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Posts Tagged ‘Scientific American’

Creativity and AI: The Next Step

Posted by hkarner - 4. Oktober 2019

Date: 03-10-2019
Source: Scientific American By Arthur I. Miller

Combining two types of machine intelligence could open new frontiers of art

In 1997 IBM’s Deep Blue famously defeated chess Grand Master Garry Kasparov after a titanic battle. It had actually lost to him the previous year, though he conceded that it seemed to possess “a weird kind of intelligence.” To play Kasparov, Deep Blue had been pre-programmed with intricate software, including an extensive playbook with moves for openings, middle game and endgame.

Twenty years later, in 2017, Google unleashed AlphaGo Zero which, unlike Deep Blue, was entirely self-taught. It was given only the basic rules of the far more difficult game of Gogo, without any sample games to study, and worked out all its strategies from scratch by playing millions of times against itself. This freed it to think in its own way. Den Rest des Beitrags lesen »

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Despite What You Might Think, Major Technological Changes Are Coming More Slowly Than They Once Did

Posted by hkarner - 15. August 2019

Date: 14-08-2019
Source: Scientific American By Wade Roush

Major technological shifts are fewer and farther between than they once were

On June 22, 1927, Charles Lindbergh flew into Dayton, Ohio, for dinner at Orville Wright’s house. It had been just a month since the young aviator’s first ever solo nonstop crossing of the Atlantic, and he felt he ought to pay his respects to the celebrated pioneer of flight.

Forty-two years later, on July 16, 1969, Apollo 11 astronaut Neil Armstrong was allowed to bring a personal guest to the Kennedy Space Center to witness the launch of NASA’s towering Saturn V rocket. Armstrong invited his hero, Charles Lindbergh.

That’s how fast technology advanced in the 20th century. One man, Lindbergh, could be the living link between the pilot of the first powered flight and the commander of the first mission to another world.

In our century, for better or worse, progress isn’t what it used to be. Northwestern University economist Robert Gordon argues that by 1970, all the key technologies of modern life were in place: sanitation, electricity, mechanized agriculture, highways, air travel, telecommunications, and the like. After that, innovation and economic growth simply couldn’t keep going at the breakneck pace set over the preceding 100 years—a period Gordon calls “the special century.” Den Rest des Beitrags lesen »

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Darwin’s Ideas on Evolution Drive a Radical New Approach to Cancer Drug Use

Posted by hkarner - 6. August 2019

Date: 06-08-2019
Source: Scientific American By James DeGregori, Robert Gatenby

Principles of evolution and natural selection drive a radical new approach to drugs and prevention strategies

IN BRIEF

  • Medical efforts to defeat cancer typically focus on malignant mutations within a cell and administer large doses of toxic drugs in an attempt to eradicate the disease.
  • A new concept emphasizes that cancer growth is stimulated by changes outside the cell, alterations in the surrounding tissue that accelerate the evolution of cancerous traits.
  • The evolutionary approach, tested in animals and humans with advanced prostate cancer, sharply limits the natural selection of cancer cells through a more judicious use of chemotherapy.

This year at least 31,000 men in the U.S. will be diagnosed with prostate cancer that has spread to other parts of their body, such as bones and lymph nodes. Most of them will be treated by highly skilled and experienced oncologists, who have access to 52 drugs approved to treat this condition. Yet eventually more than three quarters of these men will succumb to their illness. Den Rest des Beitrags lesen »

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How Wall Street Became a Cult of Risk

Posted by hkarner - 21. Juni 2019

Date: 20-06-2019
Source: Scientific American By Gillian Tett

What caused the global financial crisis? And how can the United States avoid a repeat? Those questions have sparked endless handwringing among economists, policymakers, financiers, and voters over the last decade. Little wonder: the crisis not only entailed the worst financial shock and recession in the United States since 1929; it also shook the country’s global reputation for financial competence.

Before the crisis, Wall Street seemed to epitomize the best of twenty-first-century finance. The United States had the most vibrant capital markets in the world. It was home to some of the most profitable banks; in 2006 and early 2007, Goldman Sachs’ return on equity topped an eye-popping 30 percent. American financiers were unleashing dazzling innovations that carried newfangled names such as “collateralized debt obligations,” or CDOs. The financiers insisted that these innovations could make finance not only more effective but safer, too. Indeed, Wall Street seemed so preeminent that in 2003, when I published a book about the Japanese banking crisis, Saving the Sun, I presumed that one of the ways to “fix” Japanese finance was to make it more American. Den Rest des Beitrags lesen »

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Tech Offers a Virtual Window into Future Climate Change Risk

Posted by hkarner - 25. April 2019

Date: 24-04-2019
Source: Scientific American

AI and supercomputing are rapidly shifting the way disaster planners, regulators and insurers gauge climate hazards

Accurately predicting the on-the-ground impacts of climate change remains one of the thorniest challenges facing scientists, regulators, planners and insurers.

