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Archivo del Autor: Belen De Leon

Federal jury convicts malware creators of hijacking 400,000 computers

Two Romanian men have been convicted by a federal jury for using malware to hijack 400,000 computers in order to steal credit card information, engage in cryptomining, and commit online auction fraud, among a number of other crimes.

The post Federal jury convicts malware creators of hijacking 400,000 computers appeared first on Digital Trends.


Source: Digital trends

Notre Dame Cathedral Fire: What we know so far – CNET

Almost 1000 years of French history is burning.
Source: CNET

YouTube showed a link to 9/11 info on Notre Dame fire videos – CNET

The Google-owned site says its algorithm made the “wrong call.”
Source: CNET

Westworld will return in 2020, report claims – CNET

Don’t worry the robots will be back, it’s just not clear when.
Source: CNET

The most overlooked path to commercialize AI is for companies to do it themselves

The Bessemer Process patented in 1856 by Sir Henry Bessemer is one of the inventions most closely associated with catalyzing the second industrial revolution. By reducing the impurities of iron with an innovative oxidizing air blast, the process ushered in a new wave of inexpensive, high-volume steelmaking.

Bessemer decided to license his patent to a handful of steelmakers in an effort to quickly monetize his efforts. But contrary to expectations, technical challenges and monopolistic greed prevented large steelmakers from agreeing to favorable licensing terms.

In an effort to drive adoption, Bessemer opened his own steel making plant with the intention of undercutting competitors. The approach was so successful that each partner in the endeavor walked away from the 14 year partnership with an 81x return.

Some 162 years later, new businesses continue to struggle to convince customers to adopt new technologies — even when it’s in their best interest. Following in the footsteps of founders like Bessemer, today’s innovative startups are discovering that it often makes more sense to launch “full stack” businesses that provide a traditional service optimized with proprietary automation measures.

Chris Dixon of Andreessen Horowitz popularized the term “full stack startup” in 2014, just before the deep learning revolution. In his words, a full stack startup is a company that “builds a complete, end-to- end product or service that bypasses existing companies.”

The full stack methodology gave birth to companies like Uber and Tesla prior to the apex of the deep learning revolution. And in today’s AI-first world of data and human labelers, full stack startups are poised to play an even more important role in the startup ecosystem.

Going full stack comes with the advantage of being able to operate outside traditional incentive structures that limit the ability for large players in legacy industries to implement automation measures.

Watson computer at IBM in New York City

(Photo by Andrew Spear for The Washington Post via Getty Images.)

What does DIY AI look like?

Startups like Cognition IP, a BSV portfolio company, and Atrium are good examples of this. On paper, these businesses look very similar to traditional law firms in that they employ lawyers to practice patent law and startup law, respectively. But while traditional law firms often don’t automate due to the natural incentives associated with hourly billing, full stack startups are incentivized by consumer adoption so they have much to gain from developing a faster, cheaper, better strategy.

In addition to rejiggering old incentive structures à la Bessemer, going full stack opens up opportunities for companies to integrate labeling workflows into more traditional roles, to reap the full benefits of virtuous feedback loops, and to avoid countless complex process integrations.

Data labeling is a critical responsibility for startups that rely on machine learning. Services like Amazon Mechanical Turk and Figure Eight work well when startups have relatively manageable data labeling responsibilities. But when labeling and human-plus-machine cooperative decision-making are a core part of everyday operations, startups often have to hire employees to manage that workflow internally.

Scaling these teams is expensive and operationally intensive. Going full stack opens up opportunities for companies to integrate labeling workflows into other jobs. Employees traditionally tasked with performing a consumer or enterprise service can take on the extra task at reduced expense. And if their role is assisted by a machine, they will gradually become more productive over time as their assistive models get more accurate with more labeled data.

A second and inherently related benefit of going full stack is that these startups are able to generate – and own – powerful virtuous data feedback loops. Owning data flows creates more impressive moats than merely locking down static data sets. Deep Sentinel has a natural moat in the consumer security space, for example, as it not only has accurate classifiers, but accurate classifiers that continue to improve with real world data generated in an environment it can control.

Courtesy of Flickr/Tullio Saba

Leveraging automation is a matter of balancing risks and rewards

In 1951, Ford’s VP of Operations, Del Harder, decided it was time to upgrade the company’s lines with a more fully automated system for moving materials through the production sequence. It ultimately took five years of tinkering at Ford’s Cleveland Engine Plant before the technique was ready to scale to other factories. By chaining together previously independent parts of the production sequence, Harder had created new frustrating interdependencies.

Founders today going after traditional industries like manufacturing and agriculture similarly understand that the devil is in the details when it comes to scaling. The clear advantage to startups subscribing to the full stack methodology is that they only need to worry about integrating once with their own processes.

