Adonis Diaries

Archive for April 2017

Like to come Along for the ride? And what’s on commitment and techniques?

Along for the ride

And the pilot says, “sit back, relax, and enjoy the flight.”

When you’re on one of those Disneyland boats, it takes you where Disney wants you to go. That’s why you got on.

And so you are lulled, a spectator, merely a tourist.

So different, isn’t it, from driving yourself.

You got to choose your own route and have to owning what comes of it.

(Taking on your own responsibilities is the price to pay for choosing to drive your life)

How long have you been along for the ride? When is your turn to actually drive?

Where Do The Richest Americans Live?

Sizing up the homes of Bill Gates and other top members of the new Forbes 400 list

Originally published on October 04, 2016|Mansion Global

Have you checked the newest “Forbes 400: The Full List of The Richest People in America” yet?

Surprise, Bill Gates, with a net worth of $81 billion, is ranked No. 1 for the 23rd year running. Meanwhile, his friend Warren Buffett fell to third place for the first time in 15 years with a net worth of $65.5 billion.

Thanks to soaring stock prices of hot tech firms, CEOs at the helm of those companies seem to have been accumulating wealth at a much faster pace than others. CEO Jeff Bezos gained $20 billion to boost his net worth to $67 billion, making him the second-richest person in the U.S.

Facebook CEO Mark Zuckerberg jumped into fourth place, his highest rank ever, with a net worth of $55.5 billion.

Oracle founder Larry Ellison landed at No. 5 for the first time since 2007. His net worth is $49.3 billion.

Standing on the No. 6 spot is former New York City Mayor Michael Bloomberg, CEO of the eponymous firm Bloomberg L.P., who has a net worth of $45 billion.

These six richest multi-billionaires have a combined $363.3 billion at their withdrawal, most of which is still held as stakes in the companies they founded.

Part of their fortune is vested in real estate. Mr. Gates, for one, owns a Washington mansion worth about $170 million, several horse ranches across the U.S. and shares in some luxury hotel chains through his private investment firm, Cascade.

Here, take a look at the residences the six richest moguls call home:

1. Bill Gates
Worth: $81 billion
Home: Medina, Washington

Mr. Gates, 60, spends most of his time at his 66,000-square-foot Medina, Washington, mansion, nicknamed Xanadu 2.0 after the title character’s estate in Citizen Kane. The mansion overlooks Lake Washington. It took Mr. Gates seven years and $63.2 million to build this house, which is filled with lots of high-tech features. He purchased the lot for $2 million in 1988, but it’s now worth an estimated $170 million, according to public records.

2. Jeff Bezos
Worth: $67 billion
Home: Medina, Washington

Mr. Bezos, 52, in the process of building his e-commerce empire, scooped up a vast amount of real properties over the years, earning him the No. 26 spot on The Land Report’s list of America’s largest landowners last year. In terms of residences, he has a 165,000-acre ranch in West Texas, a waterfront house in Washington state, three linked apartments in Manhattan’s Century Tower, and a 12,000-square-foot Beverly Hills estate that boasts Tom Cruise as a neighbor, according to Forbes.

His home at Medina, Washington, close to Amazon’s headquarters, boasts 5.35 acres and about 29,000 square-foot of living space. Aside from the main home, there’s also a caretaker’s cottage and a 4,500-square-foot boathouse on Lake Washington.

3. Warren Buffett
Worth: $65.5 billion
Home: Omaha, Nebraska

Although the shrewdest investor on earth holds multiple real estate investments, Mr. Buffett, 86, is known for living humbly.

His home sits on a corner in Omaha, Nebraska, which he bought in 1958 for $31,500. Mr. Buffett has lived there ever since. The house, originally built in 1921, underwent several expansions to make it a cozy and comfortable 6,500-square-foot home for the man who has a net worth of over $65.5 billion.

4. Mark Zuckerberg
Worth: $55.5 billion
Home: Palo Alto, California

The youngest richest entrepreneur docks most of his wealth in schools, health and other philanthropies. His real estate portfolio include his home in Palo Alto and a 9.9-million pied-a-terre near Dolores Park in San Francisco.

Mr. Zuckerberg, 32, purchased his first Craftsman-style 5,000-square-foot home in Palo Alto in 2011 for $7 million. He snapped up four of the houses surrounding his home in the following years for about $43.8 million to better keep his privacy. But his plan to tear down and rebuild those four homes has been stalled.

5. Larry Ellison
Worth: $49.3 billion
Home: Woodside, California

Oracle executive chairman Larry Ellison, 72, has an extensive real estate portfolio. He has bought up large parts of whole neighborhoods in Malibu and around Lake Tahoe. He owns a $70-million Beechwood Mansion in Newport, Rhode Island; a garden villa in Kyoto, Japan; and 98% of the land of Lanai, Hawaii’s sixth-largest island, which he purchased in 2012 for $500 million, according to published reports.

His estate in Woodside, California, with an estimated value of $110 million, is modeled after 16th-century Japanese architecture, complete with a man-made 2.3-acre lake.

Getty Images

6. Michael Bloomberg
Worth: $45 billion
Home: Manhattan, New York

Former New York City Mayor Michael Bloomberg, 74, has more than a dozen of properties worldwide. He spends most of his time at his Upper Estate Side townhouse, but he also owns estates in the Hamptons in New York, as well as in London, Bermuda, Colorado and Florida.

Mr. Bloomberg’s townhouse, located at 17 East 79th St., spans five stories with a limestone exterior. During his three terms as mayor, Mr. Bloomberg lived in the townhouse instead of Gracie Mansion. However, he apparently has a plan to turn it into a mega-mansion.

Since 1989, he has been gradually buying up units at 19 East 79th St., the townhouse co-op that’s right next door to his current residence. Out of the six units in the white 1880 Greek-revival-style building, Bloomberg now owns five of them, according to The New York Observer.

