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Google Tackles Challenge of How to Build an Honest Robot

Google can see a future where robots help us unload the dishwasher and sweep the floor. The challenge is making sure they dont inadvertently knock over a vase — or worse — while doing so.

Researchers at Alphabet Inc. unit Google, along with collaborators at Stanford University, the University of California at Berkeley, and OpenAI — an artificial intelligence developing company backed by Elon Musk — have some ideas about how to design robot minds that wont lead to undesirable outcomes for the person or persons they serve. They published a technical newspaper Tuesday outlining their thinking.

The motivation for the research is the immense popularity of artificial intelligence, software that can learn about the world and act within it. Todays AI systems let vehicles drive themselves, interpret speech spoken into phones, and devise trading strategies for the stock market. In the future, companies plan to use AI as personal assistants, first as software-based services like Apple Inc.s Siri and the Google Assistant, and later as smart robots that can take actions for themselves.

But before giving smart machines the ability to make decisions, people need to make sure the goals of the robots are aligned with those of their human owners.

While possible AI safety dangers have received a lot of public attention, most previous discussion has been very hypothetical and speculative, Google researcher Chris Olah wrote in a blog post accompanying the paper. We believe its essential to ground concerns in real machine learning the investigations and to start developing practical approaches for engineering AI systems that operate safely and reliably.

Enough Structure

The report describes some of their own problems robot designers may face in the future, and lists some techniques for building software that the smart machines cant subvert. The challenge is the open-ended nature of intelligence, and the puzzle is akin to one faced by regulators in other areas, like the financial system; how do you design regulations to let entities achieve their goals in a system you regulate, without being able to subvert your rules, or be unnecessarily constricted by them?

For example, if you have a clean robot( and OpenAI aims to build such a machine ), how do you make sure that your rewards dont give it positive incentives to cheat, the researchers wonder. Reward it for cleaning up a room and it might answer by sweeping dirt under the rug so its out of sight, or it might learn to turn off its cameras, preventing it from find any mess, and thereby giving it a reward. Counter these tactics by giving it an additional reward for using cleaning products and it might evolve into a system that uses bleach far too liberally because its rewarded for doing so. Correct that by making its reward for using cleaning products tied to the apparent cleanliness of its environment and the robot may eventually subverts that as well, hacking its own system to make itself think it deserves a reward regardless.

While cheating with housecleaning may not seem to be a critical problem, the researchers are extrapolating to potential future utilizes where stakes are higher. With this paper, Google and its collaborators are trying to solve problems they can only vaguely understand before they manifest in real-world systems. The mindset is roughly: Better to be somewhat prepared than not prepared at all.

With the realistic possibility of machine learning-based systems controlling industrial process, health-related systems, and other mission-critical technology, small-scale collisions seem like a very concrete threat, and are critical to prevent both intrinsically and because such accidents could cause a justifiable loss of trust in automated systems, the researchers write in the paper.

Potential Solutions

Some answers the researches propose include restriction how much control the AI system has over the human environment, so as to contain the damage, and pairing a robot with human buddy. Other ideas include programming trip wires into the AI machine to give humans a warn if it abruptly steps out of its intended routine.

The idea of smart machines running haywire is scarcely new: Goethe wrote a poem in the late 18 th century where a sorcerers apprentice makes a living broom to fetch water from a river for a basin in his home. The broom is so good at its chore that it almost deluges the house and so the sorcerer chops it up with an ax. New brooms emerge out of the fragments and continue with their tasks. Designing machines that avoid this kind of unintentionally harmful outcome is the core notion behind Googles research.

The research is part of an ongoing line of investigation that goes back more than 50 years, told Stuart Russell, a prof of computer science at the University of California at Berkeley and an writer, with Googles Peter Norvig, of the definitive volume on artificial intelligence. The fact that Google and other companies are getting involved in AI safety research is a further demo of the varied applications AI is seeing in industry, he told. And the problems theyre trying to deal with are not hypothetical: Russell had a human cleaner in Paris who conceal rubbish away in the apartment, which was only discovered by the landlord where reference is moved out, so a robot might do the same.

Anyone that believes for five seconds about whether its a good idea to build something thats more intelligent than you, theyll is understood that yes of course its a number of problems, he said.

Read more: www.bloomberg.com

Game of Thrones premiere: forget Jon Snow- we need to talk about Melisandre

Weve been bothering with life and death questions about Jon Snow for a year but the biggest revelation of the presents return belonged to the Red Woman

The season six premiere of Game of Thrones started the same way the fifth season ended, with Jon Snow lying in the snow in a pond of his own blood after being betrayed by the men of the Nights Watch. The big question in the year between that finale and the premiere was whether Jon could really be dead, whether there was some style to bring him back to life.

