Monthly Archives: January 2020

Libertarianism and assassination – Nolan Chart LLC

Posted: January 25, 2020 at 2:01 pm

The targeted assassination of guilty people is ethically superior to war. The assassination-by-drone policy of the Trump regime is ethically bad for the same reason, and therefore morally wrong, and libertarians are right to condemn it.

Over at the Washington Examiner a great online site that promotes conservative, libertarian, and fusionist views inside the Beltway Philip Klein has an article on what at first glance looks like an inconsistency in libertarian thought.(1)

On the one hand, Klein writes, prominent libertarians of the past (including presidential candidates Ron Paul and Harry Browne) long advocated assassination as a better alternative to war.

On the other hand, Libertarians were among the most vocal critics of President Trumps decision to order the killing of Iranian terrorist leader Qassem Soleimani by drone assassination this month. Klein is clearly referring to, not constitutional objections about the lack of congressional authorization, but the normative or ethics-based substantive criticism of whether its a good idea to take out a prominent foreign leader the way the Trump administration did.

Klein is correct about both hands. But there is no inconsistency. A libertarian can consider assassination a better option than war not just better strategically, but also better ethically while condemning Soleimanis killing, and indeed the Trump regimes whole policy of assassination by drone, as being ethically unacceptable.

Not only are the two positions compatible, but they are consistent. Both follow from a fundamental libertarian principle: killing innocent people is ethically wrong.

By Kleins account, Browne relied on exactly that principle to make his case for assassination:

Browne, who was the Libertarian presidential nominee in 1996 and 2000, explicitly argued that the United States should offer a bounty on the heads of our enemies. In Why Government Doesnt Work, the manifesto for his 1996 campaign, he made the case against the first Iraq War for its toll on innocent victims. Assume Saddam Hussein really was a threat, he posited. Is that a reason to kill innocent people and expose thousands of Americans to danger? Isnt there a better way for a President to deal with a potential enemy?. He wrote: Would the President be condoning cold-blooded killing? Yes but of just one guilty person, rather than of the thousands of innocents who die in bombing raids.

Soleimanis funding and arming of terrorist groups like Hamas made him an enabler of terrorism. Since terrorists and their enablers kill innocent people, they themselves are not innocent people; therefore, killing them does not violate the prohibition on killing innocents. If a libertarian bystander at the airport where Soleimani died, or a sniper stationed a mile away, had shot the terrorist enabler, there would have been no violation of libertarian principles.

In contrast, a war with Iran would invariably involve the use of weapons of mass destruction (WMD). By WMD I mean weapons that are designed to kill indiscriminately: Bombs dropped on cities by airplanes (the predominant means by which the U.S. government wages war today) qualify as WMD under this definition. It is possible to use WMD without killing innocents in some cases such as bombing a military convoy in a desert but the odds of bombing a city without killing even one innocent (one child, for example) are astronomically low. This makes a targeted assassination clearly superior to the bombing campaigns that would inevitably occur in a war. If one can accomplish a goal X by two methods, A (which means killing innocents) and B (which avoids killing innocents), then B is the ethical alternative: B is exactly what a libertarian should do.

Similarly, when Paul called for issuing letters of marque and reprisal (a term he used to mean authorizing acts by both U.S. Special Operations troops and private contractors) against terrorist leader Osama Bin Laden, he

proposed a bill that would have allowed Congress to authorize the President to specifically target Bin Laden and his associates using non-government armed forces.

The words specifically target are all-important: Paul advocated targeted killing of specific individuals, on the grounds that they were terrorists who were guilty of shedding innocent blood. Paul did not advocate the killing of innocents, but the fatal use of force against certain non-innocents and no one else.

It is virtually impossible to stretch this libertarian idea of assassination to include killing by drones. Drones carry bombs, and bombs carried by drones are no less WMD than bombs dropped from airplanes. Their use is always ethically questionable, and they should be used only in cases where innocent blood is not spilled along with the guilty.

Were any innocent lives killed in the bombing attack that killed Soleimani? I dont know; I doubt that anyone knows. I do know, by listening to the Trump administrations statements on the killing, that they do not care: whether they killed innocent people was simply not a consideration for them. That alone is enough to make Soleimanis assassination objectionable to a libertarian. While the drone attack was ethically better than bombing an Iranian city, since it killed less innocent lives, and even possibly no innocent lives at all, being ethically better does not make it ethically good. It remains an ethically bad, or wrong, action, and the U.S. policy of drone assassination that led to it remains ethically bad, or wrong, policy.