Write to Fang Block at

More from Mansion Global:

Follow Mansion Global:

Facebook | Twitter | Instagram
Write to us:

Neuroscientists Can Now Read Your Dreams With a Simple Brain Scan

Like islands jutting out of a smooth ocean surface, dreams puncture our sleep with disjointed episodes of consciousness. How states of awareness emerge from a sleeping brain has long baffled scientists and philosophers alike.

For decades, scientists have associated dreaming with rapid eye movement (REM) sleep, a sleep stage in which the resting brain paradoxically generates high-frequency brain waves that closely resemble those of when we’re awake.

Yet dreaming isn’t exclusive to REM sleep.

A series of oddball reports also found signs of dreaming during non-REM deep sleep, when the brain is dominated by slow-wave activity—the opposite of an alert, active, conscious brain.

Now, thanks to a new study published in Nature Neuroscience, we may have an answer to the tricky dilemma.

By closely monitoring the brain waves of sleeping volunteers, a team of scientists at the University of Wisconsin pinpointed a local “hot spot” in the brain that fires up when we dream, regardless of whether a person is in non-REM or REM sleep.

“You can really identify a signature of the dreaming brain,” says study author Dr. Francesca Siclari.

What’s more, using an algorithm developed based on their observations, the team could accurately predict whether a person is dreaming with nearly 90 percent accuracy, and—here’s the crazy part—roughly parse out the content of those dreams.

“[What we find is that] maybe the dreaming brain and the waking brain are much more similar than one imagined,” says Siclari.

The study not only opens the door to modulating dreams for PTSD therapy, but may also help researchers better tackle the perpetual mystery of consciousness.

“The importance beyond the article is really quite astounding,” says Dr. Mark Blagrove at Swansea University in Wales, who was not involved in the study.

The anatomy of sleep

During a full night’s sleep we cycle through different sleep stages characterized by distinctive brain activity patterns.

Scientists often use EEG to precisely capture each sleep stage, which involves placing 256 electrodes against a person’s scalp to monitor the number and size of brainwaves at different frequencies.

When we doze off for the night, our brains generate low-frequency activity that sweeps across the entire surface. These waves signal that the neurons are in their “down state” and unable to communicate between brain regions—that’s why low-frequency activity is often linked to the loss of consciousness.

These slow oscillations of non-REM sleep eventually transform into high-frequency activity, signaling the entry into REM sleep. This is the sleep stage traditionally associated with vivid dreaming—the connection is so deeply etched into sleep research that reports of dreamless REM sleep or dreams during non-REM sleep were largely ignored as oddities.

These strange cases tell us that our current understanding of the neurobiology of sleep is incomplete, and that’s what we tackled in this study, explain the authors.

Dream hunters

To reconcile these paradoxical results, Siclari and team monitored the brain activity of 32 volunteers with EEG and woke them up during the night at random intervals. The team then asked the sleepy participants whether they were dreaming, and if so, what were the contents of the dream. In all, this happened over 200 times throughout the night.

Rather than seeing a global shift in activity that correlates to dreaming, the team surprisingly uncovered a brain region at the back of the head—the posterior “hot zone”—that dynamically shifted its activity based on the occurrence of dreams.

Dreams were associated with a decrease in low-frequency waves in the hot zone, along with an increase in high-frequency waves that reflect high rates of neuronal firing and brain activity—a sort of local awakening, irrespective of the sleep stage or overall brain activity.

“It only seems to need a very circumscribed, a very restricted activation of the brain to generate conscious experiences,” says Siclari. “Until now we thought that large regions of the brain needed to be active to generate conscious experiences.”

That the hot zone leaped to action during dreams makes sense, explain the authors.

Previous work showed stimulating these brain regions with an electrode can induce feelings of being “in a parallel world.” The hot zone also contains areas that integrate sensory information to build a virtual model of the world around us. This type of simulation lays the groundwork of our many dream worlds, and the hot zone seems to be extremely suited for the job, say the authors.

If an active hot zone is, in fact, a “dreaming signature,” its activity should be able to predict whether a person is dreaming at any time. The authors crafted an algorithm based on their findings and tested its accuracy on a separate group of people.

“We woke them up whenever the algorithm alerted us that they were dreaming, a total of 84 times,” the researchers say.

Overall, the algorithm rocked its predictions with roughly 90 percent accuracy—it even nailed cases where the participants couldn’t remember the content of their dreams but knew that they were dreaming.

Dream readers

Since the hot zone contains areas that process visual information, the researchers wondered if they could get a glimpse into the content of the participants’ dreams simply by reading EEG recordings.

Dreams can be purely perceptual with unfolding narratives, or they can be more abstract and “thought-like,” the team explains.

Faces, places, movement and speech are all common components of dreams and processed by easily identifiable regions in the hot zone, so the team decided to focus on those aspects.

Remarkably, volunteers that reported talking in their dreams showed activity in their language-related regions; those who dreamed of people had their facial recognition centers activate.

This suggests that dreams recruit the same brain regions as experiences in wakefulness for specific contents,” says Siclari, adding that previous studies were only able to show this in the “twilight zone,” the transition between sleep and wakefulness. (Why be surprised? What other brain regions could be activated?)

Finally, the team asked what happens when we know we were dreaming, but can’t remember the specific details. As it happens, this frustrating state has its own EEG signature: remembering the details of a dream was associated with a spike in high-frequency activity in the frontal regions of the brain.

This raises some interesting questions, such as whether the frontal lobes are important for lucid dreaming, a meta-state in which people recognize that they’re dreaming and can alter the contents of the dream, says the team.

Consciousness arising

The team can’t yet explain what is activating the hot zone during dreams, but the answers may reveal whether dreaming has a biological purpose, such as processing memories into larger concepts of the world.

