John McAfee Thinks Hes Solved Bitcoins Greatest MysteryWho Is Satoshi Nakamoto? – Forbes

Bitcoin, a digital currency designed as an alternative to state-backed fiat money, was created in the midst of the 2008 global financial crisisbut no one knows who made it.

The bitcoin price has been climbing steadily over the last decade, making many early adopters overnight millionaires and causing millions more to ask: Who is bitcoin's creator, someone known only as Satoshi Nakamoto?

Now, amid a surge in bitcoin interest sparked by the global coronavirus pandemic, the eccentric cyber security pioneer John McAfee claims to know the answer. But of course, he's not telling.

John McAfee, the cybersecurity pioneer-turned U.S. presidential hopeful, claims to know the identity ... [+] of bitcoin's mysterious creator--but he's not telling.

McAfee, the outspoken antivirus software developer-turned curveball U.S. presidential candidate, says he's 99% sure he knows the identity of Satoshi Nakamotothe author of the bitcoin white paper, thought to be a pseudonym.

"It was a team of eleven people over a period of five years, that came up, eventually, with [bitcoin]," McAfee told bitcoin and cryptocurrency website Cointelegraph on the virtual sidelines of a now digital blockchain conference, forced online due to the coronavirus pandemic, adding he thinks Craig Wright, a computer scientist who's repeatedly claimed to be bitcoin's creator but failed to produce proof, was involved.

"How they decided who would write the paper, I dont know. But anybody who wants to know who it isI mean, you know who the options are, you've got Craig Wright possibly, I'm not going to name everyone else otherwise youll figure out who it is, but somebody wrote the white paper."

McAfee pointed to two language quirks as helping to narrow down the potential developers: That the author used British English over American English and consistently used two spaces after a period.

McAfee also claimed "the format of the document was identical to documents that [Satoshi Nakamoto] had published professionally"making it relatively easy for anyone to figure out.

"If you buy a two-hundred dollar authorship program, and you take the white paper and you run it through, and you take any one of the papers that hes publishedall of these people wrote papers by the way, only one comes out with ninety-nine percent probability it's him."

Despite that, McAfee said he doesn't want to reveal who exactly wrote bitcoin's white paper, as he fears he could "end up destroying an innocent mans life forever, and probably cause his death."

"I have spoken to him on the phone, I was actually going to divulge who he was," McAfee said, adding the author of bitcoin's white paper convinced him not to reveal it.

McAfee reportedly labelled the coronavirus pandemic a government conspiracy while waving around an AK-47 rifle before making his comments about Satoshi Nakamoto's true identity.

Earlier this year, McAfee reneged on his promise to "eat [his] own dick on national television" if the bitcoin price didn't hit $500,000 per bitcoin by the end of 2020, calling bitcoin "ancient technology" and lending his support to privacy-focused cryptocurrency monero.

John McAfee, whose personal fortune is thought to have peaked at around $100 million just ahead of ... [+] the 2008 financial crisis, has long been a fan of cryptocurrencies but has recently criticized bitcoin, calling it "worthless."

The true identity of bitcoin's creator, or group of creators, has become one of the internet's most tantalizing mysteries, with many tryingand so far failingto crack it.

In 2014, the U.S. magazine Newsweek claimed a Japanese American man living in California, Dorian Prentice Satoshi Nakamoto, was the bitcoin inventor. A claim he subsequently denied and one that has now been widely disregarded.

Others, such as cryptographic pioneer and the first person to receive a bitcoin transaction, Hal Finney, have been named as possibilities. Forbes put that question to Finney in the months before he died.

Bit gold creator Nick Szabo and bitcoin developer Gavin Andresen have also been linked to the name Satoshi Nakamoto.

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John McAfee Thinks Hes Solved Bitcoins Greatest MysteryWho Is Satoshi Nakamoto? - Forbes

Decentralized Finance Startup Focused on Bitcoin Cash Raises $1 Million for Expansion – Bitcoin News

On May 7, the decentralized finance (defi) startup General Protocols revealed the team has raised over $1 million from investors. The creators of General Protocols have introduced innovative projects on the Bitcoin Cash network such as Anyhedge, and have also participated in helping forward the Bitcoin Cash Node (BCHN) project and Flipstarter.cash.

The BCH community was pleased to hear that a startup dedicated to the Bitcoin Cash blockchain and decentralized finance (defi) has raised $1 million this week. The company called, General Protocols, is behind the Anyhedge project which is a blockchain-enforced synthetic derivatives protocol for Bitcoin Cash (BCH). News.Bitcoin.com reported on the project during the first week of April. According to the teams press release, the latest funding stems from the cryptocurrency trader Marc De Mesel and a variety of other investors. The team is thrilled to get funding to push the startups goals forward in order to deliver defi to the BCH community.

We are delighted that aligned investors are supporting us in our vision to bring defi to Bitcoin Cash, said John Nieri a.k.a. emergent_reasons, President of General Protocols. We are building a team of dedicated supporters of peer to peer electronic cash here at General Protocols.

General Protocols team members helped with the construction of Flipstarter.cash, a noncustodial fundraising platform. Additionally, the startup also volunteered efforts toward the new Bitcoin Cash full node implementation called BCHN. The project Anyhedge aims to be the first defi protocol on any branch of Bitcoin and the platform will launch in cooperation with Cryptophyls new noncustodial exchange, Detoken.

Further two former Bitcoin.com team members Marcel Chuo and Rosco Kalis have joined the General Protocols company. Kalis is well known for his work on the Cashscript protocol in order to create a new generation of smart contracts on the Bitcoin Cash network. Chuo will handle business relationships and his background includes global expansion and coordinating with well known tech firms like HTC. During the investment announcement for $1 million into General Protocols infrastructure, Kalis said he looks forward to working on the blockchain-enforced synthetic derivatives protocol for Bitcoin Cash.

Im excited to be working on Anyhedge with the great team at General Protocols, Kalis explained during the announcement.

What do you think about the $1 million dollar investment into General Protocols? Let us know in the comments below.

Image Credits: Shutterstock, Pixabay, Wiki Commons, General Protocols, Anyhedge

Disclaimer: This article is for informational purposes only. It is not a direct offer or solicitation of an offer to buy or sell, or a recommendation or endorsement of any products, services, or companies. Bitcoin.com does not provide investment, tax, legal, or accounting advice. Neither the company nor the author is responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any content, goods or services mentioned in this article.

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From the frying pan into the fire. The torture that awaits Julian Assange in the US. – The Canary

WikiLeaks founder Julian Assange is currently held in Belmarsh prison awaiting hearings that could see him extradited to the US to face prosecution for alleged espionage-related offences.

Award-winning US journalist Chris Hedges described the torture that would await Assange in the US prison system, adding they will attempt to psychologically destroy him. If extradited, Assange would likely be detained in accordance with Special Administrative Measures (SAMs). One report equates this to a regime of sensory deprivation and social isolation that may amount to torture.

US journalist Chris Hedges spoke about the treatment Assange is likely to receive in the US. He argues that the US authorities will psychologically destroy him and that conditions imposed could see him turned into a zombie to face life without parole:

Australian journalist John Pilger agrees:

If Julian is extradited to the US, a darkness awaits him. Hell be subjected to a prison regime called special administrative measures He will be placed in a cage in the bowels of a supermax prison, a hellhole. He will be cut off from all contact with the rest of humanity.

Assange is already in a precarious position, alongside all other UK prisoners. Belmarsh is a high-security Category A facility and, as with all other prisons in the UK, inmates there are at risk to infection from coronavirus (Covid-19).

On 28 April, the BBC reported that there were 1,783 possible/probable cases of coronavirus on top of 304 confirmed infections across jails in England and Wales. Also that there were 75 different custodial institutions, with 35 inmates treated in hospital and 15 deaths.

Vaughan Smith, who stood bail for Assange, reported that the virus was ripping through Belmarsh:

We know of two Covid-19 deaths in Belmarsh so far, though the Department of Justice have admitted to only one death. Julian told me that there have been more and that the virus is ripping through the prison.

Assange has a known chronic lung condition, which could lead to death should he become infected with coronavirus. Assanges lawyers requested he is released on bail to avoid succumbing to the virus, but that request was rejected.

As for the psychological effects of segregation, a European Committee for the Prevention of Torture and Inhuman or Degrading Treatment or Punishment report argued that it can can have an extremely damaging effect on the mental, somatic and social health of those concerned.

Its likely that Assange will be placed under SAMs if he is extradited to the US. The Darkest Corner, a report authored by the Allard K. Lowenstein International Human Rights Clinic and The Center for Constitutional Rights, describes how SAMs work.

In its summary, the report explains that:

SAMs are the darkest corner of the U.S. federal prison system, combining the brutality and isolation of maximum security units with additional restrictions that deny individuals almost any connection to the human world. Those restrictions include gag orders on prisoners, their family members, and their attorneys, effectively shielding this extreme use of government power from public view.

It continues:

SAMs deny prisoners the narrow avenues of indirect communication through sink drains or air vents available to prisoners in solitary confinement. They prohibit social contact with anyone except for a few immediate family members, and heavily regulate even those contacts. And they further prohibit prisoners from connecting to the social world via current media and news, limiting prisoners access to information to outdated, government-approved materials. Even a prisoners communications with his lawyer which are supposed to be protected by attorney-client privilege can be subject to monitoring by the FBI.

It ominously adds that: Many prisoners remain under these conditions indefinitely, for years or in some cases even decades. Moreover, these conditions can be used as a weapon to force a prisoner to plead guilty:

In numerous cases, the Attorney General recommends lifting SAMs after the defendant pleads guilty. This practice erodes defendants presumption of innocence and serves as a tool to coerce them into cooperating with the government and pleading guilty.

The report provides further details on how SAMs incorporate sensory deprivation and social isolation measures that may amount to torture. Also, it argues that the SAMs regime contravenes both US and international laws.

Should the UK courts agree to extradite Assange, he could face months, if not decades, of psychological torture. However, Article 3 of the European Court of Human Rights states clearly: No one shall be subjected to torture or to inhuman or degrading treatment or punishment. Under that article, the US extradition request should be rejected by the UK courts.

For a publisher to be subjected to such a nightmare scenario would be intolerable.

Featured image via Mohamed Elmaazi

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From the frying pan into the fire. The torture that awaits Julian Assange in the US. - The Canary

RAY McGOVERN: New House Documents Sow Further Doubt That Russia Hacked the DNC – Consortium News

For two and a half years the House Intelligence Committee knew CrowdStrike didnt have the goods on Russia. Now the public knows too.

Twin Pillars of Russiagate Crumble

By Ray McGovernSpecial to Consortium News

House Intelligence Committee documents released Thursday reveal that the committee was told two and half years ago that the FBI had no concrete evidence that Russia hacked Democratic National Committee computers to filch the DNC emails published by WikiLeaks in July 2016.

The until-now-buried, closed-door testimony came on Dec. 5, 2017 from Shawn Henry, a protege of former FBI Director Robert Mueller (from 2001 to 2012), for whom Henry served as head of the Bureaus cyber crime investigations unit.

Henry retired in 2012 and took a senior position at CrowdStrike, the cyber security firm hired by the DNC and the Clinton campaign to investigate the cyber intrusions that occurred before the 2016 presidential election.

The following excerpts from Henrys testimony speak for themselves. The dialogue is not a paragon of clarity; but if read carefully, even cyber neophytes can understand:

Ranking Member Mr. [Adam] Schiff: Do you know the date on which the Russians exfiltrated the data from the DNC? when would that have been?

Mr. Henry: Counsel just reminded me that, as it relates to the DNC, we have indicators that data was exfiltrated from the DNC, but we have no indicators that it was exfiltrated (sic). There are times when we can see data exfiltrated, and we can say conclusively. But in this case, it appears it was set up to be exfiltrated, but we just dont have the evidence that says it actually left.

Mr. [Chris] Stewart of Utah: Okay. What about the emails that everyone is so, you know, knowledgeable of? Were there also indicators that they were prepared but not evidence that they actually were exfiltrated?

Mr. Henry: Theres not evidence that they were actually exfiltrated. Theres circumstantial evidence but no evidence that they were actually exfiltrated.

Mr. Stewart: But you have a much lower degree of confidence that this data actually left than you do, for example, that the Russians were the ones who breached the security?

Mr. Henry: There is circumstantial evidence that that data was exfiltrated off the network.

Mr. Stewart: And circumstantial is less sure than the other evidence youve indicated.

Mr. Henry: We didnt have a sensor in place that saw data leave. We said that the data left based on the circumstantial evidence. That was the conclusion that we made.

In answer to a follow-up query on this line of questioning, Henry delivered this classic: Sir, I was just trying to be factually accurate, that we didnt see the data leave, but we believe it left, based on what we saw.

Inadvertently highlighting the tenuous underpinning for CrowdStrikes belief that Russia hacked the DNC emails, Henry added: There are other nation-states that collect this type of intelligence for sure, but the what we would call the tactics and techniques were consistent with what wed seen associated with the Russian state.

Not Transparent

Try as one may, some of the testimony remains opaque. Part of the problem is ambiguity in the word exfiltration.

The word can denote (1) transferring data from a computer via the Internet (hacking) or (2) copying data physically to an external storage device with intent to leak it.

As the Veteran Intelligence Professionals for Sanity has been reporting for more than three years, metadata and other hard forensic evidence indicate that the DNC emails were not hacked by Russia or anyone else.

Rather, they were copied onto an external storage device (probably a thumb drive) by someone with access to DNC computers. Besides, any hack over the Internet would almost certainly have been discovered by the dragnet coverage of the National Security Agency and its cooperating foreign intelligence services.

Henry testifies that it appears it [the theft of DNC emails] was set up to be exfiltrated, but we just dont have the evidence that says it actually left.

This, in VIPS view, suggests that someone with access to DNC computers set up selected emails for transfer to an external storage device a thumb drive, for example. The Internet is not needed for such a transfer. Use of the Internet would have been detected, enabling Henry to pinpoint any exfiltration over that network.

Binney

Bill Binney, a former NSA technical director and a VIPS member, filed a sworn affidavit in the Roger Stone case. Binney said: WikiLeaks did not receive stolen data from the Russian government. Intrinsic metadata in the publicly available files on WikiLeaks demonstrates that the files acquired by WikiLeaks were delivered in a medium such as a thumb drive.

The So-Called Intelligence Community Assessment

There is not much good to be said about the embarrassingly evidence-impoverished Intelligence Community Assessment (ICA) of Jan. 6, 2017 accusing Russia of hacking the DNC.

But the ICA did include two passages that are highly relevant and demonstrably true:

(1) In introductory remarks on cyber incident attribution, the authors of the ICA made a highly germane point: The nature of cyberspace makes attribution of cyber operations difficult but not impossible. Every kind of cyber operation malicious or not leaves a trail.

(2) When analysts use words such as we assess or we judge, [these] are not intended to imply that we have proof that shows something to be a fact. Assessments are based on collected information, which is often incomplete or fragmentary High confidence in a judgment does not imply that the assessment is a fact or a certainty; such judgments might be wrong. [And one might add that they commonly ARE wrong when analysts succumb to political pressure, as was the case with the ICA.]

The intelligence-friendly corporate media, nonetheless, immediately awarded the status of Holy Writ to the misnomered Intelligence Community Assessment (it was a rump effort prepared by handpicked analysts from only CIA, FBI, and NSA), and chose to overlook the banal, full-disclosure-type caveats embedded in the assessment itself.

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Then National Intelligence Director James Clapper and the directors of the CIA, FBI, and NSA briefed President Obama on the ICA on Jan. 5, 2017, the day before they gave it personally to President-elect Donald Trump.

On Jan. 18, 2017, at his final press conference, Obama saw fit to use lawyerly language on the key issue of how the DNC emails got to WikiLeaks, in an apparent effort to cover his own derriere.

Obama: The conclusions of the intelligence community with respect to the Russian hacking were not conclusive as to whether WikiLeaks was witting or not in being the conduit through which we heard about the DNC e-mails that were leaked.

So we ended up with inconclusive conclusions on that admittedly crucial point. What Obama was saying is that U.S. intelligence did not knowor professed not to knowexactly how the alleged Russian transfer to WikiLeaks was supposedly made, whether through a third party, or cutout, and he muddied the waters by first saying it was a hack, and then a leak.

From the very outset, in the absence of any hard evidence, from NSA or from its foreign partners, of an Internet hack of the DNC emails, the claim that the Russians gave the DNC emails to WikiLeaks rested on thin gruel.

In November 2018 at a public forum, I asked Clapper to explain why President Obama still had serious doubts in late Jan. 2017, less than two weeks after Clapper and the other intelligence chiefs had thoroughly briefed the outgoing president about their high-confidence findings.

Clapper replied: I cannot explain what he [Obama] said or why. But I can tell you were, were pretty sure we know, or knew at the time, how WikiLeaks got those emails. Pretty sure?

Preferring CrowdStrike; Splaining to Congress

Comey briefs Obama, June 2016 (Flickr)

CrowdStrike already had a tarnished reputation for credibility when the DNC and Clinton campaign chose it to do work the FBI should have been doing to investigate how the DNC emails got to WikiLeaks. It had asserted that Russians hacked into a Ukrainian artillery app, resulting in heavy losses of howitzers in Ukraines struggle with separatists supported by Russia. A Voice of America report explained why CrowdStrike was forced to retract that claim.

Why did FBI Director James Comey not simply insist on access to the DNC computers? Surely he could have gotten the appropriate authorization. In early January 2017, reacting to media reports that the FBI never asked for access, Comey told the Senate Intelligence Committee there were multiple requests at different levels for access to the DNC servers.

Ultimately what was agreed to is the private company would share with us what they saw, he said. Comey described CrowdStrike as a highly respected cybersecurity company.

Asked by committee Chairman Richard Burr (R-NC) whether direct access to the servers and devices would have helped the FBI in their investigation, Comey said it would have. Our forensics folks would always prefer to get access to the original device or server thats involved, so its the best evidence, he said.

Five months later, after Comey had been fired, Burr gave him a Mulligan in the form of a few kid-gloves, clearly well-rehearsed, questions:

BURR: And the FBI, in this case, unlike other cases that you might investigate did you ever have access to the actual hardware that was hacked? Or did you have to rely on a third party to provide you the data that they had collected?

COMEY: In the case of the DNC, we did not have access to the devices themselves. We got relevant forensic information from a private party, a high-class entity, that had done the work. But we didnt get direct access.

BURR: But no content?

COMEY: Correct.

BURR: Isnt content an important part of the forensics from a counterintelligence standpoint?

COMEY: It is, although what was briefed to me by my folks the people who were my folks at the time is that they had gotten the information from the private party that they needed to understand the intrusion by the spring of 2016.

In June last year it was revealed that CrowdStrike never produced an un-redacted or final forensic report for the government because the FBI never required it to, according to the Justice Department.

By any normal standard, former FBI Director Comey would now be in serious legal trouble, as should Clapper, former CIA Director John Brennan, et al. Additional evidence of FBI misconduct under Comey seems to surface every week whether the abuses of FISA, misconduct in the case against Gen. Michael Flynn, or misleading everyone about Russian hacking of the DNC. If I were attorney general, I would declare Comey a flight risk and take his passport. And I would do the same with Clapper and Brennan.

Schiff: Every ConfidenceBut No Evidence

Both pillars of Russiagatecollusion and a Russian hackhave now fairly crumbled.

Thursdays disclosure of testimony before the House Intelligence Committee shows Chairman Adam Schiff lied not only about Trump-Putin collusion, [which the Mueller report failed to prove and whose allegations were based on DNC and Clinton-financed opposition research] but also about the even more basic issue of Russian hacking of the DNC.

[See:The Democratic Money Behind Russia-gate republished today.]

Five days after Trump took office, I had an opportunity to confront Schiff personally about evidence that Russia hacked the DNC emails. He had repeatedly given that canard the patina of flat fact during an address at the old Hillary Clinton/John Podesta think tank, The Center for American Progress Action Fund.

Fortunately, the cameras were still on when I approached Schiff during the Q&A: You have every confidence but no evidence, is that right? I asked him. His answer was a harbinger of things to come. This video clip may be worth the four minutes needed to watch it.

Schiff and his partners in crime will be in for much tougher treatment if Trump allows Attorney General Barr and U.S. Attorney John Durham to bring their investigation into the origins of Russia-gate to a timely conclusion. Barrs dismissal on Thursday of charges against Flynn, after released FBI documents revealed that a perjury trap was set for him to keep Russiagate going, may be a sign of things to come.

Given the timid way Trump has typically bowed to intelligence and law enforcement officials, including those who supposedly report to him, however, one might rather expect that, after a lot of bluster, he will let the too-big-to-imprison ones off the hook. The issues are now drawn; the evidence is copious; will the Deep State, nevertheless, be able to prevail this time?

Ray McGovern works with Tell the Word, a publishing ministry of the ecumenical Church of the Saviour in inner-city Washington. A former CIA analyst, his retirement he co-founded Veteran Intelligence Professionals for Sanity.

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RAY McGOVERN: New House Documents Sow Further Doubt That Russia Hacked the DNC - Consortium News

‘Enemies of the State’ Review – Hollywood Reporter

MOVIES

6:30 PM PDT 5/6/2020bySheri Linden

Befitting a documentary executive produced by Errol Morris, Enemies of the State is polished, assured and chilling. But as director Sonia Kennebeck traces a tale of hacker culture, government surveillance and extreme family loyalty, the smooth surface buckles. Paranoia and unreliable narrators begin to dominate the disquieting mix, and the viewer starts to question how and why the story is being told. That uncertainty is the film's primary, illuminating strength, exposing built-in biases (ours as well as those of onscreen figures) and underscoring the dangerous internet-age velocity with which one person's conspiracy charges can turn into a seemingly righteous cause.

The cause in this twisting, decade-long series of events is that of Matt DeHart, celebrated by some complete with fundraising merch as a whistleblower in the good fight against covert, anti-free-speech government activity. A digital native with definite ties to gaming and vaguely delineated connections to hacktivist collective Anonymous and WikiLeaks, DeHart claimed that the FBI framed him, harassed his family and tortured him in pursuit of classified information that crossed his screen when he ran a darknet server. After law enforcement ransacked the Indiana home where he lived with his parents, DeHart said they were looking for top-secret files relating to a shocking CIA operation; the authorities said they were looking for child pornography.

Leann and Paul DeHart, with their down-home embrace of God and country, are not people you'd expect to slip across the border under cover of night, as they did in April 2013 with their son, seeking asylum in Canada. Paul is a minister, and they're both veterans of military intelligence. So too is Matt, although his career was curtailed by mental health issues. The couple speak openly to the filmmaker, distraught over their son's treatment at the hands of authorities and stressing the need to watch the watchers. They believe Matt unequivocally. "We raised him to think critically," says Paul, who once drove his son to the Russian and Venezuelan embassies in Washington so that he could try to defect.

A crucial thumb drive goes missing, and only some of the troubling aspects of DeHart's case are substantiated, but given an already well-documented history of government abuses and dirty tactics against activists, his complaints draw the alarmed interest of experts, a number of whom appear in the film. They include a McGill professor, an investigative reporter, and a fascinating assortment of attorneys who run the gamut in terms of point of view.

Kennebeck talks briefly, and revealingly, to old friends of DeHart, some from his school days. Several of them try to put an amusing, benign slant on his behavior one compares him to Leave It to Beaver troublemaker Eddie Haskell but the picture that emerges is a disturbing one: telling glimpses of a self-dramatizing manipulator, lending further fuel to the "who to believe" fire.

Nagging questions percolate as the doc moves back and forth through the timeline: Are the national security case and the child porn case parallel or connected? Focusing at first on the espionage angle, Enemies of the State seems for a while to push aside the pornography charges as pure fabrication, as do the DeHarts. In Tennessee, yet another location in this multi-state, international saga, a prosecutor and a detective remain quietly unshakable in their conviction that Matt DeHart is a predatory pedophile.

In addition to the film's new interviews and footage of the family, Kennebeck stages re-creations of key events, in particular legal hearings on both sides of the border that use actual audio: The actors' lips move, convincingly, but the voices we hear belong to their real-life counterparts. Much of the new material is cast in a frosty blue palette that emphasizes the institutional layers of bureaucracy and stealth.

In her 2016 drone-warfare documentary, National Bird, Kennebeck chronicled whistleblowers; here her central protagonist lodges complaints that relate chiefly to his personal treatment and only in the broadest sense to a larger political picture. Pitting the heavy cloak of government secrecy against the openness of a mother and father, Enemies of the Statecertainly doesn't answer every question it poses, but it's a wake-up call for all of us keep asking them of government, of public figures, and of ourselves.

Production companies: Codebreaker FilmsDirector: Sonia KennebeckProducers: Ines Hofmann Kanna, Sonia KennebeckExecutive producer: Errol MorrisDirector of photography: Torsten LappEditor: Maxine GoedickeComposer: Insa RudolphCasting directors: Erica Hart, Maren PoitrasVenue: Tribeca Film Festival (Documentary Competition)Sales: Submarine

104 minutes

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'Enemies of the State' Review - Hollywood Reporter

Trump Was Never on a Glide Path to Re-Election Even Before the Virus – Washington Monthly

The Washington Post reports that Donald Trump is despondent these days. But not over the death, illness and economic calamity as a responsible, empathetic leader would be. Rather, hes upset over his supposedly declining electoral fortunes:

Some of Trumps advisers described the president as glum and shell-shocked by his declining popularity. In private conversations, he has struggled to process how his fortunes suddenly changed from believing he was on a glide path to reelection to realizing that he is losing to the likely Democratic nominee, former vice presidentJoe Biden, in virtually every poll, including his own campaigns internal surveys, advisers said. He also has been fretting about the possibility that a bad outbreak of the virus this fall could damage his standing in the November election, said the advisers, who along with other aides and allies requested anonymity to discuss internal deliberations.

The president is also eager to resume political travel in June, including holding his signature rallies by the end of the summer in areas where there are few cases, advisers said. Trumps political team has begun discussions about organizing a high-dollar, in-person fundraiser next month, as well as preliminary planning about staging rallies and what sort of screenings might be necessary, according to Republican National Committee officials and outsider advisers. One option being considered is holding rallies outdoors, rather than in enclosed arenas, a senior administration official said.

These two short paragraphs illustrate the cloud of delusion under which Trump operates.

First, there are no areas where there are few cases. Not only is COVID-19 running rampant in rural areas, and not only would it take only a few asymptomatic contagious individuals to infect dozens or even hundreds of other rallygoers, it would literally be nearly impossible given the dearth of testing to know whether an area had few cases or not. Second, given the fact that 72 people were infected likely after attending an anti-social-distancing gathering in Wisconsin, it seems highly unlikely that holding a rally outdoors will make much of a difference in terms of the safety of the attendees.

But even from the limited point of view of Trumps personal electoral self-interest, his perspective is grounded more in wishful narcissism than fact.

Trump was not, in fact, on a glide path to re-election prior to the arrival of the virus. Joe Biden has been leading Trump by the same wide margin since this time last year. If anything, Bidens average margin hasshrunk in recent polls as Trump briefly enjoyed a sympathetic bounce due to the crisis. Sanders also dominated Trump over the same year-long period by a slightly smaller margin, and Warren held a consistent, slim lead as well.

Of course, Hillary Clinton led by significant margins throughout the 2016 only to lose in the electoral college at the very end. But there were a number of unique factors in that election from Clintons negative favorability rating to the Russian hacking and release of her campaign emails via Wikileaks, to the final intervention by James Comey. Nor are Democrats likely to make the same mistake in overlooking electoral realignment trends and mistaking where the real swing states lay as they did in 2016, or to be complacent in the face of Trump.

Meanwhile, Democrats dominated Republicans in the 2018 midterms and in most special elections since then.

So there is little reason outside of unearned confidence for Trump to believe he had a clear path to victory. In fact, fear of Biden is why Trump attempted the Ukrainian coercion scheme that led to his impeachment.

If Trump does lose in November, he will certainly claim election fraud and a number of other conspiraciesbut insofar as he strays away from conspiracies he will likely blame the virus. But theres no reason to believe he would have won, regardless. Only the same narcissism and wishful thinking that permeates the rest of his worldview.

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Trump Was Never on a Glide Path to Re-Election Even Before the Virus - Washington Monthly

How to overcome AI and machine learning adoption barriers – Gigabit Magazine – Technology News, Magazine and Website

Matt Newton, Senior Portfolio Marketing Manager at AVEVA, on how to overcome adoption barriers for AI and machine learning in the manufacturing industry

There has been a considerable amount of hype around Artificial Intelligence (AI) and Machine Learning (ML) technologies in the last five or so years.

So much so that AI has become somewhat of a buzzword full of ideas and promise, but something that is quite tricky to execute in practice.

At present, this means that the challenge we run into with AI and ML is a healthy dose of scepticism.

For example, weve seen several large companies adopt these capabilities, often announcing they intend to revolutionize operations and output with such technologies but then failing to deliver.

In turn, the ongoing evolution and adoption of these technologies is consequently knocked back. With so many potential applications for AI and ML it can be daunting to identify opportunities for technology adoption that can demonstrate real and quantifiable return on investment.

Many industries have effectively reached a sticking point in their adoption of AI and ML technologies.

Typically, this has been driven by unproven start-up companies delivering some type of open source technology and placing a flashy exterior around it, and then relying on a customer to act as a development partner for it.

However, this is the primary problem customers are not looking for prototype and unproven software to run their industrial operations.

Instead of offering a revolutionary digital experience, many companies are continuing to fuel their initial scepticism of AI and ML by providing poorly planned pilot projects that often land the company in a stalled position of pilot purgatory, continuous feature creep and a regular rollout of new beta versions of software.

This practice of the never ending pilot project is driving a reluctance for customers to then engage further with innovative companies who are truly driving digital transformation in their sector with proven AI and ML technology.

A way to overcome these challenges is to demonstrate proof points to the customer. This means showing how AI and ML technologies are real and are exactly like wed imagine them to be.

Naturally, some companies have better adopted AI and ML than others, but since much of this technology is so new, many are still struggling to identify when and where to apply it.

For example, many are keen to use AI to track customer interests and needs.

In fact, even greater value can be discovered when applying AI in the form of predictive asset analytics on pieces of industrial process control and manufacturing equipment.

AI and ML can provide detailed, real-time insights on machinery operations, exposing new insights that humans cannot necessarily spot. Insights that can drive huge impact on businesses bottom line.

AI and ML is becoming incredibly popular in manufacturing industries, with advanced operations analysis often being driven by AI. Many are taking these technologies and applying it to their operating experiences to see where economic savings can be made.

All organisations want to save money where they can and with AI making this possible.

These same organisations are usually keen to invest in further digital technologies. Successfully implementing an AI or ML technology can significantly reduce OPEX and further fuel the digital transformation of an overall enterprise.

Understandably, we are seeing the value of AI and ML best demonstrated in the manufacturing sector in both process and batch automation.

For example, using AI to figure out how to optimize the process to achieve higher production yields and improve production quality. In the food and beverage sectors, AI is being used to monitor production line oven temperatures, flagging anomalies - including moisture, stack height and color - in a continually optimised process to reach the coveted golden batch.

The other side of this is to use predictive maintenance to monitor the behaviour of equipment and improve operational safety and asset reliability.

A combination of both AI and ML is fused together to create predictive and prescriptive maintenance. Where AI is used to spot anomalies in the behavior of assets and recommended solution is prescribed to remediate potential equipment failure.

Predictive and Prescriptive maintenance assist with reducing pressure on O&M costs, improving safety, and reducing unplanned shutdowns.

Both AI, machine learning and predictive maintenance technologies are enabling new connections to be made within the production line, offering new insights and suggestions for future operations.

Now is the time for organisations to realise that this adoption and innovation is offering new clarity on the relationship between different elements of the production cycle - paving the way for new methods to create better products at both faster speeds and lower costs.

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How to overcome AI and machine learning adoption barriers - Gigabit Magazine - Technology News, Magazine and Website

Millions of historic newspaper images get the machine learning treatment at the Library of Congress – TechCrunch

Historians interested in the way events and people were chronicled in the old days once had to sort through card catalogs for old papers, then microfiche scans, then digital listings but modern advances can index them down to each individual word and photo. A new effort from the Library of Congress has digitized and organized photos and illustrations from centuries of news using state of the art machine learning.

Led by Ben Lee, a researcher from the University of Washington occupying the Librarys Innovator in Residence position, the Newspaper Navigator collects and surfaces data from images from some 16 million pages of newspapers throughout American history.

Lee and his colleagues were inspired by work already being done in Chronicling America, an ongoing digitization effort for old newspapers and other such print materials. While that work used optical character recognition to scan the contents of all the papers, there was also a crowdsourced project in which people identified and outlined images for further analysis. Volunteers drew boxes around images relating to World War I, then transcribed the captions and categorized the picture.

This limited effort set the team thinking.

I loved it because it emphasized the visual nature of the pages seeing the visual diversity of the content coming out of the project, I just thought it was so cool, and I wondered what it would be like to chronicle content like this from all over America, Lee told TechCrunch.

He also realized that what the volunteers had created was in fact an ideal set of training data for a machine learning system. The question was, could we use this stuff to create an object detection model to go through every newspaper, to throw open the treasure chest?

The answer, happily, was yes. Using the initial human-powered work of outlining images and captions as training data, they built an AI agent that could do so on its own. After the usual tweaking and optimizing, they set it loose on the full Chronicling America database of newspaper scans.

It ran for 19 days nonstop definitely the largest computing job Ive ever run, said Lee. But the results are remarkable: millions of images spanning three centuries (from 1789 to 1963) and organized with metadata pulled from their own captions. The team describes their work in a paper you can read here.

Assuming the captions are at all accurate, these images until recently only accessible by trudging through the archives date by date and document by document can be searched for by their contents, like any other corpus.

Looking for pictures of the president in 1870? No need to browse dozens of papers looking for potential hits and double-checking the contents in the caption just search Newspaper Navigator for president 1870. Or if you want editorial cartoons from the World War II era, you can just get all illustrations from a date range. (The team has already zipped up the photos into yearly packages and plans other collections.)

Here are a few examples of newspaper pages with the machine learning systems determinations overlaid on them (warning: plenty of hat ads and racism):

Thats fun for a few minutes for casual browsers, but the key thing is what it opens up for researchers and other sets of documents. The team is throwing a data jam today to celebrate the release of the data set and tools, during which they hope to both discover and enable new applications.

Hopefully it will be a great way to get people together to think of creative ways the data set can be used, said Lee. The idea Im really excited by from a machine learning perspective is trying to build out a user interface where people can build their own data set. Political cartoons or fashion ads, just let users define theyre interested in and train a classifier based on that.

A sample of what you might get if you asked for maps from the Civil War era.

In other words, Newspaper Navigators AI agent could be the parent for a whole brood of more specific ones that could be used to scan and digitize other collections. Thats actually the plan within the Library of Congress, where the digital collections team has been delighted by the possibilities brought up by Newspaper Navigator, and machine learning in general.

One of the things were interested in is how computation can expand the way were enabling search and discovery, said Kate Zwaard. Because we have OCR, you can find things it would have taken months or weeks to find. The Librarys book collection has all these beautiful plates and illustrations. But if you want to know like, what pictures are there of the Madonna and child, some are categorized, but others are inside books that arent catalogued.

That could change in a hurry with an image-and-caption AI systematically poring over them.

Newspaper Navigator, the code behind it and all the images and results from it are completely public domain, free to use or modify for any purpose. You can dive into the code at the projects GitHub.

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Millions of historic newspaper images get the machine learning treatment at the Library of Congress - TechCrunch

Udacity partners with AWS to offer scholarships on machine learning for working professionals – Business Insider India

All applicants will be able to join the AWS Machine Learning Foundations Course. While applications are on currently, enrollment for the course begins on May 19.

This course will provide an understanding of software engineering and AWS machine learning concepts including production-level coding and practice object-oriented programming. They will also learn about deep learning techniques and its applications using AWS DeepComposer. Advertisement

A major reason behind the increasing uptake of such niche courses among the modern-age learners has to do with the growing relevance of technology across all spheres the world over. In its wake, many high-value job roles are coming up that require a person to possess immense technical proficiency and knowledge in order to assume them. And machine learning is one of the key components of the ongoing AI revolution driving digital transformation worldwide, said Gabriel Dalporto, CEO of Udacity.

The top 325 performers in the foundation course will be awarded with a scholarship to join Udacitys Machine Learning Engineer Nanodegree program. In this advanced course, the students will work on ML tools from AWS. This includes real-time projects that are focussed on specific machine learning skills.

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The Nanodegree program scholarship will begin on August 19.

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Here are five apps you need to prepare for JEE Main and NEET competitive exams

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Udacity partners with AWS to offer scholarships on machine learning for working professionals - Business Insider India

Understanding The Recognition Pattern Of AI – Forbes

Image and object recognition

Of the seven patterns of AI that represent the ways in which AI is being implemented, one of the most common is the recognition pattern. The main idea of the recognition pattern of AI is that were using machine learning and cognitive technology to help identify and categorize unstructured data into specific classifications. This unstructured data could be images, video, text, or even quantitative data. The power of this pattern is that were enabling machines to do the thing that our brains seem to do so easily: identify what were perceiving in the real world around us.

The recognition pattern is notable in that it was primarily the attempts to solve image recognition challenges that brought about heightened interest in deep learning approaches to AI, and helped to kick off this latest wave of AI investment and interest. The recognition pattern however is broader than just image recognition In fact, we can use machine learning to recognize and understand images, sound, handwriting, items, face, and gestures. The objective of this pattern is to have machines recognize and understand unstructured data. This pattern of AI is such a huge component of AI solutions because of its wide variety of applications.

The difference between structured and unstructured data is that structured data is already labelled and easy to interpret. However unstructured data is where most entities struggle. Up to 90% of an organization's data is unstructured data. It becomes necessary for businesses to be able to understand and interpret this data and that's where AI steps in. Whereas we can use existing query technology and informatics systems to gather analytic value from structured data, it is almost impossible to use those approaches with unstructured data. This is what makes machine learning such a potent tool when applied to these classes of problems.

Machine learning has a potent ability to recognize or match patterns that are seen in data. Specifically, we use supervised machine learning approaches for this pattern. With supervised learning, we use clean well-labeled training data to teach a computer to categorize inputs into a set number of identified classes. The algorithm is shown many data points, and uses that labeled data to train a neural network to classify data into those categories. The system is making neural connections between these images and it is repeatedly shown images and the goal is to eventually get the computer to recognize what is in the image based on training. Of course, these recognition systems are highly dependent on having good quality, well-labeled data that is representative of the sort of data that the resultant model will be exposed to in the real world. Garbage in is garbage out with these sorts of systems.

The many applications of the recognition pattern

The recognition pattern allows a machine learning system to be able to essentially look at unstructured data, categorize it, classify it, and make sense of what otherwise would just be a blob of untapped value. Applications of this pattern can be seen across a broad array of applications from medical imaging to autonomous vehicles, handwriting recognition to facial recognition, voice and speech recognition, or identifying even the most detailed things in videos and data of all types. Machine-learning enabled recognition has added significant power to security and surveillance systems, with the power to observe multiple simultaneous video streams in real time and recognize things such as delivery trucks or even people who are in a place they ought not be at a certain time of day.

The business applications of the recognition pattern are also plentiful. For example, in online retail and ecommerce industries, there is a need to identify and tag pictures for products that will be sold online. Previously humans would have to laboriously catalog each individual image according to all its attributes, tags, and categories. Nowadays, machine learning-based recognition systems are able to quickly identify products that are not already in the catalog and apply the full range of data and metadata necessary to sell those products online without any human interaction. This is a great place for AI to step in and be able to do the task much faster and much more efficiently than a human worker who is going to get tired out or bored. Not to mention these systems can avoid human error and allow for workers to be doing things of more value.

Not only is this recognition pattern being used with images, it's also used to identify sound in speech. There are lots of apps that exist that can tell you what song is playing or even recognize the voice of somebody speaking. Another application of this recognition pattern is recognizing animal sounds. The use of automatic sound recognition is proving to be valuable in the world of conservation and wildlife study. Using machines that can recognize different animal sounds and calls can be a great way to track populations and habits and get a better all-around understanding of different species. There could even be the potential to use this in areas such as vehicle repair where the machine can listen to different sounds being made by an engine and tell the operator of the vehicle what is wrong and what needs to be fixed and how soon.

One of the most widely adopted applications of the recognition pattern of artificial intelligence is the recognition of handwriting and text. While weve had optical character recognition (OCR) technology that can map printed characters to text for decades, traditional OCR has been limited in its ability to handle arbitrary fonts and handwriting. Machine learning-enabled handwriting and text recognition is significantly better at this job, in which it can not only recognize text in a wide range of printed or handwritten mode, but it can also recognize the type of data that is being recorded. For example, if there is text formatted into columns or a tabular format, the system can identify the columns or tables and appropriately translate to the right data format for machine consumption. Likewise, the systems can identify patterns of the data, such as Social Security numbers or credit card numbers. One of the applications of this type of technology are automatic check deposits at ATMs. Customers insert their hand written checks into the machine and it can then be used to create a deposit without having to go to a real person to deposit your checks.

The recognition pattern of AI is also applied to human gestures. This is something already heavily in use by the video game industry. Players can make certain gestures or moves that then become in-game commands to move characters or perform a task. Another major application is allowing customers to virtually try on various articles of clothing and accessories. It's even being applied in the medical field by surgeons to help them perform tasks and even to train people on how to perform certain tasks before they have to perform them on a real person. Through the use of the recognition pattern, machines can even understand sign language and translate and interpret gestures as needed without human intervention.

In the medical industry, AI is being used to recognize patterns in various radiology imaging. For example, these systems are being used to recognize fractures, blockages, aneurysms, potentially cancerous formations, and even being used to help diagnose potential cases of tuberculosis or coronavirus infections. Analyst firm Cognilytica is predicting that within just a few years, machines will perform the first analysis of most radiology images with instant identification of anomalies or patterns before they go to a human radiologist for further evaluation.

The recognition pattern is also being applied to identify counterfeit products. Machine-learning based recognition systems are looking at everything from counterfeit products such as purses or sunglasses to counterfeit drugs.

The use of this pattern of AI is impacting every industry from using images to get insurance quotes to analyzing satellite images after natural disasters to assess damage.Given the strength of machine learning in identifying patterns and applying that to recognition, it should come as little surprise that this pattern of AI will continue to see widespread adoption. In fact, in just a few years we might come to take the recognition pattern of AI for granted and not even consider it to be AI. That just goes to the potency of this pattern of AI. .

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Understanding The Recognition Pattern Of AI - Forbes