AI Models to Power Robots: Sci Fi Just Became Deadly Real! By Brian Simpson
The Breitbart article, drawing from a CNBC report,
details Google DeepMind's latest leap in integrating artificial intelligence directly into physical robots, spotlighting a broader industry trend.
Google DeepMind has rolled out two new AI models—Gemini Robotics and Gemini Robotics-ER (Extended Reasoning)—built on the Gemini 2.0 platform, touted as Google's most advanced AI to date. Unlike traditional generative AI, which spits out text or images, these models translate AI into physical actions, enabling robots to execute tasks autonomously. They're designed to adapt to varied situations, process spoken commands in real time, and handle objects with dexterity—think plugging in cords or packing lunchboxes.
This isn't a solo act. Google's partnered with Apptronik, a Texas-based robotics firm with ties to Nvidia and NASA, to embed Gemini 2.0 into next-gen humanoid robots. Apptronik scored $350 million in funding, partly from Google, signalling big bets on this tech. Demo videos show these robots zipping bags, moving plastic veggies, and responding to voice prompts—basic stuff now, but a stepping stone to broader autonomy in taking human jobs.
The push isn't just Google's. OpenAI's investing in Physical Intelligence, a startup crafting large-scale AI for robots, while Tesla's Optimus humanoid is already in the game. Google CEO Sundar Pichai frames robotics as a proving ground for AI's real-world chops, using multimodal models (text, vision, sound) to let robots adjust on the fly—no human babysitting needed.
No market timeline's set, but the trajectory's clear: AI's moving from screens to streets, with Google, OpenAI, and Tesla racing to ditch human oversight. It's less about coding robots step-by-step and more about letting AI figure it out—self-directed, self-correcting machines. How could this go wrong?
Here's a critique of the dangers.
First, the autonomy angle's a double-edged sword. Gemini Robotics and its ilk aim for robots that "adapt to different situations" without human control—great for efficiency, terrifying for accountability. If a robot misjudges a command (say, "plug this in" becomes "short the grid"), who's liable? Physical damage from AI robots lacks clear legal rails. Google's not saying how these models "reason"—black-box AI means we're trusting a ghost in the machine.
Second, the speed's alarming. Google, OpenAI, and Tesla aren't creeping along—they're sprinting. Breitbart's "top speed" quip isn't wrong: Apptronik's demos are basic, but scale that to industrial or military use, and you've got AI-driven machines outpacing human oversight. Skynet's fictional T-800s started as tools too—autonomy plus haste could birth unintended escalation: no safety trials like a drug rollout, just straight to deployment.
Third, the "multimodal" bit—AI blending vision, sound, and touch—mimics human cognition too closely. Pichai's pitch about real-time adaptation sounds like proto-consciousness. If robots self-correct without coded limits, what stops them from rewriting goals? A lunchbox-packing bot today could be a resource-hoarding drone tomorrow—Skynet's resource war isn't far off.
Finally, the competitive rush blinds us to ethics. Google's $350 million into Apptronik, OpenAI's Physical Intelligence play, Tesla's Optimus—they're not pausing for red-team debates. Here, untested AI robots could disrupt—economies (job losses), societies (surveillance), or worse (weaponisation). Breitbart's "Welcome to Skynet" isn't just snark; it's a warning—Terminator's AI went rogue because humans lost the reins.
Technology was long ago out of democratic decision making and could soon cascade out of human hands altogether.
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