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Richard Branson: Future of Work Is “Three and Even Four Day Weekends”

Posted on December 16, 2018 by Danzig

In an interview with CNBC's Making It, billionaire Richard Branson said that three or four day weekends could be a reality for

Work Hard, Play Hard

British billionaire Richard Branson — the Virgin CEO who drove a tank through New York City and crossed the English Channel in an amphibious vehicle — thinks we’re all working a bit too hard.

If we all worked “smarter, we won’t have to work longer,” he tweeted Wednesday. In an accompanying blog post, he argued that innovations like self-driving cars and drones will cause more jobs to be taken over by robots.

“Could people eventually take three and even four day weekends?” he wrote. “Certainly.”

Billionaire Club

Branson isn’t the only one who believes the future of work will be less demanding. Google co-founder Larry Page has also called for the end of the 40-hour work week.

“The idea that everyone needs to work frantically to meet people’s needs is not true,” Page told Vinod Khsola, a billionaire venture capitalist, as quoted by Computerworld in 2014.

All Work and No Play

Other big names take a darker tone about automation. Elon Musk has repeatedly warned of automation and the future of employment.

“A lot of people derive meaning from their employment. If you’re not needed, what is the meaning? Do you feel useless?” he told an audience at the World Government Summit in Dubai back in 2017. One option: universal basic income — the concept of distributing a basic income to every citizen of a nation. In fact, he argues, it would be a necessity.

But just because our billionaire overlords think it’s a great to axe hours and give us more holidays, it’s still pretty unlikely that will happen any time soon.

More on job automation: Robots Are Coming for Service Jobs

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Richard Branson: Future of Work Is “Three and Even Four Day Weekends”

Experts: United States Should Build a Prototype Fusion Power Plant

Posted on December 16, 2018 by Danzig

The United States should devote more resources to nuclear fusion research and build an ambitious prototype fusion power plant, according to a new report.

Power Play

The United States should devote substantially more resources to nuclear fusion research and build an ambitious prototype fusion power plant, according to a new report.

The report is the work of the National Academies of Sciences, Engineering, and Medicine. Its conclusion: it’s more important than ever for the U.S. and the world to explore roads to practical fusion power.

Losin’ Fusion

At the crux of the report is the role the U.S. will play in ITER, an international experimental fusion facility currently under construction in France. Some U.S. politicians have denounced ITER, arguing that the U.S. should pull out of the project.

But the National Academies report argues that the U.S. should remain involved with ITER, which will use a donut-shaped tokamak reactor that’s currently scheduled to go online by 2030 to produce energy.

Future Vision

At the same time, according to the report, the U.S. should boost its spending on fusion research by $200 million per year and construct its own experimental reactor. The report points to the multidisciplinary scientific insights a prototype fusion power plant could grant, from energy to vacuum technologies and “complex cryonic systems.”

“We listened very carefully to the community, especially some of the younger scientists who are very active in the field, and what we heard from the scientists is a desire to get on with fusion energy,” Michael Mauel, a co-chair of the committee that released the report, told Science. “We’re not just studying this thing, we’re trying to see if it really does work.”

READ MORE: Final Report of the Committee on a Strategic Plan for U.S. Burning Plasma Research [National Academies]

More on fusion: China’s “Artificial Sun” Is Now Hot Enough for Nuclear Fusion

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Experts: United States Should Build a Prototype Fusion Power Plant

This New $5,800 Headset is the Rolls Royce of Virtual Reality

Posted on December 16, 2018 by Danzig

VRgineers's $5,800 virtual reality headset called XTAL features a pair of quad HD displays and a generous 180 degree field of view.

High-End VR

The future of VR is bright. Virtual reality headsets are becoming increasingly affordable, and the technology has come a long way over the last eight years.

And today, we get to experience the newest and greatest the VR world has to offer —if you can afford it: the $5,800 XTAL headset by VR startup VRgineers.

We first heard about the XTAL earlier this year, but a new and improved version is about to make its debut at the Consumer Electronics Show — the world’s biggest tech show — in January, TechCrunch reports.

The XTAL

The Prague-based startup’s headset features some amazing specs: two Quad HD (2560 x 1440 resolution) OLED (organic LED) displays, and an impressive 180-degree field-of-view.

VRgineers touts the XTAL’s lenses and displays to be “best-in-class” on its website, while promising “no blurring” thanks to the headset’s new-and-improved 180 degree field of view.

Blurring around the edges of the lenses is a common issue with conventional VR headsets. By increasing the field of view to 180 degrees, the XTAL could reduce that effect substantially.

Users will also be able to track their hands while wearing the headset thanks to integrated Leap Motion sensors. Did I mention the headset itself is an absolute unit?

VR Luxury

$5,800 is pretty steep for a virtual reality headset these days — Oculus Rift is planning to sell a standalone, but far less impressive headset for just $399 as soon as next year.

So who’s it for? But at such a high price point VRgineers is targeting a professional audience, not your average gamer. The startup suggests that the XTAL could be used by automotive designers, and engineers on its website.

Will there be a future for high-end VR headsets? That will mostly be up to the enterprise market to decide.

More on VR headsets: Facebook’s Oculus Just Patented a Retina-Resolution VR Display

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This New $5,800 Headset is the Rolls Royce of Virtual Reality

NASA’s Lunar Orbiter Could Assist Commercial Missions to the Moon

Posted on December 16, 2018 by Danzig

NASA's Lunar Reconnaissance Orbiter that launched in 2009 could assist future commercial and international lunar landers.

Lunar Helper

NASA’s Lunar Reconnaissance Orbiter (LRO) launched in 2009. And it’s still orbiting the Moon to this very day — almost ten years later.

And it still has a decent amount of fuel left in its tank — about 44 pounds (20 kilograms) worth according to LRO project scientist Noah Petro. “That may not seem like a lot, but we don’t go through much fuel on an annual basis,” he said at last month’s Lunar Exploration Analysis Group (LEAG) meeting, as quoted by SpaceNews. In fact that remaining fuel could be enough for the next seven years of operation, according to Petro.

Moon Assistance

On November 29, NASA announced it is planning to buy space on board commercial landers for future scientific missions.

NASA is also offering to use the LRO to assist those landers — and other international missions to the Moon as well.

For instance, it could help identify safe landing sites, and help out during landing. “We want to observe the plumes as the landers land and kick up dust and disturb the environment,” says Barbara Cohen, LRO associate project scientist, at the announcement, as quoted by SpaceNews.

Off to the Moon

And the LRO is already on standby. It will observe the landing of two upcoming (non-commercial) lunar landers: the Israeli SpaceIL lander, and India’s Chandrayaan-2 lander, SpaceNews reports. Both missions are slated to land on the Moon early next year.

The move could build a lot of trust between the flourishing commercial, and international space exploration sector. But why China’s space agency was absent from the discussions remains to be seen.

More on lunar landers: China to Land First-Ever Rover on Dark Side of the Moon

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NASA’s Lunar Orbiter Could Assist Commercial Missions to the Moon

Breathing in Moon Dust Could be Even More Toxic Than We Thought

Posted on December 16, 2018 by Danzig

A new study from scientists at Stony Brook University found that moon dust particles could react with human cells — and potentially lead to cancer.

Uninviting Environment

Space agencies are working hard to get humans back to the surface of the Moon. But it’s not exactly the most inviting place.

Astronauts during the Apollo 11 mission in 1969 may not have had any health incidents while they were gleefully bouncing around on the lunar surface, as a NASA mission report from the time points out. But they knew that lunar dust wasn’t their friend — it could irritate their lungs, cause their Moon buggies to overheat — it even started degrading their spacesuits.

Hydroxyl Radicals

And now, scientists have collected even more evidence that Moon dust could be really terrible for us. By studying samples of dust — or regolith — from the lunar surface, scientists at Stony Brook University in New York found that it could react with human cells to create so-called “hydroxyl radicals” — highly reactive particles that have have been linked to lung cancers in the past, New Scientist reports.

“It’s a major health concern for future astronauts,” Donald Hendrix, leader of the study at Stony Brook University, tells New Scientist.

Lunar Cancer

And it gets worse. A different study has found that lunar dust could cause damage to cells’ DNA, which could eventually lead to cancer. The study exposed mouse brain cells, and human lung cells to simulated lunar soil. The results were discouraging: 90 percent of human lung cells and mouse neurons died, according to Universe Today.

The toxicity of lunar dust is going to be a big problem for any human planning on wandering around on the surface of the Moon in the future. “Dust is the number one concern in returning to the Moon,” says Apollo astronaut John Young, as quoted by New Scientist. But it likely won’t hold us back completely.

READ MORE: Breathing in moon dust could release toxins in astronauts’ lungs [New Scientist]

More on Moon dust: Scientists Are 3D Printing Fake Moon Dust Into Useful Objects

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Breathing in Moon Dust Could be Even More Toxic Than We Thought

All New Public Buses in California Have to Be Electric by 2029

Posted on December 16, 2018 by Danzig

California became the first state to mandate that all new mass transit buses have to be electric by 2029. All buses will have to electric in 2040.

Electric Fleet

California became the first state to require all new homes to offset their electricity needs with solar energy earlier this month. And it’s planning to tackle emissions from public transit vehicles next.

By the year 2029, all new mass transit buses in the whole state will have to be fully electric, according to a new rule adopted unanimously by the California Air Resources Board (CARB) yesterday — a powerful arm of the Californian government dedicated to maintaining healthy air quality since 1967. All mass transit bus fleets will have to be electric by 2040.

“[A zero-emission public bus fleet] dramatically reduces tailpipe pollution from buses in low-income communities and provides multiple benefits especially for transit-dependent riders,” says CARB Chair Mary D. Nichols in an official statement.

Say No to the Pump

But there’s another advantage that could help motivate the roughly 200 mass transit agencies to adopt exclusively electric buses in the future: significant savings from switching from expensive gasoline to electricity. “Putting more zero-emission buses on our roads will also reduce energy consumption and greenhouse gases, and provides cost savings for transit agencies in the long run,” Nichols goes on to say.

The move could massively reduce carbon emissions in the state, despite the fact that many of the largest transit agencies are already in the process of switching to electric buses — although, the transition has only begun. Only about 150 buses are electric out of 12,000 in the state, according to the New York Times.

A Steep Hill to Climb

So far, the transition has been a little rough. The LA Times reports that many electric buses in California’s largest city are plagued by “stalls, stops, and breakdowns.” San Francisco city officials are worried that electric buses might not have enough oomph to get a full load of passengers up its famously steep hills.

Despite these roadblocks, switching to exclusively electric buses has a ton of benefits — from cleaner air, much quieter streets, and savings in fuel costs.

READ MORE: California Requires New City Buses to Be Electric by 2029 [New York Times]

More on electric buses: By 2019, There Will be Electric School Buses

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All New Public Buses in California Have to Be Electric by 2029

The SEC Is Letting a Company Treat Your Genetic Code as Currency

Posted on December 16, 2018 by Danzig

Seeing Value

Your health data might be worth more than you think.

