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Imagine walking down the street and stumbling upon a soccer match, except the competitors are robots, not humans. Would you be surprised to learn that this isnt a Steven Spielberg futuristic movie set but a real-life athletic competition? The first-ever World Humanoid Robot Sports Games kicked off in Beijing on August 15, hosted by the citys municipal government in hopes of promoting Chinas technological advancements and fostering further dialogue internationally. Lets take a look at the details of this event and muse about what it might mean for our future: Is there any precedent for this event? While this is the first-ever full-scale event of its kind, it is coming on the heels of the 10th World Robot Conference in Beijing, held August 8-12, according to the Asia Times. Thats not the only precursor event held in the Chinese capital. In April, 21 humanoid robots participated in the first-ever half-marathon. Only six completed the race, which seems rather relatable. The Tien Kung Ultra robot, created by China’s National and Local Co-built Embodied AI Robotics Innovation Center, finished the course in 2 hours and 40 minutes. In Hangzhou in May, the China Media Group World Robot Competition-Mecha Fighting Series took place. Four Unitree G1s lived out the rockem-sockem robot dream. Back in Beijing in June, a practice soccer match was held with robots facing off three-on-three. It was the first time AI was utilized instead of human intervention. The robots were even equipped with the ability to recover from falls, but that technology has room for improvement, as some of the robots had to be taken off the field on stretchers. Where is the event taking place? The competition is being held in two very special Olympic areas in Beijing. The first is the National Stadium, known as the Bird’s Nest. The second is the National Speed Skating Oval, aka the Ice Ribbon. More than 500 humanoid robots across 280 teams from 16 countries are throwing down in 26 events. What does this mean for the future? Technology is moving fast, and its hard not to have visions of ominous science fiction movies in your head when thinking about the ramifications of artificial intelligence. This event will allow the robots out of the lab and into a big stress test to look for errors in programming and design. Heres hoping we can create a world where robots support human innovation instead of, well, taking over and murdering us. A happy ending of coexistence and cooperation might not sell at the box office, but it would be a much better reality to live in. You can check out a preview of the event in the embedded video below.
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Imagine a busy train station. Cameras monitor everything, from how clean the platforms are to whether a docking bay is empty or occupied. These cameras feed into an AI system that helps manage station operations and sends signals to incoming trains, letting them know when they can enter the station. The quality of the information that the AI offers depends on the quality of the data it learns from. If everything is happening as it should, the systems in the station will provide adequate service. But if someone tries to interfere with those systems by tampering with their training dataeither the initial data used to build the system or data the system collects as its operating to improvetrouble could ensue. An attacker could use a red laser to trick the cameras that determine when a train is coming. Each time the laser flashes, the system incorrectly labels the docking bay as occupied, because the laser resembles a brake light on a train. Before long, the AI might interpret this as a valid signal and begin to respond accordingly, delaying other incoming trains on the false rationale that all tracks are occupied. An attack like this related to the status of train tracks could even have fatal consequences. We are computer scientists who study machine learning, and we research how to defend against this type of attack. Data poisoning explained This scenario, where attackers intentionally feed wrong or misleading data into an automated system, is known as data poisoning. Over time, the AI begins to learn the wrong patterns, leading it to take actions based on bad data. This can lead to dangerous outcomes. In the train station example, suppose a sophisticated attacker wants to disrupt public transportation while also gathering intelligence. For 30 days, they use a red laser to trick the cameras. Left undetected, such attacks can slowly corrupt an entire system, opening the way for worse outcomes such as backdoor attacks into secure systems, data leaks, and even espionage. While data poisoning in physical infrastructure is rare, it is already a significant concern in online systems, especially those powered by large language models trained on social media and web content. A famous example of data poisoning in the field of computer science came in 2016, when Microsoft debuted a chatbot known as Tay. Within hours of its public release, malicious users online began feeding the bot reams of inappropriate comments. Tay soon began parroting the same inappropriate terms as users on X (then Twitter), and horrifying millions of onlookers. Within 24 hours, Microsoft had disabled the tool and issued a public apology soon after. The social media data poisoning of the Microsoft Tay model underlines the vast distance that lies between artificial and actual human intelligence. It also highlights the degree to which data poisoning can make or break a technology and its intended use. Data poisoning might not be entirely preventable. But there are commonsense measures that can help guard against it, such as placing limits on data processing volume and vetting data inputs against a strict checklist to keep control of the training process. Mechanisms that can help to detect poisonous attacks before they become too powerful are also critical for reducing their effects. Fighting back with the blockchain At Florida International Universitys Sustainability, Optimization, and Learning for InterDependent networks (SOLID) lab, we are working to defend against data poisoning attacks by focusing on decentralized approaches to building technology. One such approach, known as federated learning, allows AI models to learn from decentralized data sources without collecting raw data in one place. Centralized systems have a single point of failure vulnerability, but decentralized ones cannot be brought down by way of a single target. Federated learning offers a valuable layer of protection, because poisoned data from one device