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Friday, April 24. 2015
Via SD Times
I recently attended Facebook’s F8 developer conference in San Francisco, where I had a revelation on why it is going to be impossible to succeed as a technology vendor in the long run without deeply embracing open source. Of the many great presentations I listened to, I was most captivated by the ones that explained how Facebook internally developed software. I was impressed by how quickly the company is turning such important IP back into the community.
To be sure, many major Web companies like Google and Yahoo have been leveraging open-source dynamics aggressively and contribute back to the community. My aim is not to single out Facebook, except that it was during the F8 conference I had the opportunity to reflect on the drivers behind Facebook’s actions and why other technology providers may be wise to learn from them.
Here are my 10 reasons why open-source software is effectively becoming inevitable for infrastructure and application platform companies:
Tuesday, December 16. 2014
If the brain is a collection of electrical signals, then, if you could catalog all those those signals digitally, you might be able upload your brain into a computer, thus achieving digital immortality.
While the plausibility—and ethics—of this upload for humans can be debated, some people are forging ahead in the field of whole-brain emulation. There are massive efforts to map the connectome—all the connections in the brain—and to understand how we think. Simulating brains could lead us to better robots and artificial intelligence, but the first steps need to be simple.
So, one group of scientists started with the roundworm Caenorhabditis elegans, a critter whose genes and simple nervous system we know intimately.
The OpenWorm project has mapped the connections between the worm’s 302 neurons and simulated them in software. (The project’s ultimate goal is to completely simulate C. elegans as a virtual organism.) Recently, they put that software program in a simple Lego robot.
The worm’s body parts and neural networks now have LegoBot equivalents: The worm’s nose neurons were replaced by a sonar sensor on the robot. The motor neurons running down both sides of the worm now correspond to motors on the left and right of the robot, explains Lucy Black for I Programmer. She writes:
Timothy Busbice, a founder for the OpenWorm project, posted a video of the Lego-Worm-Bot stopping and backing:
The simulation isn’t exact—the program has some simplifications on the thresholds needed to trigger a "neuron" firing, for example. But the behavior is impressive considering that no instructions were programmed into this robot. All it has is a network of connections mimicking those in the brain of a worm.
Of course, the goal of uploading our brains assumes that we aren’t already living in a computer simulation. Hear out the logic: Technologically advanced civilizations will eventually make simulations that are indistinguishable from reality. If that can happen, odds are it has. And if it has, there are probably billions of simulations making their own simulations. Work out that math, and "the odds are nearly infinity to one that we are all living in a computer simulation," writes Ed Grabianowski for io9.
Is your mind spinning yet?
Tuesday, December 02. 2014
Via PC World
It turns out that a vital missing ingredient in the long-sought after goal of getting machines to think like humans—artificial intelligence—has been lots and lots of data.
Last week, at the O’Reilly Strata + Hadoop World Conference in New York, Salesforce.com’s head of artificial intelligence, Beau Cronin, asserted that AI has gotten a shot in the arm from the big data movement. “Deep learning on its own, done in academia, doesn’t have the [same] impact as when it is brought into Google, scaled and built into a new product,” Cronin said.
In the week since Cronin’s talk, we saw a whole slew of companies—startups mostly—come out of stealth mode to offer new ways of analyzing big data, using machine learning, natural language recognition and other AI techniques that those researchers have been developing for decades.
One such startup, Cognitive Scale, applies IBM Watson-like learning capabilities to draw insights from vast amount of what it calls “dark data,” buried either in the Web—Yelp reviews, online photos, discussion forums—or on the company network, such as employee and payroll files, noted KM World.
Cognitive Scale offers a set of APIs (application programming interfaces) that businesses can use to tap into cognitive-based capabilities designed to improve search and analysis jobs running on cloud services such as IBM’s Bluemix, detailed the Programmable Web.
Cognitive Scale was founded by Matt Sanchez, who headed up IBM’s Watson Labs, helping bring to market some of the first e-commerce applications based on the Jeopardy-winning Watson technology, pointed out CRN.
