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Why artificial intelligence is shaping our world

By TheWAY - 11월 23, 2018

Why artificial intelligence is shaping our world

Artificial intelligence is already impacting our everyday lives (Credit: Getty Images)

We stand on the edge of a crucial moment in the history of our species – a time when a creation of our own inventiveness has the potential to change everything. For some, it will be humanity’s salvation, while for others, it could be our downfall. We are entering the age of artificial intelligence (AI).

While AI is still some way from the sentient machines portrayed in science fiction, the creation of algorithms that can learn, understand language and mimic some aspects of the human mind have led to huge advances. Today AI is being used in hundreds of different industries.

The machines we use on a daily basis are getting smarter, meaning that AI is no longer a futuristic technology but is increasingly integrated into every realm of our lives. From suggesting what books we might like to buy online to powering the virtual assistants that inhabit our phones and smart speakers, some of the applications are more visible than others. In truth, AI is touching our lives far more than many of us realise.

Banks are using it to detect fraud and predict changes in the stock markets
Insurance companies are employing AI to help them produce policy quotes and assess claims
It’s helping police forces to identify suspects from grainy CCTV images
In courtrooms it’s offering advice to judges about whether to grant bail conditions to criminal suspects
Machines with the capability to identify images are helping doctors spot disease.
Algorithms that use machine learning – one of the leading branches of AI – are helping self-driving cars to navigate our complex roads
They are helping linguists to decifer lost languages
And it is helping firms make decisions about who to hire and fire
Even on flights, AI is being used by air traffic controllers to help to keep us safe both in the air and on the ground.

Yet there are also deep ethical questions about how and when AI should be used. And whether the hype surrounding this technology outstrips what is really achievable.

Over the next four months, BBC Future is going to explore the technologies and trends at the cutting-edge of AI, sorting the hype from where it has real-world promise. We will probe the profound implications of letting these silicon brains into our lives and how it will shape our relationships, our work and our societies.

One of the early pioneers of computer science Alan Perlis once quipped that “a year spent in artificial intelligence is enough to make one believe in God,” out of frustration at the problems of producing computer networks that could mimic the human brain. Is the prospect of creating machines that could possibly hope to compete with our brains still out of reach? Or are they now able to do the things humans can only dream of?

What is AI really capable of? How can it help us solve our problems? And how should we feel about this future?

Join us on our journey through the research, applications and future of AI.

source: http://www.bbc.com/future/story/20181116-why-artificial-intelligence-is-shaping-our-world

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3 mobile telecommunication companies are on stiff competition in the run-up to 5G transmission

By TheWAY - 11월 22, 2018

3 mobile telecommunication companies are on stiff competition in the run-up to 5G transmission

As the world's first 5G transmission is set to take place, three mobile telecommunication companies are on stiff competition.

SK Telecom, KT, and LG Uplus started to compete in earnest after disclosing their technologies and situations for constructing commercial network to optimize 5G coverage and quality.

It is heard that they are planning to introduce their 5G strategies and major services in line with their first 5G transmission on December 1.

Because 3.5GHz frequency band that is used by 5G is more straight than 4G LTE frequency, quality of network is different depending on technology as it is affected by many obstacles.

For this reason, 3 mobile telecommunication businesses are paying attention to whether 5G can be used as an essential infrastructure for 4th industrial revolution through network stability and security.

SK Telecom introduced 5G commercial network that was installed in Myeongdong, Seoul on November 14.

SK Telecom developed "5G T-EOS", which is a 5G 3D design solution that reflects characteristics of frequency band of 3.5GHz/8GHz.

Based on 3D map, it analyzes how radio waves can reach customers and produce optimal quality depending on installation height and angle of 5G commercial network.

KT also developed 5G core equipment that applies "Control & User Plane Segmentation" (CUPS) technology along with Samsung Electronics and Cisco and established commercial network.

CUPS is an advanced standard technology that allows separate processing of signals and processing of user traffic to be implemented and expanded independently of each device.

LG Uplus has secured stable quality of 5G network by working with French Forsk, which is called as one of the world's top telecommunication design program companies. Global telecommunication businesses such as Verizon, Vodafone, and other equipment manufacturers such as Samsung, Nokia and others are using Forsk's cell design program "Aetol".

If 5G is commercialized, HD movies can be downloaded within a second at 20Gbps speed that is 20 times faster than LTE.

Industries predict that 5G can become more active in self-driving cars, artificial intelligence, Internet of Things, VR, and AR.

5G also enables remote medical care in the medical field in addition to information technology communication services and platforms, enabling medical services that go beyond the boundaries of time and space.