doesnt immediately affect the model as a whole. However, damage can still occur if the process the model uses to aggregate data is compromised. This is where another more popular potential solutionblockchaincomes into play. A blockchain is a shared, unalterable digital ledger for recording transactions and tracking assets. Blockchains provide secure and transparent records of how data and updates to AI models are shared and verified. By using automated consensus mechanisms, AI systems with blockchain-protected training can validate updates more reliably and help identify the kinds of anomalies that sometimes indicate data poisoning before it spreads. Blockchains also have a time-stamped structure that allows practitioners to trace poisoned inputs back to their origins, making it easier to reverse damage and strengthen future defenses. Blockchains are also interoperablein other words, they can talk to each other. This means that if one network detects a poisoned data pattern, it can send a warning to others. At SOLID lab, we have built a new tool that leverages both federated learning and blockchain as a bulwark against data poisoning. Other solutions are coming from researchers who are using prescreening filters to vet data before it reaches the training process, or simply training their machine learning systems to be extra sensitive to potential cyberattacks. Ultimately, AI systems that rely on data from the real world will always be vulnerable to manipulation. Whether its a red laser pointer or misleading social media content, the threat is real. Using defense tools such as federated learning and blockchain can help researchers and developers build more resilient, accountable AI systems that can detect when theyre being deceived and alert system administrators to intervene. M. Hadi Amini is an associate professor of computing and information sciences at Florida International University. Ervin Moore is a Ph.D. student in computer science at Florida International University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Nailah Williams discovered her path to a college degree in the most unlikely place: behind the wheel of her Uber. After years of jobs that forced her to choose between earning a paycheck and pursuing an education, she joined a program where Uber would cover her tuition for online classes at Arizona State University (ASU), where I teach. Created in 2018, the program covers tuition at ASU for drivers anywhere in the U.S. (or their beneficiary such as a child, spouse, or parent) who had completed at least 3,000 rides and met the rating requirements. With the newfound flexibility to work and study when she wanted, Nailah was able to complete a degree in urban planning while supporting herself and her family. Nailah isn’t alone. Across America, changes to how we work and learn are reshaping who is able to go to college. The Job-Education Problem According to the National Center for Education Statistics, 45% of full-time college students have a job, and around one in four of these are employed full-time. Historically, balancing work and school has taken a heavy toll. Past research found that the more students workedespecially beyond 1520 hours a weekthe more their grades, time spent studying, and graduation rates suffered. What happens when both work and education bend to fit students’ lives, instead of the other way around? To find out, my colleagues Spencer Perry, Basit Zafar, and I studied the unique partnership between Uber and ASU that funded Nailahs tuition. We analyzed data for hundreds of participating students and thousands of their classmates. The results were dramatic. Unlike in traditional jobs, participating students could take on more work hours with almost no impact on their grades (and vice versa). When students increased their study time by 10%, their work hours dropped by just 1% and their income, tips, and performance ratings barely changed. They passed their classes at about the same rate as a matched group of similar students attending classes in-person. Even more remarkable was who these students were. The initiative opened up ASUs online courses to a whole new population. Nearly half of participants were not in college before enrolling in the program. Their average age was 39, a full 14 years older than the typical ASU online student. They were more racially diverse, had higher financial need, and were more likely to be first-generation college students. Yet they harbored the same high expectations for their degrees and the resulting career and financial benefits. The Power of Flexibility The magic ingredient bringing college within reach for these students wasn’t just free tuition. It was flexibility. In a survey we conducted, program participants were three times as likely to say theyd enroll in college if work was flexible and classes were online than with traditional, rigid schedules. Our research points to a massive opportunity for more people to get a college education. As artificial intelligence reshapes the job market and economic uncertainty grows, millions of workers, young and old, need new skills and credentials. With newfound flexibility, students can earn while they learn, leveling up without taking on crushing debt. Universities that embrace this shift will capture new markets and expand their student populations. They should aggressively recruit gig workers and develop self-paced programs that can be done anytime, anywhere. They should create support systems for working students, from flexible on-campus jobs to childcare resources to time management coaching. The students are out there, driving for Lyft, delivering for DoorDash, or freelancing online, waiting for someone to make college work for them. Smart employers see the opportunity too. Companies like FedEx, Chipotle, Amazon, and Gap already offer tuition benefits. But the real winners will combine education support with genuinely flexible schedules and build partnerships with universities that allow their workers to plug into adaptable online learning. In our study, 30% of participants said they would have quit their job with Uber sooner if not for the partnership with ASUproof that these programs help attract and retain workers. The question isn’t whether flexibility will reshape education and employmentit’s whether institutions will embrace the change or be left behind. People like Nailah are ready and waiting for their opportunity. More partnerships like the one between ASU and Uber can help level the playing field and offer a path to success for students who have been shut out from higher education for too long.
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