Mapping out activity patterns in the dreaming brain could also lead to ways to directly manipulate our dreams using non-invasive procedures such as transcranial direct-current stimulation.

Inducing a dreamless state could help people with insomnia, and disrupting a fearful dream by suppressing dreaming may potentially allow patients with PTSD a good night’s sleep.

Dr. Giulo Tononi, the lead author of this study, believes that the study’s implications go far beyond sleep.

“[W]e were able to compare what changes in the brain when we are conscious, that is, when we are dreaming, compared to when we are unconscious, during the same behavioral state of sleep,” he says.

During sleep, people are cut off from the environment. Therefore, researchers could hone in on brain regions that truly support consciousness while avoiding confounding factors that reflect other changes brought about by coma, anesthesia or environmental stimuli.

“This study suggests that dreaming may constitute a valuable model for the study of consciousness,” says Tononi.

Image Credit: Shutterstock

Now, using an algorithm, a team of scientists say they can predict if a person is dreaming with nearly 90 percent accuracy, and roughly parse out the content of those dreams.

Like islands jutting out of a smooth ocean surface, dreams puncture our sleep with disjointed episodes of consciousness. How states of awareness emerge…

The Disturbing History of African-Americans and Medical Research Goes Beyond Henrietta Lacks

Lily Rothman. Updated: Apr 21, 2017

Ask a given person what they know about the history of the use of African-Americans as unwilling research subjects and they are likely to mention one infamous incident: Tuskegee.

“Such a failure seems almost beyond belief, or human compassion,” TIME wrote when the study made headlines in 1972, as the world learned that for four decades the U.S. Public Health Service had been conducting an experiment in which proven remedies were kept from syphilis patients in Alabama, all of whom were black men. But there’s a lot more to that history.

“Tuskegee shouldn’t be the first thing people think of,” Harriet A. Washington, the author of Medical Apartheid, tells TIME. “It’s the example that the government has admitted to and acknowledged. It’s so famous that people think it was the worst, but it was relatively mild compared to other stuff.”

With the premiere on Saturday of the HBO film The Immortal Life of Henrietta Lacks, based on Rebecca Skloot’s best-selling book of the same name, another piece of the puzzle may get a little closer to the first-to-mind fame of Tuskegee.

Lacks was, as TIME explained in its initial review of Skloot’s book, a black woman treated unsuccessfully for cervical cancer in 1951, from whose tumor doctors kept a sample of tissue. Her cells provided a breakthrough would prove invaluable to medical research, but her family was kept in the dark even as they themselves became the subjects of scientific interest.

Washington, who has interviewed the Lacks family, says that one problem with the national narrative about Tuskegee is the risk that those unaware of the larger history that surrounds both that study and the story of Henrietta Lacks might think that African-Americans are “overreacting to a single study” if they express distrust of the medical establishment.

Rather, as Skloot also notes in her book, distrust like that expressed by the Lacks family is related to what’s summed up by the subtitle of Washington’s book as The Dark History of Medical Experimentation on Black Americans From Colonial Times to the Present.

“We’re talking about something that began in the 17th century,” Washington says.

Though the line between therapeutic medicine and research was blurrier at the time, she says it’s clear that doctors in the colonial American context would often try out new ideas on white patients when they hoped that the experiment would help the person in question; they would use African slaves and Native Americans as subjects when the point of the research was to benefit others.

Perhaps the most infamous example of antebellum medical research being performed on slaves is that of J. Marion Sims, whose innovation of a revolutionary gynecological procedure was made possible by multiple practice runs on enslaved women. Washington also found that slaves’ bodies were used for experiments after they died, despite widespread belief that maintaining the body’s integrity after death was religiously necessary.

“Historically, one of the larger connections is that, if you’re talking about the appropriation of African-American bodies when enslavement was part of the law of the land, that represented an extension of slavery into eternity,” she explains.

When it comes to the 20th century, though slavery was no longer the law, Washington says that there was a widespread belief that people who did not pay for their medical care would “owe their bodies” to the medical community in return.

As a result, patients from marginalized communities, like the poor and immigrants, did not receive the same ethical consideration that others did. Though that idea would have applied to poor patients of all races, segregation at the time meant that black patients were confined in many places to “black wards,” and they were disproportionately affected.

Washington says that one big misconception she often hears is that in 1951, when Lacks was treated, what happened to Lacks would have been just the common practice at the time. In reality, she has found that — while it is true that the laws and regulations that govern such experimentation have changed between then and now — basic ethical concepts such as informed consent were already very much in play.

In fact, she says, especially in the wake of the world learning of Nazi medical experimentation, some organizations kept consent rules that were even more stringent than those in play today. “These conventions tended to be rigorously adhered to when it came to white people,” Washington notes.

And, though medical research can be complicated, she believes the basic idea — then and now — is simple: “Subjects who have normal adult intelligence are capable of understanding whether their permission has been asked.”

But, if those ethical standards have generally endured, other things have changed.

Washington points to 1980 as a turning point, thanks to changes like the law that changed the medical-research economy and a Supreme Court decision that has been interpreted to mean that living things are subject to patents.

The need for tissue on which to experiment continues, but now it can be a lot more financially valuable if things work out. Washington believes that economic pressures have led to an erosion in the application of informed consent in the years since.

That’s part of the reason why Washington is glad that Henrietta Lacks’ name is becoming more famous.

“People tend to underestimate the extent and breadth of this,” Washington says. “There’s no sphere of American medicine that was not touched by the use in research of African-Americans.”

Wikipedia co-founder Jimmy Wales exits Guardian board over conflict of interest with Wikitribune news site

Jimmy Wales, the co-founder of Wikipedia, will leave the board of the Guardian newspaper after opting to launch his own rival news operation that will compete for staff, stories and donations.

Jimmy Wales

Jimmy Wales co-founded Wikipedia

The 50-year-old, who joined the board of Guardian Media Group as a non-executive director little over a year ago, has revealed plans to launch Wikitribune, an outlet aiming to provide “factual and neutral” news coverage.