Sanchez, now chief technology officer for Cognitive Scale, is not the only Watson alumnus who has gone on to commercialize cognitive technologies.
Alert reader Gabrielle Sanchez pointed out that another Watson ex-alum, engineer Pete Bouchard, recently joined the team of another cognitive computing startup Zintera as the chief innovation office. Sanchez, who studied cognitive computing in college, found a demonstration of the company’s “deep learning” cognitive computing platform to be “pretty impressive.”
AI-based deep learning with big data was certainly on the mind of senior Google executives. This week the company snapped up two Oxford University technology spin-off companies that focus on deep learning, Dark Blue Labs and Vision Factory.
The teams will work on image recognition and natural language understanding, Sharon Gaudin reported in Computerworld.
Sumo Logic has found a way to apply machine learning to large amounts machine data. An update to its analysis platform now allows the software to pinpoint casual relationships within sets of data, Inside Big Data concluded.
A company could, for instance, use the Sumo Logic cloud service to analyze log data to troubleshoot a faulty application, for instance.
While companies such as Splunk have long offered search engines for machine data, Sumo Logic moves that technology a step forward, the company claimed.
“The trouble with search is that you need to know what you are searching for. If you don’t know everything about your data, you can’t by definition, search for it. Machine learning became a fundamental part of how we uncover interesting patterns and anomalies in data,” explained Sumo Logic chief marketing officer Sanjay Sarathy, in an interview.
For instance, the company, which processes about 5 petabytes of customer data each day, can recognize similar queries across different users, and suggest possible queries and dashboards that others with similar setups have found useful.
“Crowd-sourcing intelligence around different infrastructure items is something you can only do as a native cloud service,” Sarathy said.
With Sumo Logic, an e-commerce company could ensure that each transaction conducted on its site takes no longer than three seconds to occur. If the response time is lengthier, then an administrator can pinpoint where the holdup is occurring in the transactional flow.
One existing Sumo Logic customer, fashion retailer Tobi, plans to use the new capabilities to better understand how its customers interact with its website.
One-upping IBM on the name game is DataRPM, which crowned its own big data-crunching natural language query engine Sherlock (named after Sherlock Holmes who, after all, employed Watson to execute his menial tasks).
Sherlock is unique in that it can automatically create models of large data sets. Having a model of a data set can help users pull together information more quickly, because the model describes what the data is about, explained DataRPM CEO Sundeep Sanghavi.
DataRPM can analyze a staggeringly wide array of structured, semi-structured and unstructured data sources. “We’ll connect to anything and everything,” Sanghavi said.
The service company can then look for ways that different data sets could be combined to provide more insight.
“We believe that data warehousing is where data goes to die. Big data is not just about size, but also about how many different sources of data you are processing, and how fast you can process that data,” Sanghavi said, in an interview.
For instance, Sherlock can pull together different sources of data and respond with a visualization to a query such as “What was our revenue for last year, based on geography?” The system can even suggest other possible queries as well.
Sherlock has a few advantages over Watson, Sanghavi claimed. The training period is not as long, and the software can be run on-premise, rather than as a cloud service from IBM, for those shops that want to keep their computations in-house. “We’re far more affordable than Watson,” Sanghavi said.
Initially, DataRPM is marketing to the finance, telecommunications, manufacturing, transportation and retail sectors.
One company that certainly does not think data warehousing is going to die is a recently unstealth’ed startup run by Bob Muglia, called Snowflake Computing.
Publicly launched this week, Snowflake aims “to do for the data warehouse what Salesforce did for CRM—transforming the product from a piece of infrastructure that has to be maintained by IT into a service operated entirely by the provider,” wrote Jon Gold at Network World.
Founded in 2012, the company brought in Muglia earlier this year to run the business. Muglia was the head of Microsoft’s server and tools division, and later, head of the software unit at Juniper Networks.
While Snowflake could offer its software as a product, it chooses to do so as a service, noted Timothy Prickett Morgan at Enterprise Tech.