But on the flip side, going full stack does come with its own significant scaling expenses. Venture capital as a financing vehicle only makes sense to a certain point with respect to risk, margin and dilution, so many founders attempting to execute this strategic playbook have turned to debt financing.

Fortunately we have been in good economic times with low interest rates. Traditional full stack businesses like Tesla and Uber have both raised significant debt, and even up-and-coming players like Opendoor have turned to this financing strategy. A nasty economic downturn could certainly throw a wrench into things for just about everyone.

Progress in technology is cyclical and success is heavily dependent on execution within extremely narrow opportunistic bands of time. It’s debatable whether capital intensive, venture-backed companies like FedEx and Apple could have been successful if they were started in a different fundraising environment.

Like countless other automation technologies that preceded machine learning, the winners of the deep learning revolution will be startups whose technologies are optimized to work side-by-side with humans to generate outsized returns. Going full stack is difficult, expensive, and not the only way to win, but it’s an under-appreciated strategy that’s extremely relevant for today’s machine learning-enabled startups.


Source: TechCrunch

OpenAI Five crushes Dota2 world champs, and soon you can lose to it too

Dota2 is one of the most popular, and complex, online games in the world, but an AI has once again shown itself to supersede human skill. In matches over the weekend, OpenAI’s “Five” system defeated two pro teams soundly, and soon you’ll be able to test your own mettle against — or alongside — the ruthless agent.

In a blog post, OpenAI detailed how its game-playing agent has progressed from its younger self — it seems wrong to say previous version, since it really is the same extensive neural network as many months ago, but with much more training.

The version that played at Dota2’s premiere tournament, The International, gets schooled by the new version 99 percent of the time. And it’s all down to more practice:

In total, the current version of OpenAI Five has consumed 800 petaflop/s-days and experienced about 45,000 years of Dota self-play over 10 realtime months (up from about 10,000 years over 1.5 realtime months as of The International), for an average of 250 years of simulated experience per day.

To the best of our knowledge, this is the first time an RL [reinforcement learning] agent has been trained using such a long-lived training run.

One is tempted to cry foul at a datacenter-spanning intelligence being allowed to train for 600 human lifespans. But really it’s more of a compliment to human cognition that we can accomplish the same thing with a handful of months or years, while still finding time to eat, sleep, socialize (well, some of us) and so on.

Dota2 is an intense and complex game with some rigid rules but a huge amount of fluidity, and representing it in a way that makes sense to a computer isn’t easy (which likely accounts partly for the volume of training required). Controlling five “heroes” at once on a large map with so much going on at any given time is enough to tax a team of five human brains. But teams work best when they’re acting as a single unit, which is more or less what Five was doing from the start. Rather than five heroes, it was more like five fingers of a hand to the AI.

Interestingly, OpenAI also discovered lately that Five is capable of playing cooperatively with humans as well as in competition. This was far from a sure thing — the whole system might have frozen up or misbehaved if it had a person in there gumming up the gears. But in fact it works pretty well.

You can watch the replays or get the pro commentary on the games if you want to hear exactly how the AI won (I’ve played but I’m far from good. I’m not even bad yet). I understand they had some interesting buy-back tactics and were very aggressive. Or, if you’re feeling masochistic, you can take the AI on yourself in a limited time event later this week.

We’re launching OpenAI Five Arena, a public experiment where we’ll let anyone play OpenAI Five in both competitive and cooperative modes. We’d known that our 1v1 bot would be exploitable through clever strategies; we don’t know to what extent the same is true of OpenAI Five, but we’re excited to invite the community to help us find out!

Although a match against pros would mean all-out war using traditional tactics, low-stakes matches against curious players might reveal interesting patterns or exploits that the AI’s creators aren’t aware of. Results will be posted publicly, so be ready for that.

You’ll need to sign up ahead of time, though: The system will only be available to play from Thursday night at 6 PM to the very end of Sunday, Pacific time. They need to reserve the requisite amount of computing resources to run the thing, so sign up now if you want to be sure to get a spot.

OpenAI’s team writes that this is the last we’ll hear of this particular iteration of the system; it’s done competing (at least in tournaments) and will be described more thoroughly in a paper soon. They’ll continue to work in the Dota2 environment because it’s interesting, but what exactly the goals, means, or limitations will be are yet to be announced.


Source: TechCrunch

Game of Thrones Recap, Season 8 Episode 1: Jon Snow Still Knows Nothing

The one-time King in the North still sees the world not as it is, but as he is.
Source: Wired

First Big Survey of Births Finds Millions of Missing Women

A massive effort to catalog the number of male-female births shows that 23 million women were never born who should have been, according to the natural sex ratio.
Source: Wired

I asked the US government for my immigration file and all I got were these stupid photos

“Welcome to the United States of America.”