By the end of the episode , no one cared whether Jon Snow was dead or not. Instead, we all wondered how the hell Melisandre, the Lord of Lights most devoted maid, was still alive. Shes always been a gorgeous man-eater who dedicated birth to the shadow that killed Renly Baratheon and forced two brothers Stannis to sacrifice his own daughter to the one true deity. We knew something crazy was going on with her but who imagined that she was possibly centuries old, a woman who ran from a pin-up to something completely different by removing a necklace?.

It turns out that the red stone she wears around her neck is enchanted and either transforms her into a comely younger woman or at least makes people assure her as one. Is it a gift from the Lord of Light? Is it powered by Kings blood? Is that why she requires so much of it? Does she take it off every night, or was she making an exception now that Stannis is gone? And how does one get their hands on such a necklace?

We know quite a bit about Melisandre and her motives, but this highlights how little we really know about her or how her magical work. Considering shes been concealing this huge secret for so long, what else dont we know? Apparently, fans of the books will know that it has been hinted at and Carice Van Houton, who plays the Red Woman, said in an interview in 2012 that her character was well over 100 years old.

This is one of those revelations that is not only a shock, but leads us to more topics. Ironically, the majority of members of those questions are about Jon Snows death. The biggest theory was that Melisandre, who took a scandalous interest in Jon Snow when she first arrived at Castle Black, would raise him from the dead. However, the only hour weve assured one of the Lord of Lights priests resurrect someone( back in season three) it was a fresh corpse. Now that Jon Snow has been lying around for a day and a night, is he still eligible for a second life?

Davos Seaworth, who discovered and is protecting Jons body, certainly thinks so: he is going to turn to Melisandre for help. Will we get the answers to all our Jon Snow topics next week? Will Melisandre have to show the world what she actually looks like in order to give him a second chance? Or is he truly, really, actually dead? Were going to have to wait until Melisandre gets up from her nap to find out. It might take a long time. After all, she is very, very old.

Read more: www.theguardian.com

Machine Learning – Feature Extraction

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St. Pete author giving away hundreds of volumes to empower youth

Ricky Roberts III says his life was spiraling out of control and it lead him to author five volumes, all to empower young people.

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How to run faster, smarter AI apps on smartphones

(credit: iStock)

When you use smartphone AI apps like Siri, you’re dependent on the cloud for a lot of the processing — limited by your connection speed. But what if your smartphone could do more of the processing directly on your device — allowing for smarter, faster apps?

MIT scientists have taken a step in that direction with a new way to enable artificial-intelligence systems called convolutional neural networks (CNNs) to run locally on mobile devices. (CNN’s are used in areas such as autonomous driving, speech recognition, computer vision, and automatic translation.) Neural networks take up a lot of memory and consume a lot of power, so they usually run on servers in the cloud, which receive data from desktop or mobile devices and then send back their analyses.

The new MIT analytic method can determine how much power a neural network will actually consume when run on a particular type of hardware. The researchers used the method to evaluate new techniques for paring down neural networks so that they’ll run more efficiently on handheld devices.

The new CNN designs are also optimized to run on an energy-efficient computer chip optimized for neural networks that the researchers developed in 2016.

Reducing energy consumption

The new MIT software method uses “energy-aware pruning” — meaning they reduce a neural networks’ power consumption by cutting out the layers of the network that contribute very little to a neural network’s final output and consume the most energy.

Associate professor of electrical engineering and computer science Vivienne Sze and colleagues describe the work in an open-access paper they’re presenting this week (of July 24, 2017) at the Computer Vision and Pattern Recognition Conference. They report that the methods offered up to 73 percent reduction in power consumption over the standard implementation of neural networks — 43 percent better than the best previous method.

Meanwhile, another MIT group at the Computer Science and Artificial Intelligence Laboratory has designed a hardware approach to reduce energy consumption and increase computer-chip processing speed for specific apps, using “cache hierarchies.” (“Caches” are small, local memory banks that store data that’s frequently used by computer chips to cut down on time- and energy-consuming communication with off-chip memory.)**

The researchers tested their system on a simulation of a chip with 36 cores, or processing units. They found that compared to its best-performing predecessors, the system increased processing speed by 20 to 30 percent while reducing energy consumption by 30 to 85 percent. They presented the new system, dubbed Jenga, in an open-access paper at the International Symposium on Computer Architecture earlier in July 2017.