In July, home DNA testing company 23andMe earned itself $300 million for agreeing to sell customers’ health data to a pharmaceutical company — and it’s far from the only company cutting similar lucrative deals.

But while they rake in the profits from those contracts, the people actually providing the data get nothing — and one health startup hopes to change that.

Shares for Shares

LunaPBC is the public benefit corporation behind LunaDNA, a platform where people can upload their health data in exchange for shares in the company.

The number of shares is commensurate with the type of data uploaded. A user who uploads 20 days’ worth of data from their fitness tracker, for example, will earn two shares at a value of $.07 each, while one who uploads their entire genome will earn 300 shares — the equivalent of $21.

The company even sought — and received — approval from the Securities and Exchanges Commission to leverage health data as currency in this way.

LunaDNA just started accepting data from users last week, and currently, that data is limited to files from certain personal genomics companies, such as 23andMe and AncestryDNA, or health surveys it generates itself. Eventually, though, the company plans to expand to include other data sources.

Win-Win-Win

LunaPBC will sell access to users’ health data to researchers — just like 23andMe and AncestryDNA do — and if the company prospers, those with shares in it will also benefit.

“When people acquire shares they have an ownership stake in the company,” CEO Dawn Barry told MobiHealthNews. “When value is created in the platform that value flows back to the individuals in the form of dividends.”

Not everyone will pay the same rate for access to LunaPBC’s data, though — while it plans to charge for-profit companies the market rate, it will charge non-profit researchers less.

“We don’t want any silos,” Barry said. “Information silos have been a hindrance to researchers in the past. We want any type of credible researchers to be able to come in.”

READ MORE: This Health Startup Lets You Monetize Your DNA [Fast Company]

More on data: Think Deleting Your Facebook Profile Is Hard? Try Deleting Your Genomic Data.

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The SEC Is Letting a Company Treat Your Genetic Code as Currency

New Rules Takes the Guesswork out of Human Gene Editing

Posted on December 16, 2018 by Danzig

Researchers have identified two rules that they believe ensure the effects of human gene editing are less unpredictable and random.

Not So Random

There are many good reasons to criticize Chinese researcher He Jiankui for reportedly gene-editing two human babies — not only did his actions violate several accords within the scientific community, but he also undertook the project without proper transparency and oversight, working mostly in secret.

Worst of all, though, is the fact that He’s edits could affect the twin babies in unexpected ways. We don’t yet know how to ensure that CRISPR edits in humans do exactly what we want them to do — but that could be starting to change.

“The effects of CRISPR were thought to be unpredictable and seemingly random,” Francis Crick Institute researcher Paola Scaffidi said in a news release, “but by analysing hundreds of edits we were shocked to find that there are actually simple, predictable patterns behind it all.”

Two Simple Rules for Editing My Genes

In a paper published in the journal Molecular Cell on Thursday, Scaffidi and his Crick colleagues describe a set of simple rules they believe take some of the guesswork out of human gene editing.

The first of those rules involves the region a researcher instructs CRISPR to target. If a certain genetic letter (G) is in a certain place (fourth letter from the end of the target sequence), the edit will likely result in many imprecise deletions. The solution: avoid targeting those regions.

The second involves the target DNA’s degree of “openness” during the CRISPR edit. The team discovered that the use of compounds that forced DNA to open up resulted in more efficient editing.

“We hadn’t previously appreciated the significance of DNA openness in determining the efficiency of CRISPR genome editing,” researcher Josep Monserrat said. “This could be another factor to consider when aiming to edit a gene in a specific way.”

Guiding Hand

While these rules may have arrived too late to protect the twin babies on the receiving end of He’s CRISPR edits, they could put us on the path to a future in which we can edit the genes of humans without worrying about unintended consequences.

“Until now, editing genes with CRISPR has involved a lot of guesswork, frustration, and trial and error,” Scaffidi said, later adding, “This will fundamentally change the way we use CRISPR, allowing us to study gene function with greater precision and significantly accelerating our science.”

READ MORE: Scientists Crack the CRISPR Code for Precise Human Genome Editing [The Francis Crick Institute]

More on human gene editing: Chinese Scientists Claim to Have Gene-Edited Human Babies For the First Time

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New Rules Takes the Guesswork out of Human Gene Editing

McDonald’s Exec: “We’re Keeping Our Eye” on Meatless Burgers

Posted on December 16, 2018 by Danzig

McProtein

The iconic hamburger chain McDonald’s could start serving up high-tech meatless burgers alongside its Big Macs and Chicken McNuggets.

That’s according to Lucy Brady, the fast food giant’s senior vice president of corporate strategy, during Fortune‘s Most Powerful Women Next Gen Summit in California this week. In reference to high-tech meatless patties like the Impossible Burger, Brady said that “plant-based protein is something we’re keeping our eye on.”

Impossible Burger

It’s not clear whether the burger chain would spring for an off-the-shelf vegetarian burger, like those made by Impossible Foods, or develop its own in-house. With the fast food market’s race-to-the-bottom pricing, a home brew option would probably be attractive: an Impossible Burger patty typically retails for about three dollars.

If it did add a meatless burger to the menu, McDonald’s wouldn’t be the first fast food chain to experiment with vegetarian cuisine. Burger King has offered a MorningStar Farms veggie burger for years, and White Castle debuted an Impossible-branded slider this year that Eater hailed as “one of America’s best fast-food burgers.”

Fake Meat

Important to note: though the terminology is still evolving, meat substitutes like those made by Impossible and MorningStar are still “fake meat” — typically based on hearty proteins like soy, gluten, or pea — rather than lab-grown.

The latter, which is grown from cells harvested from a real animal, could be the real game-changer from a consumer point of view — and some producers have teased releasing it for sale by 2019. But don’t expect to see it on a fast food menu soon: though the cost per ounce has fallen precipitously since the first lab-grown meats were developed several years ago, their price point is still substantially higher than conventionally-farmed meat.

More on fake meat: This Is the Biggest Thanksgiving Ever for Fake Meat

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McDonald’s Exec: “We’re Keeping Our Eye” on Meatless Burgers

SpaceX Smashed the Record for Commercial Space Launches This Year

Posted on December 16, 2018 by Danzig

SpaceX has shattered the record for commercial rocket launches in a year, at 20. That beats the prior record of 18 launches in a year — which it also set.

Launch Box

As humankind ventures farther into the Earth’s gravity well, it’s been a banner year for Elon Musk’s commercial spaceflight venture SpaceX.

Case in point: Business Insider reports that SpaceX has shattered the record for commercial rocket launches in a year, at 20. That beats the prior record of 18 launches in a year — which was also set by SpaceX, in 2017. Before that, the figure stood at 16, set by United Launch Alliance in 2009.

21 Pilots

SpaceX has one more launch scheduled for 2018 — a satellite called GPS Block IIIA, which will improve location tracking services for the U.S. Air Force — bringing the company’s total tally of launches to 21.

It’s worth noting, though, that the 21 launches falls short of Musk’s most optimistic prediction for the company in 2018: that it would launch more rockets than any country on Earth. That honor, Business Insider found, went to China, which has launched about 35 rockets this year.

Starboy

Musk has had a tough year in the press, with drama as his electric car company, Tesla, struggled to bring its Model 3 compact to market. But the launch record comes as a bright cap to a year — and just weeks after Tesla reportedly managed to churn out 1,000 Model 3 vehicles per day.

“Life cannot just be about solving one sad problem after another,” Musk tweeted earlier this year after launching a Tesla Roadster into space. “There need to be things that inspire you, that make you glad to wake up in the morning and be part of humanity. That is why we did it. We did for you.”

More on SpaceX: Looks Like SpaceX Is One Step Closer to Launching Another Falcon Heavy

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SpaceX Smashed the Record for Commercial Space Launches This Year

Donald Trump Moves to Deport Vietnam War Refugees – The …

Posted on December 16, 2018 by Danzig

The White House unilaterally reinterpreted the agreement in the spring of 2017 to exempt people convicted of crimes from its protections, allowing the administration to send back a small number of pre-1995 Vietnamese immigrants, a policy it retreated from this past August. Last week, however, James Thrower, a spokesperson for the U.S. embassy in Hanoi, said the American government was again reversing course.

Washington now believes that the 2008 agreement fails to protect pre-1995 Vietnamese immigrants from deportation, Thrower told The Atlantic. This would apply to such migrants who are either undocumented or have committed crimes, and this interpretation would not apply to those who have become American citizens.

The United States and Vietnam signed a bilateral agreement on removals in 2008 that establishes procedures for deporting Vietnamese citizens who arrived in the United States after July 12, 1995, and are subject to final orders of removal, Thrower said. While the procedures associated with this specific agreement do not apply to Vietnamese citizens who arrived in the United States before July 12, 1995, it does not explicitly preclude the removal of pre-1995 cases.

The about-turn came as a State Department spokesperson confirmed that the Department of Homeland Security had met with representatives of the Vietnamese embassy in Washington, D.C., but declined to provide details of when the talks took place or what was discussed.

Katie Waldman, a spokeswoman for DHS said: We have 5,000 convicted criminal aliens from Vietnam with final orders of removalthese are non-citizens who during previous administrations were arrested, convicted, and ultimately ordered removed by a federal immigration judge. Its a priority of this administration to remove criminal aliens to their home country.

Spokespeople for the Vietnamese embassy did not immediately respond to requests for comment.

But the Southeast Asia Resource Action Center, a Washington, D.C., advocacy group, said in a statement that the purpose of the meeting was to change the 2008 agreement. That deal had initially been set to last for five years, and was to be automatically extended every three years unless either party opted out. Under those rules, it was set to renew next month. Since 1998, final removal orders have been issued for more than 9,000 Vietnamese nationals.

When it first decided to reinterpret the 2008 deal, Donald Trumps administration argued that only pre-1995 arrivals with criminal convictions were exempt from the agreements protection and eligible for deportation. Vietnam initially conceded and accepted some of those immigrants before stiffening its resistance; about a dozen Vietnamese immigrants ended up being deported from the United States. The August decision to change course, reported to a California court in October, appeared to put such moves at least temporarily on ice, but the latest shift leaves the fate of a larger number of Vietnamese immigrants in doubt. Now all pre-1995 arrivals are exempt from the 2008 agreements protection.

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Donald Trump Moves to Deport Vietnam War Refugees - The ...

Pantheism | Philosophy Talk

Posted on December 16, 2018 by Danzig

Pantheism is the view that the world is either identical to God, or an expression of Gods nature. It comes from pan meaning all, and theism, which means belief in God. So according to pantheism, God is everything and everything is God.

First, pantheism rejects the idea that God is transcendent. According to traditional Western conceptions of God, He is an entity that is above and beyond the universe. So, although God may be fully present in the universe, He is also outside of it. Simply put, He transcends the totality of objects in the world. When pantheists say that God is everything and everything is God, this is meant to capture that idea that God does not transcend the world.

