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Category: robotics/AI – Page 1,588

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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A robot has wowed the audience at a basketball match during the Tokyo 2020 Olympics.

The machine, which goes by the name of CUE, showed off its throwing skills during half-time of the Men’s Preliminary Round Group B game between France and the United States (which France won 83–76).

In development by Japanese company Toyota since May 2018, CUE stands 208 cm (6’ 10) tall and weighs 90 kg (200 lbs). It uses sensors on its torso to calculate the angle and distance to the basket, before using its motorised arms and knees to shoot. The whole process from lifting the ball to making the shot takes less than 15 seconds.

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We’ve seen helmets and AI that can spot brain tumors, but a new hard hat can actually treat them, too.

As part of the latest neurological breakthrough, researchers used a helmet that generates a magnetic field to shrink a deadly tumor by a third. The 53-year-old patient who underwent the treatment ultimately died due to an unrelated injury, but an autopsy of his brain showed that the procedure had removed 31% of the tumor mass in a short time. The test marked the first noninvasive therapy for a deadly form of brain cancer known as glioblastoma.

The helmet features three rotating magnets connected to a microprocessor-based electronic controller operated by a rechargeable battery. As part of the therapy, the patient wore the device for five weeks at a clinic and then at home with the help of his wife. The resulting magnetic field therapy created by the helmet was administered for two hours initially and then ramped up to a maximum of six hours per day. During the period, the patient’s tumor mass and volume shrunk by nearly a third, with shrinkage appearing to correlate with the treatment dose.

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4:47 BioAge, 8:10 Church talking about how controlling aging is no longer speculative, 10:44 urging caution as they are not really talking about turning 67 year olds into 20 year olds. Near the end Church mentions A.I. an exponential possibilities of hitting all the pathways at once.

Recently, Avi Roy, alongside Nathan Cheng & Laura Minquini, hosted the second Longevity Panel discussion, which assembled some of the brightest minds working on reversing aging, and enhancing health and life span.

As with the first event, this discussion was intended to illuminate how they are approaching longevity and to know if we are any closer in achieving it.

The talk was split into two sections: the first being open discussion guided by questions from the hosts. The talk was then opened up to the floor, allowing audience questions. Part 1 will provide the transcript from the first section of the Longevity Panel. Enjoy!

You can check out the full transcript, with addition links on the Gowing Life website:

Continue reading “Longevity Panel II — The Scientists Working of Reversing Aging | Part 1” | >

Getting blood to a wounded soldier could be the difference between life and death. A drone swarm is one way to make that happen in battle.

Blood is usually a finite quality on a battlefield. Battles can cause a number of injuries, from the minor to the critical. If a soldier can get the wound closed in time, they can staunch the loss, but keeping the patient alive may require an influx of new blood. As medics work to aid their comrades, they could receive help from an unusual source: delivery drones, bringing literal fresh blood to the battlefield.

A drone swarm capable of delivering blood was part of Autonomous Advance Force 4.0, an exercise by the United Kingdom’s armed forces in which Royal Marines Commandos trained with modern technology for future war. The July exercise took place in Cumbria and Dorset, with a release announced July 17.

The swarm consisted of six medium-heavy lift drones, Malloy Aeronautics TRV-150s. The TRV-150 can carry up to 140 lbs, at a range of up to 43 miles, with a maximum flight time of 36 minutes. Malloy drones got their start back in 2014 as a hoverbike concept, which was then proposed for the US military as a kind of ridden-drone scout. The US Army explored a large version of the drone as a “tactical resupply” vehicle in 2017. In TRV-150 form, the drone is an octocopter, with two rotors on each of four limbs.

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