Mr Wales has said he plans to hire up to 20 journalists to work on the operation.

Wikitribune will be funded by donations, putting it in direct competition with the Guardian, which frequently appeals to online readers for voluntary contributions in lieu of digital subscriptions.

He said: “Jimmy Wales will be stepping down from the GMG board by mutual agreement, given the potential for overlap in our work. We wish him well with the new project.”

Mr Wales has seized on concern around “fake news” online to promote Wikitribune, arguing “the news is broken and we can fix it“.

Guardian Media Group’s spokesman said: “We welcome all efforts to combat the rise of fake news. Our rapid growth in traffic and Guardian membership show that the demand for independent, trusted and high-quality journalism is greater than ever. ”

The left-leaning title is seeking to boost membership and donation revenues in light of a tough advertising market.

Online revenues have not risen quickly enough to make up for declining print sales, with the bulk of market growth taken up by Google and Facebook.

A spokesman for Guardian Media Group said Mr Wales’s plans meant he could no longer sit on the newspaper’s board.

<img src=”/content/dam/business/2016/07/28/55256363-guardian-business-small_trans_NvBQzQNjv4BqQJoTHvv9hWAiaCwwE8274uaCTQGAUkDgq8I833FLrys.jpg” alt=”Guardian” width=”301″ height=”189″ class=”responsive-image–fallback”/> Guardian
The Guardian is seeking to boost membership and donation revenues

The Guardian was on track to burn £90m in cash last year and has warned staff to expect further redundancies as it seeks to reach break-even in two years.

Mr Wales said: “I am a huge admirer of the Guardian and am honoured to have been involved as a member of the GMG board. I will continue to be an avid fan of their integrity for news and journalism.”

He has said he will take a hands-on role in his latest venture and remains chairman of The People’s Operator, a mobile service provider that gives a shares of its revenues to good causes.

It floated on AIM on a £100m valuation in 2014 but has struggled to build its subscriber base and now has a market capitalisation of less than £11m.

Kids Company founder Camila Batmanghelidjh ‘facing directorship ban’

@JamieGrierson. Monday 24 April 2017

Insolvency Service reportedly wants to disqualify ex-board members including Alan Yentob over roles in collapsed charity

Alan Yentob and Camila Batmanghelidjh
Alan Yentob and Camila Batmanghelidjh could be among board members forced to relinquish any directorships they hold. Photograph: Simon James/Getty Images

Former board members of the collapsed charity Kids Company – including its founder, Camila Batmanghelidjh, and the former BBC chief Alan Yentob – face being banned from serving as company directors, according to reports.

The Insolvency Service has written to lawyers acting for Kids Company’s former board members to warn them that it is minded to pursue disqualification proceedings against them, according to Sky News.

The Insolvency Service, which has powers to seek bans on directorships for individuals of up to 15 years, refused to comment.

While disqualification proceedings can be lengthy, if successful they would ultimately force Yentob and the other board members to relinquish any directorships they hold.

Yentob is listed at Companies House as a director of a television production business called I Am Curious, which he established last year.

Kids Company collapsed in the summer of 2015, a month after it received a £3m government grant backed by the then prime minister, David Cameron.

Batmanghelidjh and Kids Company staff blamed the collapse on a police investigation into sexual and physical assaults within the charity, which was ultimately dropped.

A review by the Charity Commission into the financial collapse is continuing.

Other directors potentially facing a ban include Richard Handover, a former boss of WH Smith, Andrew Webster, a former executive at the drugs company AstraZeneca, and Erica Bolton, an arts publicist.

Note: Articles that don’t even mention what companies do, in this case what Kids Company was delivering and its purposes, is beyond me. Would like to check Wikipedia to supplement why Cameron extended this grant and why the company is facing insolvency? Are drug companies behind this move?

Why we should compare Trump to Hitler

Comparing dictators to Hitler, or fascists to Nazis, is often criticised as intellectually lazy, inaccurate and even dangerous.

However, over the past year parallels drawn between the rise of Adolf Hitler and Donald Trump have been numerous.

A 1922 article from the New York Times archive resurfaced in February 2015, which massively underestimated Hitler’s capacity for destruction, dismissing much of his campaign promises as political rhetoric.

Many drew comparisons with the response to Trump’s victory, after which people were hopeful that Trump cynically uses nationalism and xenophobic anti-immigration in order to gain votes, but will be tempered from acting on his more brutal promises.

Some of the descriptions of Hitler and the rise of Nazi populism seemed very familiar…

Another condition favourable to the outburst of the movement is the widespread discontent with the existing state of affairs among all classes in the towns and cities under the increasing economic pressure.

He is a man of the ‘common people’ and hence, has the makings of a ‘popular hero’ appealing to all classes.

His program consists chiefly of half a dozen negative ideas clothed in generalities

He probably does not know himself just what he wants to accomplish.

He talks rough, shaggy, sound horse sense, and according to public opinion, a strong, active leader equipped with horse sense is the need of the hour.

In particular, one image of a sign in the US Holocaust Memorial Museum, which describes 14 early signs of fascism, went viral after acting attorney general Sally Q. Yates was fired.

Timothy Snyder, Yale professor of history and author of On Tyranny: Twenty Lessons from the Twentieth Century, released this video explaining the value of comparison.

He explains how comparing Trump to Hitler can be useful, despite key differences.

Obviously, comparing Trump to Hitler does not necessarily imply that Trump is going to perpetrate a genocide.

Nevertheless, without a proper consideration of history we are doomed to repeat its mistakes.


So the way to start the discussion about comparisons is to point out that Americans are extremely lazy about history. I mean that’s one way in which were definitely number one among major nations.