Better batteries — or maybe, no battery?

Another solution to better mobile AI is improving rechargeable batteries in cell phones (and other mobile devices), which have limited charge capacity and short lifecycles, and perform poorly in cold weather.

Recently, DARPA-funded researchers from the University of Houston (and at the University of California-San Diego and Northwestern University) have discovered that quinones — an inexpensive, earth-abundant and easily recyclable material that is low-cost and nonflammable — can address current battery limitations.

“One of these batteries, as a car battery, could last 10 years,” said Yan Yao, associate professor of electrical and computer engineering. In addition to slowing the deterioration of batteries for vehicles and stationary electricity storage batteries, it also would make battery disposal easier because the material does not contain heavy metals. The research is described in Nature Materials.

The first battery-free cellphone that can send and receive calls using only a few microwatts of power. (credit: Mark Stone/University of Washington)

But what if we eliminated batteries altogether? University of Washington researchers have invented a cellphone that requires no batteries. Instead, it harvests 3.5 microwatts of power from ambient radio signals, light, or even the vibrations of a speaker.

The new technology is detailed in a paper published July 1, 2017 in the Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies.

The UW researchers demonstrated how to harvest this energy from ambient radio signals transmitted by a WiFi base station up to 31 feet away. “You could imagine in the future that all cell towers or Wi-Fi routers could come with our base station technology embedded in it,” said co-author Vamsi Talla, a former UW electrical engineering doctoral student and Allen School research associate. “And if every house has a Wi-Fi router in it, you could get battery-free cellphone coverage everywhere.”

A cellphone CPU (computer processing unit) typically requires several watts or more (depending on the app), so we’re not quite there yet. But that power requirement could one day be sufficiently reduced by future special-purpose chips and MIT’s optimized algorithms.

* Loosely based on the anatomy of the brain, neural networks consist of thousands or even millions of simple but densely interconnected information-processing nodes, usually organized into layers. The connections between nodes have “weights” associated with them, which determine how much a given node’s output will contribute to the next node’s computation. During training, in which the network is presented with examples of the computation it’s learning to perform, those weights are continually readjusted, until the output of the network’s last layer consistently corresponds with the result of the computation. With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet are reduced by 3.7x and 1.6x, respectively, with less than 1% top-5 accuracy loss.

** The software reallocates cache access on the fly to reduce latency (delay), based on the physical locations of the separate memory banks that make up the shared memory cache. If multiple cores are retrieving data from the same DRAM [memory] cache, this can cause bottlenecks that introduce new latencies. So after Jenga has come up with a set of cache assignments, cores don’t simply dump all their data into the nearest available memory bank; instead, Jenga parcels out the data a little at a time, then estimates the effect on bandwidth consumption and latency. 

*** The stumbling block, Yao said, has been the anode, the portion of the battery through which energy flows. Existing anode materials are intrinsically structurally and chemically unstable, meaning the battery is only efficient for a relatively short time. The differing formulations offer evidence that the material is an effective anode for both acid batteries and alkaline batteries, such as those used in a car, as well as emerging aqueous metal-ion batteries.

Read more: www.kurzweilai.net

Science Can Restore America’s Faith in Democracy

In the aftermath of a contentious presidential campaign, there are signs that many Americans have lost faith in republic, with allegations of election fraud, suggestions of Russian involvement, and complaints about the electoral college. But the problem runnings still deeper: Like most other countries, individual states in the US employ the antiquated plurality voting system, in which each voter casts a vote for a single nominee, and the person who amass the largest number of votes is declared the win. If there is one thing that voting experts unanimously agree on, it is that plurality voting is a bad notion, or at least a poorly outdated one.

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About

Ariel Procaccia is deputy prof of computer science at Carnegie Mellon University. He is co-founder of the not-for-profit websites RoboVote.org and Spliddit.org, and co-editor of the Handbook of Computational Social Choice .


In fact, for centuries economists, mathematicians, political scientists, and more recently computer scientists have designed and examined better approaches to voting. The first step is to allow voters to express richer preferences, typically by asking them to rank the alternatives. One well-known method for aggregating these ranked votes into a collective option is called instant-runoff voting( also called ranked-choice voting ), whereby the candidate who is favored by the fewest voters is eradicated, and the choices of voters who favored that candidate are transferred to their next selection; the process is repeated until a single nominee wins a majority. Maine lately became the first US state to adopt instant-runoff voting; the approach will be used for choosing the governor and members of Congress and the state legislature.

