AI-Robotics Integration

Recent years have seen the rapid acceleration of artificial intelligence (AI) interest in the tech industry. Now, some companies have started to look at how AI can be combined with robotics to enhance features of both. The vast amounts of data that AI scrapes across the internet can help more quickly teach robots a larger variety of skills, though robots still require visual data that isn't as plentiful (which some research teams are trying to innovate around).

Liquid neural networks (LNN) are another recent advancement at the intersection of AI and robotics; these structures are designed to handle continuous data streams using significantly less storage capacity and computing power than traditional neural networks. Integrating LNNs into robots allows them to adjust their behavior dynamically and adapt to changing environments.

While AI-powered robotics generally focuses on simple household or factory tasks, one of the more controversial applications has been weapons for war. AI-powered drones can track and fire at targets without human intervention, and robots that operate on land are in development. Given the ethical implications of creating machines that could make potentially fatal decisions, the UN recently passed a resolution encouraging countries to regulate the use of these weapons.

While most specialty-purpose robots look very much like machines, people have long dreamed of a future where robots look like them. This future may not be far off. The first factory to mass-produce humanoid robots was recently built in Oregon, and Tesla is advertising jobs to train their humanoid robots. One humanoid robot just broke the speed record for its kind, and others can watch and learn how to perform a variety of tasks. New advances in electronic skin, which imbues robots with touch sensitivity, could also contribute to more human-like robots.

Despite this progress, there are still a variety of hurdles that humanoid robots must navigate (literally and figuratively) to reach the level of integration imagined by their creators. It is still difficult for robots to adapt to the vast array of spatial and variable elements in their environments. Bipedal robots are also more structurally unstable, leading some to question their further development. Future advancements may remedy some of these concerns. Planners should be aware of the potential for AI to accelerate the further integration of robots into local economies and communities.