And one of the ways we’re lazy about history is that as soon as anyone suggests that the past might be useful, then we say “but wait it’s not exactly the same and therefore I’m just going to discard it.”

In that way in two or three seconds we give ourselves an excuse not to think about history.

The premise of the book “On Tyranny” is not that Hitler is just like Trump or Trump is just like Hitler. The premise is that democratic republics usually fail and it’s useful for us to see how they fail.

One of the ways a democratic republic can fail is Germany in 1933. There are plenty of other examples in the book, also from the left wing Czechoslovakia in 1948 becoming communist.

The point of the book is that these things really happened over and over again and that intelligent people, no less intelligent than us, experienced them and left a record for us to learn from. (And they were far more cultured and read abundantly and discussed at length and met)

So what I’m trying to do in the book is to help us to learn from that record so we don’t have events like Germany in 1933 or Czechoslovakia in 1948.

Just saying “Hitler’s not like Trump“ or ”Trump is not like Hitler” isn’t going to save us.

Learning for the past though, could.

Early signs of fascism, went viral after acting attorney general Sally Q. Yates was fired.


View image on Twitter


Café-librairie or Library-Café with No lap tops or smartphones: To discuss, read and meet

Par Adib Y. TOHMÉ, OLJ


Nos Lecteurs ont la Parole


Quand je suis rentré au Liban au milieu des années 90, j’avais pour idée de créer un café-librairie.

J’imaginais un lieu convivial de déconnexion et d’échange dans lequel on pouvait lire tout en buvant un bon café, confortablement lové dans un canapé. Ce lieu proposerait aussi des discussions et des soirées emblématiques à thème autour d’une idée, d’un livre ou d’une pensée qui réunissent, autour d’un repas mythique, de parfaits inconnus partageant la même passion pour les livres.

Quand j’ai présenté mon projet à une personne dont l’opinion compte beaucoup pour moi, elle a éclaté d’un rire bruyant et moqueur dont j’entends les échos assourdissants jusqu’à aujourd’hui. Comme vous pouvez vous en douter, j’ai abandonné mon projet.

Plusieurs années plus tard, un client a débarqué chez moi et, avec le même enthousiasme que j’avais 20 ans plus tôt, il m’a proposé le même projet d’un lieu où on devait fermer nos portables, lire ou discuter tout en buvant un café. Je n’ai pas ri (bien que j’en eus envie).

Je l’ai regardé longuement, d’un regard nostalgique et je lui ai dit : « Revois ton étude de faisabilité, l’emplacement, le coût du loyer, les charges d’exploitation, les marges et surtout identifie ta clientèle pour ne pas mettre la clé sous la porte. »

Avant de partir, il m’a dit : « Crois-moi, il y a beaucoup de lecteurs. » Mais où sont les lecteurs ?

Il y a trop de fumeurs de narguilé, mais pas assez de lecteurs. Il y a trop de tables de poker, mais pas assez de bibliothèques. Il y a trop de bruit et de fracas, mais pas assez de silence. Il y a trop de mouvements, mais pas assez de constance. Il y a trop d’échanges, mais pas assez de pensée. Il y a trop d’images, mais pas assez de substance.

Il y a trop de gens qui savent tout, mais qui n’ont jamais ouvert un livre. Trop de gens qui parlent, mais peu qui savent écouter.

Au fond, j’étais triste de ne plus être capable de m’enthousiasmer pour de tels projets. Je n’aimais pas ma résignation. Je n’aimais pas mon sentiment qu’il n’y a plus rien à faire, de capituler devant le préjugé collectif que tout ce qui ne produit pas de profits immédiats est inutile.

Et pourtant il y a de l’utilité dans l’inutile.

Et la lecture fait partie de ces choses inutiles dont nous avons besoin pour vivre. Lire est avant tout un acte de liberté. Et je peux me permettre aujourd’hui de parler comme un vieux con et de dire qu’il faut redonner aux jeunes le goût de lire. De leur montrer la joie de s’extraire à la culture des escargots. Celle qui consiste à se coller les uns aux autres, fiers de ce qu’ils sont, qui se complimentent mutuellement, dans la médiocrité de leur vie et la vacuité de leur existence.

Grâce à la lecture, le monde peut devenir plus vaste et l’horizon plus grand. Dans le silence, dans la solitude, nous allons librement à la rencontre d’une autre pensée, d’un autre regard sur le monde.

À travers les livres, nous découvrons d’autres façons de voir la vie. Un bon livre, comme un bon film, ou un bon poème, ne nous laisse jamais intacts. Il remodèle notre cerveau, nous permet de sortir de nous-mêmes, d’apprendre l’attention au monde et nous engage à porter un regard différent sur ce qui est important.

Les médias ont pour mission de nous aplatir pour susciter facilement notre désir et nous faire perdre rapidement notre intérêt pour les choses. C’est la culture basée sur l’oubli et non sur l’apprentissage. Les médias fabriquent des consommateurs.

Les bons livres peuvent créer des citoyens. (Meme les mauvaises, si on a l’esprit de reflection)

Note 1: The budget of every ministry contain enormous amount of money for superfluous “charitable or cultural organizations and associations” that areNot submitted to any auditing. Let’s have such an association that opens these kinds of Café-librairie.

Note 2: There are plenty of Cafés that display a small shelf of targeted books for their targeted clients in Beirut

Notes 3: I have been patronizing a private library, turned over to a private university, for over 15 years. I walk 2 miles to spend 3 hours reading and taking notes. When I’m back home to post book reviews on my blog and edited notes and comments on social media. This walk was kind of enjoying a couple hours of freedom: I take care of my elderly parents who need plenty of care.

Refugee death toll crossing the sea passes 1,073 in record 2017

Why charities attacked for conducting Mediterranean rescues?

