Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI

A Russian Soyuz spacecraft carrying a humanoid robot failed to dock with the International Space Station (ISS) on Saturday morning, Russian state news agencies reported.

The Soyuz MS-14 crew ship launched from the Baikonur Cosmodrome in Kazakhstan on August 21 with the Skybot F-850, a life-sized artificially intelligent humanoid robot, on the commander’s seat.

Given the issues, emerged during the docking of the #SoyuzMS14 spacecraft with the ISS, the state commission chaired by Roscosmos Director General Dmitry Rogozin will held a meeting to consider the situation and discuss the measures to overcome the fault in the docking system. pic.twitter.com/turpSi08Rf

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https://youtube.com/watch?v=eRrQG-FLaZg

It’s a lunar lander named ‘Peregrine’, developed by the space robotics company to deliver payloads to the Moon for various companies, governments, universities, non-profits, and individuals for $1.2 million per kilogram. Astrobotic was selected by NASA in May 2019 for a $79.5 million contract to deliver up to 14 payloads to the Moon in 2021, under the agency’s Commercial Lunar Payload Services (CLPS) program.

To date, Astrobotic has signed 16 customers for lunar delivery on that first mission, totaling 28 payloads from 8 nations and comprising resource development, scientific investigation, technology demonstration, exploration, marketing, arts, and entertainment. The vehicle has already passed an industry-standard Preliminary Design Review, and the program will build and test a Structural Test Model, followed by a Critical Design Review, later this year.

Launch is currently slated for June 2021, with a planned landing a month later in Lacus Mortis, a large crater on the near side of the Moo n with payloads such as instruments to conduct new lunar science, pinpoint lander position, measure the lunar radiation environment, assess how lander and astronaut activity affects the Moon, and assist with navigation precision, among other capabilities.

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Circa 2017

In a recent article for Skeptic, Michael Shermer (the magazine’s founding publisher) put forth an argument for “why AI is not an existential threat,” where “AI” stands for “artificial intelligence” and an “existential threat” is anything that could cause human extinction or the irreversible decline of civilization.

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The Moon is a hot destination right now — especially for NASA, which wants to send people back to the lunar surface, but also for the private space industry. The most ambitious private lunar exploits are still many years off, but already, three companies claim they’ll be putting small robotic landers on the Moon in the next two years, amping up a small space race.

So far, no private entity has landed something successfully on the Moon. Only three government superpowers — the United States, China, and Russia — have ever been able to gently touch down vehicles on the lunar surface, and the Indian government may become the fourth in September. An Israeli nonprofit, SpaceIL, attempted to land the first private spacecraft on the Moon in April, but an early engine shutdown caused the vehicle to crash into the lunar surface instead. That means the door is still open for one of these three companies to make the first private lunar landing.

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The first-ever artificial intelligence simulation of the universe seems to work like the real thing — and is almost as mysterious.

Researchers reported the new simulation June 24 in the journal Proceedings of the National Academy of Sciences. The goal was to create a virtual version of the cosmos in order to simulate different conditions for the universe’s beginning, but the scientists also hope to study their own simulation to understand why it works so well.

“It’s like teaching image-recognition software with lots of pictures of cats and dogs, but then it’s able to recognize elephants,” study co-author Shirley Ho, a theoretical astrophysicist at the Center for Computational Astrophysics in New York City, said in a statement. “Nobody knows how it does this, and it’s a great mystery to be solved.” [Far-Out Discoveries About the Universe’s Beginnings].

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This sounds a little like Minority Report to us. China is looking into predictive analytics to help authorities stop suspects before a crime is committed.

According to a report from the Financial Times, authorities are tapping on facial recognition tech, and combining that with predictive intelligence to notify police of potential criminals, based on their behaviour patterns.

Guangzhou-headquartered Cloud Walk has been trialing its facial recognition system that tracks a person’s movements. Based on where someone goes, and when, it hands them a rating of how at risk they are of committing a crime.

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UNICEF is looking for startups applying #datascience, #machinelearning, #blockchain or #XR to prepare young people for the jobs of tomorrow.

The UNICEF Innovation Fund is looking to make up to 100K equity-free investments to provide early stage (seed) finance to for-profit technology start-ups that have the potential to benefit humanity.

If you’ve got a start-up using machine learning (ML), artificial intelligence (AI), blockchain or extended reality, registered in one of UNICEF’s programme countries, and have a working, open source prototype (or you are willing to make it open-source) showing promising results, the UNICEF Innovation Fund is looking for you.

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In a bid to accelerate training and inferences taken from artificial intelligence (AI) models, Intel has unveiled its two new processors. These two chips are part of its Nervana Neural Network Processor (NNP) selection.

The AI-focused chips will be called Spring Crest and Spring Hill, as they were disclosed on Tuesday at the Hot Chips Conference, held in Palo Alto, California.

The Hot Chips Conference is an annual tech symposium held annually in August.

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Under his plan, “Justice and Safety for All,” Bernie Sanders wants to ban facial recognition software for policing. As a supporter of Sanders, I’m going to have to respectfully disagree. Here’s why…

Last Sunday, presidential-hopeful Bernie Sanders released on his website what is arguably one of the most extensive plans for law enforcement oversight and criminal justice overhaul that the United States has ever seen. As a progressive, myself, and supporter of Sanders during his primary run, I fully endorse everything that’s been laid out in this plan— that is, except for one minor policy.

The plan, titled “Justice and Safety for All,” calls to “Ban the use of facial recognition software for policing.” It also calls for a “moratorium on the use of the algorithmic risk assessment tools in the criminal justice system until an audit is completed,” whereby the audit would “ensure these tools do not have any implicit biases that lead to unjust or excessive sentences.”

I’m perfectly fine with the policy on algorithmic risk assessment tools being used by our criminal justice system. It’s not a total ban; it simply serves as a temporary safety measure until it’s been proven that these algorithms won’t carry with them any unjust biases. But when it comes to Sanders’ policy on banning facial recognition software for policing, I simply cannot get behind it.

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When speaking about robots, people tend to imagine a wide range of different machines: Pepper, a social robot from Softbank; Atlas, a humanoid that can do backflip made by Boston Dynamics; the cyborg assassin from the Terminator movies; and the lifelike figures that populate the television series — West World. People who are not familiar with the industry tend to hold polarized views. Either they have unrealistically high estimations of robots’ ability to mimic human-level intelligence or they underestimate the potential of new researches and technologies.

Over the past year, my friends in the venture, tech, and startup scenes have asked me what’s “actually” going on in deep reinforcement learning and robotics. The wonder: how are AI-enabled robots different from traditional ones? Do they have the potential to revolutionize various industries? What are their capabilities and limitations? These questions tell me how surprisingly challenging it can be to understand the current technological progress and industry landscape, let alone make predictions for the future. I am writing this article with a humble attempt to demystify AI, in particular, and deep reinforcement learning enabled robotics, topics that we hear a lot about but understand superficially or not at all. To begin, I’ll answer a basic question: what are AI-enabled robots and what makes them unique?

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