Unfortunately, Klein touches on the use of drones and bombs only tangentially and not by name, and only to shrug it off with a But:

There are specific circumstances surrounding the Soleimani killing that may make it particularly objectionable to libertarians. But the idea of targeting bad actors as an alternative to large-scale bombing raids is not incompatible with noninterventionist foreign policy sentiments.

From the standpoint of libertarian principles (as opposed to noninterventionist sentiments), the targeted assassination of guilty people of those who have themselves shed innocent blood is ethically superior to war. At the same time, the assassination-by-drone policy of the Trump regime, and the Obama and Bush regimes, is ethically bad for the same reason, and therefore morally wrong and libertarians are right to condemn it.

(1) Philip Klein, Prominent libertarians once advocated assassination as an alternative to war, Washington Examiner, January 8, 2020. Web, Jan. 24, 2020. https://www.washingtonexaminer.com/opinion/prominent-libertarians-once-advocated-assassination-as-an-alternative-to-war

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Peter Thiels Latest Venture Is the American Government – New York Magazine

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Peter Thiel. Photo: Alex Wong/Getty Images

In mid-January, at the conclusion of a special meeting of the Mont Pelerin Society, the venerable free-market organization, after appearances by Condoleezza Rice and Niall Ferguson, Peter Thiel was slated to give closing remarks on Big Tech and the Question of Scale. The keynote was the latest in a series of public remarks and interviews in which the PayPal founder and Facebook investor showed his prominence in conservative politics.

Thiel has long been a political donor; in 2016, he gave $4million across various campaigns, including $1 million to a super-PAC supporting Trump, on whose behalf Thiel spoke at the Republican National Convention. Hes known to have funded right-wing hoaxer James OKeefe and has been an enthusiastic sponsor of organizations for activists and intellectuals, like The Stanford Review, a conservative publication he founded in the 1980s. Earlier this month, he announced an investment in a Midwest-focused venture-capital fund led by Hillbilly Elegy author and social conservative J.D. Vance.

But unlike other major right-wing donors, Thiel seems intent on being known for his intellect as much as his wallet. Over the past year, he has played the role of outraged patriot, endorsing Trumps trade war and bizarrely accusing Google of seemingly treasonous behavior in its China dealings. He intermittently lectures at Stanford. Vanity Fair has written about his hot-ticket L.A. dinner parties, where guests (including, at least once, the president) hold deep discussions about the issues of the day. Last year, George Mason University professor and economist Tyler Cowen called Thiel the most influential conservative intellectual with other conservative and libertarian intellectuals.

This emerging Republican macher is a far cry from the ultralibertarian seditionist who used to encourage entrepreneurs to exit the United States and start their own countries at sea. But Thiel is no stranger to inconsistency. For decades, he cultivated a reputation as a radical Silicon Valley anti-statist; in 2009, he wrote that Facebook, in which he was an early investor, might create the space for new modes of dissent and new ways to form communities not bounded by historical nation-states. Yet, six years earlier, he had co-founded the most aggressively statist company in the 21st century: Palantir, the global surveillance company used, for example, to monitor Iranian compliance with the nuclear deal. Can you really claim to uphold individual freedom if youre profiting from a mass-surveillance government contractor? Are you really a libertarian if youre a prominent supporter of Trump?

It would be easy enough to chalk up the seeming contradiction of Thiels thought to opportunism or pettiness (he famously funded a lawsuit, in secret, to bankrupt Gawker, my former employer) or perhaps even a mind less ambidextrous than incoherent. But its worth trying to understand his political journey. Thiels increasing prominence as both an intellectual in and benefactor of the conservative movement and his status as a legend in Silicon Valley makes him at least as important as more public tech CEOs like Mark Zuckerberg. In fact, he still holds sway over Zuckerberg: Recent reports suggest Thiel was the most influential voice in Facebooks decision to allow politicians to lie in ads on its platform. What Thiel believes now is likely to influence the next generation of conservative and libertarian thinkers if not what the president believes the next day.

How to square Thiels post-national techno-libertarianism with his bloodthirsty authoritarian nationalism? Strangely, he wants both. Todays Thielism is a libertarianism with an abstract commitment to personal freedom but no particular affection for democracy or even for politics as a process by which people might make collective decisions about the distribution of power and resources. Thiel has wed himself to state power not in an effort to participate in the political process but as an end run around it.