Currently, mobile network operators are establishing broadcasting networks sequentially in Seoul and other metropolitan areas.

It is expected that consumers will feel even more changes after 5G devices are released.

출처 : Korea IT Times(http://www.koreaittimes.com)

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The winners and losers of the 4th industrial revolution

By TheWAY - 11월 21, 2018

The winners and losers of the 4th industrial revolution

We are entering an age of digitization, automation, and AI

The fourth industrial revolution (4IR) is widely expected to completely disrupt the socio-economic status quo, promising “a better, cleaner, and safer world.” Unfortunately, the promises are not without negatives -- they are bundled with perils, which might exacerbate unemployment and income equality.

The labour-intensive industries, in particular, are thought to be particularly susceptive to being catastrophically hit, and it is going to be caused by digitization, automation, and artificial intelligence. It is pertinent to delineate costs and benefits, and devise a multi-stakeholder strategy to safeguard the vulnerable groups.

Considered as the “next economic growth engine,” 4IR is expected to generate approximately $3.7 trillion worth of economic benefits by 2025 -- all through enhancing productivity and efficiency.

WEF (World Economic Forum) estimates that manufacturing, logistics, and retail industries can reduce roughly 35% of their costs, resulting in savings and additional revenue. End consumers are expected to accrue some benefits. For example, online shopping can save billions of pounds for British consumers. 4IR also promises to revolutionize society by ensuring better access to services such as education, health care, finance, security, and others.

Although claimed by WEF and Accenture that 60% of the value created by digitization will be enjoyed by society, the economic loss for the average worker is inexplicable, when some 800 million globally may lose their jobs by 2030. While McKinsey Global Institute estimates that almost 50% of the “work activities” can be automated, the labour-intensive industries like manufacturing will suffer the most.

For example, in 2013, the RMG sector employed around 4.4 million workers in Bangladesh, which dropped significantly to about 3.5 million in 2018. It is argued that one machine has the potential to make 10 workers redundant.

Since women constitute 80% of this sector, they will pay the highest price, leaving millions unemployed, and bringing economic dependence on men. With very few transferable skills, this will only exasperate the situation, where millions are already unemployed.

Rising unemployment and widening income inequality, brought about by technological disruptions, can create unprecedented social and political unrest, unless countries acknowledge their future in the 4IR era. Government, academia, private sector, trade union, and civil society organizations need to come together to create a comprehensive 4IR adoption strategy.

Five points are suggested here which might facilitate the process.

Firstly, a vulnerability study needs to be conducted to categorize vulnerable sectors and groups so that customized action plans can be devised.

Secondly, a rehabilitation plan is crucial to retrain people with new skills and create alternative employment.

Thirdly, a skilled based development scheme needs to be formulated to meet future demand and enhance employability.

Fourthly, sector-wise employment programs should be outlined to sustain and boost job creation.

Lastly, a public-private partnership-based approach should be fostered, so that industries can complement government’s efforts in not just maximizing revenue, but also maintaining a conducive socio-economic and political environment.

While 4IR might look futuristic, the potentials, as well as the risks, are real. Concerted efforts need to begin as soon as possible, so that risks can be mitigated, and potentials can be exploited for a better, cleaner and safer world.

Makshudul Alom Mokul Mondal is a graduate student at the Alliance Manchester Business School, University of Manchester. He is also a Global Shaper at the World Economic Forum.

source: https://www.dhakatribune.com/opinion/op-ed/2018/11/15/the-winners-and-losers-of-the-4th-industrial-revolution

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Astronomers Creep Up to the Edge of the Milky Way’s Black Hole

By TheWAY - 11월 20, 2018

Astronomers Creep Up to the Edge of the Milky Way’s Black Hole
Hot spots have been discovered orbiting just outside the supermassive black hole at the galaxy’s center. Their motions have given us the closest look at that violent environment.

Astronomers have found something orbiting just outside the innermost possible orbit of the supermassive black hole at the Milky Way’s center.

or the first time, scientists have spotted something wobbling around the black hole at the core of our galaxy. Their measurements suggest that this stuff — perhaps made of blobs of plasma — is spinning not far from the innermost orbit allowed by the laws of physics. If so, this affords astronomers their closest look yet at the funhouse-mirrored space-time that surrounds a black hole. And in time, additional observations will indicate whether those known laws of physics truly describe what’s going on at the edge of where space-time breaks down.

