Lifeboat Foundation

But the technology could break down trust and social bonds.

Robotic Autonomy in Complex Environments with Resiliency (RACER) program successfully tested autonomous movement on a new, much larger fleet vehicle – a significant step in scaling up the adaptability and capability of the underlying RACER algorithms.

The RACER Heavy Platform (RHP) vehicles are 12-ton, 20-foot-long, skid-steer tracked vehicles – similar in size to forthcoming robotic and optionally manned combat/fighting vehicles. The RHPs complement the 2-ton, 11-foot-long, Ackermann-steered, wheeled RACER Fleet Vehicles (RFVs) already in use.

Continue reading “RACER Speeds Into a Second Phase With Robotic Fleet Expansion and Another Experiment Success” »

Teaching robots to ask for help is key to making them safer and more efficient.

Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know. The technique involves quantifying the fuzziness of human language and using that measurement to tell robots when to ask for further directions. Telling a robot to pick up a bowl from a table with only one bowl is fairly clear. But telling a robot to pick up a bowl when there are five bowls on the table generates a much higher degree of uncertainty — and triggers the robot to ask for clarification.

Because tasks are typically more complex than a simple “pick up a bowl” command, the engineers use large language models (LLMs) — the technology behind tools such as ChatGPT — to gauge uncertainty in complex environments. LLMs are bringing robots powerful capabilities to follow human language, but LLM outputs are still frequently unreliable, said Anirudha Majumdar, an assistant professor of mechanical and aerospace engineering at Princeton and the senior author of a study outlining the new method.

Erwan Plantec, Joachin W.Pedersen, Milton L.Montero, Eleni Nisioti, Sebastian Risi ITU Copenhagen 2024 https://arxiv.org/abs/2406.

OpenRead & Notes Taking.

Enables both DLR researchers and external drone operators to quickly test and develop Unmanned Aircraft Systems in real-life operations, the German Aerospace Center (DLR) reports.

Following the rapid advancement of artificial intelligence (AI) tools, engineers worldwide have been working on new architectures and hardware components that replicate the organization and functions of the human brain.

Most brain-inspired technologies created to date draw inspiration from the firing of brain cells (i.e., neurons), rather than mirroring the overall structure of neural elements and how they contribute to information processing.

Researchers at Tsinghua University recently introduced a new neuromorphic computational architecture designed to replicate the organization of synapses (i.e., connections between neurons) and the tree-like structure of dendrites (i.e., projections extending from the body of neurons).

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A group of physicists wants to use artificial intelligence to prove that reality doesn’t exist. They want to do this by running an artificial general intelligence as an observer on a quantum computer. I wish this was a joke. But I’m afraid it’s not.

Continue reading “AI could prove that reality doesn’t exist, physicists say” »

In this thought-provoking lecture, Prof. Jay Friedenberg from Manhattan College delves into the intricate interplay between cognitive science, artificial intelligence, and ethics. With nearly 30 years of teaching experience, Prof. Friedenberg discusses how visual perception research informs AI design, the implications of brain-machine interfaces, the role of creativity in both humans and AI, and the necessity for ethical considerations as technology evolves. He emphasizes the importance of human agency in shaping our technological future and explores the concept of universal values that could guide the development of AGI for the betterment of society.

00:00 Introduction to Jay Friedenberg.
01:02 Connecting Cognitive Science and AI
02:36 Human Augmentation and Technology.
03:50 Brain-Machine Interfaces.
05:43 Balancing Optimism and Caution in AI
07:52 Free Will vs Determinism.
12:34 Creativity in Humans and Machines.
16:45 Ethics and Value Alignment in AI
20:09 Conclusion and Future Work.

Continue reading “Exploring AI, Cognitive Science, and Ethics | Deep Interview with Jay Friedenberg” »

A combined team of roboticists from Stanford University and the Toyota Research Institute has found that adding audio data to visual data when training robots helps to improve their learning skills. The team has posted their research on the arXiv preprint server.

The researchers noted that virtually all training done with AI-based robots involves exposing them to a large amount of visual information, while ignoring associated audio. They wondered if adding microphones to robots and allowing them to collect data regarding how something is supposed to sound as it is being done might help them learn a task better.

Continue reading “Adding audio data when training robots helps them do a better job” »