NGOs are being blamed for our presence, when authorities should be blamed for their absence’

Lizzie Dearden@lizziedearden

The UN Refugee Agency (UNHCR) has recorded at least 1,073 people dead or missing on the treacherous passage between Libya and Italy – a grim benchmark that was not reached until the end of May last year.

At least 150 are children, Unicef said, while warning that the real figure is likely to be far higher because unaccompanied minors’ deaths frequently go unreported.

Such is the danger of death that asylum seekers embarking on flimsy dinghies have been known to write phone numbers in marker pen on life jackets, so loved ones can be notified if their body is recovered.

More than 8,300 migrants were rescued over the Easter weekend alone, with some of those taken to safety telling aid workers around 100 of their fellow passengers had died during the voyage.

Many dinghies have capsized, seeing up to 170 people crammed on board drown, while others have been found dead in boats after being suffocated, dying of hypothermia or starving while drifting at sea.

Smugglers are pushing more and more boats into the Mediterranean as the weather improves and amid rumours of a crackdown by the Libyan coastguard, which is being bolstered by Italian funding and equipment.

The unprecedented crisis has sparked intervention by several non-governmental organisations (NGOs), who have launched their own rescue ships equipped with medical staff and supplies to bolster efforts by the EU’s Operation Sophia.

Initially welcomed by European authorities, their growing role in the Mediterranean has been met with increasing suspicion by right-wing politicians and groups now accusing them of “colluding” with smugglers.

Médecins Sans Frontières (MSF), whose staff work on two rescue ships, dismissed the claims as “baseless”.

Stefano Argenziano, the group’s operations manager for migration, said it rejects any accusation of cooperation with ruthless Libyan smugglers, who have turned a humanitarian crisis into a lucrative business helping fuel the country’s ongoing war.

“It’s a ludicrous accusation that’s diverting attention from the real problem,” he told The Independent.

“The real problem is that people are dying. There’s a gap in assistance and we’re starting to wonder whether this is part of a deliberate plan to step the migration flow…a deadly deterrent.”

Mr Argenziano said interventions by EU assets, excepting the Italian coastguard, were often “very little and very late” and condemned the continent’s refusal to provide other routes to safety.

“Search and rescue is not the problem, but it is not the solution either,” he added.

“It is a necessity to save lives unless politicians can produce a safe and legal alternative.”

Following the closure of the refugee route over the Aegean Sea using the controversial EU-Turkey deal last year, cooperation has been ramping up with the fragile Libyan Government of National Accord.

Italy signed an agreement backed by the EU to reduce boat crossings over the Central Mediterranean in February but it was later suspended by the justice ministry in Tripoli and remains in limbo.

Rome agreed to supply the country’s coastguard, which is itself accused of killing and abusing migrants, with 10 new boats alongside millions of euros in funding for migration initiatives.

International organisations believe the ultimate aim – transferring responsibility for rescues to Libya and holding migrants in detention centres there – is not viable amid the ongoing conflict and the widespread enslavement, capture, torture and extortion of asylum seekers.

Rob MacGillivray, the director of Save the Children’s search and rescue programme, said pushing boats back to shore from international waters would be illegal.

“It’s not going to stop crossings and even if it did, all that would happen and the routes would shift to Algeria, Tunisia or Egypt for example,” he added, rejecting accusations of NGOs colluding with smugglers.

“Safety is not the smugglers’ first priority and they will use whatever floats to send people across the Mediterranean.

“If search and rescue providers were to finish work tomorrow, would the people smugglers just fade into the background?”

In 2015, operations were mainly undertaken by Italian law-enforcement, EUNAVFOR Med or Frontex vessels.

NGO vessels were involved in less than 5% of incidents.

But they are now deployed to respond to around half of missions by the Maritime Rescue Coordination Centre in Rome, which also draws on military, coastguard and commercial ships.

A cursory internet search reveals countless blogs accusing NGOs of colluding in illegal people smuggling, while numerous conspiracy theories have arisen over what far-right commentators label the “invasion of Europe”.

The latest politician to push for the Central Mediterranean route to be closed is Wolfgang Sobotka, the Austrian interior minister.

“A rescue in the open sea cannot be a ticket to Europe, because it hands organised traffickers every argument to persuade people to escape for economic reasons,” he told Germany’s DPA news agency.

“[Stopping crossings] is the only way to end the tragic and senseless deaths in the Mediterranean.”

Mr Sobotoka, from the right-wing Austrian People’s Party, claimed his country could put up borders in the event of any influx, saying the numbers seen in 2015 “must not be repeated”.

The government in Vienna is one of several to have implemented a limit on asylum seekers, with calls to halve the current annual cap of 17,000.

In Italy, the chief prosecutor in the Sicilian city of Catania has formed a task force on claims of links between NGOs and smugglers.

Carmelo Zuccaro admitted he had no proof and the public prosecutor decided not to investigate, but a fact finding mission was launched by the Italian parliament.

Frontex, the EU border agency, has also raised concern over smugglers’ alleged use of rescue vessels.

A confidential report leaked in December claimed migrants were given “clear indications before departure on the precise direction to be followed in order to reach the NGOs’ boats” and accused charities of warning rescued asylum seekers not to cooperate with Italian authorities.

Another report released by Frontex in February claimed search and rescue operations near the Libyan coast “unintentionally help criminals achieve their objectives at minimum cost, strengthen their business model by increasing the chances of success”.

It recognised that rescues were needed to comply with international legal obligations and said safe and legal routes were needed for refugees, but alleged sailing close to Libyan territorial waters acted as a “pull factor”.

The Malta-based charity Moas (Migrant Offshore Aid Station) pointed out that boat crossings increased even when Italy stopped its Mare Nostrum operation, while a recent Oxford University study found rescues have “little or no effect on the number of arrivals”.

A representative said migrants were being “increasingly used by politicians in Europe to fuel the rise of nationalism”, adding: “The migration phenomenon is not going away, and focusing only on patrolling the EU’s borders is definitely not the solution.”