“Sometime either this year or next year, we will see more data being created in the cloud than in an on-premises environment,” Muglia told Morgan. “Because the data is being created in the cloud, analysis of that data in the cloud is very appropriate.”
Saturday, November 29. 2014
More and more, governments are using powerful spying software to target human rights activists and journalists, often the forgotten victims of cyberwar. Now, these victims have a new tool to protect themselves.
Called Detekt, it scans a person's computer for traces of surveillance software, or spyware. A coalition of human rights organizations, including Amnesty International and the Electronic Frontier Foundation launched Detekt on Wednesday, with the goal of equipping activists and journalists with a free tool to discover if they've been hacked.
"Our ultimate aim is for human rights defenders, journalists and civil society groups to be able to carry out their legitimate work without fear of surveillance, harassment, intimidation, arrest or torture," Amnesty wroteThursday in a statement.
The open-source tool was developed by security researcher Claudio Guarnieri, a security researcher who has been investigating government abuse of spyware for years. He often collaborates with other researchers at University of Toronto's Citizen Lab.
During their investigations, Guarnieri and his colleagues discovered, for example, that the Bahraini government used software created by German company FinFisher to spy on human rights activists. They also found out that the Ethiopian government spied on journalists in the U.S. and Europe, using software developed by Hacking Team, another company that sells off-the-shelf surveillance tools.
Guarnieri developed Detekt from software he and the other researchers used during those investigations.
"I decided to release it to the public because keeping it private made no sense," he told Mashable. "It's better to give more people as possible the chance to test and identify the problem as quickly as possible, rather than keeping this knowledge private and let it rot."
Detekt only works with Windows, and it's designed to discover malware developed both by commercial firms, as well as popular spyware used by cybercriminals, such as BlackShades RAT (Remote Access Tool) and Gh0st RAT.
The tool has some limitations, though: It's only a scanner, and doesn't remove the malware infection, which is why Detekt's official site warns that if there are traces of malware on your computer, you should stop using it "immediately," and and look for help. It also might not detect newer versions of the spyware developed by FinFisher, Hacking Team and similar companies.
"If Detekt does not find anything, this unfortunately cannot be considered a clean bill of health," the software's "readme" file warns.
For some, given these limitations, Detekt won't help much.
"The tool appears to be a simple signature-based black list that does not promise it knows all the bad files, and admits that it can be fooled," John Prisco, president and CEO of security firm Triumfant, said. "Given that, it seems worthless to me, but that’s probably why it can be offered for free."
Joanna Rutkowska, a researcher who develops the security-minded operating system Qubes, said computers with traditional operating systems are inherently insecure, and that tools like Detekt can't help with that.
"Releasing yet another malware scanner does nothing to address the primary problem," she told Mashable. "Yet, it might create a false sense of security for users."
But Guarnieri disagrees, saying that Detekt is not a silver-bullet solution intended to be used in place of commercial anti-virus software or other security tools.
"Telling activists and journalists to spend 50 euros a year for some antivirus license in emergency situations isn't very helpful," he said, adding that Detekt is not "just a tool," but also an initiative to spark discussion around the government use of intrusive spyware, which is extremely unregulated.
For Mikko Hypponen, a renowned security expert and chief research officer for anti-virus vendor F-Secure, Detekt is a good project because its target audience — activists and journalists — don't often have access to expensive commercial tools.
“Since Detekt only focuses on detecting a handful of spy tools — but detecting them very well — it might actually outperform traditional antivirus products in this particular area,” he told Mashable.
Thursday, October 02. 2014
A startup called Algorithmia has a new twist on online matchmaking. Its website is a place for businesses with piles of data to find researchers with a dreamboat algorithm that could extract insights–and profits–from it all.
