Lifeboat Foundation: Safeguarding Humanity
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CORVALLIS, Ore. – Cassie the robot, invented at Oregon State University and produced by OSU spinout company Agility Robotics, has made history by traversing 5 kilometers, completing the route in just over 53 minutes.

Cassie was developed under the direction of robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Advanced Research Projects Agency of the U.S. Department of Defense.

Since Cassie’s introduction in 2017, OSU students funded by the National Science Foundation have been exploring machine learning options for the robot.

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Thrilled to see Paradromics’ $20M fund raise lead by the talented Dr. Amy Kruse! Paradromics is building a brain computer interface supported by DARPA’s Biologi… See More.

The investment demonstrates confidence in Paradromics as a well-positioned player in the $200 billion BCI therapy market. Last year, Paradromics successfully completed testing of its platform, demonstrating the largest ever electrical recording of cortical activity that exceeded more than 30000 electrode channels in sheep cortex. This recording allowed researchers to observe the brain activity of sheep in response to sound stimuli with high fidelity.

“We are combining the best of neural science and medical device engineering to create a robust and reliable platform for new clinical therapies,” said Paradromics CEO Matt Angle. “This funding round is a validation of both our technology and strategic vision in leading this important developing market.”

The current funding round follows $10M in early stage private funding as well as $15M of public funding from the National Institutes of Health (NIH) and the Department of Defense (DARPA).

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A team of scientists will embark on a new international research project led by Harvard University to search for evidence of extraterrestrial life by looking for advanced technology it may leave behind.

The Galileo Project is led by the Harvard astronomy professor Avi Loeb co-founded the project with Frank Laukien, CEO of Bruker Corporation, a Massachusetts-based manufacturer of scientific equipment.

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But while science fiction provides military planners with a tantalizing glimpse of future weaponry, from exoskeletons to mind-machine interfaces, the genre is always about more than flashy new gadgets. It’s about anticipating the unforeseen ways in which these technologies could affect humans and society – and this extra context is often overlooked by the officials deciding which technologies to invest in for future conflicts.

Imagined worlds

Like my colleague David Seed, who has studied how fiction impacts on real-life threat assumptions about nuclear terrorism, I’m interested in how science fiction informs our sense of the future. This has given me the opportunity to work with members of the armed forces, using science fiction to query assumptions and generate novel visions of the future.

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I’ve been suggesting for a long time to drop these Ai’s into open world games.

EDIT: Also see paper and results compilation video!

Today, we published “Open-Ended Learning Leads to Generally Capable Agents,” a preprint detailing our first steps to train an agent capable of playing many different games without needing human interaction data. … The result is an agent with the ability to succeed at a wide spectrum of tasks — from simple object-finding problems to complex games like hide and seek and capture the flag, which were not encountered during training. We find the agent exhibits general, heuristic behaviours such as experimentation, behaviours that are widely applicable to many tasks rather than specialised to an individual task.

The neural network architecture we use provides an attention mechanism over the agent’s internal recurrent state — helping guide the agent’s attention with estimates of subgoals unique to the game the agent is playing.

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What i would suggest is landing Atlas robots in waves on the Moon, the first wave builds a solar panel farm for power, the second repairs the first wave, the third joins the first two to begin building large scale runways, the fourth joins the first three to begin building permanent structures.

The Moon is close enough for teleoperations, and in the 2030s, when we actually do Mars, the AI could repeat the whole thing there.

Before they explore Mars, the robots explore Martian-like caves on Earth first.

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Although effective uncertainty estimation can be a key consideration in the development of safe and fair artificial intelligence systems, most of today’s large-scale deep learning applications are lacking in this regard.

To accelerate research in this field, a team from DeepMind has proposed epistemic neural networks (ENNs) as an interface for uncertainty modelling in deep learning, and the KL divergence from a target distribution as a precise metric to evaluate ENNs. In the paper Epistemic Neural Networks, the team also introduces a computational testbed based on inference in a neural network Gaussian process, and validates that the proposed ENNs can improve performance in terms of statistical quality and computational cost.

The researchers say all existing approaches to uncertainty modelling in deep learning can be expressed as ENNs, presenting a new perspective on the potential of neural networks as computational tools for approximate posterior inference.

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