With almost 37,000 asylum seekers arriving in Italy so far this year, mainly from Guinea, Nigeria and other African nations, the crisis shows no sign of slowing.

Sophie Beau, the co-founder of rescue charity SOS Mediterranée, said NGOs were being forced to act by the “failure of European states”, who should be increasing capacity themselves.

“NGOs are being blamed for our presence, when authorities should be blamed for their absence,” she added.

“There’s a humanitarian tragedy unfolding in front of our eyes at the door of Europe and we cannot just remain blind.”

Note: France wanted to depose Kaddafi because he declined to purchase French weapons: Italy is taking care of the problems that France  generated.  The USA got hold of $7 billion of gold in Libya central bank

Syria refused to have Qatar gas pipeline ending in Turkey instead of Syrian ports: Syria calamity is the problem of everyone, except Qatar…

And most horror stories in the Middle-East are of these kinds of irrational non-patient negotiations

Computer learn to understand what a cat is?

Just like the brain integrates vision and language, we developed a model that connects parts of visual things like visual snippets with words and phrases in sentences.

(Video) A 3-year-old  Girl: Okay, that’s a cat sitting in a bed. The boy is petting the elephant. Those are people that are going on an airplane. That’s a big airplane.

0:32 Fei-Fei Li: This is a three-year-old child describing what she sees in a series of photos. She might still have a lot to learn about this world, but she’s already an expert at one very important task: to make sense of what she sees.

Our society is more technologically advanced than ever. We send people to the moon, we make phones that talk to us or customize radio stations that can play only music we like.

Yet, our most advanced machines and computers still struggle at this task (making sense of what they see?).

So I’m here today to give you a progress report on the latest advances in our research in computer vision, one of the most frontier and potentially revolutionary technologies in computer science.

we have prototyped cars that can drive by themselves, but without smart vision, they cannot really tell the difference between a crumpled paper bag on the road, which can be run over, and a rock that size, which should be avoided.

We have made fabulous megapixel cameras, but we have not delivered sight to the blind.

Drones can fly over massive land, but don’t have enough vision technology to help us to track the changes of the rainforests.

Security cameras are everywhere, but they do not alert us when a child is drowning in a swimming pool.

Photos and videos are becoming an integral part of global life. They’re being generated at a pace that’s far beyond what any human, or teams of humans, could hope to view, and you and I are contributing to that at this TED.

Yet our most advanced software is still struggling at understanding and managing this enormous content. So in other words, collectively as a society, we’re very much blind, because our smartest machines are still blind. (Why Not invest in auditory detection?)

“Why is this so hard?” you may ask. Cameras can take pictures like this one by converting lights into a two-dimensional array of numbers known as pixels, but these are just lifeless numbers. They do not carry meaning in themselves. Just like to hear is not the same as to listen, to take pictures is not the same as to see, and by seeing, we really mean understanding.

In fact, it took Mother Nature 540 million years of hard work to do this task, and much of that effort went into developing the visual processing apparatus of our brains, not the eyes themselves. So vision begins with the eyes, but it truly takes place in the brain.

 for 15 years now, starting from my Ph.D. at Caltech and then leading Stanford’s Vision Lab, I’ve been working with my mentors, collaborators and students to teach computers to see. Our research field is called computer vision and machine learning.

It’s part of the general field of artificial intelligence. So ultimately, we want to teach the machines to see just like we do: naming objects, identifying people, inferring 3D geometry of things, understanding relations, emotions, actions and intentions. You and I weave together entire stories of people, places and things the moment we lay our gaze on them.

The first step towards this goal is to teach a computer to see objects, the building block of the visual world. In its simplest terms, imagine this teaching process as showing the computers some training images of a particular object, let’s say cats, and designing a model that learns from these training images.

How hard can this be? After all, a cat is just a collection of shapes and colors, and this is what we did in the early days of object modeling. We’d tell the computer algorithm in a mathematical language that a cat has a round face, a chubby body, two pointy ears, and a long tail, and that looked all fine. But what about this cat? (Laughter) It’s all curled up.

Now you have to add another shape and viewpoint to the object model. But what if cats are hidden? What about these silly cats? Now you get my point. Even something as simple as a household pet can present an infinite number of variations to the object model, and that’s just one object.

 about 8 years ago, a very simple and profound observation changed my thinking. No one tells a child how to see, especially in the early years. They learn this through real-world experiences and examples.

If you consider a child’s eyes as a pair of biological cameras, they take one picture about every 200 milliseconds, the average time an eye movement is made. So by age three, a child would have seen hundreds of millions of pictures of the real world. That’s a lot of training examples. So instead of focusing solely on better and better algorithms, my insight was to give the algorithms the kind of training data that a child was given through experiences in both quantity and quality.

Once we know this, we knew we needed to collect a data set that has far more images than we have ever had before, perhaps thousands of times more, and together with Professor Kai Li at Princeton University, we launched the ImageNet project in 2007.

Luckily, we didn’t have to mount a camera on our head and wait for many years. We went to the Internet, the biggest treasure trove of pictures that humans have ever created. We downloaded nearly a billion images and used crowdsourcing technology like the Amazon Mechanical Turk platform to help us to label these images.

At its peak, ImageNet was one of the biggest employers of the Amazon Mechanical Turk workers: together, almost 50,000 workers from 167 countries around the world helped us to clean, sort and label nearly a billion candidate images. That was how much effort it took to capture even a fraction of the imagery a child’s mind takes in in the early developmental years.

 In hindsight, this idea of using big data to train computer algorithms may seem obvious now, but back in 2007, it was not so obvious. We were fairly alone on this journey for quite a while. Some very friendly colleagues advised me to do something more useful for my tenure, and we were constantly struggling for research funding. Once, I even joked to my graduate students that I would just reopen my dry cleaner’s shop to fund ImageNet. After all, that’s how I funded my college years.

we carried on.