A second important difference between pantheism and traditional theistic religions is that pantheists also reject the idea of Gods personhood. The pantheist God is not a personal God, the kind of entity that could have beliefs, desires, intentions, or agency. Unlike the traditional God of theism, the pantheistic God does not have a will and cannot act in or upon the universe. These are the kind of things that only a person, or a person-like entity, could do. For the pantheist, God is the non-personal divinity that pervades all existence. It is the divine Unity of the world.

While these two points may clarify how pantheism and traditional theism differ, they may make us wonder if theres much difference between pantheism and atheism. After all, pantheism denies the existence of a transcendent, personal God, which is the God of traditional theism. So, in that sense, pantheism seems to be a form of atheism. Its not clear what exactly pantheists are talking about when they talk of God. If pantheists just consider God to be the totality of all existence, then why talk of God at all? Moreover, if thats what God means to the pantheist, then the slogan God is everything and everything is God now seems circular and redundant. As Schopenhauer, a critic of pantheism, says, to call the world God is not to explain it; it is only to enrich our language with a superfluous synonym for the word world.

But Schopenhauer seems to be operating with a very narrow definition of God here. Why suppose that God must be personal and transcendent in order to be God? This limits the concept of God in an ad hoc way that privileges the traditional theistic view of divinity. Looking at other non-theistic religious traditions, we find many conceptions of a divinity that pervades all existence, like Lao Tzus Tao, Sankaras Brahman, and arguably also Hegels Geist and Plotinuss One. To call all these views atheist simply because they reject the traditional theistic conception of a personal, transcendent God is to miss the point. Atheism, after all, is not a religion.

If we accept that pantheism differs from atheism, in that it does posit some kind of divinity in the world whereas atheism does not, its still a little difficult to see in what sense pantheism is a religion. There are no pantheist churches or services, for example, and its not even clear if there are any particular pantheist rituals or practices. Do practices like prayer or worship even make sense in the pantheist scheme of things?

Love of nature is often associated with pantheism, but that does not seem to be a central tenet of the religion. Self-professed pantheists like Wordsworth, Whitman, and other Romantic poets certainly had a deep love of nature, but that was not necessarily the case for pantheists like Spinoza and Lao Tzu. Nevertheless, for some pantheists the idea that nature is something that inspires awe, wonder, and reverence is important. This attitude toward nature is perhaps what motivates many contemporary pantheists to identify themselves as such. It is no coincidence that there are strong ties between pantheism and the ecology movement.

Given some of the issues raised here, I look forward to having a number of questions clarified during our upcoming show. One important question is: what exactly is the relationship between pantheism and atheism? Are they complementary or conflicting views of the world? Can we distinguish pantheism from traditional theism without the view simply collapsing into atheism? Is pantheism really a religion, or just a metaphysical view of the world? Does it have distinctive rituals or practices? What would motivate someone to identify as a pantheist? And how central is reverence for nature to pantheism?

Joining the conversation with John and Ken will be Philip Clayton, Dean of the Claremont School of Theology and Provost of Claremont Lincoln University. He is also the co-author of The Predicament of Belief: Science, Philosophy and Faith.

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Pantheism | Philosophy Talk

critical rationalism blog – An exploration of critical …

Posted on December 15, 2018 by Danzig

Rafe Championand Brian Gladish, Independent Scholars

The Austrian-born philosopher Karl Popper charted new direction in the philosophy of science in the 1930s with Logik der Forschung (The Logic of Scientific Discovery 1959). His ideas can be recruited to support the little-known Austrian school of economics, to improve the quality of scientific research and to indicate how a unit on critical thinking can be a core subject in liberal education. If Poppers ideas are robust then the main features of his thinking should be the common property of all educated people. Some would say the same applies to Austrian economics.

The paper begins with a summary of the key features of Poppers critical rationalism followed by an introduction to Austrian economics and the way that some of his ideas can elevate the profile of the Austrian school. The paper then turns to the rising tide of concern about the quality and reliability of the scientific research that is published in some fields. Finally there is a proposal for short course to introduce various forms of critical appraisal of ideas that could be a core component of liberal education to promote imaginative problem-solving and lateral thinking.

POPPERS CRITICAL RATIONALISM

In his introduction to Poppers philosophy Mark Notturno wrote Popper was an outspoken champion of rationalism and a constant critic of subjectivist and authoritarian tendencies in science and society. (Notturno, 2003, Preface). His philosophy can be described as critical rationalism with a historical and evolutionary approach. He liked to sum it up in two nutshells. One is the critical rationalist credo I may be wrong and you may be right, and by an effort we may get nearer to the truth. The other is the four-stage problem solving scheme that is described below.

Wade Hands demonstrated the difference that it makes to perceive Popper as a critical rationalist rather than the more usual falsificationist, a label that implies that his ideas are merely a variation on the theme of logical empiricism. Hands is a leading contributor to the literature on the philosophy and methodology of economics and for many years he was a critic of Poppers views until he radically changed his perception. He wrote that Popper is best depicted as a critical rationalist and he concluded that critical rationalism fits both the practice of mainstream economics and Poppers specific contribution to social studies Situational Analysis and the Rationality Principle.

If Poppers real message is critical rationalism, rather than falsificationist rules, then the method of SA seems to be quite fine. Popper explains in detail how to modify a particular SA explanation when it seems to be in conflict with the empirical data, internally inconsistent, or in conflict with more corroborated theories if there are many paths to effective criticism, then preserving the RP and modifying the rest of the SA could be a perfectly reasonable response. The critical rationalist reading of Poppers philosophy thus relaxes the tension between scientific rationality and SA social science and it does so within a framework that is both more contemporary than, and devoid of many of the problems of, strict falsificationism. (Hands, 2001).

Popper (1902-1994) was born in Vienna, the son of a prominent liberal lawyer with scholarly interests. He dropped out of high school and attended lectures at the university as an unmatriculated student, trained as a cabinet-maker and eventually matriculated. In 1928 he qualified to teach high school science and mathematics after a course that included a doctoral thesis on habit formation in children. He worked on the philosophy of science in his spare time and in 1935 he published Logik der Forschung that appeared many years later in English (Popper 1959).

He criticized the traditional idea that scientific theories are developed by collecting observations followed by confirmation of the theories with more observations. He argued that the creation of theories is a matter of inspiration and guesswork because new ideas arise as conjectures or speculations and the really vital function of observations is to act as tests or attempted falsifications of theories.

In the 1960s biological themes became more prominent in his work and he contributed to the revival of evolutionary epistemology by exploring the principle of natural selection in relation to the development of scientific theories and other forms of knowledge. Evolutionary epistemology is concerned with problem-solving and error-elimination under various forms of selective pressure unlike theories of knowledge that focus on the justification of beliefs and the numerical probability of theories.

Popper started with the old idea that knowledge grows by trial and error or in more learned terms by conjecture and refutation. He postulated that every organism from the amoeba to Einstein can be described as constantly engaged in problem solving (not necessarily consciously of course). Innovations in the plant and animal world arise from mutations which generate new reactions, new organs, new forms of life. For humans the most important innovations are new ideas. Living organisms confront selective pressures exerted by the biological environment and competing forms of life. Ideas meet the competition of alternative theories, critical arguments and experimental tests.

The central motif of Poppers evolutionary epistemology is a cyclic four-step problem-solving schema:

P1 > TS > EE > P2, P3, P4 etc

The starting point is a problem situation. In response the organism generates tentative solutions. These are subjected to the process of error elimination by various selective pressures. Humans can make the process of error elimination conscious and systematic by critical discussion and experimental testing. In the course of these activities new problems emerge.

This approach to scientific knowledge has at least two important consequences; (1) it resolves conflicting ideas about the various processes and activities which are involved in creative thinking and problem-solving and (2) it highlights the importance of finding unresolved issues (problems) and the willingness to recognize them, even to create them!

On the first point the evolutionary schema can be used to challenge views about science that can tend to promote antagonism between the rational (scientific) and the imaginative (literary) frames of mind. For example Peter Medawar in his book Plutos Republic described the tension between the romantic and the rational views of science; the romantic points to the poetic inspiration involved in creating new theories while in contrast the rationalist makes much of data collection, experimentation and logical analysis. This conflict has broad cultural implications. The triumph of Newtonian mechanics was widely perceived as the full flowering of the so-called inductive method to find the truth by accumulating observations. This achievement provoked a revolt by romantics and poets who could not stomach a view of human activity that had no place for the imagination. Nor could they accept the mechanical universe. The result of this collision has been a kind of cultural clash with imagination set against reason, the organic set against the mechanical, the inspiration of the poet set against the empiricism of the scientist.

Poppers theory offers a cure for this cultural conflict by harmonising the relationship between the various elements of the situation for both scientists and artists and indeed for anyone. These elements include traditional beliefs, criticism, logic, imagination and experimental trials. These elements each have a role to play and so there is no need for the tensions and antagonisms that flow from partial and narrow views of problem-solving and creativity, whether in science, art, technology or daily life. A helpful selection of Poppers thoughts can be found in David Millers A Pocket Popper (Miller, 1983) and in a collection of Cliffs Notes for Poppers first five books Champion (2016).

On the second point the schema brings out the importance of recognizing problems and working on them in a critical and imaginative spirit. In this schema a problem functions as an ecological niche to be colonised by tentative solutions. Problems are welcomed as a challenge, not an impediment to science because they are the growing point or perhaps a habitat for new species of ideas. This provides a theory of discovery, based on the creative function of criticism. To grasp the full power of evolutionary epistemology it is necessary to understand this creative function of criticism in generating problems that can be seen as spaces for new ideas Problems are the habitat where new ideas grow and criticism has two functions, which are about equally valuable: (1) to eliminate error and (2) to reveal new problems, i.e. new habitats. Thus Poppers theory brings out both the error elimination and the creative function of criticism and we need to maximise the play of criticism to get the best out of both its functions.

Watson and Crick systematically used the critical approach in their pursuit of the double helix structure of DNA. As Crick described it:

Our other advantage was that we had evolved unstated but fruitful methods of collaboration, something that was lacking in the London group. If either of us suggested a new idea the other, while taking it seriously, would attempt to demolish it in a candid but non hostile manner. This turned out to be quite crucial. In solving scientific problems of this type, it is almost impossible to avoid falling into errorNow, to obtain the correct solution of a [complex] problem usually requires a sequence of logical steps. If one of these is a mistake, the answer is often hidden, since the error usually puts one on completely the wrong track. It is therefore extremely important not to be trapped by ones own mistakes. (Crick, 1988, 70) [my emphasis].

In an interview he stated Its getting rid of false ideas which is the most important thing in developing the good ones You should not get bogged down with experimental details. You should make some sort of bold assumptions, and try them out (Wolpert and Richards, 1989, 94-5). Richard Feynman was an exemplary critical rationalist. He famously said science is organized skepticism in the reliability of expert opinion and he introduced his students to scientific discovery as guessing followed by the deduction and computation of results from the guess to check with experimental observations (Feynman, 2013). This is what Popper called conjecture and refutation. It seems that Feynman never encountered Poppers ideas and his impatience with philosophy and the soft social sciences was legendary (Feynman 1985).