But as climate disasters occur with alarming frequency, experts are relying more heavily on predictive technologies that leverage supercomputing and artificial intelligence to identify the where, how and why of climate impacts.

Known as “climate risk analytics,” the delivery of data-based predictive information about risks associated with wind, floods, fires, droughts and other climate disasters is rapidly proliferating, according to experts. Den Rest des Beitrags lesen »

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How Big Data Can Help Save the World

Posted by hkarner - 28. März 2019

Date: 27-03-2019
Source: Scientific American

Emerging analytic and computing tools are enabling much better use of huge data sets

Our ability to collect data far outpaces our ability to fully utilize it—yet those data may hold the key to solving some of the biggest global challenges facing us today.

Take, for instance, the frequent outbreaks of waterborne illnesses as a consequence of war or natural disasters. The most recent example can be found in Yemen, where roughly 10,000 new suspected cases of cholera are reported each week—and history is riddled with similar stories. What if we could better understand the environmental factors that contributed to the disease, predict which communities are at higher risk, and put in place protective measures to stem the spread?

Answers to these questions and others like them could potentially help us avert catastrophe.

We already collect data related to virtually everything, from birth and death rates to crop yields and traffic flows. IBM estimates that each day, 2.5 quintillion bytes of data are generated. To put that in perspective: that’s the equivalent of all the data in the Library of Congress being produced more than 166,000 times per 24-hour period. Yet we don’t really harness the power of all this information. It’s time that changed—and thanks to recent advances in data analytics and computational services, we finally have the tools to do it. Den Rest des Beitrags lesen »

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How Alexa Learns

Posted by hkarner - 8. März 2019

Date: 07-03-2019
Source: Scientific American By Ruhi Sarikaya

Researchers are finding new ways to help the voice service improve its performance

Over the past 10 years, commercial AI has enjoyed what we at Amazon call the flywheel effect: customer interactions with AI systems generate data; with more data, machine learning algorithms perform better, which leads to better customer experiences; better customer experiences drive more usage and engagement, which in turn generate more data.

Those data are used to train machine learning systems in three chief ways. The first is supervised learning, in which the training data are hand-labeled (with, say, words’ parts of speech or the names of objects in an image) and the system learns to apply labels to unlabeled data. A variation of this is weakly supervised learning, which uses easily acquired but imprecise labels to enable machine learning at scale. If a website visitor performs a search, for instance, the links she clicks indicate which search results should have been at the top of the list; that kind of implicit information can be used to automatically label data.

Training with entirely unlabeled data is called unsupervised learning. There, the most common approach is to cluster data together according to structural features; the clusters themselves define classification categories. Finally, semi-supervised learning leverages a small amount of labeled training data to extract information from much larger stores of unlabeled training data.

In recent AI research, supervised learning has predominated. But today, commercial AI systems generate far more customer interactions than we could begin to label by hand. The only way to continue the torrid rate of improvement that commercial AI has delivered so far is to reorient ourselves toward semi-supervised, weakly supervised and unsupervised learning. Our systems need to learn how to improve themselves.

The most common approach to semi-supervised learning is self-training, in which a machine learning system trained on a smattering of labeled data itself applies labels to a much larger set of unlabeled data. Because machine learning systems are statistical, their outputs have associated confidence scores. The outputs of the system are sorted according to confidence score, and those that fall within the right confidence window are used to train the system further. The system, in other words, is retrained on data that it has labeled itself.

Typically, self-training works best with high-confidence training examples. But in some contexts, Amazon researchers have found that lower-confidence examples offer greater performance improvements, as they’re more likely to capture nuances that the system hasn’t already learned.

Another way to leverage small amounts of labeled data is to lump it together with unlabeled data and apply some kind of unsupervised clustering algorithm to the result. For instance, sentences can be automatically embedded in a high-dimensional space, where they’re grouped together according to how frequently their component words co-occur with other words. Then, algorithms can generalize the labels of the labeled sentences to the unlabeled sentences in the same clusters, dramatically expanding the number of training examples available to a natural-language-understanding system.

Companies that depend on machine learning for real-time data classification have an additional semi-supervised–training option. That’s to use labeled data to train a powerful but impractically slow neural network, then use that network to label training data for a leaner, more efficient real-time network. Amazon researchers are using this approach across a range of business units.

Frequently, AI companies can also use customer feedback to automatically label data. For instance, the numerical (star) ratings associated with product reviews on Amazon.com could provide automatic data labels for a weakly supervised machine learning system that tries to infer customer sentiment from linguistic cues.

Customers of the Amazon Alexa voice service don’t typically rate Alexa’s responses to individual requests, but their interactions with Alexa do provide useful implicit signals. If Alexa’s initial response to a request is unsatisfying, the customer might cut the response off and rephrase the request. If the response to the rephrased request is allowed to play out, it’s a strong signal that the first request should have elicited the same response.

Alexa automatically analyzes a large number of such rephrased requests every month, learning how to rewrite the most common of them. That’s why, for instance, if you say to Alexa, “Play ‘Radioactive’ by Magic Dragons,” she’ll respond, “Playing ‘Radioactive’ by Imagine Dragons.”

