12-Year-Old Figures Out Netflix Lock Code With This "Genius" Trick – NDTV

A 12-year-old figured out the parental lock on Netflix (Representative Image)

Irish-Canadian author Ed O'Loughlin was left "both frightened and impressed" with the way his youngest daughter figured out a way to hack their Netflix parental code.

To allow parents a degree of control over what their children are watching, the streaming giant gives parents the option of using a PIN code to lock certain content. It turns out that Mr O'Loughlin's 12-year-old really wanted to watch 'The Umbrella Academy' on Netflix - but instead of asking her parents for permission, she simply devised a "genius" way to guess their lock code.

Her father took to Twitter on Sunday to explain how she did it using just a bit of grease and some clever thinking. "My youngest hacked our Netflix parental code. She put light grease on the remote and got me to input the code when she wasn't looking. Then she noted the numbers I'd pressed and went through the combinations later," wrote Ed O'Loughlin, adding that her trick left him impressed as well as frightened.

In a follow-up tweet, he explained that his daughter, aged 12, went into all the trouble to watch 'The Umbrella Academy'.

Mr O'Loughlin's tweet has gone viral with over 3.5 lakh 'likes' and more than 32,000 'retweets'.

Many in the comments section shared tales of their own children tricking them, while others said that the 12-year-old's guessing trick was "genius".

"Cut from the same cloth as my devious youngest daughter. She handed me the controls when I sat with my back to the window at night so she could see the reflection. I think she was about 7 at the time. Now she's 16 I sleep with one eye open," wrote one Twitter user.

"Very, very impressive. My son ran two school planners - one with all of his good comments and one with his bad ones," said another.

"The child is a genius," a Twitter user remarked.

In May this year, Netflix itself was left impressed with the way a woman managed to use her ex-boyfriend's account secretly.

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12-Year-Old Figures Out Netflix Lock Code With This "Genius" Trick - NDTV

Trump targeting WikiLeaks’ Assange as ‘political enemy’, UK court told – Reuters

LONDON (Reuters) - WikiLeaks founder Julian Assange is wanted by the United States because he is a political enemy of President Donald Trump, his London extradition hearing was told on Wednesday.

A supporter of WikiLeaks founder Julian Assange protests outside the Old Bailey, the Central Criminal Court ahead of a hearing to decide whether Assange should be extradited to the United States, in London, Britain September 9, 2020. REUTERS/Henry Nicholls

Australian-born Assange, 49, is fighting against being sent to the United States, where he is charged with conspiring to hack government computers and violating an espionage law over the release of confidential cables by WikiLeaks in 2010-2011.

Paul Rogers, a professor of peace studies at Britains Bradford University, told Londons Old Bailey court that the timing of the U.S. prosecution was connected to Assanges political views and Trumps hostility toward him.

The evidence does support very strongly ... this does appear to be a political trial, Rogers said.

Assange and WikiLeaks enraged the U.S. government a decade ago by publishing thousands of secret American documents, but he was not charged with any criminal offense at the time.

His supporters see him as a champion of free speech exposing abuses of power and hypocrisy by Washington and regard his prosecution as threat to journalism. U.S. authorities say he recklessly endangered the lives of sources with his releases.

Rogers said the Trump administration viewed Assange as a political enemy because of his opinions. Assanges defense team are arguing the U.S. case is politically-motivated, something which would bar his extradition.

The opinions and views of Mr Assange, demonstrated in his words and actions with the organization WikiLeaks over many years, can be seen as very clearly placing him in the crosshairs of dispute with the philosophy of the Trump administration, Rogers said in his statement to the court.

James Lewis, the lawyer representing the United States, challenged the assertion the case was politically-motivated, saying U.S. federal prosecutors were forbidden to consider political opinion in making their decisions.

Im not saying they are acting in bad faith, Rogers said. Im saying that at a different level, a political decision was taken to investigate this further after it had lapsed for eight years.

Assange was warned by the judge on Tuesday he would be removed from the courtroom and tried in his absence if he interrupted proceedings after Assange shouting nonsense at Lewis.

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Trump targeting WikiLeaks' Assange as 'political enemy', UK court told - Reuters

‘Absolute And Arbitrary Power’: Killing Extinction Rebellion And Julian Assange – Scoop.co.nz

Thursday, 10 September 2020, 4:05 pmArticle: Media Lens

Theuse and misuse of George Orwells truth-telling is sowidespread that we can easily miss his intended meaning. Forexample, with perfect (Orwellian) irony, the BBC has astatue of Orwell outside Broadcasting House, bearing the inscription:

Ifliberty means anything at all, it means the right to tellpeople what they do not want tohear.

Fine words, but suitablyambiguous: the BBC might argue that it is merely exercisingits liberty in endlessly channelling the worldview ofpowerful interests crass propaganda that many peoplecertainly do not want to hear.

Orwells realintention is made clearer in this secondcomment:

Journalism is printing whatsomeone else does not want printed: everything else ispublic relations.