The aim is to make better use of the many algorithms that are developed in academia but then languish after being published in research papers, says cofounder Diego Oppenheimer. Many have the potential to help companies sort through and make sense of the data they collect from customers or on the Web at large. If Algorithmia makes a fruitful match, a researcher is paid a fee for the algorithm’s use, and the matchmaker takes a small cut. The site is currently in a private beta test with users including academics, students, and some businesses, but Oppenheimer says it already has some paying customers and should open to more users in a public test by the end of the year.
“Algorithms solve a problem. So when you have a collection of algorithms, you essentially have a collection of problem-solving things,” says Oppenheimer, who previously worked on data-analysis features for the Excel team at Microsoft.
Oppenheimer and cofounder Kenny Daniel, a former graduate student at USC who studied artificial intelligence, began working on the site full time late last year. The company raised $2.4 million in seed funding earlier this month from Madrona Venture Group and others, including angel investor Oren Etzioni, the CEO of the Allen Institute for Artificial Intelligence and a computer science professor at the University of Washington.
Etzioni says that many good ideas are essentially wasted in papers presented at computer science conferences and in journals. “Most of them have an algorithm and software associated with them, and the problem is very few people will find them and almost nobody will use them,” he says.
One reason is that academic papers are written for other academics, so people from industry can’t easily discover their ideas, says Etzioni. Even if a company does find an idea it likes, it takes time and money to interpret the academic write-up and turn it into something testable.
To change this, Algorithmia requires algorithms submitted to its site to use a standardized application programming interface that makes them easier to use and compare. Oppenheimer says some of the algorithms currently looking for love could be used for machine learning, extracting meaning from text, and planning routes within things like maps and video games.
Early users of the site have found algorithms to do jobs such as extracting data from receipts so they can be automatically categorized. Over time the company expects around 10 percent of users to contribute their own algorithms. Developers can decide whether they want to offer their algorithms free or set a price.
All algorithms on Algorithmia’s platform are live, Oppenheimer says, so users can immediately use them, see results, and try out other algorithms at the same time.
The site lets users vote and comment on the utility of different algorithms and shows how many times each has been used. Algorithmia encourages developers to let others see the code behind their algorithms so they can spot errors or ways to improve on their efficiency.
One potential challenge is that it’s not always clear who owns the intellectual property for an algorithm developed by a professor or graduate student at a university. Oppenheimer says it varies from school to school, though he notes that several make theirs open source. Algorithmia itself takes no ownership stake in the algorithms posted on the site.
Eventually, Etzioni believes, Algorithmia can go further than just matching up buyers and sellers as its collection of algorithms grows. He envisions it leading to a new, faster way to compose software, in which developers join together many different algorithms from the selection on offer.
Monday, August 11. 2014
Aiming to do for Machine Learning what MySQL did for database servers, U.S. and UK-based PredictionIO has raised $2.5 million in seed funding from a raft of investors including Azure Capital, QuestVP, CrunchFund (of which TechCrunch founder Mike Arrington is a Partner), Stanford University‘s StartX Fund, France-based Kima Ventures, IronFire, Sood Ventures and XG Ventures. The additional capital will be used to further develop its open source Machine Learning server, which significantly lowers the barriers for developers to build more intelligent products, such as recommendation or prediction engines, without having to reinvent the wheel.
Being an open source company — after pivoting from offering a “user behavior prediction-as-a-service” under its old TappingStone product name — PredictionIO plans to generate revenue in the same way MySQL and other open source products do. “We will offer an Enterprise support license and, probably, an enterprise edition with more advanced features,” co-founder and CEO Simon Chan tells me.
The problem PredictionIO is setting out to solve is that building Machine Learning into products is expensive and time-consuming — and in some instances is only really within the reach of major and heavily-funded tech companies, such as Google or Amazon, who can afford a large team of PhDs/data scientists. By utilising the startup’s open source Machine Learning server, startups or larger enterprises no longer need to start from scratch, while also retaining control over the source code and the way in which PredictionIO integrates with their existing wares.
In fact, the degree of flexibility and reassurance an open source product offers is the very reason why PredictionIO pivoted away from a SaaS model and chose to open up its codebase. It did so within a couple of months of launching its original TappingStone product. Fail fast, as they say.
“We changed from TappingStone (Machine Learning as a Service) to PredictionIO (open source server) in the first 2 months once we built the first prototype,” says Chan. “As developers ourselves, we realise that Machine Learning is useful only if it’s customizable to each unique application. Therefore, we decided to open source the whole product.”
The pivot appears to be working, too, and not just validated by today’s funding. To date, Chan says its open source Machine Learning server is powering thousands of applications with 4000+ developers engaged with the project. “Unlike other data science tools that focus on solving data researchers’ problems, PredictionIO is built for every programmer,” he adds.
Other competitors Chan cites include “closed ‘black box” MLaaS services or software’, such as Google Prediction API, Wise.io, BigML, and Skytree.
Examples of who is currently using PredictionIO include Le Tote, a clothing subscription/rental service that is using PredictionIO to predict customers’ fashion preferences, and PerkHub, which is using PredictionIO to personalize product recommendations in the weekly ‘group buying’ emails they send out.
Monday, June 16. 2014
Following broad security scares like that caused by the Heartbleed bug, it can be frustratingly difficult to find out if a site you use often still has gaping flaws. But a little known community of software developers is trying to change that, by creating a searchable, public index of websites with known security issues.
Think of Project Un1c0rn as a Google for site security. Launched on May 15th, the site's creators say that so far it has indexed 59,000 websites and counting. The goal, according to its founders, is to document open leaks caused by the Heartbleed bug, as well as "access to users' databases" in Mongo DB and MySQL.
According to the developers, those three types of vulnerabilities are most widespread because they rely on commonly used tools. For example, Mongo databases are used by popular sites like LinkedIn, Expedia, and SourceForge, while MySQL powers applications such as WordPress, Drupal or Joomla, and are even used by Twitter, Google and Facebook.
Having a website’s vulnerability indexed publicly is like advertising that you leave your front doors unlocked and your flat screen in the basement. But Un1c0rn’s founder sees it as teaching people the value of security. And his motto is pretty direct. “Raising total awareness by ‘kicking in the nuts’ is our target,” said the founder, who goes by the alias SweetCorn.
“The exploits and future exploits that will be added are just exploiting people's stupidity or misconception about security from a company selling or buying bullshit protections,” he said. SweetCorn thinks Project Un1c0rn is exposing what is already visible without a lot of effort.
While the Heartbleed bug alerted the general public to how easily hackers can exploit widely used code, clearly vulnerabilities don’t begin and end with the bug. Just last week the CCS Injection vulnerability was discovered, and the OpenSSL foundation posted a security advisory.
“Billions of people are leaving information and trails in billions of different databases, some just left with default configurations that can be found in a matter of seconds for whoever has the resources,” SweetCorn said. Changing and updating passwords is a crucial practice.
Search results on the Un1c0rn site. Image: Project Un1c0rn
I reached out to José Fernandez, a computer security expert and professor at the Polytechnique school in Montreal, to get his take on Project Un1c0rn. "The (vulnerability) tests are quite objective," he said. "There are no reasons not to believe the vulnerabilities listed."
Fernandez added that the only caveat for the search engine was that a listed server could have been patched after the vulnerability scan had been run.
The project is still in its very early stages, with some indexed websites not yet updated, which means not all of the 58,000 websites listed are currently vulnerable to the same weaknesses.
“The Un1c0rn is still weak”, admitted SweetCorn. “We did this with 0.4 BitCoin, I just can't imagine what someone having enough money to spend on information mining could do.” According to SweetCorn, those funds were used to buy the domain name and rent servers.
SweetCorn is releasing few details about the backend of the project, although he says it relies heavily on the Tor network. Motherboard couldn’t independently confirm what kind of search functions SweetCorn is operating or whether they are legal. In any case, he has bigger plans for his project: making it the first peer-to-peer decentralized exploit system, where individuals could host their own scanning nodes.
“We took some easy steps, Disqus is one of them, we would love to see security researchers going on Un1c0rn, leave comments and help (us) fix stuff,” he said.
He hopes that the attention raised by his project will make people understand “what their privacy really (looks like).”
A quick scan through Un1c0rn’s database brings up some interesting results. McGill University in Montreal had some trouble with one of their MySQL databases. The university has since been notified, and their IT team told me the issue had been addressed.
The UK’s cyberspies at the GHCQ probably forgot they had a test database open (unless it’s a honeypot), though requests for comments were not answered. A search for “credit card” retrieves 573 websites, some of which might just host card data if someone digs enough.
In an example of how bugs can pervade all corners of the web, the IT team in charge of the VPN for the town of Mandurah in Australia were probably napping while the rest of the world was patching their broken version of OpenSSL. Tests run with the Qualys SSL Lab and filippo.io tools confirmed the domain was indeed vulnerable to Heartbleed.
While tools to scan for vulnerabilities across the Internet already exist. Last year, the project critical.io did a mass scan of the Internet to look for vulnerabilities, for research purposes. The data was released online and further analyzed by security experts.
But Project Un1c0rn is certainly one of the first to publicly index the vulnerabilities found. Ultimately, if Project Un1c0rn or something like it is successful and open sourced, checking if your bank or online dating site is vulnerable to exploits will be a click away.
Monday, June 09. 2014
Can computers learn to read? We think so. "Read the Web" is a research project that attempts to create a computer system that learns over time to read the web. Since January 2010, our computer system called NELL (Never-Ending Language Learner) has been running continuously, attempting to perform two tasks each day:
So far, NELL has accumulated over 50 million candidate beliefs by reading the web, and it is considering these at different levels of confidence. NELL has high confidence in 2,132,551 of these beliefs — these are displayed on this website. It is not perfect, but NELL is learning. You can track NELL's progress below or @cmunell on Twitter, browse and download its knowledge base, read more about our technical approach, or join the discussion group.
Saturday, June 07. 2014
Via ars technica
Chrome, Internet Explorer, and Firefox are vulnerable to easy-to-execute techniques that allow unscrupulous websites to construct detailed histories of sites visitors have previously viewed, an attack that revives a long-standing privacy threat many people thought was fixed.
Now, a graduate student at Hasselt University in Belgium said he has confirmed that Chrome, IE, and Firefox users are once again susceptible to browsing-history sniffing. Borrowing from a browser-timing attack disclosed last year by fellow researcher Paul Stone, student Aäron Thijs was able to develop code that forced all three browsers to divulge browsing history contents. He said other browsers, including Safari and Opera, may also be vulnerable, although he has not tested them.
"The attack could be used to check if the victim visited certain websites," Thijs wrote in an e-mail to Ars. "In my example attack vectors I only check 'https://www.facebook.com'; however, it could be modified to check large sets of websites. If the script is embedded into a website that any browser user visits, it can run silently in the background and a connection could be set up to report the results back to the attacker."
The sniffing of his experimental attack code was relatively modest, checking only the one site when the targeted computer wasn't under heavy load. By contrast, more established exploits from a few years ago were capable of checking, depending on the browser, about 20 URLs per second. Thijs said it's possible that his attack might work less effectively if the targeted computer was under heavy load. Then again, he said it might be possible to make his attack more efficient by improving his URL-checking algorithm.
I know what sites you viewed last summer
Thijs told Ars that Mozilla has already acknowledged plans to actively work on a fix. A Microsoft forum where Thijs reported his IE findings over the weekend has since been made private, but at the time of writing, it was still publicly available in this Google cache. Thijs said the issue is also under discussion by Chrome developers.
The resurrection of viable sniffing history attacks underscores a key dynamic in security. When defenders close a hole, attackers will often find creative ways to reopen it. For the time being, users should assume that any website they visit is able to obtain at least a partial snapshot of other sites indexed in their browser history. As mentioned earlier, privacy-conscious people should regularly flush their history or use private browsing options to conceal visits to sensitive sites.
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