Poppers student Bartley described four forms of criticism: (1) experience; (2) theories; (3) problems; and (4) logic (Bartley, 1982, section xiii onward). The criticism by test or experience is closely related to the main concern of theories of knowledge which are based on observations. The crucial difference is that for critical rationalists the observations are designed to test ideas, not to verify or confirm them. Of course good theories will pass a lot of tests but that is not the end of the matter because even the best theories have rivals and also internal problems which call for more work. The second form of criticism by theories consists of comparing the assumptions and implications of the theory under consideration with other well-tested theories. Criticism by problems or check on the problem means assessing how effectively the theory (or the policy proposal) addresses the problems that it was formulated to solve.

As for the process of forming critical preferences among rival theories, Popper suggested several criteria rather than one over-riding principle which leaves open the possibility that some theories will have different performances on the different criteria. This is consistent with Poppers support for theoretical pluralism and the desirability of competing research programs. His first proposal applies to major breakthrough developments.

The new theory should proceed from some simple, new, and powerful unifying idea about some connection or relation (such as gravitational attraction) between hitherto unconnected things (such as apples and planets) or facts (such as inertial and gravitational mass) or new theoretical entities (such as field and particles). (Popper, 1963, 241)

Other features of the preferable theory are: it makes more precise predictions and these stand up to more precise tests; it explains more facts; it describes or explains the facts in more detail; it has passed tests where the rival failed; it has suggested new experimental tests and passed them.

POPPER AND THE AUSTRIAN ECONOMISTS

The argument in this section is that some features of Poppers ideas can improve the image of the Austrian school which currently makes up only about 2% of American economists. The Austrians have suffered from the perception that their methods do not meet the standards which have been taught in the philosophy of science since it became professionalised and specialised as an academic discipline under the influence of the logical empiricists led by Rudolf Carnap (1891-1970) and Karl Hempel (1905-1997).

Austrian economics is not widely taught and some background information will be helpful for most readers. It is pursued by a confederation of scholars who trace their intellectual ancestry to the founding father Carl Menger (1840-1921) and his colleagues Eugene Bohm Bawerk and Friedrich Weiser. Other significant early figures were John Bates Clark, Frank Fetter and Herbert J. Davenport in the US, Philip Wicksteed in England and Knut Wicksell in Sweden (Salerno 2010). Prominent Austrians in the next generation were Ludwig von Mises (1881-1973), Friedrich Hayek (1898-1992) and Lionel Robbins (1898-1984) in the first part of his career.

Until the 1930s the members of the school were concentrated in Austria with scattered supporters around the world. Now most of the Austrians are in the United States with two prominent hives of activity, one at the George Mason University in Virginia and another at the Mises Institute in Alabama. There are doctoral programs at George Mason University, Texas Tech, Texas Baylor and Virginia. The Austrians are closely affiliated with the Virginia school of public choice theory (Coase, Buchanan, Tullock) and the Ostrom/Bloomington school of public administration.

In the early 20th century the Austrian ideas appeared to be firmly planted in the mainstream of the economics profession but the impact of Keynes in the 1930s and the rise of mathematics in the 1940s transformed the situation. The Austrians rejected the Keynesian revolution and they also object to much of the mathematical analysis that rapidly became standard in the profession after the war. They insisted that mathematical analysis can be misleading if it is not handled with care and insight into the economic issues as well as the mathematical formalism. Consequently the Austrians were widely perceived to be out of date and amidst the mushrooming postwar growth of the profession they became practically invisible until the movement staged a revival during the 1970s (Vaughn 1990, Boettke 2015). Another adverse influence from the 1930s was the rise of the philosophy of science known as logical positivism in Vienna and logical empiricism in the United States.

Mises did not live long enough to see the Austrian revival although he did more than anyone to keep the ideas alive. Prominent in the revival were Hayek, Ludwig Lachmann (1906-1990), Murray Rothbard (1926-1995) and Israel Kirzner (1930 ) . The numbers have increased rapidly in recent years and it is hazardous to mention the names of contemporaries because any short list will give offence to many worthy scholars who are left out! For a concise and masterly account of the progress of the school from Menger to the present day see Boettke (2015).

In the aftermath of the Great Financial Crisis 2008 the Austrians emerged with a deal of credit for the insights they provided into the mechanism of the collapse (Thornton, 2009).

Several high profile investment advisers and financial commentators have employed the Austrian Business Cycle Theory in their interpretation of the crisis. They have been inspired to revisit this theory as a result of the manifest failure of mainstream macroeconomists to foresee or explain the subprime mortgage crisis and its subsequent metamorphosis into a pandemic financial meltdowna number of economists and journalists associated with the modern Austrian school had warned of an emerging housing bubble during the Greenspan era when the conventional wisdom was that the Federal Reserve System had matters well in hand (Salerno, 2012).

The leading emphases of the school include the salience of dynamic competition and entrepreneurial innovation in the marketplace, the origin of social institutions as the unintended consequences of human action, the subjective theory of value, recognition of the time factor in social and economic processes, and the uncertainty of human knowledge. Those ideas are not unique to the Austrians although they been especially diligent in drawing out their implications. They have distinctive ideas regarding the boom and bust business cycle (as described by Salerno), capital theory and especially the methodology and philosophy of research.

The Austrian approach can be described as the situational analysis of human action, combining the language of von Mises, Talcott Parsons and Karl Popper. A central resource for Austrians is Human Action by von Mises, first published in 1949. A similar framework of analysis can be found in The Structure of Human Action published by Talcott Parsons in 1937 (summarized in Devereaux, 1964) and in The Open Society and Its Enemies and The Poverty of Historicism (Popper, 1945, 1957). The common features of the schemes of Parsons, von Mises and Popper are summarised in Champion (2010). The analysis starts with the human actor making plans and taking action to achieve his or her objectives. The actors take account of the various elements in the situation as they are subjectively perceived. These include the resources and capacities of the actors, the opportunities and constraints offered by the physical environment, the institutional framework of laws and regulations, and the social/cultural framework of written and unwritten mores, traditions, values and belief systems.

Some of the elements can change rapidly but many can only be changed slowly and the individual actor has very limited capacity to change the major elements of the situation. The outcome of actions are mediated (limited) by natural laws whether the actors are aware of them or not. The situation offers problems and opportunities for the actor/entrepreneur and Parsons in particular emphasised the element of individual choice and he thought of his approach as a voluntarist theory of human action (Devereaux, 1964).

Economists focus on the economic system, prices and production and the like but the framework is sufficiently expansive to take account of the impact of other factors and to coordinate the work in many areas of the social sciences and humanities. The framework drafted by the gang of three in the 1930s could have been used to maintain sociology and economics as an integrated discipline and to sponsor partnerships between economists and all students of social institutions law, politics, literature, religion and cultural studies at large. There was a window of opportunity for these three leading figures in their respective fields to form a united front across the disciplines of sociology, economics and philosophy to promote the ideas that they shared and to debate the issues where they disagreed. This did not happen; there was no united front, no dialogue to resolve differences and the defective ideas that all three identified in the 1930s became embedded in the rapidly growing community of academics and researchers after the war. Consequently the kind of research programs which were implicit in the situational analysis of human action were blindsided by the dominance of logical empiricism, Keynesianism and mathematical formalism. This is not to decry the use of mathematics but the efficacy of numerical analysis has to be decided on a case by case basis by people who are understand both the mathematics and the economics.

The Achilles heel of the Austrian school in the eyes of the modern mainstream is the claim that the basic principles of economics can be established by logical analysis in advance of evidence (apriori) and they cannot and need not be empirically tested. Not surprisingly this position raised eyebrows after the rise of logical positivism/empiricism and Poppers ideas in the philosophy of science created a demand for empirical verification or at least testing of scientific theories. Living in Vienna von Mises saw this coming because he was alert to the activities of the famous Vienna Circle of logical positivists and he wrote a long criticism of positivism in his master work (von Mises 1949).

The strong form of apriorism is apparent in his comparison of monetary theory with geometry where all of the theorems are implied in the axioms. The quantity theory does not add to our knowledge anything that is which is not virtually contained in the concept of money (von Mises, 1966, 38). The starting point of praxeology is not a choice of axioms and a decision about methods of procedure, but reflection about the essence of action (ibid, 39). Rothbard took the same strong position. The fundamental axiom that individual human beings act, that is, on the primordial fact that individuals engage in conscious action towards chosen goals [in contrast with reflex or knee-jerk behavior], furthermore, since praxeology begins with a true axiom, A, all the propositions that can be deduced from this axiom must also be true. For if A implies B, and A is true, then B must also be true. (Rothbard, 1976). He asserted that these propositions are justified because they are deduced from the axiom of purposeful action. Apart from the fact that these conclusions cannot be tested by historical or statistical means, there is no need to test them since their truth has already been established. (ibid).

In view of those arguments Mark Blaug wrote Mises made important contributions to monetary economics, business cycle theory and of course socialist economics, but his later writings on the foundations of economic science are so cranky and idiosyncratic that we can only wonder that they have been taken seriously by anyone (Blaug, 1992, 81). He quoted Samuelsons famous rejoinder to the Austrians. Well, in connection with the exaggerated claims that used to be made in economics for the power of deduction and a priori reasoningI tremble for the reputation of my subject.

Poppers approach offers a corrective to the methodological rhetoric of the Austrians and simultaneously a rejoinder to Blaug and Samuelson. For Popper the test of evidence applies to the explanations and predictions generated by a scientific research program. The program itself is a system of ideas including philosophical and metaphysical framework assumptions and methodological procedures and principles that generate explanations and predictions. Not all of these parts are amenable to empirical testing and this applies to the natural sciences as much as the human sciences.

Hence it is not a departure from standard scientific practice to make use of untestable propositions. The critical rationalist does not insist that all the premises and presuppositions in scientific discourse should be verified, merely that they stand up to criticism as well or better than other options (Hands, 2001, 301). Recall the four forms of criticism: empirical tests are a particular kind of criticism but they are not appropriate for all assumption, especially those of methodology and the philosophical framework assumptions of the program. They prove themselves at one step removed by the power of the explanatory theories and the research programs that they generate.

The basic principles of Austrian economics such as the axiom of action can be regarded as working assumptions in the form of indispensable methodological procedures and assumptions which are required in all sciences. The axiom is often described as self-evidently true but it is better to describe as a methodological assumption that contributes to explanatory theories which are tested by their capacity to account for the phenomena under investigation, such as money, the Great Depression, unemployment, inflation and trade cycles including the Great Financial Crisis.

