BRUSSELS BEHIND THE SCENES Weekly analysis and untold stories With SAMUEL STOLTON

Other Brussels behind the scenes stories: European business is embroiled in a Colombian guerrilla war George Floyds blood is on Europes hands, too The EU is trading in dead tigers Remembering Manolis Glezos Who wins from the Coronavirus blamegame?

Telling the truth in a post-truth world

In a hyper-connected and globalized world, the pursuit of truth becomes an arduous enterprise.

Fraught with geopolitical falsifications and commercially-invested fabrications, that which is regarded as true is often hauled from the hands of its purveyors and fashioned into an altogether counterfeit sculpture with remarkable rapidity.

The manifestation of a post-truth reality, where facts become fluid and malleable, fragile to the touch and bitter to the tongue, is no modern phenomenon. But it is the agency and spread afforded to a post-truth statement in the digitized world that allows for an untruth to gain such traction.

BRUSSELS BEHIND THE SCENES is a weekly newsletter which brings the untold stories about the characters driving the policies affecting our lives. Analysis not found anywhere else, The Brussels Times Samuel Stolton helps you make sense of what is happening in Brussels. If you want to receive Brussels behind the scenes straight to your inbox every week, subscribe to the newsletter here.

The issues the current context raise are manifold, but mostly hinge on the fact that truths are regarded as possessions, as Nietzsche said: The investigator into [such] truths is basically seeking just the metamorphosis of the world into man; he is struggling to understand the world as a human-like thing and acquires at best a feeling of assimilation.

What Nietzsche means here is that sheer human confusion, delight, mystery and wonder with the world mutate into a human willingness to wrest from our everyday experience a sense of understanding. We create truths in order to try and explain our everyday experiences, albeit within the limits of our own species.

When the Commissions Vice-President for Values and Transparency, Vra Jourov, told Brussels reporters recently that it was time to tell the truth about China, the truth that she wanted to perpetuate was woefully partial, possessed by her own reality and subsidized by a political currency spent on attempting to impose diplomatic pressure on the Chinese, ironically in the aftermath of Beijings attempts to disseminate their own truths regarding the coronavirus outbreak.

With vitriolic tit-for-tat geopolitical recriminations such as these, how on earth can we ever expect citizens to be delivered a truth they can trust, a truth that is not possessed by anothers interpretation of reality?

In this climate, it is hardly surprising that the recently published Reuters Institute Digital News Report drew attention to the fact that trust in the media worldwide continues to fall rapidly, with fewer than four in ten (38%) of those surveyed saying that they trust media most of the time, and less than half (46%) saying they trust the news that they themselves use.

The latter finding is particularly astonishing: nearly half of those surveyed do not trust the media that they absorb regularly. What does this say about a society in which citizens are content to subject themselves to information that they knowingly regard as untrustworthy? Has civilization really arrived at some sort of an abandoned fate whereby governments are satisfied with a populace vegetating in a state of acquiesced ignorance?

This is the mad purgatory that presents itself to the modern journalist. In a dizzying world of truths and untruths, where every other citizen doubts the very words that acquaint their gaze, any pretence to objectivity appears tenuous. The citizen is embedded in a wider ecosystem of what Hannah Arendt referred to as defactualization where there is a legitimate incapacity on behalf of the reader to discern fact from fiction.

When Jourov made the aforementioned remarks about China, she was presenting a report about the state of disinformation on the bloc, which earmarked Russia and China as having engaged in targeted influence operations and disinformation campaigns related to the coronavirus crisis.

Russia has an established track record in this arena, its Internet Research Agency, otherwise known as the troll factory, having long churned out propaganda crusades aimed at sowing division among Western rivals.

In my view, the most effective remedy against such campaigns is an increased emphasis on a complete, total and unhindered commitment to transparency.

But what does total transparency in political governance look like? Do we, as the steadfast purveyors of truth, in fact require a certain obfuscation of legal and political systems in order for our pursuit of transparency to be worthwhile? What becomes of transparency in a fully-transparent world?

In this case, one would assume that transparency becomes normalized to the extent by which truth becomes discernible and objectivity becomes attainable. How can you question or scrutinize a political body which is transparent in total terms? The answers to your questions would already be in front of your eyes.

And it was Jourov again that made me dream of the chimera of total transparency speaking to MEPs in Parliaments Civil Liberties committee on Monday, she said that the executive wants to work further on developing a culture of transparency that stretches throughout the legislative cycle, including trialogues.

For those not immersed in the Brussels policy cycle, trialogues are the three-way negotiations on legislative files between the Council and the Parliament, mediated by the Commission. They are strictly private meetings, cut off from public scrutiny entirely. A couple of years ago, I managed to attend one.

The whole affair was a bizarre carnival of messianic spirits engaged in a feverish debate into the early morning hours embellished with servings of lukewarm sandwiches and chemical-infused red wine, the air soured by bitter overtones of body odour, extracted from the pores of fatigued and policy-beaten pink bodies. Maybe its not surprising that these meetings are normally off limits.

With that being said, the internal legislative process of the EU for many across the continent does indeed remain an unfathomable covert operation. In a post-truth world, where the minds of the masses become vulnerable to the imposition of divisive narratives, such black boxes in EU law-making can be exploited as political capital by those who seek to perpetuate untruths. For total transparency to ever be achieved in Brussels, the doors should be opened up on trialogues, once and for all.

If today we are truly implanted in a post-truth society, it is only by a commitment to total transparency that we can devolve ourselves from this nauseating culture of lies, untruths and disinformation, and seek out a society where truth can once again become an attainable resource accessible to all.

BRUSSELS BEHIND THE SCENES is a weekly newsletter which brings the untold stories about the characters driving the policies affecting our lives. Analysis not found anywhere else, The Brussels Times Samuel Stolton helps you make sense of what is happening in Brussels. If you want to receive Brussels behind the scenes straight to your inbox every week, subscribe to the newsletter here.

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Telling the truth in a post-truth world - The Brussels Times

The New Republic article about Rand, which we looked at in Part 1, stood out not primarily because of what it said about her, but in how it conveyed its message. The article put a tribal prejudice toward Rand above facts and logic. That same mindset is on display, even more starkly, in Amanda Marcottes Salon article, Right-wingers finally got their Ayn Rand hero as president and its this guy.

Let me stress, again, that my goal is not to change your mind about Rand and her ideas, nor primarily to correct the many errors and misrepresentations in these articles (though Ill point out some of them along the way). Instead, the point is to explain how the two articles are fundamentally uninterested in convincing any active-minded reader. Their aim, rather, is to affirm a preset narrative about Rand. These are worse than mere smears, because their tribal mindset represents the abandonment of rational persuasion as the goal of intellectual discussion.