Astronomers already knew that the Milky Way hosts a central black hole, weighing some four million suns. From Earth, this black hole is a dense, tiny thing in the constellation Sagittarius, only as big on the sky as a strawberry seed in Los Angeles when viewed from New York. But interstellar gas glows as it swirls into the black hole, marking the dark heart of the galaxy with a single, faint point of infrared light in astronomical images. Astronomers call it Sagittarius A* (pronounced “A-star”).

For 15 years researchers have watched that point flicker — and wondered why. Occasionally, it flares up 30 times brighter in infrared light and then subsides, all within just a few minutes. Now, though, a team based at the Max Planck Institute for Extraterrestrial Physics in Garching, Germany, has measured not just this speck’s brightness but its position with staggering precision. When it flares, it also moves clockwise on the sky, tracing out a tiny circle, they find.

“They have clearly seen something moving,” said Shep Doeleman, an astronomer at the Harvard-Smithsonian Center for Astrophysics who did not participate in what he calls the team’s “extraordinary” measurements, which will be published tomorrow in Astronomy & Astrophysics. “What it is, is not exactly clear.”

But one particular interpretation stands out, the team argues. This wobbling likely comes from “hot spots,” glowing blobs of magnetically heated plasma orbiting right above the black hole’s gaping maw at almost one-third the speed of light. As these hot spots circle, the black hole’s immense gravitational forces twist space-time itself into something like a lens, one that flashes beacons of light across the cosmos like a galactic searchlight beam. The idea, first proposed in 2005 by Avery Broderick, now at the Perimeter Institute of Theoretical Physics and the University of Waterloo in Canada, and Avi Loeb of Harvard University, would explain why the black hole appears to flare.

“It seems like they’ve got something really exciting here,” added astronomer Andrea Ghez, a longtime competitor to the European team at the University of California, Los Angeles.

If these rotating flares are due to hot spots in the way that Broderick and Loeb imagined, additional flares will help reveal the black hole’s “spin,” a measure of its rotation. And it could also provide a new way to poke and prod Einstein’s theory of general relativity in the flexed space-time at the mouth of a black hole.

“To occasionally be right makes up for all the other times when I scratched my head at the blackboard,” said Broderick. “This is what makes being a scientist so much fun.”

Light from the four telescopes at the Very Large Telescope array in Cerro Paranal, Chile, can be combined to create, in effect, a single, enormous telescope.

Gravity’s Beam

Since the 1990s, Ghez’s group at UCLA and the European team, led by Reinhard Genzel of the Max Planck Institute for Extraterrestrial Physics in Garching, Germany, have used ever-sharper techniques to resolve the orbits of stars right around the galactic center. Earlier this summer, Genzel’s team published a measurement of how general relativity is affecting the light of a star now passing close to the black hole; a similar paper by Ghez’s team is now under review. “It’s a remarkable moment, in terms of these experiments’ ability to start probing how gravity works near a supermassive black hole,” Ghez said.

But since last year, the European team has had a unique tool—the power of four giant telescopes working together in a project called GRAVITY. On a typical night, the European Southern Observatory’s four 8-meter telescopes on Cerro Paranal, overlooking Chile’s Atacama desert, loll in different directions on the sky. GRAVITY pulls them together using a technique called interferometry that combines observations from multiple telescopes to produce artificial images that only a preposterously huge real telescope could make.

To do this in infrared wavelengths— close to what human eyes can perceive—requires blending light in real time to avoid losing crucial information. So on July 22, when Sagittarius A* flared, the light collected by each scope traveled through a Rube Goldberg–like setup of mirrors and fiber-optic cables that traced out a path with a total length that varies no more than 1/1,000th the width of a hair, said Frank Eisenhauer, a physicist at Max Planck in Garching and the leader of GRAVITY. Then, inside a 3-ton freezing toolbox of optical tech, these light waves mixed together, their peaks and troughs combining and canceling to produce position measurements with impossible crispness.

Video: Light from four telescopes navigates through a byzantine network of optical components inside the GRAVITY experiment. For the experiment to work, the total path lengths of each light beam has to differ by less than 1 micrometer — 1/1,000th the length of a millimeter.

Even after all that, GRAVITY still didn’t have high enough resolution to make movies of the three flares it saw — the one on July 22 and two others. But its measurements of the faint speck wiggling on the sky promises to narrow down the multiple options of what’s causing Sagittarius A* to flicker in the first place.

If you could see them up close, the flares might be lumps of hot plasma shot outward from the black hole, in jets of material focused and launched away by magnetic fields. Or they could be hot clumps out in the wide Frisbee of gas draining into the black hole, or other possible disk structures like spiral arms. In all these cases, the flaring and dimming of light would come from the material itself glowing hot, then cooling off.

Broderick and Loeb’s idea also involved plasma blobs zapped by heat. They would form close to the black hole, not unlike what happens in a solar flare. Above the surface of our sun, a briar patch of magnetic fields snag together, spurting out flares of heated plasma when the fields snap into new shapes. Something similar could happen in the gas right around a black hole, which also hosts strong, tangled magnetic fields.

In this case, though, the modulation in brightness would come not from the blob itself but from the blob’s orbit. As it whipped around in the thrall of a giant black hole, the warped space-time predicted by general relativity would focus the hot spot’s light into a beam. And as that beam swept across Earth, we would measure the black hole flickering. “The black hole is like this lighthouse lens that’s causing this thing to flash at us as it goes around,” Broderick says.

If jets caused the black hole’s flickering, that motion would be linear, as blobs traveled outward and cooled, Eisenhauer said. If clumps in the disk around the black hole were responsible, the motion wouldn’t go in any particular consistent direction. But the circular motion supports orbiting hot spots, the team argues.

“There’s one particular fact that makes me inclined to trust this result,” said astrophysicist Gunther Witzel of the Max Planck Institute for Radio Astronomy in Bonn, who has worked with the galactic center teams on both sides of the Atlantic. GRAVITY also found that the light emitted during a flare shifts in polarization, following the same rough timescale as the apparent orbital motion. That fits, too. The light emitted by a hot spot would be polarized. As the spot traveled through warped space-time, its polarization would twist throughout its orbit.

For astrophysicists, this glimpse at plasma under unique circumstances is interesting in and of itself. “We have a totally new environment, which is totally unknown,” said Nico Hamaus, a cosmologist at Ludwig Maximilian University in Munich, who also developed the early hot spot theory. “That’s why there were such vague ideas of what was going on.”

Now, though, theorists hope the hot spots may be able to shine a harsh interrogation-room lamp on Einstein’s theory of gravity itself.

Reading the Horizon

Consider a trip to a black hole. As you approach, popular accounts say, you have one last chance to turn back — the event horizon that marks the black hole’s edge. But perhaps a better place to rethink your approach would be earlier, at what astrophysicists call the innermost stable circular orbit (ISCO). The hot spots around the black hole at the center of the galaxy seem to orbit just a little outside this boundary.

That such an orbit exists is a key difference between Newton’s and Einstein’s theories of gravity. In Newtonian gravity, you can orbit an object as closely as you want, provided you keep increasing your speed. But in Einstein’s view, rotational energy summons more gravity. At some distance, going faster will only hasten your fall. “If the black hole is the drain where things disappear,” said Loeb at Harvard, “this innermost circular orbit is sort of the sink.”

To Loeb, a light source flying around this fateful rim is a gift from Mother Nature. A black hole’s mass and its rotation speed determine where the ISCO is, plus how long a hot spot will orbit at a given radius. Beyond mass and spin, general relativity holds that nothing else determines how an object orbits an astrophysical black hole. These two values should be the only distinguishing characteristics.

Ghez and Genzel have already established this particular black hole’s weight. And while they can’t yet calculate its spin, subsequent flares, especially brighter ones, should help nail it down.

A black hole’s spin drags the space around it, changing how long it takes nearby objects to orbit. As GRAVITY builds up a catalog of flares, probing how long they take to orbit at different radii around the black hole, they’ll be able to infer what the black hole’s spin is.

Of course, that’s assuming that general relativity is correct, and the orbits of objects around a black hole are determined solely by the black hole’s mass and spin. If it appears that something else is going on — that there exists some other factor affecting these orbits — it could hint that Einstein’s theory needs a tune-up.

Past that, “there’s an even more exciting opportunity on the horizon,” said Broderick. “If you’ll forgive the pun.”

The next horizon — quite literally — should come from the Event Horizon Telescope, or EHT, a separate effort now straining to resolve the space-time right around the Milky Way’s central black hole. The EHT team is currently crunching through their data, with hopes to publish at some point in 2019, they say.

EHT also hones its impossibly sharp vision through interferometry. But it operates in radio wavelengths, a thousand times longer than the infrared emission GRAVITY traces. And its component observatories span the entire world, not just a mountaintop in Chile. As Earth rotates, these observatories sweep across space, collecting even more information.

While GRAVITY measured the black hole’s position with staggering accuracy every 30 seconds during a flare, the EHT aims for something different: a long-exposure picture of radio waves warping inside the ISCO, right around the black hole’s edge.

But the hot-spotlike wobbles that GRAVITY found provide a new opportunity. “If these events happen quite often, and it looks like they do, that’s great news for everybody,” said Doeleman at Harvard, who directs the EHT.

Provided the wobbles also occur in radio wavelengths, the EHT could also track them as tiny shifts in position. And if they feel confident that something orbited the black hole during an EHT observation — say, after EHT and GRAVITY looked at the same flare on the same night — the EHT team could break their long exposure into sequential frames, using mathematical models to produce an actual movie of a circling hot spot.

“We could be testing the same thing, just in a very complementary way, with different instruments,” said Doeleman. “That’s really what science is all about.”

SOURCE: https://www.quantamagazine.org/astronomers-creep-up-to-the-edge-of-the-milky-ways-black-hole-20181030/

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Machine Learning Confronts the Elephant in the Room

By TheWAY - 11월 19, 2018

Machine Learning Confronts the Elephant in the Room

A visual prank exposes an Achilles’ heel of computer vision systems: Unlike humans, they can’t do a double take.

Art for "Machine Learning Confronts the Elephant in the Room"

Score one for the human brain. In a new study, computer scientists found that artificial intelligence systems fail a vision test a child could accomplish with ease.

“It’s a clever and important study that reminds us that ‘deep learning’ isn’t really that deep,” said Gary Marcus, a neuroscientist at New York University who was not affiliated with the work.

The result takes place in the field of computer vision, where artificial intelligence systems attempt to detect and categorize objects. They might try to find all the pedestrians in a street scene, or just distinguish a bird from a bicycle (which is a notoriously difficult task). The stakes are high: As computers take over critical tasks like automated surveillance and autonomous driving, we’ll want their visual processing to be at least as good as the human eyes they’re replacing.

It won’t be easy. The new work accentuates the sophistication of human vision — and the challenge of building systems that mimic it. In the study, the researchers presented a computer vision system with a living room scene. The system processed it well. It correctly identified a chair, a person, books on a shelf. Then the researchers introduced an anomalous object into the scene — an image of an elephant. The elephant’s mere presence caused the system to forget itself: Suddenly it started calling a chair a couch and the elephant a chair, while turning completely blind to other objects it had previously seen.

“There are all sorts of weird things happening that show how brittle current object detection systems are,” said Amir Rosenfeld, a researcher at York University in Toronto and co-author of the study along with his York colleague John Tsotsos and Richard Zemel of the University of Toronto.

Researchers are still trying to understand exactly why computer vision systems get tripped up so easily, but they have a good guess. It has to do with an ability humans have that AI lacks: the ability to understand when a scene is confusing and thus go back for a second glance.

The Elephant in the Room

Eyes wide open, we take in staggering amounts of visual information. The human brain processes it in stride. “We open our eyes and everything happens,” said Tsotsos.

Artificial intelligence, by contrast, creates visual impressions laboriously, as if it were reading a description in Braille. It runs its algorithmic fingertips over pixels, which it shapes into increasingly complex representations. The specific type of AI system that performs this process is called a neural network. It sends an image through a series of “layers.” At each layer, the details of the image — the colors and brightnesses of individual pixels — give way to increasingly abstracted descriptions of what the image portrays. At the end of the process, the neural network produces a best-guess prediction about what it’s looking at.

“It’s all moving from one layer to the next by taking the output of the previous layer, processing it and passing it along to the next layer, like a pipeline,” said Tsotsos.

Graphic: Deep neural networks learn by adjusting the strengths of their connections to better convey input signals through multiple layers to neurons associated with the right general concepts. When data is fed into a network, each artificial neuron that fires (labeled “1”) transmits signals to certain neurons in the next layer, which are likely to fire if multiple signals are received. The process filters out noise and retains only the most relevant features. — Lucy Reading-Ikkanda/Quanta Magazine

Neural networks are adept at specific visual chores. They can outperform humans in narrow tasks like sorting objects into best-fit categories — labeling dogs with their breed, for example. These successes have raised expectations that computer vision systems might soon be good enough to steer a car through crowded city streets.

They’ve also provoked researchers to probe their vulnerabilities. In recent years there have been a slew of attempts, known as “adversarial attacks,” in which researchers contrive scenes to make neural networks fail. In one experiment, computer scientists tricked a neural network into mistaking a turtle for a rifle. In another, researchers waylaid a neural network by placing an image of a psychedelically colored toaster alongside ordinary objects like a banana.

This new study has the same spirit. The three researchers fed a neural network a living room scene: A man seated on the edge of a shabby chair leans forward as he plays a video game. After chewing on this scene, a neural network correctly detected a number of objects with high confidence: a person, a couch, a television, a chair, some books.

Photo of AI identifying objects in an image.

In the unmodified image at left, the neural network correctly identifies many items in a cluttered living room scene with high probability. Add an elephant, as in the image at right, and problems arise. The chair in the lower-left corner becomes a couch, the nearby cup disappears, and the elephant gets misidentified as a chair.

Then the researchers introduced something incongruous into the scene: an image of an elephant in semiprofile. The neural network started getting its pixels crossed. In some trials, the elephant led the neural network to misidentify the chair as a couch. In others, the system overlooked objects, like a row of books, that it had correctly detected in earlier trials. These errors occurred even when the elephant was far from the mistaken objects.

Snafus like those extrapolate in unsettling ways to autonomous driving. A computer can’t drive a car if it might go blind to a pedestrian just because a second earlier it passed a turkey on the side of the road.

And as for the elephant itself, the neural network was all over the place: Sometimes the system identified it correctly, sometimes it called the elephant a sheep, and sometimes it overlooked the elephant completely.

“If there is actually an elephant in the room, you as a human would likely notice it,” said Rosenfeld. “The system didn’t even detect its presence.”

Everything Connected to Everything

When human beings see something unexpected, we do a double take. It’s a common phrase with real cognitive implications — and it explains why neural networks fail when scenes get weird.

Today’s best neural networks for object detection work in a “feed forward” manner. This means that information flows through them in only one direction. They start with an input of fine-grained pixels, then move to curves, shapes, and scenes, with the network making its best guess about what it’s seeing at each step along the way. As a consequence, errant observations early in the process end up contaminating the end of the process, when the neural network pools together everything it thinks it knows in order to make a guess about what it’s looking at.

“By the top of the neural network you have everything connected to everything, so you have the potential to have every feature in every location interfering with every possible output,” said Tsotsos.

The human way is better. Imagine you’re given a very brief glimpse of an image containing a circle and a square, with one of them colored blue and the other red. Afterward you’re asked to name the color of the square. With only a single glance to go on, you’re likely to confuse the colors of the two shapes. But you’re also likely to recognize that you’re confused and to ask for another look. And, critically, when you take that second look, you know to focus your attention on just the color of the square.

“The human visual system says, ‘I don’t have right answer yet, so I have to go backwards to see where I might have made an error,’” explained Tsotsos, who has been developing a theory called selective tuning that explains this feature of visual cognition.

Most neural networks lack this ability to go backward. It’s a hard trait to engineer. One advantage of feed-forward networks is that they’re relatively straightforward to train — process an image through these six layers and get an answer. But if neural networks are to have license to do a double take, they’ll need a sophisticated understanding of when to draw on this new capacity (when to look twice) and when to plow ahead in a feed-forward way. Human brains switch between these different processes seamlessly; neural networks will need a new theoretical framework before they can do the same.

Leading researchers in the world are working on it, though, and they’re calling for backup. Earlier this month, Google AI announced a contest to crowdsource image classifiers that can see their way through adversarial attacks. The winning entry will need to unambiguously distinguish between an image of a bird and an image of a bicycle. It would be a modest first step — but also a necessary one.

SOURCE: https://www.quantamagazine.org/machine-learning-confronts-the-elephant-in-the-room-20180920/

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Using Big Data to Give Patients Control of Their Own Health

By TheWAY - 11월 18, 2018

Using Big Data to Give Patients Control of Their Own Health

Big data, personalized medicine, artificial intelligence. String these three buzzphrases together, and what do you have?

A system that may revolutionize the future of healthcare, by bringing sophisticated health data directly to patients for them to ponder, digest, and act upon—and potentially stop diseases in their tracks.

At Singularity University’s Exponential Medicine conference in San Diego this week, Dr. Ran Balicer, director of the Clalit Research Institute in Israel, painted a futuristic picture of how big data can merge with personalized healthcare into an app-based system in which the patient is in control.

Exponential Medicine Summit 2018 Ran Balicer — Dr. Ran Balicer at Exponential Medicine

Picture this: instead of going to a physician with your ailments, your doctor calls you with some bad news: “Within six hours, you’re going to have a heart attack. So why don’t you come into the clinic and we can fix that.” Crisis averted.

Following the treatment, you’re at home monitoring your biomarkers, lab test results, and other health information through an app with a clean, beautiful user interface. Within the app, you can observe how various health-influencing life habits—smoking, drinking, insufficient sleep—influence your chance of future cardiovascular disease risks by toggling their levels up or down.

There’s more: you can also set a health goal within the app—for example, stop smoking—which automatically informs your physician. The app will then suggest pharmaceuticals to help you ditch the nicotine and automatically sends the prescription to your local drug store. You’ll also immediately find a list of nearby support groups that can help you reach your health goal.

With this hefty dose of AI, you’re in charge of your health—in fact, probably more so than under current healthcare systems.

Sound fantastical? In fact, this type of preemptive care is already being provided in some countries, including Israel, at a massive scale, said Balicer. By mining datasets with deep learning and other powerful AI tools, we can predict the future—and put it into the hands of patients.

The Israeli Advantage

In order to apply big data approaches to medicine, you first need a giant database.

Israel is ahead of the game in this regard. With decades of electronic health records aggregated within a central warehouse, Israel offers a wealth of health-related data on the scale of millions of people and billions of data points. The data is incredibly multiplex, covering lab tests, drugs, hospital admissions, medical procedures, and more.

One of Balicer’s early successes was an algorithm that predicts diabetes, which allowed the team to notify physicians to target their care. Clalit has also been busy digging into data that predicts winter pneumonia, osteoporosis, and a long list of other preventable diseases.

So far, Balicer’s predictive health system has only been tested on a pilot group of patients, but he is expecting to roll out the platform to all patients in the database in the next few months.

Truly Personalized Medicine

To Balicer, whatever a machine can do better, it should be welcomed to do. AI diagnosticians have already enjoyed plenty of successes—but their collaboration remains mostly with physicians, at a point in time when the patient is already ill.

A particularly powerful use of AI in medicine is to bring insights and trends directly to the patient, such that they can take control over their own health and medical care.

For example, take the problem of tailored drug dosing. Current drug doses are based on average results conducted during clinical trials—the dosing is not tailored for any specific patient’s genetic and health makeup. But what if a doctor had already seen millions of other patients similar to your case, and could generate dosing recommendations more relevant to you based on that particular group of patients?

Such personalized recommendations are beyond the ability of any single human doctor. But with the help of AI, which can quickly process massive datasets to find similarities, doctors may soon be able to prescribe individually-tailored medications.

Tailored treatment doesn’t stop there. Another issue with pharmaceuticals and treatment regimes is that they often come with side effects: potentially health-threatening reactions that may, or may not, happen to you based on your biometrics.

Back in 2017, the New England Journal of Medicine launched the SPRINT Data Analysis Challenge, which urged physicians and data analysts to identify novel clinical findings using shared clinical trial data.

Working with Dr. Noa Dagan at the Clalit Research Institute, Balicer and team developed an algorithm that recommends whether or not a patient receives a particularly intensive treatment regime for hypertension.

Rather than simply looking at one outcome—normalized blood pressure—the algorithm takes into account an individual’s specific characteristics, laying out the treatment’s predicted benefits and harms for a particular patient.

“We built thousands of models for each patient to comprehensively understand the impact of the treatment for the individual; for example, a reduced risk for stroke and cardiovascular-related deaths could be accompanied by an increase in serious renal failure,” said Balicer. “This approach allows a truly personalized balance—allowing patients and their physicians to ultimately decide if the risks of the treatment are worth the benefits.”

This is already personalized medicine at its finest. But Balicer didn’t stop there.

We are not the sum of our biologics and medical stats, he said. A truly personalized approach needs to take a patient’s needs and goals and the sacrifices and tradeoffs they’re willing to make into account, rather than having the physician make decisions for them.

Balicer’s preventative system adds this layer of complexity by giving weights to different outcomes based on patients’ input of their own health goals. Rather than blindly following big data, the system holistically integrates the patient’s opinion to make recommendations.

Balicer’s system is just one example of how AI can truly transform personalized health care. The next big challenge is to work with physicians to further optimize these systems, in a way that doctors can easily integrate them into their workflow and embrace the technology.

“Health systems will not be replaced by algorithms, rest assured,” concluded Balicer, “but health systems that don’t use algorithms will be replaced by those that do.”

source: https://singularityhub.com/2018/11/07/using-big-data-to-give-patients-control-of-their-own-health/#sm.0000rgzqh1ncyembz741bazuafjwp

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The 3 Ways That Artificial Intelligence Will Change Content Marketing

By TheWAY - 11월 17, 2018

The 3 Ways That Artificial Intelligence Will Change Content Marketing

Artificial Intelligence - 3d art image. Technology concept. Abstract backgrounds.GETTY ROYALTY FREE

In many ways, artificial intelligence (AI) is already influencing digital marketing in general, and content marketing in particular. But the truth is, there is so much more to come – so many more changes and improvements that AI will surely bring to content marketing.

In this blog post, I’m going to explore some of these changes in order to try to understand what the future holds – read on to discover the 3 ways that artificial intelligence will change content marketing.

What exactly is artificial intelligence?

Before I can discuss the effects of artificial intelligence – also known as AI, machine intelligence and in some cases, machine learning - on content marketing, it’s important to first understand what exactly artificial intelligence is.

So, what is AI, exactly?

Techopedia defines it as “an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:

Speech recognition
Learning
Planning
And problem solving”

For example, such a machine would be a self-driving car: a car that doesn’t need any humans to operate it in order to safely drive itself. Or, a computer that can play chess with you and take on-the-spot decisions as needed. Or, a simple every day example and something that many can relate to – the content that Netflix suggests you watch (all based on machine learning).

Build Better Content Marketing Strategies With An AI Marketing Assistant

Geometric facade of 51 Astor Place (the IBM Watson Building) at Astor Place in Manhattan, New York CityGETTY ROYALTY FREE

One of the ways that AI is already heavily impacting content marketing is with AI marketing assistants – like IBM Watson’s Lucy.

Lucy is an incredibly powerful tool that marketers can use for research, segmentation and planning - and it’s so powerful that it can do more in a minute than an entire team of marketers can achieve in months.

So, how exactly does an AI marketing assistant like Lucy work?

To start with, Lucy can absorb and analyse literally all of the data your company owns, or that has commissioned or licensed. What’s more, once it absorbs all of this data, you can ask it any question you might have, no matter how complex, and it will find the answer for you:

Which regions should I first target?
What mix of content should I create for my audience for maximum results?
What are my competitors up to?
What are the main personality traits of my audience?

These are questions that companies need to answer in order to put together a strategy that works. But finding these answers is not exactly easy when you don’t have a tool like Lucy on your side – gathering and interpreting these vast amounts of data would be a difficult, if not almost impossible task without help.

And the possibilities of marketing assistants like Lucy don’t end here:

You can create clear and complex segments of your target audience so that you can create highly personalized content
Plan your content marketing (and other marketing) strategies by seeing how different strategies would work and what results you can expect

Systems like Lucy will have a huge impact on content marketing as they become more affordable and more popular. They will help companies better understand their audience and their data in general and what’s more, they will help marketers put together more effective strategies as well as help them understand what types of outcomes they can expect.

Will AI Take Over Content Writing Jobs Completely?

Business man of freelancer working using laptop computer in home office, Communication technology and Business conceptGETTY ROYALTY FREE

Unsurprisingly, when it comes to technology this powerful, it’s easy to get a little scared: will it take my job from me? Will all content be written by machines in a few years?

If AI can analyse this much data and take human-like decisions based on this data – and even arguably better decisions, since a powerful machine can hold so much more knowledge than any human realistically can – can’t it also write the content, maybe just as well or even better than a human can?

In the world of journalism, it’s already happened – as far back as 2015. Back then, the Associated Press published a short financial news story: “Apple tops Street IQ forecasts”. The piece could very well have been written by a real human – but if you read until the end, you would see that it was actually “generated” by Automated Insights; in other words, it was written (or generated, if you will) by a so-called “robot journalist”.

Associated Press aren’t the only ones who experimented with robot journalism (or automated journalism) either; numerous top publications use it, such as Washington Post, who published as many as 850 articlesgenerated by their robot journalist over the course of one year.

That might sound gloomy to many, but it doesn’t necessarily mean that AI is taking over any and all content writing jobs. Rather, this type of AI technology can be used to free up your time and give you all the data you need so that you can focus on creating better content. It can be used to automate the little tasks – like Associated Press’s news story about Apple’s quarterly earnings – while humans can focus on writing the more complex and sensible content.

Conclusion: Will AI Take Over Content Marketing Jobs?

Clearly, AI will rapidly become an even bigger part of digital marketing. It already is taking over numerous aspects of our lives and even threatening some jobs, but how much will it really affect content marketing?

While no one can predict the future, the present does tell us that AI will have a real impact on content marketing – from creating incredibly detailed marketing strategies based on huge amounts of data to generating content automatically, marketers will start using AI more and more to help them every step of the way.

source: https://www.forbes.com/sites/lilachbullock/2018/11/12/the-3-ways-that-artificial-intelligence-will-change-content-marketing/#28a07851618f

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