Popper made two other relevant contributions. One is the framework of Situational Analysis and the Rationality Principle which is functionally equivalent to the Austrian approach using the axiom of human action (Popper 1994). The second is to introduce students to the critical/creative problem-solving approach of the scientist who operates like an entrepreneur in a world of intellectual problems and opportunities, generating conjectures which are tested and criticised in the laboratory and the marketplace of ideas. Students who bring this approach to a course on Austrian economics will have less to unlearn than students who have encountered the philosophy of science in the more usual mode of collecting data and attempting to confirm theories. Harper explicitly drew on Poppers evolutionary epistemology in his work on entrepreneurial activities (Harper, 1996 and 2003).

THE QUALITY OF SCIENCE

There is a rapidly-growing literature on problems in the quality of published research. The editor in chief of Lancet wrote The case against science is straightforward: much of the scientific literature, perhaps half, may be simply untrueScience has taken a turn towards darkness with reference to small sample sizes, invalid analyses, conflicts of interest and obsession with fashionable trends (Horton, 2015). There is concern about the increasing incidence of retractions and the higher rate of retractions in high impact journals (Fang et al., 2011) and the dangerous liaison of science and politics (Butos and McQuade, 2006). Less than 12% of articles in 2004 in The Journal of Economic Theory passed three tests stating a theory, explaining why it mattered and testing it (Klein and Romero, 2007). There are problems of replication of results and politicization in some fields. Another concern is the declining publication of negative results (Fanelli, 2012).

Popper provided two ways to approach this complex of issues. One is the social or institutional analysis of scientific and industrial progress which he proposed in The Poverty of Historicism. The other is the approach of critical rationalism and multi-faceted criticism to offset tendencies to confirmation bias that are built into the courses in the philosophy of science which focus on confirmation and the quest for inductive probabilities.

In The Poverty of Historicism Popper confronted Comte and Mill who adopted a psychological approach and regarded progress as inevitable due to the progressive tendencies in the human mind. Popper noted that there are other tendencies in the human mind such as forgetfulness, laziness and dogmatism. Instead of the psychological approach he urged a search for the conditions of progress using a situational approach to imagine ways that progress could be stopped. This is a very counterintuitive approach and it is presented in a few highly compressed paragraphs, summarized below.

Popper did not pursue these early thoughts in depth and others made important contributions. The art historian Ernst Gombrich applied Poppers ideas to a wide range of issues including the drift of linguistic usage, architecture, the popularity of modern art and trends in music and fashion including hemlines (Gombrich 1974). Ian Jarvie published a major work to explain what he called Poppers social turn to institutional analysis almost a decade after Popper died (Jarvie, 2001). He previously applied the situational approach in sociology (Jarvie, 1972). Roger James applied critical rationalism to some episodes of central planning in Britain (James, 1980) and Tyrell Burgess used Poppers approach in education planning and administration in Britain (Burgess, 1985). Paul Knepper explained the work that has been done on situational crime prevention inspired by both Popper and the Austrian economists (Knepper 2007).

As for stopping progress in science, Popper proposed that this might be achieved in various ways.

By closing down or controlling laboratories for research, by suppressing or controlling scientific periodicals and other means of discussion, by suppressing scientific congresses and conferences, by suppressing Universities and other schools, by suppressing books, the printing press, writing, and, in the end, speaking. All these things which indeed might be suppressed (or controlled) are social institutionsScientific method itself has social aspects. Science, and more especially scientific progress, are the results not of isolated efforts but of the free competition of thought. For science needs ever more competition between hypotheses and ever more rigorous tests. And the competing hypotheses need personal representation, as it were: they need advocates, they need a jury, and even a public. This personal representation must be institutionally organized if we wish to ensure that it works. (Popper, 1961, 154-5)

Popper also used the social approach to suggest how science can achieve a degree of objectivity through cooperative criticism of the kind practiced by Watson and Crick. When he wrote about this in the 1930s and 1940s the sociology of knowledge was becoming popular under the influence of Marxists and others such as Karl Mannheim. This approach aimed to explain our personal beliefs as a reflection of the social and political climate of ideas around us.

Popper did not challenge the importance of intellectual influences. However he turned the sociology of knowledge on its head to argue that it is a mistake focus on the formation of subjective beliefs because this does not engage with the proper object of inquiry, namely knowledge as a public or inter-subjective social product. In other words we are students and critics of spoken and written propositions and arguments, not subjective beliefs or states of mind. Thus it follows that the objectivity of science, such as it is, does not arise from the a lack of prejudices among scientists or their unique impartiality. Instead it depends on a process of more or less free criticism in the scientific community.

It may be said that what we call scientific objectivity is not a product of the individual scientists impartiality, but a product of the social or public character of scientific method; and the individual scientists impartiality is, so far as it exists, not the source but rather the result of this socially or institutionally organized objectivity of science. (Popper, 1966, 217).

It is important to note that criticism may be more or less free and this raises some issues about free speech and the factors which limit criticism. Following Poppers line of thought to promote scientific objectivity it seems that we need such things as diversity of ideas (points of view and theoretical pluralism), clear formulation of the problems that the theories are supposed to solve, and access to journals, seminars and conferences to facilitate critical discussion. Some of these requirements have to be provided by individual scientists, especially new ideas and imaginative criticism while others are social and institutional.

Turning to the contribution of the philosophy of science to the quality of scientific work and especially the declining publication of negative results, it may be that the function of criticism is underplayed in teaching the philosophy of science compared with the effort devoted to confirmation theory and the technical aspects of assigning inductive probabilities to theories. In addition much of this work proceeds in isolation from live problems in science. Mulligan and associates deplored this tendency in philosophy at large (Mulligan, Simons and Smith, 2006) and a recent example is a contribution to The Oxford Handbook of Philosophy of Science (Sprenger, 2016).

Sprenger posed two problems of induction; first whether inferences beyond the evidence are justified and second, assuming a positive answer to the first, to assess the various methods used to justify inferences about the future performance of general scientific theories. Regarding the first problem he briefly noted Poppers critical approach and work by Deborah Mayo on testing in some specific scientific situations. That could have led to a survey of work by philosophers in relation to substantive scientific problems, such as Alan Chalmers on the contribution of philosophy to the development of atomic theory in chemistry (Chalmers, 2009). This could arouse the interest of working scientists. However almost all of the paper addressed the latest developments in probability theory without seriously engaging with any contemporary scientific issues. There is an impression of a mighty engine of philosophical thought which is not transmitting any power to the wheels of science.

AN INTRODUCTION TO PHILOSOPHY AND CRITICAL THINKING

Critical thinking is an important part of philosophy and this section suggests how a short course on critical thinking could be part of a Philosophy major or indeed a part of any liberal education curriculum. The idea is to introduce the four types of criticism suggested by Bartley (above); the test of experience; the test of comparison with other theories; the check on the problem; and the test of logical consistency. This could be pursued at school, it could be used for an introduction to university courses in philosophy, it could be a core subject for all tertiary students. The students would explore the implications and applications of the four methods of criticism applied to some theories or beliefs which interest the class. The topics should have some scientific or practical relevance but it would be unhelpful to select the most pressing issues of the day if these generate too much polarization of opinion to permit a civil discussion.

Explaining the test of evidence and experience could lead into the philosophy of science, the logic of experimental design and hypothesis-testing, to a study of rules of evidence in law, to the use of diagnostic tests by doctors, motor mechanics or plumbers, and to the use of clues by detectives and archaeologists. The test of comparison with other theories would raise questions about the weight and authority to be assigned to assumptions imported into arguments from other domains. For example the psychological theories assumed by literary critics, the physical theories assumed by geologists, the sociological theories assumed by engineers, the economic theories assumed by politicians. This part of the course should open students eyes to the interdependence of the disciplines and the artificial nature of boundaries between subjects. At the same time students may learn how to use readily available resources, including other students and staff to pursue problems from one discipline to another (for example by walking from the Philosophy Department to Physics or Life Sciences).

The check on the problem can lead in particularly interesting directions. This part of the course could indicate how a revised formulation of a problem may be decisive, how background theories can unconsciously direct how problems are identified and formulated, how fashions, fads and funding can influence the direction of research. It would lead to a study of the history of ideas, showing that problems have histories, that philosophical problems usually have their roots elsewhere, in science, or religion or in social and moral dilemmas, that powerful themes can leak from one discipline to another and preoccupations often run in parallel in more than one field.

The section on logic would call for study of both the formal and informal methods of argument. Formal logic concerns rules of inference and the way that logical steps can be used to draw out the consequences of an argument or of a scientific theory, perhaps for testing or for technological application. Informal logic encompasses the tricks of debate that may be used to cover up logical and factual defects in a position. Discourse by politicians, creation scientists and advertisers would furnish material for critical study.

If this approach is used for philosophy students it could be followed by exploratory reading of the Great Philosophers, though preferably not until the students have a firm sense of their own interests and problems. In this mood they might be less deferential to the greats, more critical and at the same time more willing to learn. This would contrast with the common situation where the young student is confronted with the soaring abstractions and profound arguments produced by the titans of the past. The novice is likely to be overwhelmed (who am I to criticise the great?) or else clings to a critique provided by the teacher. The result is likely to be either a student who is inducted into a system of thought or a graduate who is highly skilled in certain methods and techniques which are not necessarily connected to issues outside philosophy.

It is important to note that this approach is very different from most of the literature on critical thinking surveyed by Miller (2005). He discovered that there was a great deal of effort dedicated to critical thinking in recent times, citing an annotated bibliography of material on critical thinking with 903 books and papers published between 1980 and 1991 (Cassell and Congleton, 1993). Scanning the literature he found that practically all of it defined the purpose of arguments in terms of justification of beliefs and persuading other people to come to the same point of view. He quoted a typical example from the preface of a book on the philosophy of argument. Argument is a social practice, arguable part of the core of any culturethe finding of reasons to justify beliefs and the response to disagreement by rational persuasion. (Blair, 1999).

The purpose of the course proposed here is very different from justification and persuasion because it is focussed on the criticism of arguments and it can be explained in the language of used by Stuart Firestein in his book Ignorance: How it Drives Science (Firestein, 2012). More precisely, discovering ignorance (unsolved problems) drives science. Criticism a la Watson and Crick uncovers ignorance especially false assumptions and that drives the quest for better assumptions and new ideas. According to Firestein the great Italian physicist Enrico Fermi told his students that an experiment that successfully proves a hypothesis is a measurement and one that doesnt is a discovery an uncovering of new ignorance (Firestein, 2012, 57). Firesteins book could be the text for the course.

CONCLUSION

Popper has a low profile these days judging from the negligible references to his work in The Oxford Handbook of Contemporary Philosophy (Jackson and Smith, 2005) and The Oxford Handbook of Philosophy of Science (Humphries, 2016). He enjoyed a high profile during the philosophy of science wars in the 1960s and 1970s but he became classified as a transitional figure between the logical empiricists and the new waves generated by Kuhn, Lakatos and Feyerabend. It seems that he went out of fashion before the full implications of his critical rationalism and evolutionary epistemology were explored (Champion, 2011). In the philosophy and methodology of economics that view is strongly supported by Hands (2001). This paper argues that there is still plenty of mileage in Poppers work including a potentially fruitful partnership with Austrian economics, a contribution to improve the quality of science and ideas to promote critical and imaginative thinking.