Instant-runoff voting may seem sophisticated compared to plurality voting, but, in my view, other voting systems are becoming more intriguing. An especially elegant approach dates back to the work of the marquis de Condorcet, a French nobleman and mathematician, in the 18 th century. He suggested that some candidates are objectively better than others, but voters don’t always get the order right. Furthermore, Condorcet argued, the style voters misjudge candidates can be modeled use statistical tools, and the goal of a voting system is to kind through voters’ mistakes and choose the candidate that is most likely to be the best.

The math behind Condorcet’s notions was a mystery for centuries–prompting the noted mathematician Isaac Todhunter to write that” the obscurity and self-contradiction are without any parallel, so far as our experience of mathematical works extends “– until it was elucidated by Peyton Young in 1988. Strikingly, today there is a large body of work in artificial intelligence that applies modern machine learning tools to design voting systems that construct Condorcet’s idea a reality.

So why aren’t we already using cutting-edge voting systems in national elections? Perhaps because changing election systems usually itself requires an election, where short-term political considerations may trump long-term, scientifically grounded reasoning.

For example, in a 2011 referendum, British voters repudiated a proposal to change the voting system from plurality to instant runoff–a change that was heavily supported by academics–in part because it was seen as advantageous to the unpopular leader of the Liberal Democrats, Nick Clegg. Tellingly, except for a few neighborhoods in London, the only districts in England where there was a majority in favor of the switch were Cambridge and Oxford, homes of two venerated universities. And in the US, California Governor Jerry Brown lately vetoed a bill that would have expanded instant-runoff voting across the nation( several cities in the San Francisco Bay Area already use the system for municipal elections ).

Despite these difficulties, in the last few years state-of-the-art voting systems have built the transition from hypothesi to practise, through not-for-profit online platforms that focus on facilitating elections in cities and organizations, or even merely on helping a group of friends choose where to go to dinner. For example, the Stanford Crowdsourced Democracy Team has created an online tool whereby residents of a city can vote on how to apportion the city’s budget for public projects such as parks and roads. This tool has been used by New York City, Boston, Chicago, and Seattle to apportion millions of dollars. Building on this success, the Stanford team is experimenting with groundbreaking techniques, inspired by computational thinking, to elicit and aggregate the preferences of residents.

The Princeton-based project All Our Ideas asks voters to compare pairs of notions, and then aggregates these comparisons via statistical methods, ultimately a ranking of all the ideas. To date, approximately 14 million elections have been cast utilizing this system, and it has been employed by major cities and organizations. Among its more whimsical use lawsuits is the Washington Post’s 2010 holiday gift guide, where the question was ” what gift would you like to receive this vacation season “; the disappointingly uncreative top notion, based on tens of thousands of votes, was ” fund “.

Finally, the recently launched website RoboVote( which I created with collaborators at Carnegie Mellon and Harvard) offers AI-driven voting methods to help groups of people attain smart collective decisions. Applications range from selecting a place for a family vacation or a class president, to potentially high-stakes choices such as which product prototype to develop or which movie script to produce.

These instances show that centuries of research on voting can, at long last, make a societal impact in the internet age. They demonstrate what science can do for republic, albeit on a relatively small scale, for now. As more and more people discover the benefits of advanced voting systems, we will see more religion in the power of democratic decision-making in the short term, and perhaps, in the long term, a rethinking of the way political elections are conducted in this country and around the world.

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Britney Spears’ 11 Most Iconic Outfits Of All Time

If you were alive in the late nineties and early aughts, you’re more than familiar with Britney Spears. You probably know all the lyrics( and dance moves) to “Baby One More Hour, ” you most likely still believe that Justin and Britney were the best couple ever, and of course, you’ve memorized all the singer’s most iconic seems.

Today, in celebration Spears’ 34 th birthday( Dec. 2 ), we’re taking a look at her most memorable outfits to date. Between her on-stage dress and her red carpet moments, this dame has had some looks for the books throughout her 16 -year career in the limelight.

1999

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In concert.

1999

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Performing on the Tonight Show with Jay Leno.

2000

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At the American Music Award.

2000

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At the MTV Video Music Awards.

2000

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At the Billboard Music Awards.

2000

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At the Grammy Awards.

2001

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With Justin Timberlake at the American Music Awarding.

2001

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At MTV Video Music Awards.

2002

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At the MTV Video Music Awards.

2003

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At the MTV Video Music Awards.

2003

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At MTV Bash.

TROLLS Monster Truck Paint Machine Color Changer Best Learning Colours For Kids

Learning Educational videos for toddlers, preschoolers, newborns, or anyone needing a little extra help with learn colours 🙂 BEST Learning Colors for Children, …

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