Marcottes point is captured in the subtitle: Conservatives finally have a leader who lives by Ayn Rands selfish philosophy, and hes an embarrassing clown, the clown being Donald Trump. But whatever you might think of Rand or of Trump, this is a claim thats far from self-evident. It requires a real argument. Marcottes article offers no argument. Its written for an audience that already partly or fully shares Marcottes preconceptions.

What would it take to build a case that Trump is the incarnation of Rands moral ideals? For a start, and at minimum, youd need to grasp what Rands view actually is, why she holds it, and how her radical view relates to, and contrasts with, existing views in morality. Rand once summarized her system of ideas by saying that My philosophy, in essence, is the concept of man as a heroic being, with his own happiness as the moral purpose of his life, with productive achievement as his noblest activity, and reason as his only absolute. Part of whats radical in Rands moral theory is that she argues for an individualist morality that is non-predatory.

Marcottes article offers no argument. Its written for an audience that already partly or fully shares Marcottes preconceptions.

Each individual, in her view, is responsible for achieving his own happiness by his own effort and the use of his own mind without sacrifices of anyone to anyone. That means a rational egoist neither surrenders his own values and goals to others, nor sacrifices others to himself. On Rands view, the egoist is someone guided by reason, pursuing creative achievement, building mutually beneficial relationships. It is nothing like the conventional view of a whim-driven brute who lies, cheats, and steals, walking over corpses to get his way.

From this brief indication of her view, it should be evident that what Rand means by selfishness is far different from what most people mean by that term. Regardless of whether one agrees with her conception, the fact is that Rand is saying something distinctive and new, and it takes work to understand it and think through what her morality does (and does not) look like in practice.

Marcotte, by contrast, evidently cannot imagine a moral ideal so dramatically at odds with conventional views. Apparently, the possibility of a non-predatory individualist is unreal to her, or else its pushed out of mind. Instead, Marcotte aims to patch together a narrative to affirm her prejudice against Rand. The goal is to portray Rand as a monster whose moral ideal, in practice, turns out to be a monster such as Trump.

To that end, Marcotte begins with a disturbing claim. Marcotte writes that Rand had a schoolgirl crush on a murderer, William Hickman, that she based a character on him in plans for an early story, and that she later reworked her idea of the individualistic, contemptuous hero into The Fountainhead and Atlas Shrugged.

Marcottes smear operates in part by omitting important facts.

Since Rands mature views reject any form of predation, her youthful interest in Hickman is strange enough that if you are going to raise it, it demands thoughtful exploration. A multitude of questions spring to mind: What was the nature of Rands curiosity in him? Where did she articulate it? When was this? How does it relate to her mature, principled advocacy of individual rights as sacrosanct?

READ ALSO: Why Rand Was Right to Testify Against Hollywood Communism

None of these questions interests Marcotte, who slants the episode to smear Rand. Marcottes smear operates in part by omitting important facts. Let me indicate just five.

First, its a gross distortion to call Rands reaction a schoolgirl crush, which you can see for yourself in Rands own notes on the subject. She made those notes in her personal journals, which can be found in Journals of Ayn Rand, published long after her death. Across decades, Rand wrote voluminously in her journals to sketch ideas for characters, plays, stories, novels; to engage in thinking on paper for her own understanding; to distill lessons and conclusions from her experiences with people and events.

Second, she wrote these journal entries for an audience of exactly one herself. In her journals she was continually forming, revising, changing, clarifying her views. Nothing in them was ever meant for publication, so its ludicrous to treat her journals as definitive statements of her considered view.

Third, Marcotte hand-wavingly notes that fans are quick to argue that Rand didnt endorse the murder, but elides the fact that Rand herself, in her own journal notes, repudiates Hickmans abhorrent crime.

Fourth, a relevant fact for understanding Rands interest in Hickman is that she was a fiction writer, and she was sketching ideas for a story. She was curious about the character and psychology of individuals, about what ideas and attitudes motivated them, in part for the sake of depicting the motivation of fictional characters. This is an issue central to the craft of writing fiction, which Rand (at the time, aged 23) was striving to master.

Fifth, it is impossible to read Rands notes about Hickman and the story she was planning without observing the influence of the philosopher Friedrich Nietzsche on the young Rand. That influence is manifest in the premise of the story and the lead character she envisioned for it (Rand uses concepts borrowed from Nietzsche and quotes him in her notes). Rand never got far in planning that story and decided to abandon it. Why? The project was too alien to her deepest premises, writes David Harriman, editor of Journals of Ayn Rand, who points out (along with other scholars) that Rand went on to discard Nietzsches philosophic ideas and explicitly repudiated them.

For Marcotte, such facts are pushed aside in the dash to affirm a preconception about Rand. The next step in that process is to link this fictional Rand to conservatism and President Trump.

Marcotte wheels out the trope that Rand is the backbone of modern conservativism. This metaphor obscures a complicated reality, which I mentioned in Part 1, about the nature of Rands influence on conservatives and right-leaning folks. Moreover, there are abundant counterexamples that negate this trope. The aim of Marcottes article, however, is not to convince, but to reinforce preconceptions, and her intended audience is already primed to feel loathing at the mention of conservatism. Thats the emotional context Marcottes article works to activate.

Marcottes unwarranted lumping together of Rand with conservatism reflects a definite purpose. Rands philosophy, Marcotte writes, serves as a pseudo-intellectual rationalization, beloved by assorted Republicans, for a reactionary movement that rose up to reject the feminist and anti-racist movements of the 20th century. One giveaway here is the word reactionary.

In this mindset, its unimaginable that someone could have a view different from ones own that is grounded in reasonable argument.

Even if you reject conservatism (as I do), Marcottes characterization of it betrays, not a reasoned opposition, but a tribal opposition. Were there conservatives who were racist and misogynistic? Yes, and there still are. But the sweeping claim in Marcottes article is that conservatives were reactionary: meaning, they stubbornly opposed progress. They could have had no legitimate basis for their concerns about, for example, the growth of government regulations, or the cost of burgeoning welfare programs, or the budget. Regardless of whether you share those concerns, some conservative intellectuals actually did voice reasoned objections to these developments. But for Marcotte and her intended audience, these outsiders, members of an opposing tribe, can be nothing but wrong and evil. In this mindset, its unimaginable that someone could have a view different from ones own that is grounded in reasonable argument.

In linking Rand with conservatism, Marcotte is uninterested in the fact which contradicts her narrative that Rand wrote at length about her philosophic opposition to the conservative movement (see, for instance, the essay Conservatism: An Obituary). Whats more, nowhere in Marcottes article will you learn that Rand was a fierce opponent of racism. Nor will you learn about Rands distinctive, profound opposition to the conventional notion that a womans place is in the home; or that a woman is somehow intellectually or morally inferior to a man. Among Rands fictional heroes are two women, Kira Argounova (in We the Living) and Dagny Taggart (in Atlas Shrugged), who shatter stereotyped roles for women. Long before it was imaginable in our culture, Dagny Taggart took it for granted that she could run a vast railroad network, and she did so superlatively; it was at most an afterthought for her that anyone might object. Kira Argounova, fascinated by buildings and bridges, wanted to be an engineer, and her will to achieve her goals in life was indomitable.

