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Cyber Security Software Market Report 2020 (Based on 2020 COVID-19 Worldwide Spread) by Key Players, Types, Applications, Countries, Market Size,…

In the forecast period of 2020 to 2025, Cyber Security Software Market is projected to rise at a compound annual growth rate (CAGR) of XX million. The Cyber Security Software Market Research Report from ReportsnReports offers analysis and insights into the various factors that are expected to be prevalent during the forecasted period, as well as their impacts on the markets development.

For this study, the globalCyber Security Software Marketbased upon the components, usage, application, the main participant, and the region, has Prepared by ReportsnReports Industry Research Firm.

COVID-19 Impact Analysis:

Due to the effects of COVID-19, the implementation of Cyber Security Software Marketis expected to witness a rapid advance, thereby resulting in the fast growth of the Cyber Security Software Market. This is mainly due to the rapid adoption of the technology to map the spread of the disease and implement preventive measures. Hence, various government organizations are utilizing the Cyber Security Software Market technology for varied applications during the pandemic.

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Internet security or cyber security is a branch of computer security specifically related to internet. The Internet has given rise to new opportunities almost in every field such as business, sports, education or entertainment and many others. However, the internet has its own drawbacks like cyber crime, where the computer used for various types of thefts and crime. Various types of cyber crimes include hacking, software piracy, denial of service attack, and cyber terrorism. The purpose of cyber security is to establish rules and measures to use against cyber crimes over the internet.This report elaborates the market size, market characteristics, and market growth of the Cyber Security Software industry, and breaks down according to the type, application, and consumption area of Cyber Security Software. The report also conducted a PESTEL analysis of the industry to study the main influencing factors and entry barriers of the industry.

In Chapter 3.4 of the report, the impact of the COVID-19 outbreak on the industry was fully assessed. Fully risk assessment and industry recommendations were made for Cyber Security Software in a special period. This chapter also compares the markets of Pre COVID-19 and Post COVID-19.In addition, chapters 8-12 consider the impact of COVID-19 on the regional economy.

Key players in the global Cyber Security Software market covered in Chapter 13:HerjavecFireEye Inc.Symantec CorpCisco Systems Inc.Dell Root 9BIBMAVG TechnologiesPalo Alto NetworksFortinet Inc.CyberArk Software Ltd.Imperva Inc.ProofpointTrend Micro Inc.Check Point Software Technologies Ltd.

In Chapter 6, on the basis of types, the Cyber Security Software market from 2015 to 2025 is primarily split into:Network SecurityCloud SecurityWireless SecurityOthers

In Chapter 7, on the basis of applications, the Cyber Security Software market from 2015 to 2025 covers:AerospaceGovernmentFinancial ServicesTelecommunicationHealthcareOthers

Geographically, the detailed analysis of production, trade of the following countries is covered in Chapter 4.2, 5:United StatesEuropeChinaJapanIndia

Geographically, the detailed analysis of consumption, revenue, market share and growth rate of the following regions are covered in Chapter 8, 9, 10, 11, 12:North America (Covered in Chapter 8)United StatesCanadaMexicoEurope (Covered in Chapter 9)GermanyUKFranceItalySpainOthersAsia-Pacific (Covered in Chapter 10)ChinaJapanIndiaSouth KoreaSoutheast AsiaOthersMiddle East and Africa (Covered in Chapter 11)Saudi ArabiaUAESouth AfricaOthersSouth America (Covered in Chapter 12)BrazilOthers

Years considered for this report:Historical Years: 2015-2019Base Year: 2019Estimated Year: 2020Forecast Period: 2020-2025

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The Goal of Cyber Security Software Market Report is to provide a complete market evaluation which includes insightful observations, information, actual data, market data verified by the industry, and forecasts with a proper set of hypotheses and methodologies. The study also analyzes global companies, including patterns in growth, opportunities for industry, investment strategies, and conclusions from experts. The study focuses on globally performing key players to clarify, identify and analyze the multiple aspects of the demand for Cyber Security Software Market.

About Us: ReportsnReports.com is your single source for all market research needs. Our database includes 500,000+ market research reports from over 95 leading global publishers & in-depth market research studies of over 5000 micro markets.We provide 24/7 online and offline support to our customers.

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Was Einstein wrong? Why some astrophysicists are questioning the theory of space-time – Space.com

As in history, revolutions are the lifeblood of science. Bubbling undercurrents of disquiet boil over until a new regime emerges to seize power. Then everyone's attention turns to toppling their new ruler. The king is dead, long live the king.

This has happened many times in the history of physics and astronomy. First, we thought Earth was at the center of the solar system an idea that stood for over 1,000 years. Then Copernicus stuck his neck out to say that the whole system would be a lot simpler if we are just another planet orbiting the sun. Despite much initial opposition, the old geocentric picture eventually buckled under the weight of evidence from the newly invented telescope.

Then Newton came along to explain that gravity is why the planets orbit the sun. He said all objects with mass have a gravitational attraction towards each other. According to his ideas we orbit the sun because it is pulling on us, the moon orbits Earth because we are pulling on it. Newton ruled for two-and-a-half centuries before Albert Einstein turned up in 1915 to usurp him with his General Theory of Relativity. This new picture neatly explained inconsistencies in Mercury's orbit, and was famously confirmed by observations of a solar eclipse off the coast of Africa in 1919.

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Instead of a pull, Einstein saw gravity as the result of curved space. He said that all objects in the universe sit in a smooth, four-dimensional fabric called space-time. Massive objects such as the sun warp the space-time around them, and so Earth's orbit is simply the result of our planet following this curvature. To us that looks like a Newtonian gravitational pull. This space-time picture has now been on the throne for over 100 years, and has so far vanquished all pretenders to its crown. The discovery of gravitational waves in 2015 was a decisive victory, but, like its predecessors, it too might be about to fall. That's because it is fundamentally incompatible with the other big beast in the physics zoo: Quantum theory.

The quantum world is notoriously weird. Single particles can be in two places at once, for example. Only by making an observation do we force it to 'choose'. Before an observation we can only assign probabilities to the likely outcomes. In the 1930s, Erwin Schrdinger devised a famous way to expose how perverse this idea is. He imagined a cat in a sealed box accompanied by a vial of poison attached to a hammer. The hammer is hooked up to a device that measures the quantum state of a particle. Whether or not the hammer smashes the vial and kills the cat hinges on that measurement, but quantum physics says that until such a measurement is made, the particle is simultaneously in both states, which means the vial is both broken and unbroken and the cat is alive and dead.

Such a picture cannot be reconciled with a smooth, continuous fabric of space-time. "A gravitational field cannot be in two places at once," said Sabine Hossenfelder, a theoretical physicist at the Frankfurt Institute for Advanced Studies. According to Einstein, space-time is warped by matter and energy, but quantum physics says matter and energy exist in multiple states simultaneously they can be both here and over there. "So where is the gravitational field?" asks Hossenfelder. "Nobody has an answer to that question. It's kind of embarrassing," she said.

Try and use general relativity and quantum theory together, and it doesn't work. "Above a certain energy, you get probabilities that are larger than one," said Hossenfelder. One is the highest probability possible it means an outcome is certain. You can't be more certain than certain. Equally, calculations sometimes give you the answer infinity, which has no real physical meaning. The two theories are therefore mathematically inconsistent. So, like many monarchs throughout history, physicists are seeking a marriage between rival factions to secure peace. They're searching for a theory of quantum gravity the ultimate diplomatic exercise in getting these two rivals to share the throne. This has seen theorists turn to some outlandish possibilities.

Arguably the most famous is string theory. It's the idea that sub-atomic particles such as electrons and quarks are made from tiny vibrating strings. Just as you can play strings on a musical instrument to create different notes, string theorists argue that different combinations of strings create different particles. The attraction of the theory is that it can reconcile general relativity and quantum physics, at least on paper. However, to pull that particular rabbit out of the hat, the strings have to vibrate across eleven dimensions seven more than the four in Einstein's space-time fabric. As yet there is no experimental evidence that these extra dimensions really exist. "It might be interesting mathematics, but whether it describes the space-time in which we live, we don't really know until there is an experiment," said Jorma Louko from the University of Nottingham.

Partly inspired by string theory's perceived failings, other physicists have turned to an alternative called Loop Quantum Gravity (LQG). They can get the two theories to play nicely if they do away with one of the central tenets of general relativity: That space-time is a smooth, continuous fabric. Instead, they argue, space-time is made up of a series of interwoven loops that it has structure at the smallest size scales. This is a bit like a length of cloth. At first glance it looks like one smooth fabric. Look closely, however, and you'll see it is really made of a network of stitches. Alternatively, think of it like a photograph on a computer screen: Zoom in, and you'll see it is really made of individual pixels.

The trouble is that when LQG physicists say small, they mean really small. These defects in space-time would only be apparent on the level of the Planck scale around a trillionth of a trillionth of a trillionth of a meter. That's so tiny that there would be more loops in a cubic centimeter of space than cubic centimeters in the entire observable universe. "If space-time only differs on the Planck scale then this would be difficult to test in any particle accelerator," says Louko. You'd need an atom smasher a 1,000-trillion-times more powerful than the Large Hadron Collider (LHC) at CERN. How, then, can you detect space-time defects that small? The answer is to look across a large area of space.

Light arriving here from the furthest reaches of the universe has traveled through billions of light years of space-time along the way. While the effect of each space-time defect would be tiny, over those distances interactions with multiple defects might well add up to a potentially observable effect. For the last decade, astronomers have been using light from far-off Gamma Ray Bursts to look for evidence in support of LQG. These cosmic flashes are the result of massive stars collapsing at the ends of their lives, and there is something about these distant detonations we currently cannot explain. "Their spectrum has a systematic distortion to it," said Hossenfelder, but no one knows if that is something that happens on the way here or if it's something to do with the source of the bursts themselves. The jury is still out.

To make progress, we might have to go a step further than saying space-time isn't the smooth, continuous fabric Einstein suggested. According to Einstein, space-time is like a stage that remains in place whether actors are treading its boards or not even if there were no stars or planets dancing around, space-time would still be there. However, physicists Laurent Freidel, Robert Leigh, and Djordje Minic think that this picture is holding us back. They believe space-time doesn't exist independently of the objects in it. Space-time is defined by the way objects interact. That would make space-time an artifact of the quantum world itself, not something to be combined with it. "It may sound kooky," said Minic, "but it is a very precise way of approaching the problem."

The attraction of this theory called modular space-time is that it might help solve another long-standing problem in theoretical physics regarding something called locality, and a notorious phenomenon in quantum physics called entanglement. Physicists can set up a situation whereby they bring two particles together and link their quantum properties. They then separate them by a large distance and find they are still linked. Change the properties of one and the other will change instantly, as if information has traveled from one to the other faster than the speed of light in direct violation of relativity. Einstein was so perturbed by this phenomenon that he called it 'spooky action at a distance'.

Modular space-time theory can accommodate such behavior by redefining what it means to be separated. If space-time emerges from the quantum world, then being closer in a quantum sense is more fundamental than being close in a physical sense. "Different observers would have different notions of locality," said Minic, it depends on the context. It's a bit like our relationships with other people. We can feel closer to a loved one far away than the stranger who lives down the street. "You can have these non-local connections as long as they are fairly small," said Hossenfelder.

Freidel, Leigh, and Minic have been working on their idea for the last five years, and they believe they are slowly making progress. "We want to be conservative and take things step-by-step," said Minic, "but it is tantalizing and exciting". It's certainly a novel approach, one that looks to "gravitationalize" the quantum world rather than quantizing gravity as in LQG. Yet as with any scientific theory, it needs to be tested. At the moment the trio are working on how to fit time into their model.

This may all sound incredibly esoteric, something only academics should care about, but it could have a more profound effect on our everyday lives. "We sit in space, we travel through time, and if something changes in our understanding of space-time this will impact not only on our understanding of gravity, but of quantum theory in general," said Hossenfelder. "All our present devices only work because of quantum theory. If we understand the quantum structure of space-time better that will have an impact on future technologies maybe not in 50 or 100 years, but maybe in 200," she said.

The current monarch is getting long in tooth, and a new pretender is long overdue, but we can't decide which of the many options is the most likely to succeed. When we do, the resulting revolution could bear fruit not just for theoretical physics, but for all.

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Einstein discussed a link between physics and biology in a letter, 70 years before it was confirmed – Scroll.in

Since the dawn of the electronic age, it has never been easier for researchers to engage with the general public gaining access to precious resources otherwise unavailable.

This is illustrated perfectly in our latest publication, in which we introduce a previously unknown letter written in 1949 by none other than Albert Einstein. In it, the German-born mathematician and physicist discusses bees, birds and whether new physics principles could come from studying animal senses.

We first came across it in 2019, after Judith Davys a retiree living in the United Kingdom read an article we had published on the mathematical abilities of bees. She reached out to us to share the 72-year-old letter, which Einstein had addressed to her late husband Glyn Davys. We spent a year investigating the precious document.

Einstein was one of the greatest thinkers of the twentieth century, as well as an excellent communicator. His imagination helped shape many technologies that define the information age today. For example, Einsteins theory of general relativity governs the large-scale structure of the universe, which in turn enables corrections for the GPS system used on our smartphones.

In 1921, Einstein was awarded a Nobel Prize for his study of the photoelectric effect. This effect describes how light can remove electrons from atoms a principle that underpins the operation of todays solar cells.

In 1933, Einstein left Germany to work at Princeton University in the United States. It was here, in April 1949, he met scientist Karl von Frisch at a lecture.

Von Frisch was visiting Princeton to present his new research on how honeybees navigate more effectively using the polarisation patterns of light scattered from the sky. He used this information to help translate bees now-famous dance language, for which he eventually received his own Nobel Prize.

The day after Einstein attended von Frischs lecture the two researchers shared a private meeting. Although this meeting wasnt formally documented, the recently discovered letter from Einstein provides some insight into what may have been discussed.

We suspect Einsteins letter is a response to a query he received from Glyn Davys. In 1942, as the Second World War raged, Davys had joined the British Royal Navy. He trained as an engineer and researched topics including the budding use of radar to detect ships and aircraft. This nascent technology was kept top secret at the time.

By complete coincidence, bio-Sonar sensing had been discovered in bats at the same time, alerting people to the idea that animals may have different senses from humans. While any previous correspondence from Davys to Einstein appears lost, we were interested in what may have prompted him to write to the famous physicist.

So we set out to trawl through online archives of news published in England in 1949. From our search, we found von Frischs findings of bee navigation were already big news by July of that year, and he had even been covered in The Guardian newspaper in London.

The news specifically discussed how bees use polarised light to navigate. As such, we think this is what spurred Davys to write to Einstein. It is also likely Davyss initial letter to Einstein specifically mentioned bees and von Frisch, as Einstein responded: I am well acquainted with Mr v Frischs admirable investigations.

It seems von Frischs ideas about bee sensory perception remained in Einsteins thoughts since the two scientists crossed paths at Princeton six months earlier.

In his letter to Davys, Einstein also suggests that for bees to extend our knowledge of physics, new types of behaviour would need to be observed. Remarkably, it is clear through his writing that Einstein envisaged new discoveries could come from studying animals behaviours.

Einstein wrote:

It is thinkable that the investigation of the behaviour of migratory birds and carrier pigeons may someday lead to the understanding of some physical process which is not yet known.

Now, more than 70 years since Einstein sent his letter, research is indeed revealing the secrets of how migratory birds navigate while flying thousands of kilometres to arrive at a precise destination.

In 2008, research on thrushes fitted with radio transmitters showed, for the first time, that these birds use a form of a magnetic compass as their primary orientation guide during flight.

One theory for the origin of magnetic sense in birds is the use of quantum randomness and entanglement. Both of these physics concepts were first proposed by Einstein. But although Einstein was one of the founders of quantum physics, he was uncomfortable with its implications.

God does not play with dice, he famously stated, to express his opposition to the randomness which lay at the heart of quantum mechanics.

In an influential 1935 paper, Einstein and co-authors Boris Podolsky and Nathan Rosen introduced the concept of quantum entanglement. Interestingly, it was introduced as a conceptual failure of quantum mechanics, rather than one of its defining centrepieces, as we now understand it.

Perhaps ironically, one of the leading theories for the origin of magnetic sense in birds is the use of quantum randomness and entanglement. This theory suggests radical-pair chemical reactions in cryptochromes signalling proteins found in certain plants and animals are affected by the Earths magnetic field, and thus form the basis of a birds biological magnetic compass.

Although Einstein disagreed with entanglement, his willingness to speculate on how we might learn new things from animal sensory perception suggests he would have been delighted by how new research on bird migration is pushing the boundaries of our understanding of physics.

Indeed, Einsteins letter to Davys is a testament to how open he was to new possibilities for the field of physics being observed in nature. It illustrates, once again, how mindful he was of what one might discover when taking a different view of the world.

Adrian Dyer is an Associate Professor and Andrew Greentree is a Professor of Quantum Physics and Australian Research Council Future Fellow at RMIT University.

Jair Garcia is a Research fellow at the same institute.

This article first appeared on The Conversation.

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Einstein discussed a link between physics and biology in a letter, 70 years before it was confirmed - Scroll.in

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New superstring theory says black holes may be portals to other universes – The Next Web

We dont know very much about our universe. Were fairly certain it exists, but we dont know how it got here, how long its been here, or how big it is. Heck, we dont even know if our universe is unique.

Ever since Albert Einstein came up with the theory of relativity and other scientists realized that classical physics and quantum mechanics dont really line up, weve been trying to reconcile those worlds.

Many theoretical physicists believe that bridging the gap between obvious reality (classical physics) and the wacky quantum realm could help us finally understand the true nature of our universe.

As far as we know, theres no such thing as a gods eye viewof the universe. We cant just zoom out in space and time and figure out whats going on like were dealing with a 3D model.

Instead, we have to use math to describe all the features of the universe beyond those we can directly measure with sensors and observations. Basically, scientists take the cosmic events they can observe and measure, and use them as data-points to help inform hypotheses about all the things that could happen beyond our field of observation.

And, when it comes to describing the universe, we need a theoretical framework that can unify classical and quantum physics with an explanation that makes sense of mysterious occurrences in both worlds. Thats where singularities come in.

Einstein and his longtime research partner Roger Penrose spent a lot of effort trying to figure out singularities because theyre among the most powerful, exoticobjects in existence that we know of. They literally bend light, space, and time. If we can figure out whats really going on inside a black hole, well be well on our way to determining whats happening everywhere else in our universe.

The problem: We have absolutely no idea how to physically study a black hole. As far as we know, anything that gets close enough to slip over the event horizon of a singularity is gone forever.

Scientists have long posited that black holes could contain exotic space materials that could have been present at the universes genesis event most commonly thought to be the Big Bang.

But, thats just a guess. As to whats actually inside of them: we can only theorize.

M-theory, or string theory, has long been considered a strong candidate for unifying quantum and classical physics. At the risk of grossly oversimplifying, string theory is exactly what it sounds like: instead of being made up of infinite particles, the universe is made up of strings that connect everything to everything else.

And then theres superstring theory. This adds supersymmetry to the mix which, again grossly oversimplified, accounts for fermions and bosons, particulate objects that are essential to quantum mechanics.

An international team of researchers recently published a pre-print paper that uses superstring theory to posit a unified explanation of classical and quantum physics that not only explains the origin story for our universe, it also theorizes the existence of innumerable other universes.

And it all relies on black holes.

Per the teams paper:

We show that an S-Brane which arises in the inside of the black hole horizon when the Weyl curvature reaches the string scale induces a continuous transition between the inside of the black hole and the beginning of a new universe.

This provides a simultaneous resolution of both the black hole and Big Bang singularities.

And there you have it, in one fell swoop weve figured out that rather than being destructive vacuums from which nothing can escape, black holes are objects of creation. Theyre pregnant with young universes that, one far away day, could mature to contain stars and planets and life just like our own.

No. Not really. I mean, maybe. The scientists arent saying any of this is true. In fact, this pre-print paper isnt actually saying anything is true: its positing mathematical possibilities that could explain why black holes act the way they do.

One the one hand, they could just be sucking everything into them, as Einstein figured, because of regular old gravity-related stuff. Thats pretty much what relativity is; the more massive something is, the more powerful its gravitational pull should be. And black holes are incredibly massive.

But, if they are just acting out extreme classical physics, then we have no way of explaining how they function in the quantum realm. And the problem with that is, were pretty sure quantum mechanics drives the machinations of black holes.

So we need a better answer.

And even though superstring theory and the idea that black holes exist to feed energy (or dark energy maybe?) to other universes might seem unbelievable, it does make a modicum of sense.

The bulk of the paper is dedicated to describing the theory in mathematical terms, so the physicistsdo show their proverbial work. But, because this is a pre-print, its still awaiting recognizable peer-review. And we should take everything it says with a grain of salt until then.

Ultimately, this is a pretty wacky take on the typical theory of everything. But Occams Razor tells us the simplest explanation is often the correct one. And when you see a giant tear in the fabric of the universe that appears to be pouring unfathomable amounts of energy somewhere, it makes sense to make the basic assumption its a portal.

H/t: Interesting Engineering

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New superstring theory says black holes may be portals to other universes - The Next Web

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The black hole: finding hope in the darkest of places – Big Issue

Professor Heino Falcke finds faith, hope and love in the depths of black holes.

A little over two years ago I had the privilege to present the first ever image of a supermassive black hole to the world on behalf of a global collaboration. It was an amazing experience that reminded me again how human science is and that some goals you can only achieve together.

We had taken this iconic picture using the entire world as one giant telescope. When we gave it back to the world, it embraced it much more than I had ever imagined: 4.5 billion people saw that one image and it was covered by newspapers and websites around the globe. Most astonishingly, however, when I stood at the football pitch the following weekend, watching my son play, all his teammates certainly not all from academic backgrounds had seen that image of a black hole too. Suddenly, I wasnt the nameless dad of any more, but he became the son of the black hole guy.

Black holes seem so utterly useless to society, so why do they capture peoples imagination? Why do we study them? All of the technology we use today goes back to some seemingly useless curiosity. When Einstein, Planck, Bohr and Heisenberg developed the foundations of space, time and quantum physics, they didnt mean to develop the basic physics that enables GPS navigation systems, computers or DNA printers that can produce vaccines. They were just curious, and the same curiosity drives us today.

What we sometimes forget is science is not only about facts, it is driven by inspiration and it can inspire

Black holes have an extra twist though. They have become modern scientific myths. Next to their mathematical and physical beauty, they also represent our ultimate fear of death, destruction and eternal darkness. Now that we can image them, it feels like we are looking at the gates of hell from a safe distance.

Their edge is exotic to say the least. Time seems to come to a standstill and everything that goes inside never comes back at least according to the theory of Einstein. What lies beyond black holes event horizon remains one of the big scientific questions of modern science, located at the crossroads of quantum physics and the theory of space and time. Some very fundamental discovery may still be waiting for us hidden in the shadow of black holes.

However, what lies beyond is also a deep-rooted human question. Whoever climbs a hill wants to stand on its top and see what lies beyond. This is why we study black holes, this is why we do science: we want to look further. This is also why humans are deeply spiritual beings. We long to look further than our eyes can see. It is this longing where science and religion have common roots. What we sometimes forget is science is not only about facts, it is driven by inspiration and it can inspire.

I may be a bit old-fashioned, as a scientist who maintains his religious convictions, but I still think together we can make the world flourish

Some hills you have to climb on your own, but there are mountains that require more than just yourself. When we developed the daring idea to image the black hole it was clear that a world-sized network telescope was needed. We had to look at the black hole literally from all angles. This required bringing together institutions and scientists from very different cultural backgrounds. That wasnt easy and it wasnt always fun. However, everybody was driven by the same desire to see what we had only seen in our dreams so far. Together we turned dreams into a reality.

Many problems today require a global approach too. Collaboration in science can serve as an example. It doesnt mean naively ignoring the fact that people are different, but making use of their differences. Every angle counts as tedious as it sometimes can be.

Science and technology are an indispensable tool to address our problems today and they show us clearly where the problems are even if some dont want to hear this. Many of these problems we only have because of an unsustainable use of science and technology. Science has its limits and its perils, and the outlook it provides is not always uplifting. Occasionally this leads us scientists to appear as doomsday prophets that only see into the black holes of our future. Hence, as much as we need science, we need hope too. We need to share that we want to look beyond together. The solution is never science alone, the solutions is always us. I may be a bit old-fashioned, as a scientist who maintains his religious roots and convictions, but I still think together we can make the world flourish and look beyond. To do this we will need hard facts, but we will need faith, hope and love too. And a little bit of curiosity, of course.

Light in the Darkness: Black Holes, The Universe and Us by Professor Heino Falcke is out now (Wildfire, 20)

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How a weird theory of gravity could break cause-and-effect – Livescience.com

Astronomers have known that galaxies across the universe are behaving badly. Some are spinning too fast, while others are just way too hot and still others glommed into super structures too quickly.

But they don't know why. Perhaps some new hidden particle, like dark matter, could explain the weirdness. Or perhaps gravity is acting on these coalescing clusters of stars in a way scientists hadn't expected.

For decades, astronomers have debated the possibilities. While most astronomers believe that dark matter exists, some still think that we need to modify our theory of gravity. However, new research has found a critical flaw in modified gravity theories: They allow for effects to occur without causes and for information to travel faster than the speed of light. This is bad for modified gravity.

Related: The 15 weirdest galaxies in our universe

"It may change this research area considerably, forcing it in rather new directions," lead researcher and Tufts University astrophysicist Mark Hertzberg told Live Science.

Something funny is going on in the universe. For instance, based on what scientists would predict based on the masses of galaxies, stars orbit around the centers of them far too quickly; the temperature of the gas inside of galaxy clusters is far too hot; and large structures appeared in our universe far too soon.

At galactic and cosmological scales, either astronomers' understanding of the force of gravity is totally off, or there's a new ingredient in our universe that exerts gravity but is otherwise invisible. The latter idea is known as cold dark matter (CDM), which is the name given to a hypothetical form of matter that is as yet unknown to physics. The "cold" is there to note that whatever exotic particle might be responsible for the dark matter, it moves relatively slowly, in contrast to other potential dark matter candidates like the neutrino an example of a candidate for hot dark matter particles.

Related: The 11 biggest unanswered questions about dark matter

"If one gives up the principles of causality and locality, then it means we are essentially unable to explain the structure of the Standard Model of Particle Physics and General Relativity."

By filling galaxies with a form of matter that is invisible to light, the CDM hypothesis is wildly successful at explaining the majority of observations of galaxies and the larger universe. It is by far the most commonly accepted explanation for why the universe behaves as it does.

But the CDM hypothesis isn't perfect. Whatever it is, it sits outside the Standard Model of particle physics, meaning we have no idea what it is. Also, it has difficulty explaining something called the Baryonic Tully-Fisher Relation. The observed relationship shows that the total mass of normal matter, called baryonic matter, of a galaxy is proportional to the fourth power of the rotation speed. But CDM models predict that the relationship should be to the third power, predicting that galaxies spin slower for a certain amount of mass than they actually do.

What else could be going on?

An alternative to the whole CDM idea is a modified understanding of gravity. The simplest models fall under a class called MOND, for Modified Newtonian Dynamics. These models replace Newtonian physics (think Force = mass x acceleration) with other relationships that match the observed rotation rate of stars inside galaxies. While these models were popular when dark matter was first discovered in the 1970s and 1980s, they have failed to account for observations of galaxy clusters and the larger universe; as such, most scientists have all but rejected these models.

But the inadequacies of CDM to explain internal galactic dynamics provide an opening for MOND to survive. If a "MONDian" theory wants to compete on the galactic stage, however, it must be compatible with our other theories of physics, such as the special theory of relativity and quantum mechanics. So that's exactly what Hertzberg and his team set out to do. The results of their study were published in May to the preprint database arXiv, so the study hasn't been peer-reviewed.

"The only possibility to obtain something new [within the framework of relativity and quantum mechanics] is to add new degrees of freedom," Hertzberg told Live Science. In other words, in order to get MONDian theories to work with known physics, you have to add a whole bunch of funky stuff to theories. In examining that funky stuff, Hertzerg and collaborators found "some theoretical problems lurking in these attempts."

For instance, Hertzberg and his collaborators examined whether MONDian theories protect two principles: locality and causality. Locality is the concept that objects are directly influenced only by their surroundings in order for one object to influence another, it must transmit that influence via something like a force that travels at a finite speed. Causality is the simple notion that all events have a cause.

If a theory violates locality and/or causality, it is unlikely to fit in with our theories of physics, which do protect both principles

"If one gives up the principles of causality and locality, then it means we are essentially unable to explain the structure of the Standard Model of Particle Physics and General Relativity, as they are some of the central principles that go into constructing these theories in the first place," Hertzberg said. "In other words, if causality were badly broken in nature, we likely would have seen it already in various corrections to particle physics in the lab or tests of gravity in space."

In other words, we should've noticed by now.

Since all available evidence indicates that locality and causality are preserved (at least at macroscopic scales), then they should be obeyed by any new theory of physics. The team of physicists put MONDian theories to the test and found that they contain features that allow for non-locality and acausality. In other words, if MONDian theories are correct, then it's possible for events to happen without a cause and for effects to travel instantaneously, which violates the speed-of-light limit in the universe.

"Since we found that the existing proposals for radically new dark matter and MOND-like theories have some form of acausality, then it suggests they may not be embedded into fundamental physics, at least in their present form," Hertzberg said.

It might indeed be possible for locality and causality to be violated on galactic scales, but this would be extremely difficult to reconcile with everything else we know about physics.

As to the future of MONDian theories, Hertzberg speculated, "it motivates attempts to try to construct some classes of similar models that somehow maintain causality, but this looks difficult to achieve. In our paper, we show that a generalized form of these models fails the above tests for consistency."

Still, the "cold dark matter" paradigm has difficulty explaining the details of galactic physics. But there could be far more mundane reasons for this rather than upending all known physics. Modeling how galaxies form and evolve, even just accounting for all the messy processes where normal matter plays a role, is very difficult. Perhaps, a more sophisticated understanding of galaxies will provide an explanation for the observed Baryonic Tully-Fisher Relation.

And CDM is by far the best explanation we have.

"What is great about CDM is that it is theoretically on firm ground, and passes all the above theoretical consistency tests, even though it is not part of the Standard Model of Particle Physics," Hertzberg said. "The reason I say it is on firm ground is that there is no known theoretical reason why there shouldn't be some stable, neutral particles out there in the universe that don't couple to us very much. So CDM is reinforced, for now, as the leading idea."

Originally published on Live Science.

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AI & Machine Learning: Substance Behind the Hype? – CIO Insight

Its become inevitable in IT. Something new appears on the horizon and the hype machine ramps up to warp speed as it drafts a new term into its sales and marketing patter. In some cases, companies relabel their existing wares to align with the new term without making any actual change to the product.

Sometimes the hype is justified, often it is not. How about artificial intelligence (AI) and machine learning (ML)? Gartner believes they are over-hyped according to its recent Gartner Magic Quadrant for Data Science and Machine-Learning Platforms.

Case in point: a recent interview with a software vendor led to the confession that the AI capabilities spoken about in their brochures werent there yet. In other words, they were taking advantage of the hype to get more eyes viewing their software.

Gartner doesnt dismiss AI and ML as being without wholly substance. In fact, it goes on to name the top 20 candidates, explaining their strengths and weaknesses. These platforms are already proving valuable to data scientists and analysts in sourcing data, constructing models, analyzing data, and spotting trends. That value is translating into sales. Gartner reports heavier investment in AI during the COVID-19 pandemic. The analyst firms best advice on how to see beyond the glowing marketing promises is to tightly focus ML and AI into actual use cases that deliver tangible business value.

Read more on COVID-19s impact on IT spending patterns.

And IT has to be cognizant of how the hype may be influencing top management. CEOs and board room members are being assailed on all sides by the wonders of this or that AI platform. This may cause them to demand the replacement of existing analytics and business intelligence tools at once!

Calm heads must prevail for a number of reasons. Here are five to keep in mind.

If real value can be gained, push ahead with AI and ML investments. Gartner noted that the market generated $4 billion in 2019 and is growing at 17% per year. But not all tools are the same. Some platforms are focused on the data scientist and require highly trained personnel. A few can afford such personnel, but many cant. Other tools aim to democratize AI and ML. That may work for some organizations and not others.

Gartner listed the usual suspects as its leaders in the Magic Quadrant such as long time BI pioneers SAS, IBM Watson, and MathWorks. SAS Visual Data Mining and Machine Learning currently rules the roost, according to Gartner, with the two others not far behind.

But beware the incursion from the cloud giants Google, Microsoft, and Amazon. The latter was late to the party and is now coming on strong. There are also a lot of others competing in a crowded market. Those earning high markets from Gartner include Dataiku, Databricks, Tibco, Alteryx, DataRobot, KNIME, RapidMiner, and H2O.ai.

The question remains: Will SAS, IBM, and MathWorks be able to maintain their grip on the market? Or will they be overwhelmed by the cloud brigade? Amazon SageMaker is making a big play right now and is gaining major traction. Not to be outdone, the launch of a unified AI platform from Google is imminent.

Regardless of the hype, this market is primed for major growth in the coming years. Those who win will be those who see through the marketing blitz to direct AI and ML initiatives towards the attainment of strategic business objectives.

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Quantcast uses machine learning and AI to take on walled garden giants in the fight for the open internet – SiliconANGLE News

Media and publishing used to be the domain of specialized companies who controlled the content. The internet broke that model, and today anyone can go online and publish a blog, a podcast, or star in their own video.

But the big tech companies want to take control, closing content into walled gardens. But thats not what the majority of publishers, big or small, want.

We get to hear the perspectives of the publishers at every scale, and they consistently tell us the same thing: They want to more directly connect to consumers; they dont want to be tied into these walled gardens which dictate how they must present their content and in some cases what content theyre allowed to present, said Dr. Peter Day (pictured, right), chief technology officer at Quantcast Corp.

Day and Shruti Koparkar (pictured, left), head of product marketing at Quantcast, spoke with John Furrier, host of theCUBE, SiliconANGLE Medias livestreaming studio, duringThe Cookie Conundrum: A Recipe for Success event. They discussed the importance of smart technology for the post-cookie future of digital marketing. (* Disclosure below.)

Quantcast has cast itself as a champion of the open internet as it sets out to find the middle ground between the ability to scale provided by walled gardens and access to individual-level user data. Urgency for the quest is provided by Goliath company Google, which announced it will no longer be supporting third-party cookies on its Chrome browser as of January 2022.

Our approach to a world without third-party cookies is grounded in three fundamental things, Koparkar stated. First is industry standards: We think its really important to participate and to work with organizations who are defining the standards that will guide the future of advertising, Koparkar said, naming IAB Technology Laboratorys Project Rearc and Prebid as open projects Quantcast is involved with.

The companys engineering team also participates in meetings with the World Wide Web Consortium (W3C) to keep on top of what is happening with web browsers and to monitor what Google is up to with its Federated Learning of Cohorts (FLoC) project.

The second fundamental principle to Quantcasts strategy is interoperability. With multiple identity solutions from Unified ID 2.0 to FLoC already existing, and more on the way, We think it is important to build a platform that can ingest all of these signals, and so thats what weve done, Koparkar said referring to the release of Quantcasts intelligent audience platform.

Innovation is the third principle. Being able to take in multiple signals, not only IDs and cohorts, but also contextual first-party consent, time, language, geolocation and many others is increasingly important, according to Kopackar.

All of these signals can help us understand user behavior, intent and interests in absence of third-party cookies, she said.

But these signals are raw, messy, complex and ever-changing. What you need is technology like AI and machine learning to bring all of these signals together, combine them statistically, and get an understanding of user behavior, intent and interest, and then act on it, Koparkar stated. And the only way to bring them all together to obtain coherent understanding is through intelligent technologies such as machine learning, she added.

The foundation of our platform has always been machine learning from before it was cool, Day said. Many of the core team members at Quantcast have doctorate degrees in statistics and ML, which means it drives the companys decision-making.

Data is only useful if you can make sense of it, if you can organize it, and if you can take action on it, Day said. And to do that at this kind of scale its absolutely necessary to use machine learning technology.

Watch the complete video interview below, and be sure to check out more of SiliconANGLEs and theCUBEs coverage of The Cookie Conundrum: A Recipe for Success event. (* Disclosure: TheCUBE is a paid media partner for The Cookie Conundrum: A Recipe for Success event. Neither Quantcast Corp., the sponsor for theCUBEs event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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Machine Learning and the Future of Business: 5 Ways AI Is the New Business Frontier – TechAcute

Artificial intelligence (AI) was something out of science fiction a few decades ago. Today, AI is something scientists are studying in research labs and a new tool for a wide range of businesses.

Many of us use AI systems already. For example, many of our devices use biometric authentication to allow us to log in. The power of AI is applicable throughout several industries, and its constantly evolving to deliver better technology to companies worldwide.

However, what is the true use of AI in business? In this article, well cover a few reasons why AI is the future of business and what we can expect to see over the next few years.

Aside from biometric authentication, AI has many benefits that we are moving towards in the future. Lets take a look at why AI is the future of business.

Providing a good customer experience is essential for any business to succeed. Unfortunately, humans can only do so much. However, an AI machine can chat with customers and fulfill support inquiries 24/7. Whats more, the entire process is automated. If your customers still want to speak to a human, the AI can schedule a time for a call, email, or live chat.

AI can keep your current customers happy and potentially attract new clients. Some good examples of AI customer service tools are chatbots and autoresponders.

There are numerous ways you can make your employees more productivehowever, AI adds-on to that productivity by completing tasks quickly and at any hour of the day. With a well-developed AI, you can allocate all the tedious tasks to the machines and give your living breathing employees more time to focus on things that matter the most.

An AI can handle things like responding to message requests, booking appointments, sending out reminders, tracking data (see below), and much more.

Data is the lifeblood of any company. Without tracking useful insights, a company cant know what theyre doing right or wrong. However, tracking data accurately on a large scale can be incredibly difficult. Thats where AI can come in handy. AI can track data quickly and accurately so that businesses get all the information they need whenever they need it.

With AI, companies can increase their return on investment (ROI) across all platforms, including marketing and sales. Whats more, machine learning (ML) software for data analytics isnt extremely expensive to implement.

Recruiting new employees can be daunting and time-consuming. However, its an essential task every business needs to grow. With AI, companies can streamline their recruitment processes for better efficiency.

Image: Stefan Amer / Scopio

For starters, AI can speed up the review process for new candidates. Instead of your recruiting team looking for discrepancies in candidate applications, a machine can do it much faster and with greater accuracy.

AI will not only help companies find better candidates, but it will also allow more people to apply since the recruiters can manage more applications. Whats more, AI can potentially reduce prehire costs whenever a company is seeking new talent.

Every company wants to make more money, and AI can help in several ways. Above, we mention that AI can free up employees to focus on more profitable tasks. AI can also help reduce costs and bring value to companies through a wide range of applications, such as:

By investing in the right AI, companies can expect to see an increase to their respective bottom lines relatively quickly. More profit equals more growth opportunities for the business.

Several companies are hesitant to implement AI into their processes. It seems reasonable since, from the surface, AI seems like a highly technical product that requires a team of tech wizards to get started. However, there are countless AI products on the market that are seamless and easy to set up.

If you look at all the benefits above, its easy to see how AI can help any business across any industry. Furthermore, AI is constantly evolving. So, if there isnt a use for it in your business yet, theres likely to be a solution shortly.

Photo credit: The feature image has been done by Maksim Chernyshev. The photo in the body of the article was taken by Stefan Amer.

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Google Cloud Mainstreams AI With Vertex Platform – The Next Platform

For the past several years, tech giants have been trying to make artificial intelligence in its many guises HPC, data analytics, and other advanced workloads more available and easier to use for enterprises.

Traditional OEMs such as Hewlett Packard Enterprise, Dell Technologies, and Lenovo are using a combination of hardware, software, and services to make technologies that in years gone by would only be employed by research institutions and the largest of corporations more widely accessible.

The public clouds, particularly those that have their own hyperscale applications driven by machine learning and scale, also function a bit like OEMs when it comes to these workloads.

They have exposed the tools they build for their own use through their clouds, giving customers another option to be a modern computing organization. In many cases, this reduces time to market for AI-driven applications and can also reduce costs particularly huge capital outlays for buying GPU-laden infrastructure but also for high salaried AI experts who are in short supply and high demand.

The global AI space is expected to grow from $27.23 billion in 2019 to almost $267 billion by 2027, according to a report from Fortune Business Insights. While on-premises deployments will grab revenue share, the cloud deployment segment [will] gain traction owing to less implementation expenses, the report states. Also, the cloud offers tools and pre-trained networks, which makes building AI applications convenient.

Amazon Web Services the largest of the hyperscale cloud providers offers a range of services, from Fraud Detector and Forecast (for predicting demand) to Kendra (enterprise search) and CodeGuru (automating code reviews). Microsoft Azure offers an AI platform that includes services reaching from machine learning to knowledge search to various apps and agents.

IBM Cloud hosts a range of capabilities based on the companys Watson AI technology and Oracle Cloud includes an array of AI services and optimized infrastructure.

For about a decade, Google has focused on AI and machine learning, seeing such technologies as keys for advancing the capabilities throughout its ever-expanding array of services. That has been on display this week during the companys virtual Google I/O 2021 developer conference. In his keynote address, Sundar Pichar, CEO of both Google and its parent company, Alphabet, spoke about how Google continues to infuse AI and machine learning into everything from search to security to Android-based devices.

Even a new facility aimed at accelerating Googles quantum computing capabilities includes AI in its name: the Quantum AI campus in Santa Barbara, California, which will be down the road from the University of California campus where Urs Hotzle, senior vice president for technical infrastructure at Google, was a professor of computer science before joining the search engine giant as one of its earliest employees.

At the same time, Google Cloud took steps to make it easier for data scientists and developers to pull together AI-based applications and for enterprises to get those applications deployed. Vertex AI is a platform that encompasses a range of existing machine learning services with a unified user interface and API. Developers using Vertex AI can train an AI model using almost 80 percent fewer lines of code than platforms from other cloud providers, which opens up the development of such models and the management of machine learning projects to a wider range of data scientists and machine learning engineers with varying levels of skill, according to Google.

Today, data scientists grapple with the challenge of manually piecing together ML point solutions, creating a lag time in model development and experimentation, resulting in very few models making it into production, Craig Wiley, director of product for Vertex AI and AI applications at Google Cloud, wrote in a blog post. To tackle these challenges, Vertex AI brings together the Google Cloud services for building ML under one unified UI and API, to simplify the process of building, training, and deploying machine learning models at scale. In this single environment, customers can move models from experimentation to production faster, more efficiently discover patterns and anomalies, make better predictions and decisions, and generally be more agile in the face of shifting market dynamics.

Andrew Moore, vice president and general manager of cloud AI and industry solutions at Google Cloud, said the goals of Vertex AI were to remove orchestration burdens from data scientists and engineers and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production.

Organizations using Vertex AI will get access to the same AI toolkit which includes such capabilities as computer vision, language and conversation as well as structured data Google engineers use internally for the companys own operations, as well as new MLOps features like Vertex Vizier to speed up experimentation, Vertex Feature Store (a fully managed feature where data scientists and developers can offer, share and reuse machine learning features) and Vertex Experiments to use faster model selection to accelerate the deployment of machine learning models into deployment.

An experimental application called Vertex ML Edge Manager will enable organizations to deploy and monitor models on the edge via automated processes and APIs, enabling the data to stay on the device or on site. Other tools, such as Vertex Model Monitoring, Vertex ML Metadata and Vertex Pipelines are designed to streamline machine learning workflows.

AutoML enables developers and engineers with little machine learning experience to train models targeted at specific business needs and includes a central managed registry for all datasets across vision, natural language and tabular data types, while enterprises use BigQuery ML to export datasets from Googles managed BigQuery cloud data warehouse into Vertex AI. Vertex Data Labeling enables accurate labels for data collection.

Vertex AI also integrates with such open-source frameworks as TensorFlow, PyTorch and scikit-learn.

Google is promising more innovations around Vertex AI, which will be important for the company as it tries to gain ground on AWS and Azure, who together accounted for more than half of global cloud revenues in the first quarter in a market that saw spending reach more than $39 billion, a 37 percent year-over-year increase, according to Synergy Research Group.

Google Cloud is the third on the list and was among several other companies those being Alibaba, Tencent and Baidu as cloud providers that saw growth rates that surpassed the overall growth in the market.

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