Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 1456

Ray Kurzweil — Singularitarian Immortalist, Director of Engineering at Google, famous inventor, author of How to Create a Mind http://GF2045.com/speakers/.

A world-class prolific inventor and leading futurist author, “the restless genius” (Wall Street Journal) points to 2045 for the technological singularity when A.I. will surpass human intelligence in his New York Times best seller The Singularity is Near, Amazon’s #1 book in science and philosophy.

In this video Ray Kurzweil discusses his predictions about radical life extension, singularity, life expansion and the imminence of physical immortality. He invites participants to the second international Global Future 2045 congress (June 2013) http://www.GF2045.com.

“If we have radical life extension only, we would get profoundly bored, we’d have profound existential ennui, running out of things to do, and new ideas, but that’s not what’s going to happen. In addition to radical life extension, we’re going to have radical life expansion, we’re going to have millions of virtual environments to explore, we’re going to literally expand our brains.”

Continue reading “Ray Kurzweil — Immortality by 2045” | >

Today’s world is one big maze, connected by layers of concrete and asphalt that afford us the luxury of navigation by vehicle. For many of our road-related advancements — GPS lets us fire fewer neurons thanks to map apps, cameras alert us to potentially costly scrapes and scratches, and electric autonomous cars have lower fuel costs — our safety measures haven’t quite caught up. We still rely on a steady diet of traffic signals, trust, and the steel surrounding us to safely get from point A to point B.

“If people can use the risk map to identify potentially high-risk road segments, they can take action in advance to reduce the risk of trips they take. Apps like Waze and Apple Maps have incident feature tools, but we’re trying to get ahead of the crashes — before they happen,” says He.

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A deep model was trained on historical crash data, road maps, satellite imagery, and GPS to enable high-resolution crash maps that could lead to safer roads.

Continue reading “Deep learning helps predict traffic crashes before they happen” | >

From 2019: Long before we can certify that neural networks can drive cars, we need to prove that they can multiply.

This work is still in its very early stages, but in the last year researchers have produced several papers which elaborate the relationship between form and function in neural networks. The work takes neural networks all the way down to their foundations. It shows that long before you can certify that neural networks can drive cars, you need to prove that they can multiply.

The Best Brain Recipe

Neural networks aim to mimic the human brain — and one way to think about the brain is that it works by accreting smaller abstractions into larger ones. Complexity of thought, in this view, is then measured by the range of smaller abstractions you can draw on, and the number of times you can combine lower-level abstractions into higher-level abstractions — like the way we learn to distinguish dogs from birds.

Continue reading “Foundations Built for a General Theory of Neural Networks” | >

Despite the continued progress that the state of the art in machine learning and artificial intelligence (AI) has been able to achieve, one thing that still sets the human brain apart — and those of some other animals — is its ability to connect the dots and infer information that supports problem-solving in situations that are inherently uncertain. It does this remarkably well despite sparse, incomplete, and almost always less than perfect data. In contrast, machines have a very difficult time inferring new insights and generalizing beyond what they have been explicitly trained on or exposed to.

How the brain evolved to achieve these abilities and what are the underlying ‘algorithms’ that enable them to remain poorly understood. The development and investigation of mathematical models will lead to a deep understanding of what the brain is doing and how are not mature and remain a very active area of research.

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Intech Company is the ultimate source of the latest AI news. It checks trusted websites and collects bests pieces of AI information.

Continue reading “Sorry AI — The Brain Is Still The Best Inference Machine Out There” | >

Exoplanet hunters have found thousands of planets, most orbiting close to their host stars, but relatively few alien worlds have been detected that float freely through the galaxy as so-called rogue planets, not bound to any star. Many astronomers believe that these planets are more common than we know, but that our planet-finding techniques haven’t been up to the task of locating them.

Most exoplanets discovered to date were found because they produce slight dips in the observed light of their host stars as they pass across the star’s disk from our viewpoint. These events are called transits.

NASA.

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Blue Origin is set to launch William Shatner on their second crewed spaceflight of its New Shepard rocket. Takeoff is currently scheduled on Wednesday, October 13 at 9:00 am CDT / 14:00 UTC from Corn Ranch, Texas.

New Shepard is designed to take people and payloads to suborbital space and back. It is expected to start sending space tourists this year. Ticket reservations are still on hold.

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Continue reading “WATCH LIVE: Blue Origin Launch William Shatner into Space on Jeff Bezos New Shepard Rocket” | >

There is no doubt that artificial intelligence (AI) is on the cusp of achieving significant disruption across several sectors in the world — one can simply look to companies like American company Alfi (NASDAQ: ALF) which is attempting to revolutionize the ad-tech industry with privacy-conscious AI —. It is becoming a key driver of productivity and gross domestic product growth for many nations and is pushing the boundaries of technology as we know it.

According to a report, the United States leads the AI pack today, with China in a close 2nd and the European Union in 3rd. Out of 100 total available points in the report’s scoring methodology, the United States leads with 44.2 points, China with 32.3, and the European Union with 23.5.

Although it may seem like the U.S. has an unassailable lead, the fact is that China is rapidly catching up and stands today as a full-spectrum peer competitor of the U.S. in many applications of AI.

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China’s national share of smart-computing power is 52%, compared to 19% in the U.S.

Recently, the China Academy of Information and Communications Technology (CAICT) released a white paper on the country’s computing power. According to the paper, which was translated by ChinAI, the country’s computing power reached 135 exaFlops (EFlops), an increase of 48 EFlops from last year. One EFlop is equivalent to the computing power of roughly two million laptops.

So, what’s the point in all this computing speed? China is accelerating its computing power for a faster AI adoption. It is evident in the way it prioritizes its resources for next-generation computing. Beijing divides its AI needs into basic-, smart-, and super-computing. Between 2016 and 2,020 the country dropped its basic-computing share to 57% from 95% and increased smart-computing to 41% from 3%.

And according to the paper, China’s national share of smart-computing power is 52%, compared to 19% in the U.S. While the statistics need to be taken with a pinch of salt, it sure does reveal something about the direction in which China is moving.

Continue reading “China’s closing the AI gap with U.S.” | >

Thanks to artificial intelligence, drones can now fly autonomously at remarkably high speeds, while navigating unpredictable, complex obstacles using only their onboard sensing and computation.

This feat was achieved by getting the drone’s neural network to learn flying by watching a sort of “simulated expert” – an algorithm that flew a computer-generated drone through a simulated environment full of complex obstacles. Now, this “expert” could not be used outside of simulation, but its data was used to teach the neural network how to predict the best trajectory, based only on the data from the sensors.

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