READ ALSO: Howard Roark Laughed: Humor in The Fountainhead

All of this, and more, Marcotte must brush aside in order to shoehorn Rands ideas into the same category as the reactionary right, the opposing political tribe that Marcotte and many of her readers hate. Doing so, in defiance of the facts, is part of Marcottes larger effort to present Donald Trump as the full, perfect embodiment of Rands moral theory of selfishness. Linking Trump and Rand serves to smear each with the taken-for-granted evil of the other.

Whats the argument for that link? There is none and, tellingly, no attempt to engage with obvious objections or counterarguments. What Marcotte conveys is a disdain for the sheer possibility that anyone could hold a different view on the subject. Regardless of your assessment of President Trump, the claim that hes the embodiment of Ayn Rands moral ideas should give pause to anyone with even an elementary grasp of her outlook.

What leaps off the pages of Atlas Shrugged is not that Rand glamorizes all businesspeople, but rather that she draws a bright moral dividing line. On one side are productive business leaders, who use their minds to create real value, exchanging it in trade for mutual advantage. It is such producers who are the business heroes she valorizes for their achievements.

On the other side of that moral line are the businessmen who rely on political pull to handicap their competitors, who extort protections and corporate welfare, and who lie, cheat, and exploit others in their grubbing for unearned wealth. Such villains, in todays world, embody the scourge of cronyism.

Marcottes disdain for argument, for evidence, indeed, for the intellect of her readers is blatant in what she takes as a credible source on Rands ideas.

Just on the basis of this sketch of one aspect of Rands view, Donald Trump is far from an obvious manifestation of her moral theory. The evidence, in my view, is that his actions and statements contradict the virtue of selfishness; that, for instance, Trumps business career has relied on pull peddling and that, as president, he feeds that cronyism dynamic. My colleague Ben Bayer has argued convincingly that Trump negates Rands view of selfishness; and others still have pointed out ways in which Trump is actually more like an Ayn Rand villain.

But my aim here is not to convince you of either of those points. Rather its to indicate that any claim that Trump embodies Rands concept of selfishness would need to build an argument for that, and take seriously counterpoints and obvious objections if your goal is to convince.

Thats precisely what Marcotte disdains. I say disdain, because any reputable magazine would expect its writers to Google the topic theyre pitching, to see if anyones written on it before. Try it yourself; you should find at least two articles on the subject by my colleague Onkar Ghate. One evaluates the Trump phenomenon generally; the other considers what Rand might have thought of Trump. You might also find my article on how Trumps foreign policy clashes with Rands philosophy. And again, we at ARI are hardly the only ones to voice our perspective on this issue. Marcotte, however, does not even gesture toward engaging with these contrasting views; doing so would imply that there could be a credible view different from her preconception.

READ ALSO: On Thanksgiving, Celebrate Production

Marcottes disdain for argument, for evidence, indeed, for the intellect of her readers is blatant in what she takes as a credible source on Rands ideas. For a credible third-party source, where does Marcotte turn? To one of a number of the established, published scholars of Rands ideas? No. To an expert on the field of ethics, who has some awareness of how Rands ideas relate to the intellectual landscape? No.

Who, then? Marcotte turns to a guy with a blog. She cites someone who posted blog entries while reading his way through Atlas Shrugged. To pretend that this blog is a credible source is journalistic malpractice. If a journalist wrote about, say, Marxs Das Kapital, or Darwins Origin of Species to take two influential works that defied conventional thinking and presented a random blogger with no evident expertise as an authority on the subject, it would be laughable.

What Marcottes article exhibits even more blatantly than Sammons piece in the New Republic is a tribalist mindset.

The tribal mind is insular and keen to stay that way. Outsiders are viewed with suspicion, often hostility. The sheer possibility that outsiders might have different views and beliefs, and hold them for good reasons, is simply alien. Thats largely because the tribalist himself has fastened onto his beliefs and pieties, not through a thoughtful weighing of the evidence and by following the logic, but through conformity with the group. Theres just what his own tribe believes. All else has to be wrong. Its beyond the pale, worthy only of contempt and disdain.

Theres an underlying commonality between a Trump rally and the Marcotte and Sammon articles: they put a tribal narrative above facts and logic.

We can observe two important consequences of this tribalist mindset on display in Marcottes article about Rand. One is Marcottes disdain for facts and logic. A tribalist sees no need to convince others of his views: why take the effort of trying to communicate with outsiders, who by virtue of being outside the tribe must be wrong? Besides, if he himself didnt need evidence and logic to swallow his groups beliefs and pieties, why would anyone else?

Second, the tribalist does feel a strong need to affirm and reinforce for himself and fellow tribe members that their ways and beliefs are right, and that outsiders are wrong, if not evil, too.

A critical reading of Marcottes and Sammons articles makes clear that a major, if not the prime, aim is to rally certain readers. To activate them emotionally, not cognitively. For those readers, the common takeaway is that, despite Rands distinctive views, she can be lumped in with the hated right-wing/conservative tribe.

These articles offer the reassurance that, despite Rands enduring prominence and ongoing cultural influence, she is unworthy of serious attention. That the Objectivist movement is nosediving. That Rand, finally, is canceled.

What the Marcotte and Sammon articles do to Rand in print, Donald Trump does to his enemies in speeches at loyalist rallies. The approach is the same. The president can spellbind the audience with innuendo, pseudo-facts, and arbitrary assertions, precisely because they reinforce a conclusion many already came in with: Trump is right, his opponents in the enemy tribe are victimizing him.

No attempt is made to convince anyone in the stands. The conclusions, so congenial to the tribe, are already known. The facts or rather, innuendo, insinuation, hints and arbitrary allegations are conjured up, trimmed, shorn of context, bent, distorted to affirm the tribes common prejudices against its enemies. Theres an underlying commonality between a Trump rally and the Marcotte and Sammon articles: they put a tribal narrative above facts and logic.

There are fascinating questions to explore about the impact of Ayn Rands ideas and their cultural influence. Such questions, however, are shoved to the wayside in the Marcotte and Sammon articles. The driving impulse to cancel Rand in the eyes of their tribal audience hardly original to these articles is its own kind of cultural indicator.

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When Tribal Journalists Try to 'Cancel' Ayn Rand (Part 2) - New Ideal

Looking at the greatest business books of all-time to understand the power of influence took me on a trip down memory lane that reminded me of important, success-generating stuff I should never have forgotten! And its insightful stuff you should know for yourself and for those you lead and care about.

In recent times, Ive switched my Switzer Show podcast to be a rerun of one my favourite media activities of all time Talking Business which I pioneered for a decade with my friends at Stellar Inflight, who produced this for Qantas.