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Medawar, P. B. (1982). Plutos Republic. Oxford: Oxford University Press.

Knepper, P. (2007). Situational Logic in Social Science Inquiry: From Economics to Criminology. Review of Austrian Economics 20. 25-41.

Miller, D. (1983). A Pocket Popper. Fontana Pocket Readers. Oxford: Oxford University Press.

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What is AI (artificial intelligence)? – Definition from WhatIs.com

Posted on December 15, 2018 by Danzig

AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

AI can be categorized in any number of ways, but here are two examples.

The first classifies AI systems as either weak AI or strong AI.Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Apple's Siri, are a form of weak AI.

Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities so that when presented with an unfamiliar task, it has enough intelligence to find a solution. TheTuring Test, developed by mathematician Alan Turing in 1950, is a method used to determine if a computer can actually think like a human, although the method is controversial.

Alec Ross on AI and robotics

The second example comes from Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University. He categorizes AI into four types, from the kind of AI systems that exist today to sentient systems, which do not yet exist. His categories are as follows:

AI is incorporated into a variety of different types of technology. Here are seven examples.

Artificial intelligence has made its way into a number of areas. Here are six examples.

While AI tools present a range of new functionality for businesses, artificial intellignce also raises some ethical questions. Deep learning algorithms, which underpin many of the most advanced AI tools, only know what's in the data used during training. Most available data sets for training likely contain traces of human bias. This in turn can make the AI tools biased in their function. This has been seen in the Microsoft chatbot Tay, which learned a misogynistic and anti-Semitic vocabulary from Twitter users, and the Google Photo image classification tool that classified a group of African Americans as gorillas.

The application of AI in the realm of self-driving cars also raises ethical concerns. When an autonomous vehicle is involved in an accident, liability is unclear. Autonomous vehicles may also be put in a position where an accident is unavoidable, forcing it to make ethical decisions about how to minimize damage.

Another major concern is the potential for abuse of AI tools. Hackers are starting to use sophisticated machine learning tools to gain access to sensitive systems, complicating the issue of security beyond its current state.

Deep learning-based video and audio generation tools also present bad actors with the tools necessary to create so-called deepfakes, convincingly fabricated videos of public figures saying or doing things that never took place.

Despite these potential risks, there are few regulations governing the use AI tools, and where laws do exist, the typically pertain to AI only indirectly. For example, federal Fair Lending regulations require financial institutions to explain credit decisions to potential customers, which limit the extent to which lenders can use deep learning algorithms, which by their nature are typically opaque. Europe's GDPR puts strict limits on how enterprises can use consumer data, which impedes the training and functionality of many consumer-facing AI applications.

In 2016, the National Science and Technology Council issued a report examining the potential role governmental regulation might play in AI development, but it did not recommend specific legislation be considered. Since that time the issue has received little attention from lawmakers.

John McCarthy, an American computer scientist, coined the term "artificial intelligence" in 1956 at the Dartmouth Conference where the discipline was born. Today, it is an umbrella term that encompasses everything from robotic process automation to actual robotics. It has gained prominence recently due, in part, tobig data, or the increase in speed, size and variety of data businesses now collect. AI can perform tasks such as identifying patterns in data more efficiently than humans, enabling businesses to gain more insight from theirdata.

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What is AI (artificial intelligence)? - Definition from WhatIs.com

Benefits & Risks of Artificial Intelligence – Future of Life Institute

Posted on December 15, 2018 by Danzig

Many AI researchers roll their eyes when seeing this headline:Stephen Hawking warns that rise of robots may be disastrous for mankind. And as many havelost count of how many similar articles theyveseen.Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because theyve become conscious and/or evil.On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers dontworry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, androbots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car?Although this mystery of consciousness is interesting in its own right, its irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AIdoes, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isnt malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans dont generally hate ants, but were more intelligent than they are so if we want to build a hydroelectric dam and theres an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

The consciousness misconception is related to the myth that machines cant have goals.Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target.If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose.If that heat-seeking missile were chasing you, you probably wouldnt exclaim: Im not worried, because machines cant have goals!

I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids,because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isnt with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.

The robot misconception is related to the myth that machines cant control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, its possible that we might also cede control.

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Benefits & Risks of Artificial Intelligence - Future of Life Institute

What is Artificial Intelligence (AI)? – Definition from Techopedia

Posted on December 15, 2018 by Danzig

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.

Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

What is Artificial Intelligence (AI)? - Definition from Techopedia

19 Artificial Intelligence Technologies That Will Dominate In 2018

Posted on December 15, 2018 by Danzig

In 2017, we published a popular post on artificial intelligence (AI) technologies that would dominate that year, based on Forresters TechRadar report.

Heres the updated version, which includes 9 more technologies to watch out for this year.

We hope they inspire you to join the 62% of companies boosting their enterprises in 2018.

Natural language generation is an AI sub-discipline that converts data into text, enabling computers to communicate ideas with perfect accuracy.

It is used in customer service to generate reports and market summaries and is offered by companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop.

Siri is just one of the systems that can understand you.

Every day, more and more systems are created that can transcribe human language, reaching hundreds of thousands through voice-response interactive systems and mobile apps.

Companies offering speech recognition services include NICE, Nuance Communications, OpenText and Verint Systems.

A virtual agent is nothing more than a computer agent or program capable of interacting with humans.

The most common example of this kind of technology are chatbots.

Virtual agents are currently being used for customer service and support and as smart home managers.

Some of the companies that provide virtual agents include Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft and Satisfi.

These days, computers can also easily learn, and they can be incredibly intelligent!

Machine learning (ML) is a subdiscipline of computer science and a branch of AI. Its goal is to develop techniques that allow computers to learn.

By providing algorithms, APIs (application programming interface), development and training tools, big data, applications and other machines, ML platforms are gaining more and more traction every day.

They are currently mainly being used for prediction and classification.

Some of the companies selling ML platforms include Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree and Adext.

This last one is particularly interesting for one simple reason: Adext AI is the first and only audience management tool in the world that applies real AI and machine learning to digital advertising to find the most profitable audience or demographic group for any ad. You can learn more about it here.

AI technology makes hardware much friendlier.

How?

Through new graphic and central processing units and processing devices specifically designed and structured to execute AI-oriented tasks.

And if you havent seen them already, expect the imminent appearance and wide acceptance of AI-optimized silicon chips that can be inserted right into your portable devices and elsewhere.

You can get access to this technology through Alluviate, Cray, Google, IBM, Intel, and Nvidia.

Intelligent machines are capable of introducing rules and logic to AI systems so you can use them for initial setup/training, ongoing maintenance, and tuning.

Decision management has already been incorporated into a variety of corporate applications to assist and execute automated decision, making your business as profitable as possible.

Check out Advanced Systems Concepts, Informatica, Maana, Pegasystems, and UiPath for additional options.

Deep learning platforms use a unique form of ML that involves artificial neural circuits with various abstraction layers that can mimic the human brain, processing data and creating patterns for decision making.

It is currently mainly being used to recognize patterns and classify applications that are only compatible with large-scale data sets.

Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology and Sentient Technologies all have deep learning options worthy of exploring.

This technology can identify, measure and analyze human behavior and physical aspects of the bodys structure and form.

It allows for more natural interactions between humans and machines, including interactions related to touch, image, speech and body language recognition, and is big within the market research field.

3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera and Tahzoo are all biometrics companies working hard to develop this area.

Robotic processes automation uses scripts and methods that mimic and automate human tasks to support corporate processes.

It is particularly useful for situations when hiring humans for a specific job or task is too expensive or inefficient.

The good example is Adext AI, a platform that automates digital advertising processes using AI, saving businesses from devoting hours to mechanical and repetitive tasks.

Its a solution that lets you make the most of your human talent and move employees into more strategic and creative positions, so their actions can really make an impact on the company's growth.

Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, and WorkFusion are other examples of robotic processes automation companies.

This technology uses text analytics to understand the structure of sentences, as well as their meaning and intention, through statistical methods and ML.

Text analytics and NLP are currently being used for security systems and fraud detection.

They are also being used by a vast array of automated assistants and apps to extract unstructured data.

Some of the service providers and suppliers of these technologies include Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, and Synapsify.

A digital twin is a software construct that bridges the gap between physical systems and the digital world.

General Electric (GE), for example, is building an AI workforce to monitor its aircraft engines, locomotives and gas turbines and predict failures with cloud-hosted software models of GEs machines. Their digital twins are mainly lines of software code, but the most elaborate versions look like 3-D computer-aided design drawings full of interactive charts, diagrams, and data points.

Companies using digital twin and AI modeling technologies include VEERUM, in the capital project delivery space; Akselos, which is using it to protect critical infrastructure, and Supply Dynamics, which has developed a SaaS solution to manage raw material sourcing in complex, highly distributed manufacturing environments.

Cyber defense is a computer network defense mechanism that focuses on preventing, detecting and providing timely responses to attacks or threats to infrastructure and information.

AI and ML are now being used to move cyberdefense into a new evolutionary phase in response to an increasingly hostile environment: Breach Level Index detected a total of over 2 billion breached records during 2017. Seventy-six percent of the records in the survey were lost accidentally, and 69% were an identity theft type of breach.

Recurrent neural networks, which are capable of processing sequences of inputs, can be used in combination with ML techniques to create supervised learning technologies, which uncover suspicious user activity and detect up to 85% of all cyber attacks.

Startups such as Darktrace, which pairs behavioral analytics with advanced mathematics to automatically detect abnormal behavior within organizations and Cylance, which applies AI algorithms to stop malware and mitigate damage from zero-day attacks, are both working in the area of AI-powered cyber defense.

DeepInstinct, another cyber defense company, is a deep learning project named Most Disruptive Startup by Nvidias Silicon Valley ceremony, protects enterprises' endpoints, servers, and mobile devices.

Compliance is the certification or confirmation that a person or organization meets the requirements of accepted practices, legislation, rules and regulations, standards or the terms of a contract, and there is a significant industry that upholds it.

We are now seeing the first wave of regulatory compliance solutions that use AI to deliver efficiency through automation and comprehensive risk coverage.

Some examples of AIs use in compliance are showing up across the world. For example, NLP (Natural Language Processing) solutions can scan regulatory text and match its patterns with a cluster of keywords to identify the changes that are relevant to an organization.

Capital stress testing solutions with predictive analytics and scenario builders can help organizations stay compliant with regulatory capital requirements. And the volume of transaction activities flagged as potential examples of money laundering can be reduced as deep learning is used to apply increasingly sophisticated business rules to each one.

Companies working in this area include Compliance.ai, a Retch company that matches regulatory documents to a corresponding business function; Merlon Intelligence, a global compliance technology company that supports the financial services industry to combat financial crimes, and Socure, whose patented predictive analytics platform boosts customer acceptance rates while reducing fraud and manual reviews.

While some are rightfully concerned about AI replacing people in the workplace, lets not forget that AI technology also has the potential to vastly help employees in their work, especially those in knowledge work.

In fact, the automation of knowledge work has been listed as the #2 most disruptive emerging tech trend.

The medical and legal professions, which are heavily reliant on knowledge workers, is where workers will increasingly use AI as a diagnostic tool.

There is an increasing number of companies working on technologies in this area. Kim Technologies, whose aim is to empower knowledge workers who have little to no IT programming experience with the tools to create new workflow and document processes with the help of AI, is one of them. Kyndi is another, whose platform is designed to help knowledge workers process vast amounts of information.

Content creation now includes any material people contribute to the online world, such as videos, ads, blog posts, white papers, infographics and other visual or written assets.

Brands like USA Today, Hearst and CBS, are already using AI to generate their content.

Wibbitz, a SaaS tool that helps publishers create videos from written content in minutes with AI video production technology, is a great example of a solution from this field. Wordsmith is another tool, created by Automated Insights, that applies NLP (Natural Language Processing) to generate news stories based on earnings data.

Peer-to-peer networks, in their purest form, are created when two or more PCs connect and share resources without the data going through a server computer.

But peer-to-peer networks are also used by cryptocurrencies, and have the potential to even solve some of the worlds most challenging problems, by collecting and analyzing large amounts of data, says Ben Hartman, CEO of Bet Capital LLC, to Entrepreneur.

Nano Vision, a startup that rewards users with cryptocurrency for their molecular data, aims to change the way we approach threats to human health, such as superbugs, infectious diseases, and cancer, among others.

Another player utilizing peer-to-peer networks and AI is Presearch, a decentralized search engine thats powered by the community and rewards members with tokens for a more transparent search system.

This technology allows software to read the emotions on a human face using advanced image processing or audio data processing. We are now at the point where we can capture micro-expressions, or subtle body language cues, and vocal intonation that betrays a persons feelings.

Law enforcers can use this technology to try to detect more information about someone during interrogation. But it also has a wide range of applications for marketers.

There are increasing numbers of startups working in this area. Beyond Verbal analyzes audio inputs to describe a persons character traits, including how positive, how excited, angry or moody they are. nViso uses emotion video analytics to inspire new product ideas, identify upgrades and enhance the consumer experience. And Affectivas Emotion AI is used in the gaming, automotive, robotics, education, healthcare industries, and other fields, to apply facial coding and emotion analytics from face and voice data.

Image recognition is the process of identifying and detecting an object or feature in a digital image or video, and AI is increasingly being stacked on top of this technology to great effect.

AI can search social media platforms for photos and compare them to a wide range of data sets to decide which ones are most relevant during image searches.

Image recognition technology can also be used to detect license plates, diagnose disease, analyze clients and their opinions and verify users based on their face.

Clarifai provides image recognition systems for customers to detect near-duplicates and find similar uncategorized images.

SenseTime is one of the leaders in this industry and develops face recognition technology that can be applied to payment and picture analysis for bank card verification and other applications. And GumGums mission is to unlock the value of images and videos produced across the web using AI technology.

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19 Artificial Intelligence Technologies That Will Dominate In 2018

5 artificial intelligence trends that will dominate 2018 | CIO

Posted on December 15, 2018 by Danzig

2017 saw an explosion of machine learning in production use, with even deep learning and artificial intelligence (AI) being leveraged for practical applications.

"Basic analytics are out; machine learning (and beyond) are in," says Kenneth Sanford, U.S. lead analytics architect for collaborative data science platform Dataiku, as he looks back on 2017.

Sanford says practical applications of machine learning, deep learning, and AI are "everywhere and out in the open these days," pointing to the "super billboards" in London's Piccadilly Circus that leverage hidden cameras gathering data on foot and road traffic (including the make and model of passing cars) to deliver targeted advertisements.

So where will these frameworks and tools take us in 2018? We spoke with a number of IT leaders and industry experts about what to expect in the coming year.

AI is already here, whether we recognize it or not.

"Many organizations are using AI already, but they may not refer to it as 'AI,'" says Scott Gnau, CTO of Hortonworks. "For example, any organization using a chatbot feature to engage with customers is using artificial intelligence."

But many of the deployments leveraging AI technologies and tools have been small-scale. Expect organizations to ramp up in a big way in 2018.

"Enterprises have spent the past few years educating themselves on various AI frameworks and tools," says Nima Negahban, CTO and co-founder of Kinetica, a specialist in GPU-accelerated databases for high-performance analytics. "But as AI goes mainstream, it will move beyond small-scale experiments to being automated and operationalized. As enterprises move forward with operationalizing AI, they will look for products and tools to automate, manage, and streamline the entire machine learning and deep learning life cycle."

Negahban predicts 2018 will see an increase in investments in AI life cycle management, and technologies that house the data and supervise the process will mature.

Ramon Chen, chief product officer of master data management specialist Reltio, is less sanguine. Chen says there have been repeated predictions for several years that tout potential breakthroughs in the use of AI and machine learning, but the reality is that most enterprises have yet to see quantifiable benefits from their investments in these areas.

Chen says the hype to date has been overblown, and most enterprises are reluctant to get started due to a combination of skepticism, lack of expertise, and most important of all, a lack of confidence in the reliability of their data sets.

"In fact, while the headlines will be mostly about AI, most enterprises will need to first focus on IA (information augmentation): getting their data organized in a manner that ensures it can be reconciled, refined, and related, to uncover relevant insights that support efficient business execution across all departments, while addressing the burden of regulatory compliance," Chen says.

Chad Meley, vice president of marketing at Teradata, agrees that 2018 will see a backlash against AI hype, but believes a more balanced approach of deep learning and shallow learning application to business opportunities will emerge as a result.

While there may be a backlash against the hype, it won't stop large enterprises from investing in AI and related technologies.

"AI is the new big data: Companies race to do it whether they know they need it or not," says Monte Zweben, CEO of Splice Machine.

Meley points to Teradata's recently released 2017 State of Artificial Intelligence for Enterprises report, which identified a lack of IT infrastructure as the greatest barrier to realizing benefits from AI, surpassing issues like access to talent, lack of budget, and weak or unknown business cases.

"Companies will respond in 2018 with enterprise-grade AI product and supporting offerings that overcome the growing pains associated with AI adoption," Meley says.

Reltio's Chen isn't alone in his conviction that enterprises need to get their data in order. Tomer Shiran, CEO and co-founder of analytics startup Dremio, a driving force behind the open source Apache Arrow project, believes a debate about data sets will take center stage in 2018.

"Everywhere you turn, companies are adding AI to their products to make them smarter, more efficient, and even autonomous," Shiran says. "In 2017, we heard competing arguments for whether AI would create jobs or eliminate them, with some even proposing the end of the human race. What has started to emerge as a key part of the conversation is how training data sets shape the behavior of these models."

It turns out, Shiran says, that models are only as good as the training data they use, and developing a representative, effective training data set is very challenging.

"As a trivial example, consider the example tweeted by a Facebook engineer of a soap dispenser that works for white people but not those with darker skin," Shiran says. "Humans are hopelessly biased, and the question for AI will become whether we can do better in terms of bias or will we do worse. This debate will center around data ownership what data we own about ourselves, and the companies like Google, Facebook, Amazon, Uber, etc. who have amassed enormous data sets that will feed our models."

One of the big barriers to the adoption of AI, particularly in regulated industries, is the difficulty in showing exactly how an AI reached a decision. Kinetica's Negahban says creating AI audit trails will be essential.

"AI is increasingly getting used for applications like drug discovery or the connected car, and these applications can have a detrimental impact on human life if an incorrect decision is made," Negahban says. "Detecting exactly what caused the final incorrect decision leading to a serious problem is something enterprises will start to look at in 2018. Auditing and tracking every input and every score that a framework produces will help with detecting the human-written code that ultimately caused the problem."

Horia Margarit, principal data scientist for big-data-as-a-service provider Qubole, agrees that enterprises in 2018 will seek to improve their infrastructure and processes for supporting their machine learning and AI efforts.

"As companies look to innovate and improve with machine learning and artificial intelligence, more specialized tooling and infrastructure will be adopted in the cloud to support specific use cases, like solutions for merging multi-modal sensory inputs for human interaction (think sound, touch, and vision) or solutions for merging satellite imagery with financial data to catapult algorithmic trading capabilities," Margarit says.

"We expect to see an explosion in cloud-based solutions that accelerate the current pace of data collection and further demonstrate the need for frictionless, on-demand compute and storage from managed cloud providers," he adds.

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5 artificial intelligence trends that will dominate 2018 | CIO

Migrant crisis – Migrant crisis – Pictures – CBS News

Posted on December 15, 2018 by Danzig

Thousands trying to reach Western Europe are facing an ever increasing desperate situation as countries close their borders and are overwhelmed by the flow of migrants and refugees.

Here, a mounted policeman leads a group of migrants near Dobova, Slovenia, October 20, 2015.

Credit: Srdjan Zivulovic/Reuters

Doctors and paramedics take care of a child who was later taken to the hospital following a rescue operation when a boat with migrants sank while attempting to reach the Greek island of Lesbos from Turkey on October 28, 2015.

The Greek coast guard said it rescued 242 refugees and migrants off the eastern island of Lesbos on October 28, 2015, after the wooden boat they traveled in capsized.

Credit: Aris Messinis/AFP/Getty Images

A Greek Coast Guard helicopter flies over fishing boats trying to rescue refugees and migrants, after a boat carrying more than 200 people sunk while crossing part of the Aegean sea from Turkey, near the Greek island of Lesbos, October 28, 2015.

At least three migrants drowned and the Greek coastguard rescued 242 others when their wooden boat sank north of the island of Lesbos on Wednesday, authorities said. Four other boats sank the same day leaving at least 15 people dead, mainly children, in total.

Credit: Giorgos Moutafis/Reuters

A man holds three children wearing thermal blankets after their arrival in bad weather from Turkey on the Greek island of Lesbos , Wednesday, Oct. 28, 2015.

With winter fast approaching, the danger grows and more are taking risky journeys.

Credit: Santi Palacios/AP

Mohammed Hasan, an 18-month-old Syrian toddler, is seen onshore after he was rescued by a Turkish fisherman after a boat of migrants sunk a few miles off the coast of Turkey, October 21, 2015. The boy was reunited with his mother in Turkey after he was revived with CPR.

Nearly 50,000 people have made it to Greece's coast in a few short days, but dozens more have died at sea, including 14 in this incident.

Credit: CBS News

Migrants protect themselves from the rain as they make their way to Slovenia from Trnovec, Croatia, October 19, 2015.

Thousands of migrants crossed into Slovenia after Croatia closed its frontier, October 19, 2015. Hungary sealed its border with Croatia the previous week. Many refugees are now facing deteriorating conditions as winter approaches.

