Hype Cycle : Machine Learning

Hype Cycle : Machine Learning
Hype Cycle : Machine Learning
Hype Cycle : Machine Learning

Hype Cycle : Machine Learning

Project from Universal Everything is a series of films exploring human-machine collaboration, here presenting performative dance with human and abstracted forms:

Hype Cycle ­is a series of futurist films exploring human-machine collaboration through performance and emerging technologies.

Machine Learning is the second set of films in the Hype Cycle series. It builds on the studio’s past experiments with motion studies, and asks: when will machines achieve human agility?

Set in a spacious, well-worn dance studio, a dancer teaches a series of robots how to move. As the robots’ abilities develop from shaky mimicry to composed mastery, a physical dialogue emerges between man and machine – mimicking, balancing, challenging, competing, outmanoeuvring.

Can the robot keep up with the dancer? At what point does the robot outperform the dancer? Would a robot ever perform just for pleasure? Does giving a machine a name give it a soul?

These human-machine interactions from Universal Everything are inspired by the Hype Cycle trend graphs produced by Gartner Research, a valiant attempt to predict future expectations and disillusionments as new technologies come to market.

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This teenager invented an AI-based app that can quickly diagnose eye disease
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Launching the Future of Space Communications

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Communicating from space wasn’t always so easy. During our third attempt to land on the moon in 1970, the Apollo 13 crew had to abort their mission when the spacecraft’s oxygen tank suddenly exploded and destroyed much of the essential equipment onboard. Made famous in the movie ‘Apollo 13’ by Ron Howard and starring Tom Hanks, our NASA engineers on the ground talked to the crew and fixed the issue. Back in 1970 our ground crew could only communicate with their ground teams for 15 percent of their orbit – adding yet another challenge to the crew. Thankfully, our Apollo 13 astronauts survived and safely returned to Earth. 

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Now, our astronauts don’t have to worry about being disconnected from their teams! With the creation of the TDRS program in 1973, space communications coverage increased rapidly from 15 percent coverage to 85 percent coverage. And as we’ve continued to add TDRS spacecraft, coverage zoomed to over 98 percent!

Launching The Future Of Space Communications

TDRS is a fleet of satellites that beam data from low-Earth-orbiting space missions to scientists on the ground. These data range from cool galaxy images from the Hubble Space Telescope to high-def videos from astronauts on the International Space Station! TDRS is operated by our Space Network, and it is thanks to these hardworking engineers and scientists that we can continuously advance our knowledge about the universe!  

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What’s up next in space comm? Only the coolest stuff ever! LASER BEAMS. Our scientists are creating ways to communicate space data from missions through lasers, which have the ability to transfer more data per minute than typical radio-frequency systems. Both radio-frequency and laser comm systems send data at the speed of light, but with laser comm’s ability to send more data at a time through infrared waves, we can receive more information and further our knowledge of space.

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How are we initiating laser comm? Our Laser Communications Relay Demonstration is launching in 2019! We’re only two short years away from beaming space data through lasers! This laser communications demo is the next step to strengthen this technology, which uses less power and takes up less space on a spacecraft, leaving more power and room for science instruments.

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Watch the TDRS launch live online at 8:03 a.m. EDT on Aug. 18: https://www.nasa.gov/nasalive

Join the conversation on Twitter: @NASA_TDRS and @NASALasercomm!

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7 years ago

Only in Japan : Azuma, the First Hologram Communication Robot

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8 years ago
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7 years ago
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7 years ago
Uncanny Rd

Uncanny Rd

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Uncanny Road is an experimental tool for collectively synthesizing a never-ending road using Generative Adversarial Neural Networks. It is based on the pix2pixHD project, published by @nvidia and UC Berkeley (Project, Paper and Code), that allows for photorealistic image-to-image translation. The pix2pix model was trained using adversarial learning on the Cityscapes dataset, containing thousands of street images.

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Small digital payments have been tried again and again—in fact, Web inventor Tim Berners-Lee tried to embed micropayment capability into the original World Wide Web, but without success. So far, inherent transaction costs have been an unsurpassable hurdle.

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