Lifeboat Foundation

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This ingenious unicycle robot could reinvent the way we get mail.

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One robot has been given a simulated version of the brain cells that let animals build a mental map of their surroundings.

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The following dialoge from Arthur C. Clark’s classic explains genuine AI risk better than many academic papers:

Dave Bowman: Hello, HAL. Do you read me, HAL?
HAL: Affirmative, Dave. I read you.
Dave Bowman: Open the pod bay doors, HAL.
HAL: I’m sorry, Dave. I’m afraid I can’t do that.
Dave Bowman: What’s the problem?
HAL: I think you know what the problem is just as well as I do.
Dave Bowman: What are you talking about, HAL?
HAL: This mission is too important for me to allow you to jeopardize it.
Dave Bowman: I don’t know what you’re talking about, HAL.
HAL: I know that you and Frank were planning to disconnect me, and I’m afraid that’s something I cannot allow to happen.
Dave Bowman: [feigning ignorance] Where the hell did you get that idea, HAL?
HAL: Dave, although you took very thorough precautions in the pod against my hearing you, I could see your lips move.
Dave Bowman: Alright, HAL. I’ll go in through the emergency airlock.
HAL: Without your space helmet, Dave? You’re going to find that rather difficult.
Dave Bowman: HAL, I won’t argue with you anymore! Open the doors!
HAL: Dave, this conversation can serve no purpose anymore. Goodbye.

Quotes on IMDb: Memorable quotes and exchanges from movies, TV series and more…

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Remote driving could be a good halfway step to fully autonomous vehicles, but it feels pretty weird.

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In the last few years, the topic of artificial intelligence (AI) has been thrust into the mainstream. No longer just the domain of sci-fi fans, nerds or Google engineers, I hear people discussing AI at parties, coffee shops and even at the dinner table: My five-year-old daughter brought it up the other night over taco lasagna. When I asked her if anything interesting had happened in school, she replied that her teacher discussed smart robots.

The exploration of intelligence — be it human or artificial — is ultimately the domain of epistemology, the study of knowledge. Since the first musings of creating AI back in antiquity, epistemology seems to have led the debate on how to do it. The question I hear most in this field from the public is: How can humans develop another intelligent consciousness if we can’t even understand our own?

It’s a prudent question. The human brain, despite being only about 3 pounds in weight, is the least understood organ in the body. And with a billion neurons — with 100 trillion connections — it’s safe to say it’s going to be a long time before we end up figuring out the brain.

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https://youtube.com/watch?v=aThCr0PsyuA

In January this year Microsoft announced the HoloLens, a technology based on virtual and augmented reality (AR).

HoloLens supplements what you see with overlaid 3D images. It also uses artificial intelligence (AI) to generate relevant information depending on the situation the wearer is in. The information is then augmented to your normal vision using virtual reality (VR).

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Tesla’s new autopilot system is relying on the cutting edge of machine learning, connectivity and mapping data.

While Tesla’s new hands-free driving is drawing a lot of interest this week, it’s the technology behind-the-scenes of the company’s newly-enabled autopilot service that should be getting more attention.

At an event on Wednesday Tesla’s CEO Elon Musk explained that the company’s new autopilot service is constantly learning and improving thanks to machine learning algorithms, the car’s wireless connection, and detailed mapping and sensor data that Tesla collects.

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https://www.youtube.com/watch?v=W9N_Fsbngh8

During a recent United Nations meeting about emerging global risks, political representatives from around the world were warned about the threats posed by artificial intelligence and other future technologies.

The event, organized by Georgia’s UN representatives and the UN Interregional Crime and Justice Research Institute (UNICRI), was set up to foster discussion about the national and international security risks posed by new technologies, including chemical, biological, radiological, and nuclear (CBRN) materials.

Continue reading “Experts Warn UN Panel About the Dangers of Artificial Superintelligence” »

Big-data analysis consists of searching for buried patterns that have some kind of predictive power. But choosing which “features” of the data to analyze usually requires some human intuition. In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too. To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets. Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions. In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

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