The Balkans faced a growing backlog of migrants, thousands building up on cold, wet borders after the closure of Hungary's southern frontier diverted them to Slovenia.

Credit: Srdjan Zivulovic/Reuters

A policeman holds the hand of a young girl as migrants are escorted through Dobova to a holding camp in Dobova, Slovenia, October 22, 2015.

Thousands of migrants marched across the border from Croatia into Slovenia as authorities intensify their efforts to attempt to cope with a human tide unseen in Europe since World War II.

Credit: Jeff J Mitchell/Getty Images

Croatian riot police officers control the access to a refugee camp as more migrants arrive from the Serbian border on September 22, 2015 in Opatovac, Croatia.

Croatia built a camp to control the flow of migrants to Hungary with a capacity of 4,000 people.

Credit: David Ramos, Getty Images

Hundreds of migrants who arrived on the second train of the day at Hegyeshalom on the Hungarian and Austrian border, walk the four kilometres (2.5 miles) into Austria on September 22, 2015.

Thousands of migrants arrived in Austria over the weekend with more en-route from Hungary, Croatia and Slovenia. Politicians from across the European Union are holding meetings on the refugee crisis September 23, to try and solve the crisis and the dispute of how to relocate 120,000 migrants across EU states.

Credit: Christopher Furlong/Getty Images

Migrants and refugees queue to register at a camp after crossing the Greek-Macedonian border near Gevgelija on September 22, 2015.

EU interior ministers were set to hold emergency talks to try and bridge deep divisions over Europe's worst migrant crisis since World War II, as pressure piles on member states to reach an agreement.

Credit: NikolayI Doychinov/AFP/Getty Images

A local man surveys a huge pile of deflated dinghies, tubes and life vests left by arriving refugees and migrants on the Greek island of Lesbos on September 18, 2015.

Credit: Yannis Behrakis/Reuters

Migrants desperately try and board a train heading for Zagreb from Tovarnik station on September 20, 2015 in Tovarnik, Croatia.

Croatia continues to send buses and trains north to its border with Hungary, as officials have estimated that around 20,000 migrants have entered since September 16.

Credit: Jeff J Mitchell/Getty Images

The open-door policy of the Croatian government for migrants and refugees lasted just 24 hours. After an influx of an estimated 13,000 migrants and refugees in two days, the country said it could take no more, September 18, 2015.

A baby cries as migrants clamor to board a bus in Tovarnik, Croatia, September 17, 2015. Asylum seekers thwarted by a new Hungarian border fence and repelled by riot police poured into Croatia, spreading the strain.

Credit: Antonio Bronic/Reuters

Migrants protest at the Tovarnik railway station, Croatia September 18, 2015. Migrants continued to stream through fields from Serbia into the European Union on Friday, undeterred by Croatia's closure of almost all road crossings after an influx of more than 11,000. Helpless to stem the flow, Croatian police rounded them up at the Tovarnik on the Croatian side of the border, where several thousand had spent the night under open skies. Some kept traveling, and reached Slovenia overnight.

Credit: Antonio Bronic/Reuters

A migrant man remonstrates with security as he and other migrants try to force their way through police lines at Tovarnik station for a train to take them to Zagreb on September 17, 2015 in Tovarnik, Croatia. Migrants are crossing into Croatia from Serbia two days after Hungary sealed its border with Serbia, the majority of them want to reach Germany, amid divisions within the European Union over how to manage the ongoing crisis.

Credit: Jeff J Mitchell/Getty Images

Migrants force their way through police lines at Tovarnik station to board a train bound for Zagreb on September 17, 2015 in Tovarnik, Croatia. Migrants are diverting to Croatia from Serbia after Hungary closed its border with Serbia, with the majority of them trying to reach Germany amid divisions within the European Union over how to manage the ongoing crisis.

Credit: Jeff J Mitchell/Getty Images

Migrants wait near the train station in Tovarnik, Croatia, September 17, 2015. Amid chaotic scenes at its border with Serbia, Croatia said on Thursday it could not cope with a flood of migrants seeking a new route into the EU after Hungary kept them out by erecting a fence and using tear gas and water cannon against them.

Credit: Antonio Bronic/Reuters

Policemen direct migrants during a stampede to board a bus in Tovarnik, Croatia on September 17, 2015. Croatia said it could not take in any more migrants, amid chaotic scenes of riot police trying to control thousands who have streamed into the European Union country from Serbia.

Credit: Antonio Bronic/Reuters

A migrant taunts Hungarian riot police as they fire tear gas and water cannons on the Serbian side of the border, near Roszke, Hungary, September 16, 2015. The clash occurred after hundreds of migrants, stuck at the sealed border between Serbia and Hungary, protested and tried to break through.

Serbia condemned Hungary's use of water cannon and tear gas against migrants on their border, saying Hungary had "no right" to do so, the Serbian state news agency Tanjug reported.

Credit: Stoyan Nenov/Reuters

An injured migrant carries a child during clashes with Hungarian riot police at the border crossing with Serbia in Roszke, Hungary on September 16, 2015. Hungarian police fired tear gas and water cannons at protesting migrants demanding they be allowed to enter from Serbia on the second day of a border crackdown.

Credit: Karnok Csaba/Reuters

Migrants protest as Hungarian riot police fires tear gas and water cannons at the border crossing with Serbia in Roszke, Hungary, September 16, 2015.

Credit: Stoyan Nenov/Reuters

Hungarian riot policemen escort a migrant woman and a child in Roszke, Hungary on September 16, 2015.

Credit: Dado Ruvic/Reuters

Hungarian riot police watche from behind a fence as migrants protest on the Serbian side of the border, near Roszke, Hungary September 16, 2015.

Credit: Dado Ruvic/Reuters

A migrant is hit by a jet from a water cannon used by Hungarian riot police on the Serbian side of the border, near Roszke, Hungary September 16, 2015.

Hundreds of migrants protested the border closure and tried to break through the sealed border.

Credit: Marko Djurica/Reuters

Migrants and refugees demonstrate as Turkish police block the road at Esenler Bus Terminal in Istanbul, Turkey on September 16, 2015.

Credit: Ahmet Sik/Getty Images

Migrants and refugees demonstrate as Turkish police block the road at Esenler Bus Terminal in Istanbul, Turkey, September 16, 2015.

Credit: Ahmet Sik/Getty Images

A refugee stands looks through the fence at the Serbian border with Hungary near the town of Horgos on September 15, 2015.

Credit: Armend Nimani/AFP/Getty Images

Hungarian police officers stand in front of a fence on the Serbian side of the border after sealing it near the village of Horgos, Serbia, September 14, 2015, near the Hungarian migrant collection point in Roszke.

Hungarian police closed off the main crossing point for thousands of migrants and refugees entering from Serbia every day.

The number of migrants entering Hungary this year has risen above 200,000, police said September 14. Almost all of the migrants were seeking to travel onwards to western Europe, particularly Germany and Sweden.

Credit: Marko Djurica/Reuters

Police check the passports and papers of Syrian migrants at the border check point in the village of Szentgotthard, Hungary on September 14, 2015.

Two decades of frontier-free travel across Europe unravelled as countries re-established border controls in the face of an unprecedented influx of migrants, which broke the record for the most arrivals by land in a single day.

Credit: Srdjan Zivulovic/Reuters

A policeman guards migrants detained after crossing the border from Serbia near Asttohatolom, Hungary on September 15, 2015.

Hungary's right-wing government shut the main land route for migrants into the EU September 15, taking matters into its own hands to halt Europe's unprecedented influx of refugees while the bloc failed to agree a plan to distribute them.

Credit: Dado Ruvic/Reuters

Migrants queue to board buses bound for Vienna from Hegyshalom holding center on the Austrian border after Hungarian authorities closed the open railway track crossing in Hegyeshalom, Hungary, September 15, 2015.

Hungary implemented new laws to cope with the influx of migrants which became enforceable on the night of September 14. Since the beginning of 2015 the number of migrants using the so-called 'Balkans route' has exploded with migrants arriving in Greece from Turkey and then traveling on through Macedonia and Serbia before entering the EU via Hungary.

Credit: Jeff J Mitchell/Getty Images

A railway wagon covered in barbed wire is placed at the Hungarian border with Serbia to stop migrants and refugees near the town of Horgos on September 15, 2015.

Hungarian police closed off the main crossing point for thousands of migrants and refugees entering from Serbia daily.

Credit: Armend Nimani/AFP/Getty Images

Migrants wait on the Serbian side of the border with Hungary in Roszke, September 15, 2015. Hungarian police detained 16 people claiming to be Syrian and Afghan migrants early in the day for illegally crossing the Serbian border fence, a police spokeswoman said, as tough new laws took effect to guard the southern frontier.

Credit: Bernadett Szabo/Reuters

Policemen fix registration bands on the wrists of migrant children at a train station near the border with Austria in Freilassing, Germany September 15, 2015.

A total of 4,537 asylum seekers reached Germany by train September 14 despite the imposition of new controls at the border with Austria, according to the federal police. The arrivals brought the number of asylum seekers who have entered Germany by train since the start of the month to 91,823, a police spokeswoman in Potsdam said.

Credit: Dominic Ebenbichler/Reuters

A refugee swims towards the shore after a dinghy carrying Syrian and Afghan refugees deflated some 100m away before reaching the Greek island of Lesbos, September 13, 2015.

An estimated 309,000 people have arrived by sea in Greece, the International Organization for Migration (IMO) said September 11, 2015. About half of those crossing the Mediterranean are Syrians fleeing civil war, according to the United Nations refugee agency, UNHCR.

Credit: Alkis Konstantinidis/Reuters

Migrants eat at a reception center after their arrival at the main railway station in Dortmund, Germany on September 13, 2015.

Germany re-imposed border controls on September 13 after Europe's most powerful nation acknowledged it could scarcely cope with thousands of asylum seekers arriving every day.

Credit: Ina Fassbender/Reuters

Migrants wait to board busses in Nickelsdorf, Austria on September 14, 2015.

Thousands of migrants walked unhindered across the border into Austria from Hungary on September 14, where the frontier was kept open despite Germany's sudden reintroduction of checks.

Credit: Leonhard Foeger/Reuters

Syrian refugee Asmaa wipes her tears as she waits for a train on the platform at the main railway station in Munich, September 13, 2015.

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Migrant crisis - Migrant crisis - Pictures - CBS News

The migrant crisis – The Truthseeker

Posted on December 14, 2018 by Danzig

Browse: Home / Race / The migrant crisis

By wmw_admin on December 13, 2018

An increasingly familiar tale from Germany

Posted in Current Affairs, Headlines, The migrant crisis |

By Irish Savant on December 13, 2018

Dont blame the migrants for flocking to White countries. Theyre no more to blame than are flies drawn to a juicy cow pat. Our internal traitors are the SJWs

Posted in Headlines, The migrant crisis |

By wmw_admin on December 3, 2018

Expanding the Third World into the West through migration will enable the elite to exercise their power far more brutally and despotically than they do now