26-05-2025
Humanoid robots are the next step for AI. Here's how to train yours
The next phase of artificial intelligence (AI) is robots, which will help with the global labour shortage, an Nvidia executive told Euronews Next.
"We are at a very interesting point in time. The promise of robotics has existed for a long time. It's been in our imaginations and science fiction," Rev Lebaredian, vice president of Omniverse and simulation technology at Nvidia, told Euronews Next at the Computex technology fair in Taiwan.
He said that despite tech companies trying to build a general-purpose robot for years, the issue has been that, despite being able to build the physical robot, programming it has always been a challenge.
"AI has changed all that. We now have the technology to make robots really programmable in a general-purpose way and make it so that normal people can programme them, not just specific robot programming engineers," he said.
Companies such as Tesla are racing to build humanoid robots and have made strides. Last week, Elon Musk's company said its Optimus robot had learned to perform household chores.
However, there is still much for robots to learn.
For Nvidia, the company says robots should learn their tasks in the virtual world for safety, but also because it would take too long to train robots with humans.
"The only way to actually create these robots, intelligent ones, is to employ simulation," Lebaredian said.
"The fundamental problem that we have with physical AI is that AI is data hungry. You have to feed into your AI factory lots and lots of quality data to give it life experience to train from".
He said that with large language models (LLMs), there is a large amount of data online to train them.
But he said in physical AI, there is no such data that can be mined.
"To get all of the information we need to train a robot on how to pick up an object, we have to go create it somehow," he said.
"Collecting it from the real world is not possible. We can't create enough data. Even if you can, in some cases, it's dangerous, it's time-consuming, and it is expensive".
What is needed is "a way to go from fossil data to renewable data sources," Lebaredian said. And the best renewable data source for physical data is a physical simulator, he added.
Once your robot is tested, or has "graduated" and looks like it is working well, it can then go to its first employer.
"A new college graduate is trained on a corpus of publicly available data. You study from textbooks and information that everybody has access to everywhere. And you have a generalist that enters your company, and they're useful," Lebaredian told Euronews Next.
"But they're not really useful until you train them for a few years on the specific proprietary information and data in your company that's about your domain and your particular practices and how things are done," he added.
In robot terms, it means that you could then specialise your robot with your own data to make it work best for you.
Lebaredian did not specify a date when humanoid robots would come into our lives, but he said it would be "soon".
The first use cases for them would be in factories and warehouses.
"I think industrial use is going to be the first one because even if we can build a perfect robot that you can use in your home, it's not clear that all humans will want one," according to Lebaredian.
"But industry, there is a great need for it. There aren't enough young people replacing the older skilled workers who are retiring in every country".
Global labour shortages have reached historically high levels in the past decade, according to the OECD.
Population declines, as well as ageing populations, and the fact that many people do not want the "three D" jobs, which, according to the Nvidia executive, were "jobs that are dangerous, dull, and dirty".
Taiwan has jumped on this robotics need and is set to launch a five-year plan to boost the robotics industry in a bid to plug labour shortages, the government announced last week.
Taiwan's population decline would strain the economy and the nation's ability to care for vulnerable and elderly people, Peter Hong, who heads the National Science and Technology Council's (NSTC) Department of Engineering and Technologies, was reported as saying, according to local media.
Lebaredian said that after factory use, humanoid robots could help in retail, as he hears a lot of companies saying they cannot hire enough people to stack shelves.
He also said they could be used in mines, nuclear reactors, or even in space. Eventually, he said they could be used to take care of the elderly if the demand is there.
But just as we get excited about this next phase of AI, LLMs are still getting much wrong, which is causing them to sometimes make things up. Errors caused by a robot in the physical world could be much more dangerous.
However, Lebaredian believes that just like autonomous vehicles seem like science fiction at first, people eventually get used to them, and the technology improves.
"In generative AI, yes, there's still some stuff that's inaccurate, but I think you have to admit, in the last two and a half years since ChatGPT was introduced, accuracy and the quality of what it's producing have increased exponentially as well," he said.
But he added that perhaps chatbots will never be quite right because we want humans to perform the tasks.
"There's actually no right answer for a lot of that stuff," he said.
"But for tasks that we have in industry, that is actually something that's very measurable, for example, did it accurately pick up this object and move it over here and do that safely and robustly?"
He said those systems can be created, tested, and made sure they are safe before deployment. We can create these systems, test them, and make sure that they're working well before deploying them.
"We have machinery and systems that we create that are quite dangerous if they're not set up right. But we've managed to create nuclear reactors and these systems, and keep them safe somehow. We can do the same with physical AI," he said.
Women's jobs are at a higher risk of automation by artificial intelligence (AI) than those occupied by men, according to a new study from the United Nations.
The recent report from the UN's International Labour Organisation (ILO) and Poland's National Research Institute of the Ministry of Digital Affairs (NASK) found that automation could replace just under 10 per cent of female-dominated positions in high-income countries compared to the 3.5 per cent it could replace for men.
The biggest disparity between male and female-dominated jobs happens in high-income countries, where 41 percent of all high-income work for women could be exposed to AI, compared to 28 percent of men's jobs.
In Europe and Central Asia, 39 per cent of women's jobs could be affected compared to 26 percent of men.
The patterns identified by the study "reflect both occupational structures," and that AI-exposed jobs are "concentrated in higher-income countries".
Overall, the ILO found that one in four workers globally work in an occupation with some AI exposure.
To reach these findings, the survey was conducted with1,640 people employed in various fields in Poland, with the results analysed by a small group of international experts.
Researchers then developed an AI that used this survey data alongside national job information to identify how likely 2,500 professions and over 29,000 work tasks would be automated.
The study found that clerical occupations like data entry clerks, typists, word processing operators, accountants, and bookkeeping clerks are the most exposed to AI, due to some of the tasks performed in those professions, like taking meeting notes or scheduling appointments.
Other professions identified with a large AI exposure are web and media developers, database specialists, financial, and software-related jobs.
The study notes that these numbers reflect the "potential exposure," but that they don't reflect any actual job losses.
Full replacement by AI is still "limited," the report continued, noting that human involvement is still needed to oversee certain tasks.
"As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of generative AI," the report reads.
What could impact the number of jobs lost or AI adoption more broadly are technological constraints, infrastructure gaps, and skills shortages, the report continued.
The report asks governments, employees, and workers organisations to shape "inclusive strategies" that can help protect job quality and productivity in endangered fields.
"It's easy to get lost in the AI hype," Janine Berg, senior economist at the ILO, said in a statement. "What we need is clarity and context".