Currently, Alexa’s rewrite procedures are general: anyone who requests music by Magic Dragons has the same likelihood of receiving music from Imagine Dragons instead. But the underlying technology could be adapted to provide customers with personalized query responses. It may be, for instance, that among the many, many customers requesting music by Imagine Dragons, there are a few who are really trying to find the Magic Dragons, the former Wednesday-night house band at the Spread Eagle pub in Ipswich, England.

Amazon researchers are exploring a host of other techniques for doing unsupervised learning, from monitoring the ordinary operating parameters of cloud servers in order to recognize anomalies; to using the Amazon.com product hierarchy to draw connections between customers’ product searches; to bootstrapping natural-language–understanding systems in new languages by automatically translating texts into a language with existing machine learning systems, using those systems to label the text, and automatically translating the labeled text back into the target language.

The promise of commercial AI is the promise of machine learning at scale. But that’s not just a matter of throwing more data at existing problems. More and more, it also means finding ingenious ways to use that data efficiently, without human involvement.

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AI’s Big Challenge

Posted by hkarner - 28. Februar 2019

Date: 27-02-2019
Source: Scientific American By Garrett Kenyon

To make it truly intelligent, researchers need to rethink the way they approach the technology

The recently signed executive order establishing the American AI Initiative correctly identifies artificial intelligence as central to American competitiveness and national defense. However, it is unclear if AI has accomplished anywhere near as much as many have claimed. Indeed, current technology exhibits no convincing demonstration of anything remotely approaching “intelligence.”

To maintain U.S. supremacy in AI, the best way forward is to adopt a strategy hewing more closely to the way humans learn, which will put us on the best path to the economic growth and widespread social benefits promised by full-fledged artificial intelligence.

Here’s the challenge with most deep learning neural networks, which reflect the prevailing approach to AI: calling them both deep and intelligent assumes they achieve ever more abstract and meaningful representations of the data at deeper and deeper levels of the network. It further assumes that at some point they transcend rote memorization to achieve actual cognition, or intelligence. But they do not. Den Rest des Beitrags lesen »

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Can We Avoid the Potential Dangers of AI, Robots and Big Tech Companies?

Posted by hkarner - 29. November 2018

Date: 29-11-2018
Source: Scientific American By Karl Frederick Rauscher

We can if we’re proactive enough about anticipating what could go wrong

If you plan to live another 10 years, you should expect to live in a world with machines doing things you don’t like doing today. Shooting for another 20? Even more will be done without your lifting the proverbial finger. It’s not only menial tasks such as cleaning, laundry and dishes. High-end services previously not accessible to you will now be in your economic grasp. Your personal robot will know you better than you know yourself. This almost unimaginable lifestyle could become routine for the masses, given the tangible achievements of artificial intelligence (AI) and robotics to date and the low-latency-coupled-with-high-bandwidth-connectivity that 5G is on track to provide.

Despite the excitement of the likely new reality, however, AI, robots and big companies are three things a lot of people are afraid of. While the last one has been around for a long time, the former are things we’ll have to learn to live with. The imminent rollout of 5G infrastructure could usher in a technology revolution perhaps greater than any that have preceded it. The new networks will be a thousand times faster than 4G, which means that an entire HD film, for example, could be downloaded in seconds. High-bandwidth uploads will also be possible, which will mean that what the robot sees—constituting a massive amount of data—can be sent to, and interact in real-time with, a brain in the cloud. Robots will also be able to communicate at high speeds with each other, and network delays will be so tiny that they’ll be comparable to the unnoticeable delays within our bodies between nerve cells and our brain. Major network operators are beginning 5G rollouts in select cities by end of 2018—just around the corner. Den Rest des Beitrags lesen »

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Angela Merkel’s New Momentum

Posted by hkarner - 23. November 2018

Date: 22-11-2018
Source: Scientific American by Javier Solana

Javier Solana was EU High Representative for Foreign and Security Policy, Secretary-General of NATO, and Foreign Minister of Spain. He is currently President of the ESADE Center for Global Economy and Geopolitics, Distinguished Fellow at the Brookings Institution, and a member of the World Economic Forum’s Global Agenda Council on Europe.

Since German Chancellor Angela Merkel announced that she will not seek another term and will step down as her party’s leader at the end of this year, political obituaries have been rolling in. But far from bowing out quietly, Merkel will use her remaining time in office to cement her legacy as a defender of the European project.

MADRID – Upon Albert Einstein’s death in 1955, the New York Times published a letter to the editor with a marvelous anecdote. Shortly after the atomic bombs had fallen on Hiroshima and Nagasaki, Einstein was asked, “Why is it that when the mind of man has stretched so far as to discover the structure of the atom we have been unable to devise the political means to keep the atom from destroying us?” His answer was timeless: “That is simple, my friend. It is because politics is more difficult than physics.” Den Rest des Beitrags lesen »

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