In this line attributedto him (although there is some debateabout where it originated), Orwell was talking about power real journalism challenges the powerful. And thisis the essential difference between the vital work ofWikiLeaks and the propaganda role performed bystate-corporate media like the BBC every day on virtuallyevery issue.

On September 6, the Mail on Sunday ran twoeditorials, side by side. The first was titled, Asinister, shameful attack on free speech. It decried theExtinction Rebellion action last Friday to blockadethree newspaper printing presses owned by Rupert MurdochsUK News. The second editorial, as we will see below, was afeeble call not to send Julian Assange to the US, on the eveof his crucial extradition hearing inLondon.

Extinction Rebellions protest, lasting justa few hours, temporarily prevented the distribution ofMurdoch newspapers, such as the Sun and The Times, as wellas other titles printed by Murdochs presses, includingthe Daily Mail, Mail on Sunday and the DailyTelegraph.

The Mail on Sunday editorial predictablycondemned the protesters supposed attempt atcensorship, declaring it:

athrowback to the very worst years of trade union militancy,which came close to strangling a free press and which wasonly defeated by the determined action of RupertMurdoch.

The paperfumed:

The newspaper blockade was ashameful and dangerous attempt to crush free speech, and itshould never be repeated.

This was thepropaganda message that was repeated across much of themainstream media, epitomised by the emptyrhetoric of Prime Minister Boris Johnson:

Afree press is vital in holding the government and otherpowerful institutions to account on issues critical for thefuture of our country, including the fight against climatechange. It is completely unacceptable to seek to limit thepublics access to news in this way.

Johnsonscomments could have been pure satire penned by Chris Morris,Mark Steel or the late Jeremy Hardy. Closer to the grubbytruth, a different Johnson Samuel describedthe free press as Scribbling on the backs ofadvertisements.

As Media Lens has repeatedlydemonstrated over the past 20 years, it is thestate-corporate media, including BBC News, that hasendlessly limited the publics access to news bydenying the public the full truth about climate breakdown,UK/US warmongering, including wars on Iraq, Afghanistan andLibya, the arming of Saudi Arabia and complicity in thatbrutal regimes destruction of Yemen, UK governmentsupport for the apartheid state of Israel even as it crushesthe Palestinian people, the insidious prising open of theNHS to private interests, and numerous other issues ofpublic importance.

When has the mythical freepress ever fully and properly held to account BorisJohnson or any of his predecessors in 10 Downing Street? Whocan forget that Tony Blair, steeped in the blood of so manyIraqis, is still held in esteem as an elder statesman whoseviews are sought out by mainstream news outlets,including BBC News and the Guardian? As John Pilger saidrecently:

Always contrast JulianAssange with Tony Blair. One will be fighting for his lifein court on 7 Sept for the crime of exposing warcrimes while the other evades justice for the paramountcrime of Iraq.

Health Secretary MattHancock, who has presided over a national public healthdisaster with soaring rates of mortality during thecoronavirus pandemic, had the affront to tweeta photograph of himself with a clutch of right-wing papersunder his arm, declaring:

Totallyoutrageous that Extinction Rebellion are trying to suppressfree speech by blockading newspapers. They must be dealtwith by the full force of the law.

Itis Hancock himself, together with government colleagues andadvisers not least Johnson and his protector, DominicCummings who should be dealt with by the full forceof the law. As Richard Horton, editor of The Lancetmedical journal, saidof Boris Johnson in May:

you droppedthe ball, Prime Minister. That was criminal. And you knowit.

Extinction Rebellion (XR) explainedsuccinctly via Twitter their reason for their totallyoutrageous action:

Dear Newsagents,we are sorry for disruption caused to your business thismorning. Dear Mr. Murdoch, we are absolutely not sorry forcontinuing to disrupt your agenda this morning. @rupertmurdoch#FreeTheTruth#ExtinctionRebellion#TellTheTruth

Anarticleon the XR website, simply titled, We do not have a freepress, said:

We are in an emergencyof unprecedented scale and the papers we have targeted arenot reflecting the scale and urgency of what is happening toour planet.

One of the XR protesterswas Steve, a former journalist for 25 years who hadworked for the Sun, Daily Mail, the Telegraph and The Times.He was filmed on location during the protest. He explainedthat he was participating, in part, because he is worriedabout the lack of a future for his children. And a majorreason for how we got to this point is that journalistsare:

stuck inside a toxic system wherethey dont have any choice but to tell the stories thatthese newspapers want to be told.

Hecontinued:

Every person who works onNews International or a Mail newspaper knows what story isor isnt acceptable for their bosses. And their bossesknow that because they know whats acceptable to Murdochor Rothermere or the other billionaires that run 70 per centof our media.

Steve said he left thatsystem because he couldnt bear the way itworked.

The most recent reportby the independent MediaReform Coalition on UK media ownership, published in2019, revealed the scale of the problem of extremelyconcentrated media ownership. Just three companies Rupert Murdochs News UK, Daily Mail Group and Reach(publisher of the Mirror titles) dominate 83 per centof the national newspaper market (up from 71 per cent in2015). When online readers are included, just five companies News UK, Daily Mail Group, Reach, Guardian and Telegraph dominate nearly 80 per cent of the market.

As wenotedof XRs worthy action:

Before anyonedenounces this as an attack on the free press there is no free press. There is a billionaire-owned,profit-maximising, ad-dependent corporate press that hasknowingly suppressed the truth of climate collapse and theneed for action to protect corporateprofits.

Zarah Sultana, Labour MP forCoventry South, indicatedher support too:

A tiny number ofbillionaires own vast swathes of our press. Their papersrelentlessly campaign for right-wing politics, promoting theinterests of the ruling class and scapegoating minorities. Afree press is vital to democracy, but too much of our pressisnt free at all.

By contrast,Labour leader Keir Starmer once again demonstrated hisestablishment credentials as a safe pair of hands bycondemning XRs protest. Craig Murray commented:

Ata time when the government is mooting designating ExtinctionRebellion as Serious Organised Crime, right wing bequiffedmuppet Keir Starmer was piously condemning the group,stating: The free press is the cornerstone of democracyand we must do all we can to protectit.

Starmer had also commented:

Denyingpeople the chance to read what they choose is wrong and doesnothing to tackle climate change.

Butdenying people the chance to read what they would choose the corporate-unfriendly truth on climate change isexactly what the corporate media, misleadingly termedmainstream media, is all about.

Media activistand lecturer Justin Schlosberg made a number of cogentobservations on press freedom in a Twitter thread(beginning here):

9times out of 10 when people in Britain talk about protectingpress freedom what they really mean is protecting presspower.

He pointed out the giantmyth promulgated by corporate media, forever trying toresist any attempt to curb their power; namelythat:

Britains mainstream [sic]press is a vital pillar of our democracy, covering adiversity of perspectives and upholding professionalstandards of journalismthe reality is closer to the exactinverse of such claims. More than 10 million people votedfor a socialist party at the last election (13 million in2017) and polls have consistently shown that majority ofBritish public opposeausterity.

Schlosbergcontinued:

The diversity of ournational press [ ] covers the political spectrum fromliberal/centre to hard right and has overwhelmingly backedausterity economics for the best part of the last 4decades [moreover] the UK press enjoys an unrivalledinternational reputation for producing a diatribe of fake,racist and misogynistic hate speech over anything that canbe called journalism.

He rightlyconcluded:

ironically one of thegreatest threats to democracy is a press that continues toweave myths in support of its vested interests, and a BBCthat continues to uncritically absorbthem.

Alongside the Mail on Sundaysbillionaire-owned, extremist right-wing attack on climateactivists highlighting a non-existent free press, thepaper had an editorialthat touched briefly on the danger to all journalists shouldWikiLeaks co-founder Julian Assange be extradited from theUK to the US:

the charges against MrAssange, using the American Espionage Act, might be usedagainst legitimate journalists in thiscountry.

The implication was thatAssange is not to be regarded as a legitimatejournalist. Indeed, the billionaire Rothermere-ownedviewspaper a more accurate description thannewspaper made clear its antipathy towardshim:

Mr Assanges revelations ofleaked material caused grave embarrassment to Washington andare alleged to have done material damagetoo.

The term embarrassmentrefers to the exposure of US criminal actions threateningthe great rogue states ability to commit similar crimesin future: not embarrassing (Washington is without shame),but potentially limiting.

The Mail on Sundaycontinued:

Mr Assange has been aspectacular nuisance during his time in this country,lawlessly jumping bail and wasting police time by takingrefuge in embassy of Ecuador. The Mail on Sunday disapprovesof much of what he has done, but we must also ask if hiscurrent treatment is fair, right orjust.

The insinuations and subtlesmears embedded in these few lines have been repeatedlydemolished (see this extensive analysis,for example). And there was no mention that Nils Melzer, theUNSpecial Rapporteur on Torture, as well as numerous doctors, healthexperts and humanrights organisations, have strongly condemned the UKsappalling abuse of Assange and demanded his immediaterelease.

Melzer has accusedthe British government of torturingAssange:

the primary purpose of tortureis not necessarily interrogation, but very often torture isused to intimidate others, as a show to the public whathappens if you dont comply with the government. That isthe purpose of what has been done to Julian Assange. It isnot to punish or coerce him, but to silence him and to do soin broad daylight, making visible to the entire world thatthose who expose the misconduct of the powerful no longerenjoy the protection of the law, but essentially will beannihilated. It is a show of absolute and arbitrarypower.

Melzer also spoke about theprice he will pay for challenging thepowerful:

I am under no illusions thatmy UN career is probably over. Having openly confronted twoP5-States (UN security council members) the way I have, I amvery unlikely to be approved by them for another high-levelposition. I have been told, that my uncompromisingengagement in this case comes at a politicalprice.

This is the reality of theincreasingly authoritarian world we are living in.

Theweak defence of Assange now being seen in even right-wingmedia, such as the Mail on Sunday, indicates a real fearthat any journalist could in future be targeted bythe US government for publishing material that might angerWashington.

In an interviewthis week, Barry Pollack, Julian Assanges US lawyer,warned of the very dangerous precedent that could beset in motion with Assanges extradition to theUS:

The position that the U.S. istaking is a very dangerous one. The position the U.S. istaking is that they have jurisdiction all over the world andcan pursue criminal charges against any journalist anywhereon the planet, whether theyre a U.S. citizen or not. Butif theyre not a U.S. citizen, not only can the U.S.pursue charges against them but that person has no defenseunder the First Amendment.

In starkcontrast to the weak protestations of the Mail on Sunday andthe rest of the establishment media, Noam Chomsky pointedout the simple truth in a recent interviewon RT (note the dearth of Chomsky interviews on BBC News,and consider why his views are not soughtafter):

Julian Assange committed thecrime of letting the general population know things thatthey have a right to know and that powerful states dontwant them to know.

Likewise, JohnPilger issueda strong warning:

This week, one of themost important struggles for freedom in my lifetime nearsits end. Julian Assange who exposed the crimes of greatpower faces burial alive in Trumps America unless he winshis extradition case. Whose side are youon?

Pilger recommended an excellent in-depthpiece by Jonathan Cook, a former Guardian/Observerjournalist, in which Cook observed:

Foryears, journalists cheered Assanges abuse. Now theyvepaved his path to a US gulag.

PeterOborne is a rare example of a right-leaning journalist whohas spoken out strongly in defence of Assange. Oborne wrotelast week in Press Gazettethat:

Future generations of journalistswill not forgive us if we do not fightextradition.

He set out the followingscenario:

Lets imagine a foreigndissident was being held in Londons Belmarsh Prisoncharged with supposed espionage offences by the Chineseauthorities.

And that his real offence wasrevealing crimes committed by the Chinese Communist Party including publishing video footage of atrocities carriedout by Chinese troops.

To put it another way, thathis real offence was committing the crime ofjournalism.

Let us further suppose the UN SpecialRapporteur on Torture said this dissident showed all thesymptoms typical for prolonged exposure to psychologicaltorture and that the Chinese were putting pressure on theUK authorities to extradite this individual where he couldface up to 175 years in prison.

The outrage fromthe British press would bedeafening.

Obornecontinued:

There is one crucialdifference. It is the US trying to extradite the co-founderof Wikileaks.

Yet there has been scarcely a word inthe mainstream British media in hisdefence.

In fact, as we haverepeatedly highlighted,Assange has been the subject of a propagandablitz by the UK media, attackingand smearinghim, over and over again, often in the pages of theliberal Guardian.

At the time of writing,neither ITV political editor Robert Peston nor BBCNews political editor LauraKuenssberg appear to have reported the Assangeextradition case. They have not even tweeted about it once,even though they are both very active on Twitter. In fact,the last time Peston so much as mentioned Assange on hisTwitter feed was 2017.Kuenssbergs record is even worse; her Twitter silenceextends all the way back to 2014.These high-profile journalists are supposedly primeexemplars of the very best high-quality UK newsbroadcasters, maintaining the values of a free press,holding politicians to account and keeping the publicinformed.

On September 7, John Pilger gave an addressoutside the Old Bailey in London, just before JulianAssanges extradition hearing began there. His words werea powerful rebuke to those so-called journalists thathave maintained a cowardly silence, or worse. Theofficial truth-tellers of the media thestenographers who collaborate with those in power, helpingto sell their wars are, Pilger says, Vichyjournalists.

He continued:

Itis said that whatever happens to Julian Assange in the nextthree weeks will diminish if not destroy freedom of thepress in the West. But which press? The Guardian? TheBBC, The New York Times, the Jeff Bezos WashingtonPost?

No, the journalists in theseorganizations can breathe freely. The Judases on theGuardian who flirted with Julian, exploited hislandmark work, made their pile then betrayed him, havenothing to fear. They are safe because they areneeded.

Freedom of the press now rests with thehonorable few: the exceptions, the dissidents on theinternet who belong to no club, who are neither rich norladen with Pulitzers, but produce fine, disobedient,moral journalism those like JulianAssange.

DC &DE

Scoop Media

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'Absolute And Arbitrary Power': Killing Extinction Rebellion And Julian Assange - Scoop.co.nz

Tips and Murmurs: Bob Carr whistles a different tune on Julian Assange – Crikey

Bob Carr whistles a new tune on Julian Assange (if only he'd done anything about it?) while Qantas seems to be reaping the pandemic rewards. Plus other tips from the Crikey bunker.

That was then, this is now Ah, the luxury of being a former politician, when you finally have the platform and influence to really make a difference. Just days after former foreign affairs minister Julie Bishop lamented the cuts in the foreign aid budget -- a process she had overseen and defended in 2014, 2015, 2016, 2017 and 2018 -- we have Bob Carr, who continues his newfound interest in the case of Julian Assange:

"He faces the prospect of a living death inside an American prison, in very cruel conditions, because he let the world know about an American war crime," he told Nine News. Not much to quarrel with that.

So why, back in 2012 (when Carr held the obscure post of Australian foreign affairs minister), did he say of Assange: "Theres an amorality about whats been at work here; secrets being released for the sake of being released without inherent justification.

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Tips and Murmurs: Bob Carr whistles a different tune on Julian Assange - Crikey

Combatting COVID-19 misinformation with machine learning (VB Live) – VentureBeat

Presented by AWS Machine Learning

As machine learning has evolved, so have best practices, especially in the wake of COVID-19. Join this VB Live event to learn from experts about how machine learning solutions are helping companies respond in these uncertain times and the lessons learned along the way.

Register here for free.

Misinformation around COVID-19 is driving human behavior across the world. Here in the information age, sensationalized clickbait headlines are crowding out actual fact-based content, and, as a result misinformation spreads virally. Conversations within small communities become the epicenter of false information, and that misinformation spreads as people talk, both online and off. As the number of misinformed people grow, this infodemic grows.

The spread of misinformation around COVID-19 is especially problematic, because it could overshadow the key messaging around safety measures from public health and government officials.

In an effort to counter misinformed narratives in central and west Africa, Novetta Mission Analytics (NMA) is working with Africa CDC (Center for Disease Control) to discover and identify narratives and behavior patterns around the disease, says David Cyprian, product owner at Novetta. And machine learning is key.

They supply data that measures the acceptability, impact, and effectiveness of public health and social measures. In turn, the Africa CDC analysis of the data enables them to generate tailored guidelines for each country.

With all these different narratives out there, we can use machine learning to quantify which ones are really affecting the largest population, Cyprian explains. We uncover how quickly these things are spreading, how many people are talking about the issues, and whether anyone is actually criticizing the misinformation itself.

NMA uncovered trending phrases that indicate worry around the disease, mistrust about official messaging, and criticisms of local measures to combat the disease. They found that herbal remedies are becoming popular, as is the idea of herd immunity.

We know all of these different narratives are changing behavior, Cyprian says. Theyre causing people to make decisions that make it more difficult for the COVID-19 response community to be effective and implement countermeasures that are going to mitigate the effects of the virus.

To identify these narrative threads, Novetta ingests publicly-available social media at scale and pairs it with a collection of domestic and international news media. They process and analyze that raw social and traditional media content in their ML platform built on AWS to identify where people are talking about these things, and where events are happening that drive the conversations. They also use natural language processing for directed sentiment analysis to discover whether narratives are being driven by mistrust of a local government entity, the west, or international organizations, as well as identifying influencers that are engendering a lot of positive sentiment among users and building trust.

Pieces of content are tagged as positive or negative to local and global pandemic measures and public entities, creating small human-labeled data sets about specific micronarratives for specific populations that might be trading in misinformation.

By fusing rapid ingestion with a human labeling process of just a few hundred artifacts, theyre able to kick off machine learning and apply it to the scale of social media. This allows them to have more than one learning model that is used for all the problem sets.

We dont have a one-size-fits-all approach, says Cyprian. Were always tuning and researching accuracy for specific narratives, and then were able to provide large, near-real-time insights into how these narratives are propagating or spreading in the field.

Built on AWS, their machine learning architecture allows their development team to focus on what they do well, which is develop new applications and new widgets to be able to analyze this data.

They dont need to worry about any server management, or scaling, since thats taken care of for them with Amazon EC2 and S3. Their microservices architecture uses some additional features that Amazon offers, particularly Elastic Kubernetes Service (EKS), to orchestrate their services, and Amazon Elastic Container Registry (ECR), to store images and run vulnerability testing before they deploy.

Novettas approach is cross-disciplinary, bringing in domain experts from the health field, media analysts, machine learning research engineers, and software developers. They work in small teams to solve problems together.

In my experience, thats been the best way for machine learning to make a practical difference, he says. I would just urge folks who are facing these similar difficult problems to enable their people to do what people do well, and then have the machine learning engineers help to harden, verify, and scale those efforts so you can bring countermeasures to bear quickly.

To learn more about the impact machine learning solutions can deliver and lessons learned along the way, dont miss this round table with leaders from Kabbage and Novetta, as well as Michelle K. Lee, VP of the Amazon Machine Learning Solutions Lab.

Dont miss out!

Register here for free.

Youll learn:

Speakers:

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Combatting COVID-19 misinformation with machine learning (VB Live) - VentureBeat

This artist used machine learning to create realistic portraits of Roman emperors – The World

Some people have spent their quarantine downtime bakingsourdough bread. Others experiment with tie-dye. But others namely Toronto-based artist Daniel Voshart have createdpainstaking portraits of all 54 Roman emperors of the Principate period, which spanned from 27 BC to 285 AD.

The portraits help people visualize what the Roman emperors would have looked like when they were alive.

Included are Vosharts best artistic guesses of the faces of emperors Augustus, Nero, Caligula, Marcus Aurelius and Claudius, among others. They dont look particularly heroic or epic rather, they look like regular people, with craggy foreheads, receding hairlines and bags under their eyes.

To make the portraits, Voshart used a design software called Artbreeder, which relies on a kind of artificial intelligence called generative adversarial networks (GANs).

Voshart starts by feeding the GANs hundreds of images of the emperors collected from ancient sculpted busts, coins and statues. Then he gets a composite image, which he tweaks in Photoshop. To choose characteristics such as hair color and eye color, Voshart researches the emperors backgrounds and lineages.

It was a bit of a challenge, he says. About a quarter of the project was doing research, trying to figure out if theres something written about their appearance.

He also needed to find good images to feed the GANs.

Another quarter of the research was finding the bust, finding when it was carved because a lot of these busts are recarvings or carved hundreds of years later, he says.

In a statement posted on Medium, Voshartwrites: My goal was not to romanticize emperors or make them seem heroic. In choosing bust/sculptures, my approach was to favor the bust that was made when the emperor was alive. Otherwise, I favored the bust made with the greatest craftsmanship and where the emperor was stereotypically uglier my pet theory being that artists were likely trying to flatter their subjects.

Related:Battle of the bums: Museums complete over best artistic behinds

Voshart is not a Rome expert. His background is in architecture and design, and by day he works in the art department of the TV show "Star Trek: Discovery," where he designs virtual reality walkthroughs of the sets before they'rebuilt.

But when the coronavirus pandemic hit, Voshart was furloughed. He used the extra time on his hands to learn how to use the Artbreeder software.The idea for the Roman emperor project came from a Reddit threadwhere people were posting realistic-looking images theyd created on Artbreeder using photos of Roman busts. Voshart gave it a try and went into exacting detail with his research and design process, doing multiple iterations of the images.

Voshart says he made some mistakes along the way. For example, Voshart initially based his portrait of Caligula, a notoriously sadistic emperor, on a beautifully preserved bust in the Metropolitan Museum of Art. But the bust was too perfect-looking, Voshart says.

Multiple people told me he was disfigured, and another bust was more accurate, he says.

So, for the second iteration of the portrait, Voshart favored a different bust where one eye was lower than the other.

People have been telling me my first depiction of Caligula was hot, he says. Now, no ones telling me that.

Voshart says people who see his portraits on Twitter and Reddit often approach them like theyd approachTinder profiles.

I get maybe a few too many comments, like such-and-such is hot. But a lot of these emperors are such awful people!

I get maybe a few too many comments, like such-and-such is hot. But a lot of these emperors are such awful people! Voshart says.

Voshart keeps a list on his computer of all the funny comparisons people have made to present-day celebrities and public figures.

Ive heard Nero looks like a football player. Augustus looks like Daniel Craigmy early depiction of Marcus Aurelius looks like the Dude from 'The Big Lebowski.'

But the No. 1 comment? Augustus looks like Putin.

Related:UNESCO says scammers are using its logo to defraudartcollectors

No one knows for sure whether Augustus actually looked like Vladimir Putin in real life.Voshart says his portraits are speculative.

Its definitely an artistic interpretation, he says. Im sure if you time-traveled, youd be very angry at me."

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This artist used machine learning to create realistic portraits of Roman emperors - The World

Machine Learning Chips Market Dynamics Analysis to Grow at Cagr with Major Companies and Forecast 2026 – The Scarlet

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Global Machine Learning Chips Market by Companies:

The company profile section of the report offers great insights such as market revenue and market share of global Machine Learning Chips market. Key companies listed in the report are:

Market Segment AnalysisThe research report includes specific segments by Type and by Application. Each type provides information about the production during the forecast period of 2015 to 2026. Application segment also provides consumption during the forecast period of 2015 to 2026. Understanding the segments helps in identifying the importance of different factors that aid the market growth.Segment by TypeNeuromorphic ChipGraphics Processing Unit (GPU) ChipFlash Based ChipField Programmable Gate Array (FPGA) ChipOther

Segment by ApplicationRobotics IndustryConsumer ElectronicsAutomotiveHealthcareOther

Global Machine Learning Chips Market: Regional AnalysisThe report offers in-depth assessment of the growth and other aspects of the Machine Learning Chips market in important regions, including the U.S., Canada, Germany, France, U.K., Italy, Russia, China, Japan, South Korea, Taiwan, Southeast Asia, Mexico, and Brazil, etc. Key regions covered in the report are North America, Europe, Asia-Pacific and Latin America.The report has been curated after observing and studying various factors that determine regional growth such as economic, environmental, social, technological, and political status of the particular region. Analysts have studied the data of revenue, production, and manufacturers of each region. This section analyses region-wise revenue and volume for the forecast period of 2015 to 2026. These analyses will help the reader to understand the potential worth of investment in a particular region.Global Machine Learning Chips Market: Competitive LandscapeThis section of the report identifies various key manufacturers of the market. It helps the reader understand the strategies and collaborations that players are focusing on combat competition in the market. The comprehensive report provides a significant microscopic look at the market. The reader can identify the footprints of the manufacturers by knowing about the global revenue of manufacturers, the global price of manufacturers, and production by manufacturers during the forecast period of 2015 to 2019.The major players in the market include Wave Computing, Graphcore, Google Inc, Intel Corporation, IBM Corporation, Nvidia Corporation, Qualcomm, Taiwan Semiconductor Manufacturing, etc.

Global Machine Learning Chips Market by Geography:

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Some of the Major Highlights of TOC covers in Machine Learning Chips Market Report:

Chapter 1: Methodology & Scope of Machine Learning Chips Market

Chapter 2: Executive Summary of Machine Learning Chips Market

Chapter 3: Machine Learning Chips Industry Insights

Chapter 4: Machine Learning Chips Market, By Region

Chapter 5: Company Profile

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Machine Learning Chips Market Dynamics Analysis to Grow at Cagr with Major Companies and Forecast 2026 - The Scarlet

Demonstration Of What-If Tool For Machine Learning Model Investigation – Analytics India Magazine

Machine learning era has reached the stage of interpretability where developing models and making predictions is simply not enough any more. To make a powerful impact and get good results on the data it is important to investigate and probe the dataset and the models. A good model investigation involves digging deep into the understanding of the model to find insights and inconsistencies in the developed model. This task usually involves writing a lot of custom functions. But, with tools like What-If, it makes the probing task very easy and saves time and efforts for programmers.

In this article we will learn about:

What-If tool is a visualization tool that is designed to interactively probe the machine learning models. WIT allows users to understand machine learning models like classification, regression and deep neural networks by providing methods to evaluate, analyse and compare the model. It is user friendly and can be used not only by developers but also by researchers and non-programmers very easily.

WIT was developed by Google under the People+AI research (PAIR) program. It is open-source and brings together researchers across Google to study and redesign the ways people interact with AI systems.

This tool provides multiple features and advantages for users to investigate the model.

Some of the features of using this are:

WIT can be used with a Google Colab notebook or Jupyter notebook. It can also be used with Tensorflow Board.

Let us take a sample dataset to understand the different features of WIT. I will choose the forest fire dataset available for download on Kaggle. You can click here for downloading the dataset. The goal here is to predict the areas affected by forest fires given the temperature, month, amount of rain etc.

I will implement this tool on google collaboratory. Before we load the dataset and perform the processing, we will first install the WIT. To install this tool use,

!pip install witwidget

Once we have split the data, we can convert the columns month and day to categorical values using label encoder.

Now we can build our model. I will use sklearn ensemble model and implement the gradient boosting regression model.

Now that we have the model trained, we will write a function to predict the data since we need to use this for the widget.

Next, we will write the code to call the widget.

This opens an interactive widget with two panels.

To the left, there is a panel for selecting multiple techniques to perform on the data and to the right is the data points.

As you can see on the right panel we have options to select features in the dataset along X-axis and Y-axis. I will set these values and check the graphs.

Here I have set FFMC along the X-axis and area as the target. Keep in mind that these points are displayed after the regression is performed.

Let us now explore each of the options provided to us.

You can select a random data point and highlight the point selected. You can also change the value of the datapoint and observe how the predictions change dynamically and immediately.

As you can see, changing the values changes the predicted outcomes. You can change multiple values and experiment with the model behaviour.

Another way to understand the behaviour of a model is to use counterfactuals. Counterfactuals are slight changes made that can cause a model to flip its decision.

By clicking on the slide button shown below we can identify the counterfactual which gets highlighted in green.

This plot shows the effects that the features have on the trained machine learning model.

As shown below, we can see the inference of all the features with the target value.

This tab allows us to look at the overall model performance. You can evaluate the model performance with respect to one feature or more than the one feature. There are multiple options available for analysis of the performance.

I have selected two features FFMC and temp against the area to understand performance using mean error.

If multiple training models are used their performance can be evaluated here.

The features tab is used to get the statistics of each feature in the dataset. It displays the data in the form of histograms or quantile charts.

The tab also enables us to look into the distribution of values for each feature in the dataset.

It also highlights the features that are most non-uniform in comparison to the other features in the dataset.

Identifying non-uniformity is a good way to reduce bias in the model.

WIT is a very useful tool for analysis of model performance. Ability to inspect models in a simple no-code environment will be of great help especially in the business perspective.

It also gives insights to factors beyond training the model like understanding why and how that model was created and how the dataset is fitting in the model.

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Demonstration Of What-If Tool For Machine Learning Model Investigation - Analytics India Magazine

Machine Learning & Big Data Analytics Education Market Size is Thriving Worldwide 2020 | Growth and Profit Analysis, Forecast by 2027 – The Daily…

Fort Collins, Colorado The Global Machine Learning & Big Data Analytics Education Market research report offers insightful information on the Global Machine Learning & Big Data Analytics Education market for the base year 2019 and is forecast between 2020 and 2027. Market value, market share, market size, and sales have been estimated based on product types, application prospects, and regional industry segmentation. Important industry segments were analyzed for the global and regional markets.

The effects of the COVID-19 pandemic have been observed across all sectors of all industries. The economic landscape has changed dynamically due to the crisis, and a change in requirements and trends has also been observed. The report studies the impact of COVID-19 on the market and analyzes key changes in trends and growth patterns. It also includes an estimate of the current and future impact of COVID-19 on overall industry growth.

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The report has a complete analysis of the Global Machine Learning & Big Data Analytics Education Market on a global as well as regional level. The forecast has been presented in terms of value and price for the 8 year period from 2020 to 2027. The report provides an in-depth study of market drivers and restraints on a global level, and provides an impact analysis of these market drivers and restraints on the relationship of supply and demand for the Global Machine Learning & Big Data Analytics Education Market throughout the forecast period.

The report provides an in-depth analysis of the major market players along with their business overview, expansion plans, and strategies. The main actors examined in the report are:

The Global Machine Learning & Big Data Analytics Education Market Report offers a deeper understanding and a comprehensive overview of the Global Machine Learning & Big Data Analytics Education division. Porters Five Forces Analysis and SWOT Analysis have been addressed in the report to provide insightful data on the competitive landscape. The study also covers the market analysis and provides an in-depth analysis of the application segment based on the market size, growth rate and trends.

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The research report is an investigative study that provides a conclusive overview of the Global Machine Learning & Big Data Analytics Education business division through in-depth market segmentation into key applications, types, and regions. These segments are analyzed based on current, emerging and future trends. Regional segmentation provides current and demand estimates for the Global Machine Learning & Big Data Analytics Education industry in key regions in North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa.

Global Machine Learning & Big Data Analytics Education Market Segmentation:

In market segmentation by types of Global Machine Learning & Big Data Analytics Education , the report covers-

In market segmentation by applications of the Global Machine Learning & Big Data Analytics Education , the report covers the following uses-

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Overview of the table of contents of the report:

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Improving The Use Of Social Media For Disaster Management – Texas A&M University Today

The algorithm could be used to quickly identify social media posts related to a disaster.

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There has been a significant increase in the use of social media to share updates, seek help and report emergencies during a disaster. Algorithms keeping track of social media posts that signal the occurrence of natural disasters must be swift so that relief operations can be mobilized immediately.

A team of researchers led by Ruihong Huang, assistant professor in the Department of Computer Science and Engineering at Texas A&M University, has developed a novel weakly supervised approach that can train machine learning algorithms quickly to recognize tweets related to disasters.

Because of the sudden nature of disasters, theres not much time available to build an event recognition system, Huang said. Our goal is to be able to detect life-threatening events using individual social media messages and recognize similar events in the affected areas.

Text on social media platforms, like Twitter, can be categorized using standard algorithms called classifiers. This sorting algorithm separates data into labeled classes or categories, similar to how spam filters in email service providers scan incoming emails and classify them as either spam or not spam based on its prior knowledge of spam messages.

Most classifiers are an integral part of machine learning algorithms that make predictions based on carefully labeled sets of data. In the past, machine learning algorithms have been used for event detection based on tweets or a burst of words within tweets. To ensure a reliable classifier for the machine learning algorithms, human annotators have to manually label large amounts of data instances one by one, which usually takes several days, sometimes even weeks or months.

The researchers also found that it is essentially impossible to find a keyword that does not have more than one meaning on social media depending on the context of the tweet. For example, if the word dead is used as a keyword, it will pull in tweets talking about a variety of topics such as a phone battery being dead or the television series The Walking Dead.

We have to be able to know which tweets that contain the predetermined keywords are relevant to the disaster and separate them from the tweets that contain the correct keywords but are not relevant, Huang said.

To build more reliable labeled datasets, the researchers first used an automatic clustering algorithm to put them into small groups. Next, a domain expert looked at the context of the tweets in each group to identify if it was relevant to the disaster. The labeled tweets were then used to train the classifier how to recognize the relevant tweets.

Using data gathered from the most impacted time periods for Hurricane Harvey and Hurricane Florence, the researchers found that their data labeling method and overall weakly-supervised system took one to two person-hours instead of the 50 person-hours that were required to go through thousands of carefully annotated tweets using the supervised approach.

Despite the classifiers overall good performance, they also observed that the system still missed several tweets that were relevant but used a different vocabulary than the predetermined keywords.

Users can be very creative when discussing a particular type of event using the predefined keywords, so the classifier would have to be able to handle those types of tweets, Huang said. Theres room to further improve the systems coverage.

In the future, the researchers will look to explore how to extract information about the users location so first responders will know exactly where to dispatch their resources.

Other contributors to this research include Wenlin Yao, a doctoral student supervised by Huang from the computer science and engineering department; Ali Mostafavi and Cheng Zhang from the Zachry Department of Civil and Environmental Engineering; and Shiva Saravanan, former intern of the Natural Language Processing Lab at Texas A&M.

The researchers described their findings in the proceedings from the Association for the Advancement of Artificial Intelligences 34th Conference on Artificial Intelligence.

This work is supported by funds from the National Science Foundation.

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Improving The Use Of Social Media For Disaster Management - Texas A&M University Today