly manned security guarding and monitoring model. RAD delivers these tremendous cost savings via a suite of stationary and mobile robotic solutions that complement, and at times, directly replace the need for human personnel in environments better suited for machines. All RAD technologies, AI-based analytics and software platforms are developed in-house. The Company's operations and internal controls have been validated through successful completion of its SOC 2 Type 2 audit, reinforcing the Company's credibility with enterprise and government clients who require strict data protection and security compliance. RAD has a prospective sales pipeline of over 35 Fortune 500 companies and numerous other client opportunities. RAD expects to continue to attract new business as it converts its existing sales opportunities into deployed clients generating a recurring revenue stream. Each Fortune 500 client has the potential of making numerous reorders over time. About Artificial Intelligence Technology Solutions (AITX) AITX is an innovator in the delivery of artificial intelligence-based solutions that empower organizations to gain new insight, solve complex challenges and fuel new business ideas. Through its next-generation robotic product offerings, AITX's RAD, RAD-R, RAD-M and RAD-G companies help organizations streamline operations, increase ROI, and strengthen business. AITX technology improves the simplicity and economics of patrolling and guard services and allows experienced personnel to focus on more strategic tasks. Customers augment the capabilities of existing staff and gain higher levels of situational awareness, all at drastically reduced cost. AITX solutions are well suited for use in multiple industries such as enterprises, government, transportation, critical infrastructure, education, and healthcare. To learn more, visit and or follow Steve Reinharz on X @SteveReinharz. CAUTIONARY DISCLOSURE ABOUT FORWARD-LOOKING STATEMENTS The information contained in this publication does not constitute an offer to sell or solicit an offer to buy securities of Artificial Intelligence Technology Solutions, Inc. (the "Company"). This publication contains forward-looking statements, which are not guarantees of future performance and may involve subjective judgment and analysis. The information provided herein is believed to be accurate and reliable, however the Company makes no representations or warranties, expressed or implied, as to its accuracy or completeness. The Company has no obligation to provide the recipient with additional updated information. No information in this publication should be interpreted as any indication whatsoever of the Company's future revenues, results of operations, or stock price.
Forbes
a day ago
- Automotive
- Forbes
Why Reliability Is The Hardest Problem In Physical AI
Dr. Jeff Mahler: Co-Founder, Chief Technology Officer, Ambi Robotics; PhD in AI and Robotics from UC Berkeley. getty Imagine your morning commute. You exit the highway and tap the brakes, but nothing happens. The car won't slow down. You frantically search for a safe place to coast, heart pounding, hoping to avoid a crash. Even after the brakes are repaired, would you trust that car again? Trust, once broken, is hard to regain. When it comes to physical products like cars, appliances or robots, reliability is everything. It's how we come to count on them for our jobs, well-being or lives. As with vehicles, reliability is critical to the success of AI-driven robots, from the supply chain to factories to our homes. While the stakes may not always be life-or-death, dependability still shapes how we trust robots, from delivering packages before the holidays to cleaning the house just in time for a dinner party. Yet despite the massive potential of AI in the physical world, reliability remains a grand challenge for the field. Three key factors make this particularly hard and point to where solutions might emerge. 1. Not all failures are equal. Digital AI products like ChatGPT make frequent mistakes, yet hundreds of millions of active users use them. The key difference is that these mistakes are usually of low consequence. Coding assistants might suggest a software API that doesn't exist, but this error will likely be caught early in testing. Such errors are annoying but permissible. In contrast, if a robot AI makes a mistake, it can cause irreversible damage. The consequences range from breaking a beloved item at home to causing serious injuries. In principle, physical AI could learn to avoid critical failures with sufficient training data. In practice, however, these failures can be extremely rare and may need to occur many times before AI learns to avoid them. Today, we still don't know what it takes in terms of data, algorithms or computation to achieve high dependability with end-to-end robot foundation models. We have yet to see 99.9% reliability on a single task, let alone many. Nonetheless, we can estimate that the data scale needed for reliable physical AI is immense because AI scaling laws show a diminishing performance with increased training data. The scale is likely orders of magnitude higher than for digital AI, which is already trained on internet-scale data. The robot data gap is vast, and fundamentally new approaches may be needed to achieve industrial-grade reliability and avoid critical failures. 2. Failures can be hard to diagnose. Another big difference between digital and physical AI is the ability to see how a failure occurred. When a chatbot makes a mistake, the correct answer can be provided directly. For robots, however, it can be difficult to observe the root causes of issues in the first place. Limitations of hardware are one problem. A robot without body-wide tactile sensing may be unable to detect a slippery surface before dropping an item or unable to stop when backing into something behind it. The same can happen in the case of occlusions and missing data. If a robot can't sense the source of the error, it must compensate for these limitations—and all of this requires more data. Long-time delays present another challenge. Picture a robot that sorts a package to the wrong location, sending it to the wrong van for delivery. The driver realizes the mistake when they see one item left behind at the end of the day. Now, the entire package history may need to be searched to find the source of the mistake. This might be possible in a warehouse, but in the home, the cause of failure may not be identified until the mistake happens many times. To mitigate these issues, monitoring systems are hugely important. Sensors that can record the robot's actions, associate them with events and find anomalies can make it easier to determine the root cause of failure and make updates to the hardware, software or AI on the robot. Observability is critical. The better that machines get at seeing the root cause of failure, the more reliable they will become. 3. There's no fallback plan. For digital AI, the internet isn't just training data; it's also a knowledge base. When a chatbot realizes it doesn't know the answer to something, it can search through other data sources and summarize them. Entire products like Perplexity are based on this idea. For physical AI, there's not always a ground truth to reference when planning actions in real-world scenarios like folding laundry. If a robot can't find the sheet corners, it's not likely to have success by falling back to classical computer vision. This is why many practical AI robots use human intervention, either remote or in-person. For example, when a Waymo autonomous vehicle encounters an unfamiliar situation on the road, it can ask a human operator for additional information to understand its environment. However, it's not as clear how to intervene in every application. When possible, a powerful solution is to use a hybrid AI robot planning system. The AI can be tightly scoped to specific decisions such as where to grasp an item, and traditional methods can be used to plan a path to reach that point. As noted above, this is limited and won't work in cases where there is no traditional method to solve the problem. Intervention and fallback systems are key to ensuring reliability with commercial robots today and in the foreseeable future. Conclusion Despite rapid advances in digital GenAI, there's no obvious path to highly reliable physical AI. It isn't just a technical hurdle; it's the foundation for trust in intelligent machines. Solving it will require new approaches to data gathering, architectures for monitoring/interventions and systems thinking. As capabilities grow, however, so does momentum. The path is difficult, but the destination is worth it. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