That’s the first thing you read when you find out your green card application was approved. Those long-awaited words are printed on fancier-than-usual paper, an improvement on the usual copy machine printed paper that the government sends to periodically remind you that you, like millions of other people, are stuck in the same slow bureaucratic system.

First you cry — then you cry a lot. And then you celebrate. But then you have to wait another week or so for the actual credit card-sized card — yes, it’s green — to turn up in the mail before it really kicks in.

It took two years to get my green card, otherwise known as U.S. permanent residency. That’s a drop in the ocean to the millions who endure twice, or even three times as long. After six years as a Brit in New York, I could once again leave the country and arrive without worrying as much that a grumpy border officer might not let me back in because they don’t like journalists.

The reality is, U.S. authorities can reject me — and any other foreign national — from entering the U.S. for almost any reason. As we saw with President Trump’s ban on foreign nationals from seven Muslim-majority nations — since ruled unconstitutional — the highly vetted status of holding a green card doesn’t even help much. You have almost no rights and the questioning can be brutally invasive — as I, too, have experienced before, along with the stare-downs and silent psychological warfare they use to mentally shake you down.

I was curious what they knew about me. With my green card in one hand and empowered by my newfound sense of immigration security, I filed a Freedom of Information request with U.S. Citizenship and Immigration Services to obtain all of the files the government had collected on me in order to process my application.

Seven months later, disappointment.

USCIS sent me a disk with 561 pages of documents and a cover letter telling me most of the interesting bits were redacted, citing exemptions such as records relating to officers and government staff, investigatory material compiled for law enforcement purposes, and techniques used by the government to decide an applicant’s case.

But I did get almost a decade’s worth of photos taken by border officials entering the United States.

Seven years of photos taken at the U.S. border. (Source: Homeland Security/FOIA)

What’s interesting about these encounters is that you can see me getting exponentially fatter over the years while my sense of style declines at about the same rate.

Each photo comes with a record from a web-based system called the Customer Profile Management Service (CPMS), which stores all the photos of foreign nationals visiting or returning to the U.S. from a camera at port of entries.

Immigration officers and border officials use the Identity Verification Tool (IVT) to visually confirm my identity and review my records at the border and my interview, as well as checking for any “derogatory” information that might flag a problem in my case.

The government’s IDENT system, which immigration staff and border officials use to visually verify an applicant’s identity along with any potentially barring issues, like a criminal record. (Source: FOIA)

Everyone’s file will differ, and my green card case was somewhat simple and straightforward compared to others.

Some 90 percent of my file are things my lawyer submitted — my application, my passport and existing visa, my bank statements and tax returns, my medical exam, and my entire set of supporting evidence — such as my articles, citations, and letters of recommendation. The final 10 percent were actual responsive government documents, and some random files like photocopied folders.

And there was a lot of duplication.

From the choice files we are publishing, the green card process appears highly procedural and offered little to nothing in terms of decision making by immigration officers. Many of the government-generated documents were mostly box-ticking exercises, such as verifying the authenticity of documents along the chain of custody. A single typo can derail an entire case.

The government uses several Homeland Security systems to check my immigration records against USCIS’ Central Index System, and verifying my fingerprints against my existing records stored in its IDENT system to ensure it’s really me at the interview.

USCIS’ Central Index System, a repository of data held by the government as applications go through the immigration process. (Source: FOIA)

During my adjustment-of-status interview with an immigration officer, my “disposition” was recorded but redacted. (Spoiler alert: it was probably “sweaty and nervous.”)

A file filled out by an immigration officer at an adjustment of status interview, which green card candidates are subject to. (Source: FOIA)

Following the interview, the immigration officer checks to make sure that the interview procedures are properly carried out. Homeland Security also pulls in data from the FBI to check to see if my name is on a watchlist, but also to confirm my identity as the real person applying for the green card.

And, in the end, two years of work and waiting came down to was a single checked box following my interview. “Approved.”

The final adjudication of an applicant’s green card. (Source: FOIA)

It’s no secret that you can FOIA for your green card file. Some are forced to file to obtain their case files in order to appeal their denied applications.

Runa Sandvik, a senior director of information security at The New York Times, obtained her border photographs from Homeland Security some years ago. Nowadays, it’s just as easy to request your files. Fill out one form and email it to the USCIS.

For me, next stop is citizenship. Just five more years to go.

Read more:


Source: TechCrunch

TED 2019: I Promise Not to Roll My Eyes at Your TED Talk

When cynicism and ironic detachment are the dominant modes of social discourse, it can feel frankly transgressive to earnestly celebrate passionate people who are trying to change the world.
Source: Wired

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