Ten years is a long time to do anything and my exit from something I loved coincided with my Switzer TV programme that went for over a decade, until Rupert and News Corp decided to close down the Sky Business channel.

What Im doing on The Switzer Show is tracking down legends, both new and old, to deliver on what so many people are and should be interested in the secrets of their success. As I wrote in my article last week, legends leave clues but someone often has to pry them out of them.

Over 30 years of talking, analysing and listening to business legends make speeches, these people who have been great at business are not gifted when it comes to teaching what others have to do to emulate them. A lot of these high achievers do things innately and never think that others dont see or think something that is so obvious to them.

Its like natural born leaders, who are a lot less on the ground than those who learn to lead. John Maxwell, the author of the book 21 Irrefutable Laws of Leadership, argues convincingly that leadership can be learnt. I think becoming a business builder is a skill that can be taught and learnt.

For three decades Ive tried to do business education via all my media, my books and websites and nowadays we have the Grow Your Business website, which has an online business coaching product that were developing.

I believe coaching and mentoring are critical game changers to anyone wanting any kind of success. When I want to lose weight, I go to my dietician expert. If Gerry Harvey asks me to play tennis with ex-pros who Im not good enough to play with, I get some coaching so I dont look hopeless.

Its the big lesson Ive taken as an educator and business builder myself if you want to progress, you have to tap into good influences.

This week I interview Ruslan Kogan for The Switzer Show podcast and two big take-outs came from the interview.

First, Ruslan, who Ive known and interviewed for over a decade, is a better bloke than I took him for and this was shown in his revelation about what he really thinks about Gerry Harvey.

Second, his view on his parents and how their courageous decision to become immigrants and then encourage Ruslan and his sister to work hard to become successful, is something that was quite unforgettable. At the time I told him that Id recently written about how Aussie Home Loans John Symond and Wizard/Yellow Brick Roads Mark Bouris both singled out their parents as critical influences in their success story.

These people were lucky, and I guess I was too because my parents were hardworking small business owners who sacrificed to give their kids opportunities. But what does someone do if they dont have these close relations who are good at inspiring and influencing?

Ive said this before that Jim Collins, author of the best-selling book Good to Great, admitted he didnt have life-changing influences in his life so he pretended he had an advisory board of mentors by reading books. This meant he counted the great Peter Drucker as a mentor along with others, just how Jim has become the same for others, and especially entrepreneurs who knew they needed help, inspiration and direction.

Thats why most of us trying to go from ordinary to extraordinary or from say good to great, need a hand from a coach, a mentor or in the absence of these a book or a website.

The key message from me is not only that you need a coach or a mentor but more importantly you have to be, as Cat Stevens once sang: On the road to find out.

And the poet Robert Frost gave us a huge clue when he wrote: Two roads diverged in a yellow wood. I took the one less travelled by, And that has made all the difference.

Ruslan Kogan and Gerry Harvey have been inspired travellers down the scary road of business. Ruslan compared this unknown, spooky world of business (as demonstrated by whats happened this year) to the kind of challenging environment that immigrants, like his parents, encountered in coming to Australia from Communist Eastern Europe.

And it is clear that someone like Ruslan, who used Gerrys fame to get attention for himself when he started out as a disruptor business, respects his rivals achievements in building the biggest retail business in the country.

This is the kind of maturity that can only come when someone has learnt from the lessons on the road or at the coalface of achieving something substantial and from business greats that have gone before him.

This five-year Kogan share price chart shows a lot has been learnt by Ruslan and it shows the power of great influences.

Note how his business has roared out of the Coronavirus crash, which says it all.

By the way, Forbes says the greatest business books of all-time, which look like they have been largely based on sales were:

1. Think and Grow Rich, by Napolean Hill

2. Rich Dad, Poor Dad, by Robert Kiyosaki

3. The E-Myth, by Michael Gerber

4. The 22 Immutable Laws of Marketing, by Al Ries and Jack Trout

5. How to Win Friends & Influence People, by Dale Carnegie

6. The Hard Thing About Hard Things, by Ben Horowitz

7. Blue Ocean Strategy, by W. Chan Kim and Rene Mauborgne

8. Shoe Dog, by Phil Knight

And they are all about insights from the very people who have seen what others miss and thats because the majority dont go looking for what the minority of successful people have to know.

If you cant get a coach or a mentor, start hanging out with books and videos that give you a sneaky way of getting winning clues from legends.

Click here to take a free 21-day trial to theSwitzer Report, a leading investment newsletter and website for self-directed investors.

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Gerry Harvey, John Symond, Mark Bouris look for the clues they leave about success - Switzer Financial News

Napolean Hill once said, "Opinions are the cheapest commodities on earth. Everyone has a flock of opinions ready to be wished upon anyone who will accept them. If you are influenced by "opinions" when you reach DECISIONS, you will not succeed in any undertaking."

It's a quote that has long resonated with Dr. Ari Bernstein, MD, and you could say it's one which the 41-year-old from Long Island, New York, lives by.

When asked, the doctor is the first to admit he learned the inherent truth behind the quote the hard way, and explained, "Something that stayed with me from childhood is a lack of encouragement. It seemed as if people tried to limit my potential by telling me what I couldn't do, and so I made it my goal to prove to the world what I could do and help others do the same."

It turned out that what Dr. Ari 'could do' was demonstrate a natural affinity with the fields of medicine and science.

"I found my true calling early on," explained the doctor. "As a young child, I attended programs at Cold Spring Harbor Laboratory and science summer camp, and I knew instinctively what I wanted to do for the rest of my life."

After studying pre-med and psychology at Long Island University, Dr. Ari went on to finish his medical schooling at St. George's University and completed his internal medicine residency at NewYork-Presbyterian Queens.

Finally, in a position to give something back, and in particular, encourage and mentor other aspiring medical professionals, Dr. Ari remains passionate about using his knowledge and experience to make an impact on the lives of others.

The doctor explained, "During my college years, I volunteered at the Cold Spring Harbor Laboratory, where they were working on the Human Genome Project. I was also lucky enough to benefit from helping out a postdoctoral fellow at Columbia University College of Physicians and Surgeons in a genetics lab. The lessons I learned proved invaluable, and the encouragement I was given proved priceless. I am keen to help others realize that they should never let people pigeon-hole them and tell them what they can't do because I believe you can pretty much do anything if you set your mind to it and are willing to put the work in."

As a keen advocate of self-development, it is somewhat surprising that Dr. Ari Bernstein couldn't work for five years because of health issues. The issues were only eventually fixed after four separate surgical procedures, the implantation of a spinal cord stimulator, and bucketfuls of self-belief.

The doctor revealed, "You could say my passion for medicine and helping others is what saved me. The odds of me making a full recovery were completely against me, but here I stand, and here I am. Through perseverance and self-belief, I bounced back, and my sole goal is to help others realize that nothing is impossible."

As the good doctor's story proves, everyone in life at some point either needs or has the opportunity to lend a helping hand. Stepping up to the plate when it's your turn is what makes the world go around. As Anne Frank once wrote, "No one has ever become poor by giving."

Originally posted here:
Dr Ari Bernstein Talks About the Importance of Mentoring the Next Generation of Medical Professionals - LatestLY

According to the approach embraced by McRO and BASCOM, while machine learning algorithms bringing a slight improvement can pass the eligibility test, algorithms paving the way for a whole new technology can be excluded from the benefits of patent protection simply because there are no alternatives.

In the past decade or so, humanity has gone through drastic changes as Artificial intelligence (AI) technologies such as recommendation systems and voice assistants have seeped into every facet of our lives. Whereas the number of patent applications for AI inventions skyrocketed, almost a third of these applications are rejected by the U.S. Patent and Trademark Office (USPTO) and the majority of these rejections are due to the claimed invention being ineligible subject matter.

The inventive concept may be attributed to different components of machine learning technologies, such as using a new algorithm, feeding more data, or using a new hardware component. However, this article will exclusively focus on the inventions achieved by Machine Learning (M.L.) algorithms and the effect of the preemption test adopted by U.S. courts on the patent-eligibility of such algorithms.

Since the Alice decision, the U.S. courts have adopted different views related to the role of the preemption test in eligibility analysis. While some courts have ruled that lack of preemption of abstract ideas does not make an invention patent-eligible [Ariosa Diagnostics Inc. v. Sequenom Inc.], others have not referred to it at all in their patent eligibility analysis. [Enfish LLC v. Microsoft Corp., 822 F.3d 1327]

Contrary to those examples, recent cases from Federal Courts have used the preemption test as the primary guidance to decide patent eligibility.

In McRO, the Federal Circuit ruled that the algorithms in the patent application prevent pre-emption of all processes for achieving automated lip-synchronization of 3-D characters. The court based this conclusion on the evidence of availability of an alternative set of rules to achieve the automation process other than the patented method. It held that the patent was directed to a specific structure to automate the synchronization and did not preempt the use of all of the rules for this method given that different sets of rules to achieve the same automated synchronization could be implemented by others.

Similarly, The Court in BASCOM ruled that the claims were patent eligible because they recited a specific, discrete implementation of the abstract idea of filtering contentand they do not preempt all possible ways to implement the image-filtering technology.

The analysis of the McRO and BASCOM cases reveals two important principles for the preemption analysis:

Machine learning can be defined as a mechanism which searches for patterns and which feeds intelligence into a machine so that it can learn from its own experience without explicit programming. Although the common belief is that data is the most important component in machine learning technologies, machine learning algorithms are equally important to proper functioning of these technologies and their importance cannot be understated.

Therefore, inventive concepts enabled by new algorithms can be vital to the effective functioning of machine learning systemsenabling new capabilities, making systems faster or more energy efficient are examples of this. These inventions are likely to be the subject of patent applications. However, the preemption test adopted by courts in the above-mentioned cases may lead to certain types of machine learning algorithms being held ineligible subject matter. Below are some possible scenarios.

The first situation relates to new capabilities enabled by M.L. algorithms. When a new machine learning algorithm adds a new capability or enables the implementation of a process, such as image recognition, for the first time, preemption concerns will likely arise. If the patented algorithm is indispensable for the implementation of that technology, it may be held ineligible based on the McRO case. This is because there are no other alternative means to use this technology and others would be prevented from using this basic tool for further development.

For example, a M.L. algorithm which enabled the lane detection capability in driverless cars may be a standard/must-use algorithm in the implementation of driverless cars that the court may deem patent ineligible for having preemptive effects. This algorithm clearly equips the computer vision technology with a new capability, namely, the capability to detect boundaries of road lanes. Implementation of this new feature on driverless cars would not pass the Alice test because a car is a generic tool, like a computer, and even limiting it to a specific application may not be sufficient because it will preempt all uses in this field.

Should the guidance of McRO and BASCOM be followed, algorithms that add new capabilities and features may be excluded from patent protection simply because there are no other available alternatives to these algorithms to implement the new capabilities. These algorithms use may be so indispensable for the implementation of that technology that they are deemed to create preemptive effects.

Secondly, M.L. algorithms which are revolutionary may also face eligibility challenges.

The history of how deep neural networks have developed will be explained to demonstrate how highly-innovative algorithms may be stripped of patent protection because of the preemption test embraced by McRO and subsequent case law.

Deep Belief Networks (DBNs) is a type of Artificial Neural Networks (ANNs). The ANNs were trained with a back-propagation algorithm, which adjusts weights by propagating the outputerror backwardsthrough the network However, the problem with the ANNs was that as the depth was increased by adding more layers, the error vanished to zero and this severely affected the overall performance, resulting in less accuracy.

From the early 2000s, there has been a resurgence in the field of ANNs owing to two major developments: increased processing power and more efficient training algorithms which made trainingdeep architecturesfeasible. The ground-breaking algorithm which enabled the further development of ANNs in general and DBNs in particular was Hintons greedy training algorithm.

Thanks to this new algorithm, DBNs has been applicable to solve a variety of problems that were the roadblock before the use of new technologies, such as image processing,natural language processing, automatic speech recognition, andfeature extractionand reduction.

As can be seen, the Hiltons fast learning algorithm revolutionized the field of machine learning because it made the learning easier and, as a result, technologies such as image processing and speech recognition have gone mainstream.

If patented and challenged at court, Hiltons algorithm would likely be invalidated considering previous case law. In McRO, the court reasoned that the algorithm at issue should not be invalidated because the use of a set of rules within the algorithm is not a must and other methods can be developed and used. Hiltons algorithm will inevitably preempt some AI developers from engaging with further development of DBNs technologies because this algorithm is a base algorithm, which made the DBNs plausible to implement so that it may be considered as a must. Hiltons algorithm enabled the implementation of image recognition technologies and some may argue based on McRO and Enfish that Hiltons algorithm patent would be preempting because it is impossible to implement image recognition technologies without this algorithm.

Even if an algorithm is a must-use for a technology, there is no reason to exclude it from patent protection. Patent law inevitably forecloses certain areas from further development by granting exclusive rights through patents. All patents foreclose competitors to some extent as a natural consequence of exclusive rights.

As stated in the Mayo judgment, exclusive rights provided by patents can impede the flow of information that might permit, indeed spur, invention, by, for example, raising the price of using the patented ideas once created, requiring potential users to conduct costly and time-consuming searches of existing patents and pending patent applications, and requiring the negotiation of complex licensing arrangements.

The exclusive right granted by a patents is only one side of the implicit agreement between the society and the inventor. In exchange for the benefit of the exclusivity, inventors are required to disclose their invention to the public so this knowledge becomes public, available for use in further research and for making new inventions building upon the previous one.

If inventors turn to trade secrets to protect their inventions due to the hostile approach of patent law to algorithmic inventions, the knowledge base in this field will narrow, making it harder to build upon previous technology. This may lead to the slow-down and even possible death of innovation in this industry.

The fact that an algorithm is a must-use, should not lead to the conclusion that it cannot be patented. Patent rights may even be granted for processes which have primary and even sole utility in research. Literally, a microscope is a basic tool for scientific work, but surely no one would assert that a new type of microscope lay beyond the scope of the patent system. Even if such a microscope is used widely and it is indispensable, it can still be given patent protection.

According to the approach embraced by McRO and BASCOM, while M.L. algorithms bringing a slight improvement, such as a higher accuracy and higher speed, can pass the eligibility test, algorithms paving the way for a whole new technology can be excluded from the benefits of patent protection simply because there are no alternatives to implement that revolutionary technology.

Considering that the goal of most AI inventions is to equip computers with new capabilities or bring qualitative improvements to abilities such as to see or to hear or even to make informed judgments without being fed complete information, most AI inventions would have the higher likelihood of being held patent ineligible. Applying this preemption test to M.L. algorithms would put such M.L. algorithms outside of patent protection.

Thus, a M.L. algorithm which increases accuracy by 1% may be eligible, while a ground-breaking M.L. algorithm which is a must-use because it covers all uses in that field may be excluded from patent protection. This would result in rewarding slight improvements with a patent but disregarding highly innovative and ground-breaking M.L. algorithms. Such a consequence is undesirable for the patent system.

This also may result in deterring the AI industry from bringing innovation in fundamental areas. As an undesired consequence, innovation efforts may shift to small improvements instead of innovations solving more complex problems.

Image Source: Author: nils.ackermann.gmail.com Image ID:102390038

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Effects of the Alice Preemption Test on Machine Learning Algorithms - IPWatchdog.com

A new project called Keen is launching today from Googles in-house incubator for new ideas, Area 120, to help users track their interests. The app is like a modern rethinking of the Google Alerts service, which allows users to monitor the web for specific content. Except instead of sending emails about new Google Search results, Keen leverages a combination of machine learning techniques and human collaboration to help users curate content around a topic.

Each individual area of interest is called a keen a word often used to reference someone with an intellectual quickness.

The idea for the project came about after co-founder C.J. Adams realized he was spending too much time on his phone mindlessly browsing feeds and images to fill his downtime. He realized that time could be better spent learning more about a topic he was interested in perhaps something he always wanted to research more or a skill he wanted to learn.

To explore this idea, he and four colleagues at Google worked in collaboration with the companys People and AI Research (PAIR) team, which focuses on human-centered machine learning, to create what has now become Keen.

To use Keen, which is available both on the web and on Android, you first sign in with your Google account and enter in a topic you want to research. This could be something like learning to bake bread, bird watching or learning about typography, suggests Adams in an announcement about the new project.

Keen may suggest additional topics related to your interest. For example, type in dog training and Keen could suggest dog training classes, dog training books, dog training tricks, dog training videos and so on. Click on the suggestions you want to track and your keen is created.

When you return to the keen, youll find a pinboard of images linking to web content that matches your interests. In the dog training example, Keen found articles and YouTube videos, blog posts featuring curated lists of resources, an Amazon link to dog training treats and more.

For every collection, the service uses Google Search and machine learning to help discover more content related to the given interest. The more you add to a keen and organize it, the better these recommendations become.

Its like an automated version of Pinterest, in fact.

Once a keen is created, you can then optionally add to the collection, remove items you dont want and share the Keen with others to allow them to also add content. The resulting collection can be either public or private. Keen can also email you alerts when new content is available.

Google, to some extent, already uses similar techniques to power its news feed in the Google app. The feed, in that case, uses a combination of items from your Google Search history and topics you explicitly follow to find news and information it can deliver to you directly on the Google apps home screen. Keen, however, isnt tapping into your search history. Its only pulling content based on interests you directly input.

And unlike the news feed, a keen isnt necessarily focused only on recent items. Any sort of informative, helpful information about the topic can be returned. This can include relevant websites, events, videos and even products.

But as a Google project and one that asks you to authenticate with your Google login the data it collects is shared with Google. Keen, like anything else at Google, is governed by the companys privacy policy.

Though Keen today is a small project inside a big company, it represents another step toward the continued personalization of the web. Tech companies long since realized that connecting users with more of the content that interests them increases their engagement, session length, retention and their positive sentiment for the service in question.

But personalization, unchecked, limits users exposure to new information or dissenting opinions. It narrows a persons worldview. It creates filter bubbles and echo chambers. Algorithmic-based recommendations can send users searching for fringe content further down dangerous rabbit holes, even radicalizing them over time. And in extreme cases, radicalized individuals become terrorists.

Keen would be a better idea if it were pairing machine-learning with topical experts. But it doesnt add a layer of human expertise on top of its tech, beyond those friends and family you specifically invite to collaborate, if you even choose to. That leaves the system wanting for better human editorial curation, and perhaps the need for a narrower focus to start.

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According to PwC, AIs potential global economic impact will reach USD 15.7 trillion by 2030. However, the enterprises who look to deploy AI are often hampered by the lack of time, trust and talent. Especially, with the highly regulated sectors such as healthcare and finance, convincing the customers to imbibe AI methodologies is an uphill task.

Of late, the AI community has seen a sporadic shift in AI adoption with the advent of AutoML tools and introduction of customised hardware to cater to the needs of the algorithms. One of the most widely used AutoML tools in the industry is H2O Driverless AI. And, when it comes to hardware Intel has been consistently updating its tool stack to meet the high computational demands of the AI workflows.

Now H2O.ai and Intel, two companies who have been spearheading the democratisation of the AI movement, join hands to develop solutions that leverage software and hardware capabilities respectively.

AI and machine-learning workflows are complex and enterprises need more confidence in the validity of their AI models than a typical black-box environment can provide. The inexplicability and the complexity of feature engineering can be daunting to the non-experts. So far AutoML has proven to be the one stop solution to all these problems. These tools have alleviated the challenges by providing automated workflows, code ready deployable models and many more.

H2O.ai especially, has pioneered in the AutoML segment. They have developed an open source, distributed in-memory machine learning platform with linear scalability that includes a module called H2OAutoML, which can be used for automating the machine learning workflow, that includes automatic training and tuning of many models within a user-specified time-limit.

Whereas, H2O.ais flagship product Driverless AI can be used to fully automate some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment.

But, for these AI based tools to work seamlessly, they need the backing of hardware that is dedicated to handle the computational intensity of machine learning operations.

Intel has been at the forefront of digital revolution for over half a century. Today, Intel flaunts a wide range of technologies, including its Xeon Scalable processors, Optane Solid State Drives and optimized Intel software libraries that bring in a much needed mix of enhanced performance, AI inference, network functions, persistent memory bandwidth, and security.

Integrating H2O.ais software portfolio with hardware and software technologies from Intel has resulted in solutions that can handle almost all the woes of an AI enterprise from automated workflows to explainability to production ready code that can be deployed anywhere.

For example, H2O Driverless AI, an automatic machine-learning platform enables data science experts and beginners to streamline their AI tasks within minutes that usually take months. Today, more than 18,000 companies use open source H2O in mission-critical use cases for finance, insurance, healthcare, retail, telco, sales, and marketing.

The software capabilities of H2O.ai combined with hardware infrastructure of Intel, that includes 2nd Generation Xeon Scalable processors, Optane Solid State Drives and Ethernet Network Adapters, can empower enterprises to optimize performance and accelerate deployment.

Enterprises that are looking for increasing productivity while increasing the business value of to enjoy the competitive advantages of AI innovation no longer have to wait thanks to hardware backed AutoML solutions.

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Patrick Opie, founder of Scout9, and his dog, Orin. (Photo courtesy of Patrick Opie)

After adopting his first dog last year, Patrick Opie was struggling with figuring out what Orin, his mini Australian shepherd, needed and when.

The struggle went beyond coping with normal puppy stuff, like when a dog chews up a favorite pair of shoes or pees where hes not supposed to. Opie was buying products that were irrelevant or unfit for his dog and he was spending too much time researching what to get each month.

Those things add up, Opie said. Thats where I realized I really wished there was a product or something that could help navigate or work with you to help you find what you need to get going.

Opies new adventures in dog parenthood led him to create Scout9, a Seattle startup that offers an intuitive and economical way for new dog owners to prepare for each step of their dogs development through the use of an autonomous Personal Pocket Scout.

Its a timely venture considering reports that the COVID-19 pandemic has led to a national surge in pet adoptions and fostering. As the pet industry heads toward $100 billion in annual spending, pet tech and web-based services are right in the mix, especially in dog friendly Seattle.

Opie was frustrated by his own mess-ups when it came to buying the right food and the right type of kennel as well as milestones he missed including when to start socialization and training for Orin.

Think of it like if Im Batman and I just got a dog, Opie said. I would want to have an Alfred who can kind of help me figure out the baseline: These are the things you need to think about, these are the things that I suggest you should do.'

Opies Alfred-the-butler vision is instead an online platform that relies on machine learning technology to create a dynamic timeline for milestones in the dogs life. Its not breed specific, but is instead based on some parameters given to the tool, such as the dogs initial age and size. Scout works by scouring the internet for relevant information and learning along the way what the human user accepts and rejects.

Scout will surface food choices, for instance, and do the shopping if given permission, by searching for the best available deals. The user has the ability to set their budget, so that Scout avoids overspending and gets the most out of the money it is allotted. Purchases can be automated so food shows up on time and Scout will learn and grow as your pet does.

A user can also take Scouts recommendations and go find food or other items on Amazon or somewhere else.

Scout9 will make money a couple different ways, either by collecting a commission from retailers whose affiliated links show up in the tool, or by charging users a service fee on transactions that are made by Scout on the users behalf.

Using Orin as a test case for the first year, Opie said he went from spending $1,700 on supplies down to $1,100 using his tool, for a 35 percent savings.

Opie, who is working on the new company with two friends, was previously a consultant at Boston Consulting Group and he spent more than three years at Accenture. He also worked as a developer at DevHub, and in April teamed with DevHub co-founder Mark Michael to create a virtual Gumwall to raise money for restaurant workers during the early days of the health crisis.

His goal is for dogs to be the jumping off point for Scout9 and the Personal Pocket Scout, and he envisions it being applied beyond raising puppies to such scenarios as raising a baby or buying a new house.

It definitely is an idea that will be across all life transitions, Opie said. My team all loves dogs. Weve been through that experience. Its easier for us to execute on that vision.

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This startup could be a dog owners best friend as it uses machine learning to help guide key decisions - GeekWire

Protesters and police in Seattle's Capitol Hill neighborhood. (Getty Images)

When incidents of police brutality occur, typically departments enact police reforms and fire bad cops, but machine learning could potentially predict when a police officer may go over the line.

Rayid Ghani is a professor at Carnegie Mellon and joined Seattles Morning News to discuss using machine learning in police reform. Hes working on tech that could predict not only which cops might not be suited to be cops, but which cops might be best for a particular call.

AI and technology and machine learning, and all these buzzwords, theyre not able to to fix racism or bad policing, they are a small but important tool that we can use to help, Ghani said. I was looking at the systems called early intervention systems that a lot of large police departments have. Theyre supposed to raise alerts, raise flags when a police officer is at risk of doing something that they shouldnt be doing, like excessive use of force.

What level of privacy can we expect online?

What we found when looking at data from several police departments is that these existing systems were mostly ineffective, he added. If theyve done three things in the last three months that raised the flag, well thats great. But at the same time, its not an early intervention. Its a late intervention.

So they built a system that works to potentially identify high risk officers before an incident happens, but how exactly do you predict how somebody is going to behave?

We build a predictive system that would identify high risk officers We took everything we know about a police officer from their HR data, from their dispatch history, from who they arrested , their internal affairs, the complaints that are coming against them, the investigations that have happened, Ghani said.

Can the medical system and patients afford coronavirus-related costs?

What we found were some of the obvious predictors were what you think is their historical behavior. But some of the other non-obvious ones were things like repeated dispatches to suicide attempts or repeated dispatches to domestic abuse cases, especially involving kids. Those types of dispatches put officers at high risk for the near future.

While this might suggest that officers who regularly dealt with traumatic dispatches might be the ones who are higher risk, the data doesnt explain why, it just identifies possibilities.

It doesnt necessarily allow us to figure out the why, it allows us to narrow down which officers are high risk, Ghani said. Lets say a call comes in to dispatch and the nearest officer is two minutes away, but is high risk of one of these types of incidents. The next nearest officer is maybe four minutes away and is not high risk. If this dispatch is not time critical for the two minutes extra it would take, could you dispatch the second officer?

So if an officer has been sent to a multiple child abuse cases in a row, it makes more sense to assign somebody else the next time.

Thats right, Ghani said. Thats what that were finding is they become high risk It looks like its a stress indicator or a trauma indicator, and they might need a cool-off period, they might need counseling.

But in this case, the useful thing to think about also is that they havent done anything yet, he added. This is preventative, this is proactive. And so the intervention is not punitive. You dont fire them. You give them the tools that they need.

Listen to Seattles Morning News weekday mornings from 5 9 a.m. on KIRO Radio, 97.3 FM. Subscribe to thepodcast here.

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How machine learning could reduce police incidents of excessive force - MyNorthwest.com

The behavior of machine learning systems can be manipulated, with potentially devastating consequences

In March 2019, security researchers at Tencent managed to trick a Tesla Model S into switching lanes.