If we wanted to construct a genealogy of late Thielism, one place to start might be a relatively little-read essay Thiel wrote in 2015 for the conservative religious journal First Things. Thiel is a Christian, though clearly a heterodox believer, and in Against Edenism, he makes the case that science and technology are natural allies to what he sees as the inborn optimism of Christianity. Christians are natural utopians, Thiel believes, and because there will be no returning to the prelapsarian paradise of Eden, they should support technological progress, although it may mean joining with atheist optimists, personified in the essay by Goethes Faust. At least Faust was motivated to try to do something about everything that was wrong with the world, even if he did, you know, sell his immortal soul to the Devil.

Thiel suggests that growth is essentially a religious obligation building the kingdom of heaven today, here on Earth and that stagnation is, well, demonic the chaotic sea where the demon Leviathan lives. This binary appears frequently in Thiels writing, where progress is always aligned with technology and the individual, and chaos with politics and the masses. If Thiel has an apocalyptic fear of stasis, you can begin to see why his politics have changed over the past few years, as it has become less clear whether the booming technology industry has actually added much to the economy or to human happiness, let alone demonstrated progress.

Where some of his fellow libertarians have moved toward the center, attempting to build a liberaltarianism with a relatively strong welfare state and mass democratic appeal, others have found themselves articulating a version of what Tyler Cowen, in a recent blog post, called state capacity libertarianism, a concept he says was influenced by Thiels thinking. In its essence, its the admission that strong states remain necessary to maintain and extend capitalism and markets. Where Thiel would differ with state-capacity libertarians like Cowen is that he isnt merely a believer in strong states in the abstract as agents of economic progress. He is purported to be a specifically American national conservative, at least per his conference-keynote schedule. Thiel has suggested in the past that such a conservative nationalism is the only thing that can provide the cohesion necessary to re-create a strong state. Identity politics, he suggested in an address at the Manhattan Institute, the free-market think tank, is a distraction that stops us from acting at the scale that we need to be focusing on for this country. MAGA politics is the only way to grow.

This is the context in which it makes sense for a gay, cosmopolitan libertarian like Thiel to throw his support behind a red-meat conservative like Senate candidate Kris Kobach of Kansas. The technological progress Thiel associates with his own personal freedom and power is threatened by market failure and political chaos. A strong centralized state can restore order, breed progress, and open up new technologies, markets, and financial instruments from which Thiel might profit. And as long as it allows Thiel to make money and host dinner parties, who cares if its borders are cruelly and ruthlessly enforced? Who cares if its leader is an autocrat? Who cares, for that matter, if its democratic? In fact, it might be better if it werent: If the lefts commitment to identity politics is divisive enough to prevent technological advancement, its threat outstrips the kind of bellicose religious authoritarianism that Kobach represents. A Thielist government would be aggressive toward China, a country Thiel is obsessed with while also seeming, in its centralized authority and close ties between government and industry, very much like it.

There is, of course, another context in which it makes sense for Thiel to join forces with social conservatives and nationalists: his bank account. Thiels ideological shifts have matched his financial self-interest at every turn. His newfound patriotism is probably best understood as an alliance of convenience. The U.S. government is the vessel best suited for reaching his immortal techno-libertarian future (and a lower tax rate), and he is happy to ride it as long as it and he are traveling in the same direction. And if it doesnt work out, well, he did effectively buy New Zealand citizenship.

*This article appears in the January 20, 2020, issue ofNew York Magazine. Subscribe Now!

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Six things to know about the primary election – Shelby Star

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In just over a month voters will decide who they want to see on the ballot in the 2020 general election. Early voting for the March 3 primary will begin in the middle of February and run up until Saturday, Feb. 29.

Heres everything you need to know about when, where and how to vote:

Where:

Two polling locations will be open for early voting, the Market Place Shopping Center, 1740 E. Dixon Blvd near Hobby Lobby and Bargain Hunt and the Kings Mountain Fire Museum, 269 Cleveland Ave.

How long do I have to vote?

Polls will open Thursday, Feb. 13, and will remain open every weekday through the 28th. Saturday, Feb. 29 will be the final day of early voting. Polls will open from 8 a.m.-7:30 p.m. every day except for Feb. 29, when they will close at 3 p.m.

Do I need to register?

The deadline to register to vote or to make any changes to current voter registration has passed. However, voters will be allowed to same-day register and vote during the early voting period.

Do I need an ID?

A federal district court has temporarily blocked North Carolinas voter photo ID requirement from taking effect. Unless the courts direct otherwise, this means that voters will not be required to provide photo ID when they vote in the primary election on March 3.

Registration information:

All Cleveland County registered voters are eligible to participate in the upcoming Presidential Preference and Primary Election. Three parties - Republican, Democrat, Libertarian conduct semi-closed primaries. Two parties - Green, Constitution conduct closed primaries.

This means that if you want to vote for a particular candidate, you must pick which primary you wish to participate in. Registered Republicans, Democrats, Libertarians, Green Party and Constitution Party voters must all vote for their parties only.

Unaffiliated voters will choose one party to vote for in the primary election.

Who is on the ballot?

Depending on which primary you choose, your ballot could have as many as 13 races to vote in or as few as one. Green, Libertarian and Constitution party primaries only decide who they want to see on the presidential ballot later this year. Republican and Democrat voters will decide which candidates get to appear in presidential, school board, county commission, governor and other state and federal ballots in November.

Sample ballots are available at the Cleveland County Board of Elections.

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Joe Rogans Endorsement Is One of the Most Influential in America – VICE

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Joe Rogan is one of the most influential people in media. That doesn't mean he's a good interviewer or a responsible communicator when speaking to a large and devoted audience, but it is a fact. It's hard to pinpoint the exact size of his podcast's audience, but Rogans official YouTube channel has 7.3 million subscribers and he recently claimed his podcast gets 190 million downloads a month.

When Elon Musk goes on Rogans show and smokes a blunt, Tesla stocks take a tumble (though as Rogan notes at every opportunity, they quickly bounced back). It was a big deal when Bernie Sanders sat down with Rogan for an hour-long interview in August, and an even bigger deal earlier this week when Rogan said that he would probably vote for Sanders in the upcoming election. Sanders is not the first presidential candidate to go on Rogan's podcastTulsi Gabbard has been on several timesnor is Sanders the first candidate to get something resembling an endorsement from Rogan. Rogan hosted and voted for Libertarian Gary Johnson in 2016.

On Thursday, Sanders tweeted a clip from Rogan's podcast highlighting his endorsement, in which Rogan said he likes Sanders for career-long consistency in his politics.

Rogan's endorsement of Sanders is notable because unlike Johnson, Sanders has an actual shot at taking the White House, and in a close race, gaining the support of even a portion of Rogans massive, loyal audience could be a difference maker. Whats less clear is why the Sanders campaign embraced and promoted the endorsement, knowing that Rogan is controversial and hated by parts of his base. Rogans endorsement is so influential, his audience so large, that its not even clear Sanders needed to acknowledge it because Rogans audience rivals (and is likely larger than) his own. Who is Sanders reaching with a Joe Rogan video clip that Rogan hasnt already reached?

Rogan's endorsement, and the video Sanders shared on Twitter in particular, has caused some controversy among people who argue that Rogan is a bigot who should be marginalized, ignored, or disavowed.

Rogan hasn't wielded his power with much responsibility: He's given people like Chuck Johnson, Milo Yiannopoulos, Alex Jones, Stefan Molyneux, and Gavin McInnes access to his gigantic audience, and Rogan rarely challenges his guests on their views, allowing them to launder their bad ideas on his show. Data & Society researcher Becca Lewis has argued that Rogan giving a platform to these people has led his audience down more extremist rabbit holes on YouTube. Lewis describes Rogan as a "libertarian influencer with mainstream appeal."

"When [Rogan] hosts other members of the Intellectual Dark Web, it's easy to get drawn into that world," Lewis told Motherboard in 2018. That Rogan is an entry point to other YouTube and podcast influencers speaks to his own influence; whether Rogan's endorsement matters doesn't depend on whether Rogan himself is GOOD or BAD, it's whether his endorsement moves the needle. And given how much discussion there is about his endorsement and what we know about Rogan's overall influence, it almost certainly does.

A big part of Rogan's appeal is that he's an average Joe. Sitting down with him for an interview is not the same as doing a quick spot on CNN or Fox News. His interviews are long (often more than three hours), meandering, and silly. It gives subjects the chance to speak at length and often put their foot in their mouth. For his listeners, a recommendation from Rogan is like a recommendation from a friend, if your friend was talking to millions of people at once. It has the appearance of raw, emotional authenticity. It is the exact opposite of a measured, calculated endorsement from the

New York Times.

What seems to have made lots of people mad, however, is that Sanders has embraced the endorsement. Whats worth noting is that its not clear that Sanders sharing the video is actually going to earn him any more voters. Sanders, of course, has a huge audience, but Sanders reach is almost certainly smaller than Rogans. Sanders clip has 3.2 million views on Twitter. Rogans audience fluctuates and its notoriously difficult to get reliable podcast statistics (especially if you dont work for that podcast), but if Rogans 190 million downloads per month figure is accurate, we could conservatively estimate that each episode is getting far more than 3.2 million downloads.

This is why its impossible to amplify Joe Rogan: He has an audience bigger than nearly anyone in the country, and to ignore that he exists and that people like him is to remove yourself from reality. Theres little danger in Sanders tweeting this video and radicalizing people because Rogans audience is already huge. But sharing the video and tacitly accepting Rogans endorsement feels like an unforced error, or at least a risk Sanders didnt need to take: Hes opened himself up to criticism from parts of his base who care about social justice, marginalized people, and stand against the people and ideas Rogan has allowed to be laundered on his show, without really standing to gain anything.

"The goal of our campaign is to build a multi-racial, multi-generational movement that is large enough to defeat Donald Trump and the powerful special interests whose greed and corruption is the root cause of the outrageous inequality in America," the Sanders campaign told Motherboard in a statement. "Sharing a big tent requires including those who do not share every one of our beliefs, while always making clear that we will never compromise our values. The truth is that by standing together in solidarity, we share the values of love and respect that will move us in the direction of a more humane, more equal world."

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State Election Board Releases Official 2020 Voter Registration Statistics – The Marlow Review

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Official Oklahoma voter registration statistics released yesterday show 2,090,107 Oklahomans are registered to vote heading into the 2020 election cycle. Oklahomas official voter registration statistics are counted every year on January 15.

"These statistics continue a decades-long trend of growth for Independents and Republicans as a share of the Oklahoma electorate," said State Election Board Secretary Paul Ziriax. "And although they are relatively small in overall numbers, Libertarians now have more than 11,000 voters for the first time in state history."

The largest number of Oklahoma's voters are Republicans, who make up more than 48.3% of registered voters. Two years ago, Republicans accounted for 46.8% of registered voters.

Democrats are the second-largest party at 35.3% of registered voters, down from 38.2% in January 2018. Democrats had long been the largest political party in Oklahoma, but were passed by Republicans in January 2015.

Independents, or "no party" voters, are now 15.9% of Oklahoma voters, up from 14.8% two years ago.

The Libertarian Party, which gained recognition in 2016, now has 11,171 registered voters, more than double the number in January 2018.

Oklahomas registered voters:

JAN. 15, 2020 JAN. 15, 2018

DEMOCRATS 738,256.35.3% 769,772.38.2%

REPUBLICANS 1,008,569.48.3% 942,621.46.8%

LIBERTARIANS 1,171.less than 1% 4,897.less than 1%

INDEPENDENTS 332,111.15.9% 298,867.14.8%

TOTAL 2,090,107 2,016,157

HISTORICAL VOTER REGISTRATION IN OKLAHOMA

The State Election Board began recording statewide voter registration statistics by party in 1960.

YEAR DEM REP IND OTHER 1960 82.0% 17.6% 0.4% N/A

1980 75.8% 22.8% 1.4% N/A

2000* 56.7% 35.0% 8.3% *

2020* 35.3% 48.3% 15.9% *

*Minor parties account for less than 1 percent of voters in Oklahoma.

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Federated machine learning is coming – here’s the questions we should be asking – Diginomica

Posted: at 1:58 pm

A few years ago, I wondered how edge data would ever be useful given the enormous cost of transmitting all the data to either the centralized data center or some variant of cloud infrastructure. (It is said that 5G will solve that problem).

Consider, for example, applications of vast sensor networks that stream a great deal of data at small intervals. Vehicles on the move are a good example.

There is telemetry from cameras, radar, sonar, GPS and LIDAR, the latter about 70MB/sec. This could quickly amount to four terabytes per day (per vehicle). How much of this data needs to be retained? Answers I heard a few years ago were along two lines:

My counterarguments at the time were:

Introducing TensorFlow federated, via The TensorFlow Blog:

This centralized approach can be problematic if the data is sensitive or expensive to centralize. Wouldn't it be better if we could run the data analysis and machine learning right on the devices where that data is generated, and still be able to aggregate together what's been learned?

Since I looked at this a few years ago, the distinction between an edge device and a sensor has more or less disappeared. Sensors can transmit via wifi (though there is an issue of battery life, and if they're remote, that's a problem); the definition of the edge has widened quite a bit.

Decentralized data collection and processing have become more powerful and able to do an impressive amount of computing. The case is point in Intel's Introducing the Intel Neural Compute Stick 2 computer vision and deep learning accelerator powered by the Intel Movidius Myriad X VPU, that can stick into a Pi for less than $70.00.

But for truly distributed processing, the Apple A13 chipset in the iPhone 11 has a few features that boggle the mind: From Inside Apple's A13 Bionic system-on-chip Neural Engine, a custom block of silicon separate from the CPU and GPU, focused on accelerating Machine Learning computations. The CPU has a set of "machine learning accelerators" that perform matrix multiplication operations up to six times faster than the CPU alone. It's not clear how exactly this hardware is accessed, but for tasks like machine learning (ML) that use lots of matrix operations, the CPU is a powerhouse. Note that this matrix multiplication hardware is part of the CPU cores and separate from the Neural Engine hardware.

This should beg the question, "Why would a smartphone have neural net and machine learning capabilities, and does that have anything to do with the data transmission problem for the edge?" A few years ago, I thought the idea wasn't feasible, but the capability of distributed devices has accelerated. How far-fetched is this?

Let's roll the clock back thirty years. The finance department of a large diversified organization would prepare in the fall a package of spreadsheets for every part of the organization that had budget authority. The sheets would start with low-level detail, official assumptions, etc. until they all rolled up to a small number of summary sheets that were submitted headquarters. This was a terrible, cumbersome way of doing things, but it does, in a way, presage the concept of federated learning.

Another idea that vanished is Push Technology that shared the same network load as centralizing sensor data, just in the opposite direction. About twenty-five years, when everyone had a networked PC on their desk, the PointCast Network used push technology. Still, it did not perform as well as expected, often believed to be because its traffic burdened corporate networks with excessive bandwidth use, and was banned in many places. If Federated Learning works, those problems have to be addressed

Though this estimate changes every day, there are 3 billion smartphones in the world and 7 billion connected devices.You can almost hear the buzz in the air of all of that data that is always flying around. The canonical image of ML is that all of that data needs to find a home somewhere so that algorithms can crunch through it to yield insights. There are a few problems with this, especially if the data is coming from personal devices, such as smartphones, Fitbit's, even smart homes.

Moving highly personal data across the network raises privacy issues. It is also costly to centralize this data at scale. Storage in the cloud is asymptotically approaching zero in cost, but the transmission costs are not. That includes both local WiFi from the devices (or even cellular) and the long-distance transmission from the local collectors to the central repository. This s all very expensive at this scale.

Suppose, large-scale AI training could be done on each device, bringing the algorithm to the data, rather than vice-versa? It would be possible for each device to contribute to a broader application while not having to send their data over the network. This idea has become respectable enough that it has a name - Federated Learning.

Jumping ahead, there is no controversy that training a network without compromising device performance and user experience, or compressing a model and resorting to a lower accuracy are not alternatives. In Federated Learning: The Future of Distributed Machine Learning:

To train a machine learning model, traditional machine learning adopts a centralized approach that requires the training data to be aggregated on a single machine or in a datacenter. This is practically what giant AI companies such as Google, Facebook, and Amazon have been doing over the years. This centralized training approach, however, is privacy-intrusive, especially for mobile phone usersTo train or obtain a better machine learning model under such a centralized training approach, mobile phone users have to trade their privacy by sending their personal data stored inside phones to the clouds owned by the AI companies.

The federated learning approach decentralizes training across mobile phones dispersed across geography. The presumption is that they collaboratively develop machine learning while keeping their personal data on their phones. For example, building a general-purpose recommendation engine for music listeners. While the personal data and personal information are retained on the phone, I am not at all comfortable that data contained in the result sent to the collector cannot be reverse-engineered - and I havent heard a convincing argument to the contrary.

Here is how it works. A computing group, for example, is a collection of mobile devices that have opted to be part of a large scale AI program. The device is "pushed" a model and executes it locally and learns as the model processes the data. There are some alternatives to this. Homogeneous models imply that every device is working with the same schema of data. Alternatively, there are heterogeneous models where harmonization of the data happens in the cloud.

Here are some questions in my mind.

Here is the fuzzy part: federated learning sends the results of the learning as well as some operational detail such as model parameters and corresponding weights back to the cloud. How does it do that and preserve your privacy and not clog up your network? The answer is that the results are a fraction of the data, and since the data itself is not more than a few Gb, that seems plausible. The results sent to the cloud can be encrypted with, for example, homomorphic encryption (HE). An alternative is to send the data as a tensor, which is not encrypted because it is not understandable by anything but the algorithm. The update is then aggregated with other user updates to improve the shared model. Most importantly, all the training data remains on the user's devices.

In CDO Review, The Future of AI. May Be In Federated Learning:

Federated Learning allows for faster deployment and testing of smarter models, lower latency, and less power consumption, all while ensuring privacy. Also, in addition to providing an update to the shared model, the improved (local) model on your phone can be used immediately, powering experiences personalized by the way you use your phone.

There is a lot more to say about this. The privacy claims are a little hard to believe. When an algorithm is pushed to your phone, it is easy to imagine how this can backfire. Even the tensor representation can create a problem. Indirect reference to real data may be secure, but patterns across an extensive collection can surely emerge.

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Expert: Don’t overlook security in rush to adopt AI – The Winchester Star

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MIDDLETOWN Lord Fairfax Community College hosted technologist Gary McGraw on Wednesday night. He spoke of the cutting edge work being done at the Berryville Institute of Machine Learning, which he co-founded a year ago.

The talk was part of the colleges Tech Bytes series of presentations by industry professionals connected to technology.

The Berryville Institute of Machine Learning is working to educate tech engineers and others about the risks they need to think about while building, adopting and designing machine learning systems. These systems involve computer programs called neural networks that learn to perform a task such as facial recognition by being trained on lots of data, such as by the use of pictures, McGraw said.

Its important that we dont take security for granted or overlook security in the rush to adopt AI everywhere, McGraw said.

One easily relatable adaptation of this technology is in smartphones, which are using AI to analyze conversations, photos and web searches, all to process peoples data, he said.

There should be privacy by default. There is not. They are collecting your data you are the product, he said.

The institute anticipates within a week or two releasing a report titled An Architectural Risk Analysis of Machine Learning Systems in which 78 risks in machine learning systems are identified.

McGraw told the audience that, while not interchangeable terms, artificial intelligence and machine learning have been sold as magic technology that will miraculously solve problems. He said that is wrong. The raw data used in machine learning can be manipulated and it can open up systems to risks, such as system attacks that could compromise information, even confidential information.

McGraw cited a few of those risks.

One risk is someone fooling a machine learning system by presenting malicious input of data that can cause a system to make a false prediction or categorization. Another risk is if an attacker can intentionally manipulate the data being used by a machine learning system, the entire system can be compromised.

One of the most often discussed risks is data confidentiality. McGraw said data protection is already difficult enough without machine learning. In machine learning, there is a unique challenge in protecting data because it is possible that through subtle means information contained in the machine learning model could be extracted.

LFCC Student Myra Diaz, who is studying computer science at the college, attended the program.

I like it. I am curious and so interested to see how can we get a computer to be judgmental in a positive way, such as judging what it is seeing, Diaz said.

Remaining speakers for this years Tech Bytes programs are:

6 p.m. Feb. 19: Kay Connelly, Informatics.

1 p.m. March 11:Retired Secretary of the Navy Richard Danzig

6 p.m. April 8: Heather Wilson, Analytics, L Brands

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Uncover the Possibilities of AI and Machine Learning With This Bundle – Interesting Engineering

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If you want to be competitive in an increasingly data-driven world, you need to have at least a baseline understanding of AI and machine learningthe driving forces behind some of todays most important technologies.

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Jenkins Creator Launches Startup To Speed Software Testing with Machine Learning — ADTmag – ADT Magazine

Posted: at 1:58 pm

Jenkins Creator Launches Startup To Speed Software Testing with Machine Learning

Kohsuke Kawaguchi, creator of the open source Jenkins continuous integration/continuous delivery (CI/CD) server, and Harpreet Singh, former head of the product group at Atlassian, have launched a startup that's using machine learning (ML) to speed up the software testing process.

Their new company, Launchable, which emerged from stealth mode on Thursday, is developing a software-as-a-service (SaaS) product with the ability to predict the likelihood of a failure for each test case, given a change in the source code. The service will use ML to extract insights from the massive and growing amount of data generated by the increasingly automated software development process to make its predictions.

"As a developer, I've seen this problem of slow feedback from tests first-hand," Kawaguchi told ADTmag. "And as the guy who drove automation in the industry with Jenkins, it seemed to me that we could make use of all that data the automation is generating by applying machine learning to the problem. I thought we should be able to train the machine on the model and apply quantifiable metrics, instead of relying on human experience and gut instinct. We believe we can predict, with meaningful accuracy, what tests are more likely to catch a regression, given what has changed, and that translates to faster feedback to developers."

The strategy here is to run only a meaningful subset of tests, in the order that minimizes the feedback delay.

Kawaguchi (known as "KK") and Singh worked together at CloudBees, the chief commercial supporter of Jenkins. Singh left that company in 2018 to serve as GM of Atlassian's Bitbucket cloud group. Kawaguchi became an elite developer and architect at CloudBees, and he's been a part of the community throughout the evolution of this technology. His departure from the company was amicable: Its CEO and co-founder Sacha Labourey is an investor in the startup, and Kawaguchi will continue to be involved with the Jenkins community, he said.

Software testing has been a passion of Kawaguchi's since his days at Sun Microsystems, where he developed Jenkins as a fork of the Hudson CI server in 2011. Singh also worked at Sun and served as the first product manager for Hudson before working on Jenkins. They will serve as co-CEOs of the new company. They reportedly snagged $3.2 million in seed funding to get the ball rolling.

"KK and I got to talking about how the way we test now impacts developer productivity, and how machine learning could be used to address the problem," Singh said. "And then we started talking about doing a startup. We sat next to each other at CloudBees for eight years; it was an opportunity I couldn't pass up."

An ML engine is at the heart of the Launchable SaaS, but it's really all about the data, Singh said.

"We saw all these sales and marketing guys making data-driven decisions -- even more than the engineers, which was kind of embarrassing," Singh said. "So it became a mission for us to change that. It's kind of our north star."

The co-execs are currently talking with potential partners and recruiting engineers and data scientists. They offered no hard release date, but they said they expect a version of the Launchable SaaS to become generally available later this year.

Posted by John K. Waters on 01/23/2020 at 8:41 AM

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Red Hat Survey Shows Hybrid Cloud, AI and Machine Learning are the Focus of Enterprises – Computer Business Review

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The data aspect in particular is something that we often see overlooked

Open source enterprise software firm Red Hat now a subsidiary of IBM have conducted its annual survey of its customers which highlights just how prevalent artificial intelligence and machine learning is becoming, while a talent and skill gap is still slowing down companies ability to enact digital transformation plans.

Here are the top three takeaways from Red Hats customer survey;

When asked to best describe their companies approach to cloud infrastructure 31 percent stated that they run a hybrid cloud, while 21 percent said their firm has a private cloud first strategy in place.

The main reason cited for operating a hybrid cloud strategy was the security and cost benefits it provided. Some responders noted that data integration was easier within a hybrid cloud.

Not everyone is fully sure about their approach yet, as 17 percent admitted they are in the process of establishing a cloud strategy, while 12 percent said they have no plans at all to focus on the cloud.

When it comes to digital transformation there has been a notable rise in the amount of firms that undertaken transformation projects. In 2018; under a third of responders (31 percent) said they were implementing new processes and technology, this year that number has nearly doubled as 58 percent confirm they are introducing new technology.

Red Hat notes that: The drivers for these projects vary. And the drivers also vary by the role of the respondent. System administrators care most about simplicity. IT architects focus on user experience and innovation. For managers, simplicity, user experience, and innovation are all tied for top priority. Developers prioritize innovationwhich, overall, was cited as the most important reason to do digital transformation projects.

However, one in ten surveyed said they are facing a talent and skillset gap that is slowing down the pace at which they can transform their business. The skillset is being made worse by the amount of new technologies that are being brought to market such as artificial intelligence, machine learning and containerisation, the use of which is expected to grow significantly in the next 24 months.

Artificial intelligence, machine learning models and processes is the clear emerging technology for firms in 2019, as 30 percent said that they are planning to implement an AI or ML project within the next 12 months.

However, enterprises are worried about the compatibility and complexity of implementing AI or ML, with 29 percent stating they are worried about evolving software stacks.

One in five (22 percent) responders are worried about getting access to the right data. The data aspect in particular is something that we often see overlooked; obtaining relevant data and cleansing or transforming it in ways that its a useful input for models can be one of the most challenging aspects of an AI project, Red Hat notes.

Red Hats survey was created by compiling 876 qualified responses from Red Hat customers during August and September of 2019.

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