Insight No. 82: Break-through Technologies – “Robot Dexterity”

Insights, Technology

This Robot Hand Taught Itself How to Grab Stuff Like a Human.

Robots are teaching themselves to handle the physical world.

For all the talk about machines taking jobs, industrial robots are still clumsy and inflexible. A robot can repeatedly pick up a component on an assembly line with amazing precision and without ever getting bored—but move the object half an inch, or replace it with something slightly different, and the machine will fumble ineptly or paw at thin air.

But while a robot can’t yet be programmed to figure out how to grasp any object just by looking at it, as people do, it can now learn to manipulate the object on its own through virtual trial and error.

One such project is Dactyl, a robot that taught itself to flip a toy building block in its fingers. Dactyl, which comes from the San Francisco nonprofit OpenAI, consists of an off-the-shelf robot hand surrounded by an array of lights and cameras. Using what’s known as reinforcement learning, neural-network software learns how to grasp and turn the block within a simulated environment before the hand tries it out for real. The software experiments, randomly at first, strengthening connections within the network over time as it gets closer to its goal.

It usually isn’t possible to transfer that type of virtual practice to the real world, because things like friction or the varied properties of different materials are so difficult to simulate. The OpenAI team got around this by adding randomness to the virtual training, giving the robot a proxy for the messiness of reality.

Further breakthroughs for robots to master the advanced dexterity are needed in a real warehouse or factory. But if researchers can reliably employ this kind of learning, robots might eventually assemble our gadgets, load our dishwashers, and even help Grandma out of bed. 

Insight No. 79: Breaking-through Technologies: “Dueling Neural Networks” – Machines become creative

Insights, Technology

Artificial intelligence is getting very good at identifying things: show it a million pictures, and it can tell you with uncanny accuracy which ones depict a pedestrian crossing a street. But AI is hopeless at generating images of pedestrians by itself.

The problem is, creating something entirely new requires imagination and until now that has perplexed AIs.

The solution first occurred to Ian Goodfellow in 2014. The approach, known as a generative adversarial network, or GAN, takes two neural networks the simplified mathematical models of the human brain that underpin most modern machine learning and pits them against each other in a digital cat-and-mouse game.

Both networks are trained on the same data set. One, known as the generator, is tasked with creating variations on images it’s already seen perhaps a picture of a pedestrian with an extra arm. The second, known as the discriminator, is asked to identify whether the example it sees is like the images it has been trained on or a fake produced by the generator basically, is that three-armed person likely to be real?

Over time, the generator can become so good at producing images that the discriminator can’t spot fakes. Essentially, the generator has been taught to recognize, and then create, realistic-looking images of pedestrians.

The technology has become one of the most promising advances in AI in the past decade, able to help machines produce results that fool even humans.

GANs have been put to use creating realistic-sounding speech and photorealistic fake imagery. In one compelling example, researchers from chipmaker Nvidia primed a GAN with celebrity photographs to create hundreds of credible faces of people who don’t exist. Another research group made not-unconvincing fake paintings that look like the works of van Gogh. Pushed further, GANs can reimagine images in different waysmaking a sunny road appear snowy, or turning horses into zebras.

The results aren’t always perfect: GANs can conjure up bicycles with two sets of handlebars, say, or faces with eyebrows in the wrong place. But because the images and sounds are often startlingly realistic, some experts believe there’s a sense in which GANs are beginning to understand the underlying structure of the world they see and hear. And that means AI may gain, along with a sense of imagination, a more independent ability to make sense of what it sees in the world.

Thank you * Danke * Merci!

arts, Creatures, Food, Insights, poems, Psychologie, psychology, Reisen, Tipps, Travel, Uncategorized

Yesterday, revisiting “The Red Box” after a break, I surprisingly noticed that The Red Box has hit respectively has already exceeded the 200 followers mark!

TIME TO THANK ALL OF YOU !!!

…not only for your interest in my sometimes a bit weird mix of posts but also for sharing your expertise, thoughts, opinions, emotions, poems, recipes (= culinary poems), teasers for traveling, arts and much more treasures with me and others. You are awesome and an enrichment.

The about 6-months old, tiny blog “The Red Box” was born spontaneously. I put my cartoons, a couple of pencil drawings and own Haikus online in order to gain some social media insight, frankly speaking.
Soon, I noticed that I apparently enjoy it to research, illustrate and write the posts very much. In times of struggling with my health, the blogging is also a nice distraction and the topics are a reminder of the diversity, beauty & uniqueness of life.

In the meantime, I`ve published a bouquet of 716 posts without really noticing it.
To be honest, I am in particular surprised about the huge global coverage of “The Red Box”.
I like the idea very much that all of us sharing so many interests and issues beyond geographical, culture-, gender-, age-related borders. Well, we are all human beings with the same basic needs, I guess.

Admittedly, I am often curious who is visiting my blog “The Red Box”…who are the readers on the remote island Vanuatu or in Nepal for instance? Are there any readers in my German neighborhood, I could meet in person? What do we have in common or not?

However, whereever and whoever you are…I wish all of you simply the best.

At this occasion, I finally have to thank all the amazing international artists who generously allowed me to publish photos of their art work accompanied by their CVs and artists statements – and who agreed on my personal thoughts & comments in regard of their fascinating work. Merci!

Of course, all visitors are welcome to provide some feedback or comments any time.

Stay awesome!

Cartoon No. 56: Lessons Learnt (Gelerntes)

arts, Cartoons, humor, Kunst, Psychologie, psychology

Hr Hirschfeld - Kröte schlucken

The Turtle: What is it you can learn from the crane?

The Wayfarer: Balance!

The Turtle: And what does the snail teaches you, over there?

The Wayfarer: Patience!

The Turtle: And what is the lesson of the toad, over there?

The Wayfarer: …that toads aren`t easily to swollow!

 

Note: “To swollow a toad” = German proverb that outlines that there might be issues in life that are very difficult to accept. “To swollow a bitter pill” is a similar proverb (also used in German language) but it`s even harder to swollow a disgusting, big, living toad than a small pill.

Tipp No. 66: Ball-shaped Robot “SPHERO BOLT” For Creative Learning & Gaming” – About Giving & Taking (Xmas Gifts)

Technology, Tipps, Uncategorized

With a LED matrix and advanced sensors, the Sphero BOLT app-enabled robot provides many opportunities to be creative and have fun while learning.

FIT FOR THE DIGITAL WORLD OR ARE YOU JUST A USER?

To Take: Sphero BOLT’s eye-catching, programmable 8×8 light matrix opens up an endless array of coding and gaming capabilities. Use advanced sensors to track speed, acceleration, and direction, or drive BOLT without having to aim your robot thanks to the compass. BOLT also features infrared communication, allowing your robot to “talk” with other BOLTs. Equipped with Bluetooth SMART and a strong scratch-resistant shell, this robot’s brawn matches its brilliance.

To Give: about € 170 ,- or USD 150,- 

web: https://www.sphero.com/de_de/sphero-bolt

Source: Courtesy: Pixabay A therapist agrees to work with a client on two specific problems over the course of a three-month engagement. In the first month, the two specific problems are solved. The therapist and client continue to work together for the remaining two months, solving an additional three problems. At the end of the […]

via Why We Love Our Problems and How to Stop Loving Them — PushUP24

Shared No. 38: Why We Love Our Problems and How to Stop Loving Them — PushUP24

Insights, Psychologie, psychology, Uncategorized