All they had to do was place a few inconspicuous stickers on the road. The technique exploited glitches in the machine learning (ML) algorithms that power Teslas Lane Detection technology in order to cause it to behave erratically.

Machine learning has become an integral part of many of the applications we use every day from the facial recognition lock on iPhones to Alexas voice recognition function and the spam filters in our emails.

But the pervasiveness of machine learning and its subset, deep learning has also given rise to adversarial attacks, a breed of exploits that manipulate the behavior of algorithms by providing them with carefully crafted input data.

Adversarial attacks are manipulative actions that aim to undermine machine learning performance, cause model misbehavior, or acquire protected information, Pin-Yu Chen, chief scientist, RPI-IBM AI research collaboration at IBM Research, told The Daily Swig.

Adversarial machine learning was studied as early as 2004. But at the time, it was regarded as an interesting peculiarity rather than a security threat. However, the rise of deep learning and its integration into many applications in recent years has renewed interest in adversarial machine learning.

Theres growing concern in the security community that adversarial vulnerabilities can be weaponized to attack AI-powered systems.

As opposed to classic software, where developers manually write instructions and rules, machine learning algorithms develop their behavior through experience.

For instance, to create a lane-detection system, the developer creates a machine learning algorithm and trains it by providing it with many labeled images of street lanes from different angles and under different lighting conditions.

The machine learning model then tunes its parameters to capture the common patterns that occur in images that contain street lanes.

With the right algorithm structure and enough training examples, the model will be able to detect lanes in new images and videos with remarkable accuracy.

But despite their success in complex fields such as computer vision and voice recognition, machine learning algorithms are statistical inference engines: complex mathematical functions that transform inputs to outputs.

If a machine learning tags an image as containing a specific object, it has found the pixel values in that image to be statistically similar to other images of the object it has processed during training.

Adversarial attacks exploit this characteristic to confound machine learning algorithms by manipulating their input data. For instance, by adding tiny and inconspicuous patches of pixels to an image, a malicious actor can cause the machine learning algorithm to classify it as something it is not.

Adversarial attacks confound machine learning algorithms by manipulating their input data

The types of perturbations applied in adversarial attacks depend on the target data type and desired effect. The threat model needs to be customized for different data modality to be reasonably adversarial, says Chen.

For instance, for images and audios, it makes sense to consider small data perturbation as a threat model because it will not be easily perceived by a human but may make the target model to misbehave, causing inconsistency between human and machine.

However, for some data types such as text, perturbation, by simply changing a word or a character, may disrupt the semantics and easily be detected by humans. Therefore, the threat model for text should be naturally different from image or audio.

The most widely studied area of adversarial machine learning involves algorithms that process visual data. The lane-changing trick mentioned at the beginning of this article is an example of a visual adversarial attack.

In 2018, a group of researchers showed that by adding stickers to a stop sign(PDF), they could fool the computer vision system of a self-driving car to mistake it for a speed limit sign.

Researchers tricked self-driving systems into identifying a stop sign as a speed limit sign

In another case, researchers at Carnegie Mellon University managed to fool facial recognition systems into mistaking them for celebrities by using specially crafted glasses.

Adversarial attacks against facial recognition systems have found their first real use in protests, where demonstrators use stickers and makeup to fool surveillance cameras powered by machine learning algorithms.

Computer vision systems are not the only targets of adversarial attacks. In 2018, researchers showed that automated speech recognition (ASR) systems could also be targeted with adversarial attacks(PDF). ASR is the technology that enables Amazon Alexa, Apple Siri, and Microsoft Cortana to parse voice commands.

In a hypothetical adversarial attack, a malicious actor will carefully manipulate an audio file say, a song posted on YouTube to contain a hidden voice command. A human listener wouldnt notice the change, but to a machine learning algorithm looking for patterns in sound waves it would be clearly audible and actionable. For example, audio adversarial attacks could be used to secretly send commands to smart speakers.

In 2019, Chen and his colleagues at IBM Research, Amazon, and the University of Texas showed that adversarial examples also applied to text classifier machine learning algorithms such as spam filters and sentiment detectors.

Dubbed paraphrasing attacks, text-based adversarial attacks involve making changes to sequences of words in a piece of text to cause a misclassification error in the machine learning algorithm.

Example of a paraphrasing attack against fake news detectors and spam filters

Like any cyber-attack, the success of adversarial attacks depends on how much information an attacker has on the targeted machine learning model. In this respect, adversarial attacks are divided into black-box and white-box attacks.

Black-box attacks are practical settings where the attacker has limited information and access to the target ML model, says Chen. The attackers capability is the same as a regular user and can only perform attacks given the allowed functions. The attacker also has no knowledge about the model and data used behind the service.

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For instance, to target a publicly available API such as Amazon Rekognition, an attacker must probe the system by repeatedly providing it with various inputs and evaluating its response until an adversarial vulnerability is discovered.

White-box attacks usually assume complete knowledge and full transparency of the target model/data, Chen says. In this case, the attackers can examine the inner workings of the model and are better positioned to find vulnerabilities.

Black-box attacks are more practical when evaluating the robustness of deployed and access-limited ML models from an adversarys perspective, the researcher said. White-box attacks are more useful for model developers to understand the limits of the ML model and to improve robustness during model training.

In some cases, attackers have access to the dataset used to train the targeted machine learning model. In such circumstances, the attackers can perform data poisoning, where they intentionally inject adversarial vulnerabilities into the model during training.

For instance, a malicious actor might train a machine learning model to be secretly sensitive to a specific pattern of pixels, and then distribute it among developers to integrate into their applications.

Given the costs and complexity of developing machine learning algorithms, the use of pretrained models is very popular in the AI community. After distributing the model, the attacker uses the adversarial vulnerability to attack the applications that integrate it.

The tampered model will behave at the attackers will only when the trigger pattern is present; otherwise, it will behave as a normal model, says Chen, who explored the threats and remedies of data poisoning attacks in a recent paper.

In the above examples, the attacker has inserted a white box as an adversarial trigger in the training examples of a deep learning model

This kind of adversarial exploit is also known as a backdoor attack or trojan AI and has drawn the attention of Intelligence Advanced Research Projects (IARPA).

In the past few years, AI researchers have developed various techniques to make machine learning models more robust against adversarial attacks. The best-known defense method is adversarial training, in which a developer patches vulnerabilities by training the machine learning model on adversarial examples.

Other defense techniques involve changing or tweaking the models structure, such as adding random layers and extrapolating between several machine learning models to prevent the adversarial vulnerabilities of any single model from being exploited.

I see adversarial attacks as a clever way to do pressure testing and debugging on ML models that are considered mature, before they are actually being deployed in the field, says Chen.

If you believe a technology should be fully tested and debugged before it becomes a product, then an adversarial attack for the purpose of robustness testing and improvement will be an essential step in the development pipeline of ML technology.

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Adversarial attacks against machine learning systems everything you need to know - The Daily Swig