Alibaba Debuts AI That Helps Robots Understand Their Surroundings

Chinese tech giant Alibaba has introduced an artificial intelligence (AI) model designed to power robotics.

As CNBC reported Tuesday (Feb. 10), the company’s RynnBrain was developed to help robots grasp their physical surroundings and identify objects.

The report pointed to a video from Alibaba’s DAMO Academy showing a robot identifying fruit and placing it in a basket. These tasks seem simple, CNBC added, though they involve complex AI that shapes a robot’s understanding of individual items and movement.

As the report noted, robotics falls under the umbrella category of what’s known as “physical AI,” which includes machines that depend on artificial intelligence, like self-driving cars. China has prioritized development in this area as it competes with the U.S. on tech dominance.

The report added that Alibaba is not alone in building models for physical AI, joining tech giants that include Nvidia, Google and Tesla.

Despite these efforts, there is emerging evidence to suggest that the real-world productivity of humanoid robots is still not up to snuff, as covered here recently.

Industry research has found that robot workers are functioning at less than half the efficiency of their human counterparts workers in early deployments, based on metrics such as how fast they can complete a task, reliability and sustained output.

“Although robots can carry out individual actions competently, they still have trouble matching humans on tasks requiring fluid sequences, adaptation or uninterrupted execution in dynamic settings,” PYMNTS wrote. “This productivity shortfall is beginning to reshape expectations. Companies experimenting with humanoid robots are increasingly treating them as long-term bets rather than short-term efficiency tools.”

Meanwhile, recent PYMNTS Intelligence research examines fears about robotics from workers in the Labor Economy and non-Labor Economy spaces.

The former group are people doing labor-intensive, task-based or contingent work such as gig work, freelancing, contracting and hourly wage roles, and who are often seen as most vulnerable to automation.

Non-Labor Economy workers are more often salaried employees in stable roles with predictable pay, longer-term employment contracts and clearer career paths.

“If any segment of the workforce were going to show concern about the threats of robotics it would be them. But the data suggests that shift has not yet happened,” PYMNTS wrote.

The research shows that a little more than 32% of Labor Economy workers are worried that new technologies may reduce the need for people in their roles or lower demand for their skills, slightly above the 31.8% reported by non-Labor Economy workers.

“When the question turns specifically to automation and robotics, 29.3% of Labor Economy workers say advances could reduce the need for the work they do, compared with 26.8% of non-Labor Economy workers, a modest but notable difference,” PYMNTS added.