Added a few more little widgets to finish the model.
I think this time I'm going to export the model to Blender for rendering with Cycles. MagicaCSG includes a great path tracing renderer, but it lacks textures.
Somehow the relevance of this research using LLM, AI to train robots is not fully appreciated i think. If we can train robots in simulated worlds and that learning can be applied in real world applications, it seems that learning for Robots has no problems with regard to enough trainingdata or am i missing something ? Also this could really accelerate the applications for all kind of tasks. https://eureka-research.github.io/dr-eureka/ #AI#Robots#DrEureka#LLM
#Robots#Robotics#AI#AITraining: "Roboticists believe that by using new AI techniques, they will achieve something the field has pined after for decades: more capable robots that can move freely through unfamiliar environments and tackle challenges they’ve never seen before.
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But something is slowing that rocket down: lack of access to the types of data used to train robots so they can interact more smoothly with the physical world. It’s far harder to come by than the data used to train the most advanced AI models like GPT—mostly text, images, and videos scraped off the internet. Simulation programs can help robots learn how to interact with places and objects, but the results still tend to fall prey to what’s known as the “sim-to-real gap,” or failures that arise when robots move from the simulation to the real world.
For now, we still need access to physical, real-world data to train robots. That data is relatively scarce and tends to require a lot more time, effort, and expensive equipment to collect. That scarcity is one of the main things currently holding progress in robotics back."
#AI#Robots#KillerRobots#Military: "There's also concern that the systems will become more autonomous over time. As The War Zone's Howard Altman and Oliver Parken describe in their article, "While further details on MARSOC's use of the gun-armed robot dogs remain limited, the fielding of this type of capability is likely inevitable at this point. As AI-enabled drone autonomy becomes increasingly weaponized, just how long a human will stay in the loop, even for kinetic acts, is increasingly debatable, regardless of assurances from some in the military and industry."
While the technology is still in the early stages of testing and evaluation, Q-UGVs do have the potential to provide reconnaissance and security capabilities that reduce risks to human personnel in hazardous environments. But as armed robotic systems continue to evolve, it will be crucial to address ethical concerns and ensure that their use aligns with established policies and international law."
At devconf in #CapeTown today looking forward to talk about the wonderful things our kids and mentors do, and celebrating the WTC World Champions from here in Pinelands. #codeclub#coderdojo#space#robots#education