In 2009, the ImageNet project delivered a database of 15 million images across 22,000 classes of objects and things organized by everyday English words. In both quantity and quality, this was an unprecedented scale. As an example, in the case of cats, we have more than 62,000 cats of all kinds of looks and poses and across all species of domestic and wild cats. We were thrilled to have put together ImageNet, and we wanted the whole research world to benefit from it, so in the TED fashion, we opened up the entire data set to the worldwide research community for free. (Applause)

Now that we have the data to nourish our computer brain, we’re ready to come back to the algorithms themselves.

As it turned out, the wealth of information provided by ImageNet was a perfect match to a particular class of machine learning algorithms called convolutional neural network, pioneered by Kunihiko Fukushima, Geoff Hinton, and Yann LeCun back in the 1970s and ’80s.

Just like the brain consists of billions of highly connected neurons, a basic operating unit in a neural network is a neuron-like node. It takes input from other nodes and sends output to others. Moreover, these hundreds of thousands or even millions of nodes are organized in hierarchical layers, also similar to the brain. (maybe Not that hierarchical?)

In a typical neural network we use to train our object recognition model, it has 24 million nodes, 140 million parameters, and 15 billion connections.

That’s an enormous model. Powered by the massive data from ImageNet and the modern CPUs and GPUs to train such a humongous model, the convolutional neural network blossomed in a way that no one expected. It became the winning architecture to generate exciting new results in object recognition.

This is a computer telling us this picture contains a cat and where the cat is. Of course there are more things than cats, so here’s a computer algorithm telling us the picture contains a boy and a teddy bear; a dog, a person, and a small kite in the background; or a picture of very busy things like a man, a skateboard, railings, a lampost, and so on.

Sometimes, when the computer is not so confident about what it sees, we have taught it to be smart enough to give us a safe answer instead of committing too much, just like we would do, but other times our computer algorithm is remarkable at telling us what exactly the objects are, like the make, model, year of the cars.

We applied this algorithm to millions of Google Street View images across hundreds of American cities, and we have learned something really interesting: first, it confirmed our common wisdom that car prices correlate very well with household incomes. But surprisingly, car prices also correlate well with crime rates in cities, or voting patterns by zip codes.

So wait a minute. Is that it? Has the computer already matched or even surpassed human capabilities? Not so fast. So far, we have just taught the computer to see objects. This is like a small child learning to utter a few nouns. It’s an incredible accomplishment, but it’s only the first step.

Soon, another developmental milestone will be hit, and children begin to communicate in sentences. So instead of saying this is a cat in the picture, you already heard the little girl telling us this is a cat lying on a bed.

 to teach a computer to see a picture and generate sentences, the marriage between big data and machine learning algorithm has to take another step. Now, the computer has to learn from both pictures as well as natural language sentences generated by humans. Just like the brain integrates vision and language, we developed a model that connects parts of visual things like visual snippets with words and phrases in sentences.

About four months ago, we finally tied all this together and produced one of the first computer vision models that is capable of generating a human-like sentence when it sees a picture for the first time. Now, I’m ready to show you what the computer says when it sees the picture that the little girl saw at the beginning of this talk.

14:30 (Video) Computer: A man is standing next to an elephant. A large airplane sitting on top of an airport runway.

14:40 FFL: Of course, we’re still working hard to improve our algorithms, and it still has a lot to learn. (Applause)

14:50 And the computer still makes mistakes.

14:53 (Video) Computer: A cat lying on a bed in a blanket.

14:57 FFL: So of course, when it sees too many cats, it thinks everything might look like a cat.

15:04 (Video) Computer: A young boy is holding a baseball bat. (Laughter)

15:08 FFL: Or, if it hasn’t seen a toothbrush, it confuses it with a baseball bat.

15:14 (Video) Computer: A man riding a horse down a street next to a building. (Laughter)

15:19 FFL: We haven’t taught Art 101 to the computers.

15:24 (Video) Computer: A zebra standing in a field of grass.

15:27 FFL: And it hasn’t learned to appreciate the stunning beauty of nature like you and I do.

15:33 So it has been a long journey. To get from age zero to three was hard.

The real challenge is to go from three to 13 and far beyond. Let me remind you with this picture of the boy and the cake again. So far, we have taught the computer to see objects or even tell us a simple story when seeing a picture.

15:58 (Video) Computer: A person sitting at a table with a cake.

16:02 FFL: But there’s so much more to this picture than just a person and a cake. What the computer doesn’t see is that this is a special Italian cake that’s only served during Easter time. The boy is wearing his favorite t-shirt given to him as a gift by his father after a trip to Sydney, and you and I can all tell how happy he is and what’s exactly on his mind at that moment.

This is my son Leo. On my quest for visual intelligence, I think of Leo constantly and the future world he will live in. When machines can see, doctors and nurses will have extra pairs of tireless eyes to help them to diagnose and take care of patients.

Cars will run smarter and safer on the road. Robots, not just humans, will help us to brave the disaster zones to save the trapped and wounded.

We will discover new species, better materials, and explore unseen frontiers with the help of the machines.

17:14 Little by little, we’re giving sight to the machines. First, we teach them to see. Then, they help us to see better. For the first time, human eyes won’t be the only ones pondering and exploring our world. We will not only use the machines for their intelligence, we will also collaborate with them in ways that we cannot even imagine.

17:40 This is my quest: to give computers visual intelligence and to create a better future for Leo and for the world.

Patsy Z shared this link TED

Why the body-bending poses in cat pictures are a huge challenge to AI technology:

This is how we’re teaching computers to understand pictures.|By Fei-Fei Li




April 2017

Blog Stats

  • 1,516,553 hits

Enter your email address to subscribe to this blog and receive notifications of new posts by

Join 822 other subscribers
%d bloggers like this: