How SEO Has Shaped the Web

January 19, 2018

With the benefit of hindsight, big-name thinker Anil Dash has concluded that SEO has contributed to the ineffectiveness of Web search. He examines how we got here in his article, “Underscores, Optimization & Arms Races” at Medium.  Starting with the year 2000, Dash traces the development of Internet content management systems (CMS’s), of which he was a part. (It is a good brief summary for anyone who wasn’t following along at the time.) WordPress is an example of a CMS.

As Google’s influence grew, online publishers became aware of an opportunity—they could game the search algorithm to move their site to the top of “relevant” results by playing around with keywords and other content details. The question of whether websites should bow to Google’s whims seemed to go unasked, as site after site fell into this pattern, later to be known as Search Engine Optimization. For Dash, the matter was symbolized by a question over hyphens or underbars to represent spaces in web addresses. Now, of course, one can use either without upsetting Google’s algorithm, but that was not the case at first. When Google’s Matt Cutts stated a preference for the hyphen in 2005, most publishers fell in line. Including Dash, eventually and very reluctantly; for him, the choice represented nothing less than the very nature of the Internet.

He writes:

You see, the theory of how we felt Google should work, and what the company had often claimed, was that it looked at the web and used signals like the links or the formatting of webpages to indicate the quality and relevance of content. Put simply, your search ranking with Google was supposed to be based on Google indexing the web as it is. But what if, due to the market pressure of the increasing value of ranking in Google’s search results, websites were incentivized to change their content to appeal to Google’s algorithm? Or, more accurately, to appeal to the values of the people who coded Google’s algorithm?

Eventually, even Dash and his CMS caved and switched to hyphens. What he did not notice at the time, he muses, was the unsettling development of the  entire SEO community centered around appeasing these algorithms. He concludes:

By the time we realized that we’d gotten suckered into a never-ending two-front battle against both the algorithms of the major tech companies and the destructive movements that wanted to exploit them, it was too late. We’d already set the precedent that independent publishers and tech creators would just keep chasing whatever algorithm Google (and later Facebook and Twitter) fed to us. Now, the challenge is to reform these systems so that we can hold the big platforms accountable for the impacts of their algorithms. We’ve got to encourage today’s newer creative communities in media and tech and culture to not constrain what they’re doing to conform to the dictates of an opaque, unknowable algorithm.

Is that doable, or have we gone too far toward appeasing the Internet behemoths to turn back?

Cynthia Murrell, January 19, 2018

Out with the Old, in with the New at Google

January 17, 2018

It may have started with its finance app, but Google is making some drastic changes you might want to keep an eye on. We discovered the tip of the iceberg with Google Blog piece, “Stay on Top of Finance Information on Google.”

According to the story:

Now under a new search navigation tab called “Finance,” you’ll have easier access to finance information based on your interests, keeping you in the know about the latest market news and helping you get in-depth insights about companies. On this page, you can see performance information about stocks you’ve chosen to follow, recommendations on other stocks to follow based on your interests, related news, market indices, and currencies.

As part of this revamped experience, we’re retiring a few features of the original Google Finance, including the portfolio, the ability to download your portfolio, and historical tables. However, a list of the stocks from your portfolio will be accessible through Your Stocks in the search result, and you can get notifications when there are any notable changes on their performance.

Not a big shock, but a big part of Google trying to freshen things up. The company has been in hot water with a string of YouTube videos deemed too much. So, with moves like improving its algorithm to weed out fake news, changes to Google Home, and even Maps, Google is sending a message. The message is one of change and one we hope is for the better.

Patrick Roland, January 17, 2018

AI Makes Life-Saving Medical Advances

January 2, 2018

Too often we discuss the grey area around AI and machine learning. While that is incredibly important during this time, it is also not all this amazing technology can do. Saving lives, for instance. We learned a little more on that front from a recent Digital Journal story, “Algorithm Repairs Corrupted Digital Images.”

According to the story:

University of Maryland researchers have devised a technique exploits the power of artificial neural networks to tackle multiple types of flaws and degradations in a single image in one go.

The researchers achieved image correction through the use of a new algorithm. The algorithm operates artificial neural networks simultaneously, so that the networks apply a range of different fixes to corrupted digital images. The algorithm was tested on thousands of damage digital images, some with severe degradations. The algorithm was able to repair the damage and return each image to its original state.

The application of such technology crosses the business and consumer divide, taking in everything from everyday camera snapshots to lifesaving medical scans. The types of faults digital images can develop include blurriness, grainy noise, missing pixels and color corruption.

Very promising from a commercial and medical standpoint. Especially, the medical side. This news, coupled with the story in Forbes about AI disrupting healthcare norms in 2018 makes for a big promise. We are looking forward to seeing what the new year brings for medical AI.

Patrick Roland, January 2, 2018

Turning to AI for Better Data Hygiene

December 28, 2017

Most big data is flawed in some way, because humans are imperfect beings. That is the premise behind ZDNet’s article, “The Great Data Science Hope: Machine Learning Can Cure Your Terrible Data Hygiene.” Editor-in-Chief Larry Dignan explains:

The reality is enterprises haven’t been creating data dictionaries, meta data and clean information for years. Sure, this data hygiene effort may have improved a bit, but let’s get real: Humans aren’t up for the job and never have been. ZDNet’s Andrew Brust put it succinctly: Humans aren’t meticulous enough. And without clean data, a data scientist can’t create algorithms or a model for analytics.

 

Luckily, technology vendors have a magic elixir to sell you…again. The latest concept is to create an abstraction layer that can manage your data, bring analytics to the masses and use machine learning to make predictions and create business value. And the grand setup for this analytics nirvana is to use machine learning to do all the work that enterprises have neglected.

I know you’ve heard this before. The last magic box was the data lake where you’d throw in all of your information–structured and unstructured–and then use a Hadoop cluster and a few other technologies to make sense of it all. Before big data, the data warehouse was going to give you insights and solve all your problems along with business intelligence and enterprise resource planning. But without data hygiene in the first place enterprises replicated a familiar, but failed strategy: Poop in. Poop out.

What the observation lacks in eloquence it makes up for in insight—the whole data-lake concept was flawed from the start since it did not give adequate attention to data preparation. Dignan cites IBM’s Watson Data Platform as an example of the new machine-learning-based cleanup tools, and points to other noteworthy vendors investigating similar ideas—Alation, Io-Tahoe, Cloudera, and HortonWorks. Which cleaning tool will perform best remains to be seen, but Dignan seems sure of one thing—the data that enterprises have been diligently collecting for the last several years is as dirty as a dustbin lid.

Cynthia Murrell, December 28, 2017

New York Begins Asking If Algorithms Can Be Racist

December 27, 2017

The whole point of algorithms is to be blind to everything except data. However, it is becoming increasingly clear that in the wrong hands, algorithms and AI could have a very negative impact on users. We learned more in a recent ACLU post, “New York Takes on Algorithm Discrimination.”

According to the story:

A first-in-the-nation bill, passed yesterday in New York City, offers a way to help ensure the computer codes that governments use to make decisions are serving justice rather than inequality.

 

Algorithms are often presumed to be objective, infallible, and unbiased. In fact, they are highly vulnerable to human bias. And when algorithms are flawed, they can have serious consequences.

 

The bill, which is expected to be signed by Mayor Bill de Blasio, will provide a greater understanding of how the city’s agencies use algorithms to deliver services while increasing transparency around them. This bill is the first in the nation to acknowledge the need for transparency when governments use algorithms…

This is a very promising step toward solving a very real problem. From racist coding to discriminatory AI, this is a topic that is creeping into the national conversation. We hope others will follow in New York’s footsteps and find ways to prevent this injustice from going further.

Patrick Roland, December 27, 2017

A Look at Chinese Search Engine Sogou

December 25, 2017

An article at Search Engine Watch draws our attention to one overseas search contender—“What Do You Need to Know About Chinese Search Engine Sogou?” Sogu recently announced terms for a proposed IPO, so writer Rebecca Sentance provides a primer on the company. She begins with some background—the platform was launched in 2004, and the name translates to “searching dog.” She also delves into the not-so-clear issue of where Sogu stands in relation to China’s top search engine, Baidu, and some other contenders for the second-place, so see the article for those details.

I was interested in what Sentance writes about Sogou’s use of AI and natural language search:

It also plans to shift its emphasis from more traditional keyword-based search to answer questions, in line with the trend towards natural language search prompted by the rise of voice search and digital assistants. Sogou has joined major search players such as Bing, Baidu and of course Google in investing in artificial intelligence, but its small size may put it at a disadvantage. A huge search engine like Baidu, with an average of more than 583 million searches per day, has access to reams more data with which to teach its machine learning algorithms.

But Sogou has an ace up its sleeve: it is the only search engine formally allowed to access public messages on WeChat – a massive source of data that will be particularly beneficial for natural language processing. Plus, as I touched on earlier, language is something of a specialty area for Sogou, as Sogou Pinyin gives it a huge store of language data with which to work. Sogou also has ambitious plans to bring foreign-language results to Chinese audiences via its translation technology, which will allow consumers to search the English-speaking web using Mandarin search terms.

The article wraps up by looking at Sogou’s potential effect on search markets; basically, it could have a large impact within China, especially if Baidu keeps experiencing controversy. For the rest of the world, though, the impact should be minimal. Nevertheless, this is one company worth keeping an eye on.

Cynthia Murrell, December 25, 2017

Google Is Taught Homosexuality Is Bad

December 12, 2017

The common belief is that computers and software are objectives, inanimate objects capable of greater intelligence than humans.  The truth is that humans developed computers and software, so the objective, inanimate objects are only as smart as their designers.  What is even more hilarious is the sentiment analysis AI development process requires tons of data for the algorithms to read and teach itself to recognize patterns.  The data used is “contaminated” with human emotion and prejudices.  Motherboard wrote about how artificial bias pollutes AI in the article, “Google’s Sentiment Analyzer Thinks Being Gay Is Bad.”

The problem when designing AI is that if it is programmed with polluted and biased data, then these super intelligent algorithms will discriminate against people rather than being objective.  Google released its Cloud Natural Language API that allows developers to add Google’s deep learning models into their own applications.  Along with entity recognition, the API included a sentiment analyzer that detected when text contained a positive or negative sentiment.  However, it has a few bugs and returns biased results, such as saying being gay is bad, certain religions are bad, etc.

It looks like Google’s sentiment analyzer is biased, as many artificially intelligent algorithms have been found to be. AI systems, including sentiment analyzers, are trained using human texts like news stories and books. Therefore, they often reflect the same biases found in society. We don’t know yet the best way to completely remove bias from artificial intelligence, but it’s important to continue to expose it.

The problem with programming AI algorithms is that it is difficult to feed it data free of human prejudices. It is difficult to work around these prejudices, because they are so ingrained in most data.  Programmers are kept on their toes to find a solution, but it is not a one size fits all one.  Too bad they cannot just stick with numbers and dictionaries.

Whitney Grace, December 12, 2017

Big Shock: Social Media Algorithms Are Not Your Friend

December 11, 2017

One of Facebook’s founding fathers, Sean Parker, has done a surprising about-face on the online platform that earned him billions of dollars. Parker has begun speaking out against social media and the hidden machinery that keeps people interested. We learned more from a recent Axios story,Sean Parker Unloads on Facebook ‘Exploiting’ Human Psychology.

According to the story:

Parker’s I-was-there account provides priceless perspective in the rising debate about the power and effects of the social networks, which now have scale and reach unknown in human history. He’s worried enough that he’s sounding the alarm.

According to Parker:

The thought process that went into building these applications, Facebook being the first of them, … was all about: ‘How do we consume as much of your time and conscious attention as possible?’

 

And that means that we need to sort of give you a little dopamine hit every once in a while, because someone liked or commented on a photo or a post or whatever. And that’s going to get you to contribute more content, and that’s going to get you … more likes and comments.

What’s at stake here isn’t just human psychology being exploited, though. It’s a major part of the story, but, as Forbes pointed out, we are on the cusp of social engineering via social media. If more people like Parker don’t stand up and offer a solution, we fear there could be serious repercussions.

Patrick Roland, December 11, 2017

Google Told to Rein in Profits

December 5, 2017

Google makes a lot of money with their advertising algorithms.  Every quarter their profit looms higher and higher, but the San Francisco Gate reports that might change in the article, “Google Is Flying High, But Regulatory Threats Loom.”  Google and Facebook are being told they need to hold back their hyper efficient advertising machines.  Why?  Possible Russian interference in the 2016 elections and the widespread dissemination of fake news.

New regulations would require Google and Facebook to add more human oversight into their algorithms.  Congress already has a new bill on the floor with new regulations for online political ads to allow more transparency.  Social media sites like Twitter and Facebook already making changes, but Google has not done anything and will not get a free pass.

It’s hard to know whether Congress or regulators will actually step up and regulate the company, but there seems to be a newfound willingness to consider such action,’ says Daniel Stevens, executive director of the Campaign for Accountability, a nonprofit watchdog that tracks Google spending on lobbyists and academics. ‘Google, like every other industry, should not be left to its own devices.’

Google has remained mostly silent, but has made a statement that they will increase “efforts to improve transparency, enhance disclosures, and reduce foreign abuse.”  Google is out for profit like any other company in the world.  The question is if they have the conscience to comply or will find a way around it.

Whitney Grace, December 5, 2017

 

Big Data and Search Solving Massive Language Processing Headaches

December 4, 2017

Written language can be a massive headache for those needing search strength. Different spoken languages can complicate things when you need to harness a massive amount of data. Thankfully, language processing is the answer, as software architect Federico Thomasetti wrote in his essay, “A Guide to Natural Language Processing.”

According to the story:

…the relationship between elements can be used to understand the importance of each individual element. TextRank actually uses a more complex formula than the original PageRank algorithm, because a link can be only present or not, while textual connections might be partially present. For instance, you might calculate that two sentences containing different words with the same stem (e.g., cat and cats both have cat as their stem) are only partially related.

 

The original paper describes a generic approach, rather than a specific method. In fact, it also describes two applications: keyword extraction and summarization. The key differences are:

  • the units you choose as a foundation of the relationship
  • the way you calculate the connection and its strength

Natural language processing is a tricky concept to wrap your head around. But it is becoming a thing that people have to recognize. Currently, millions of dollars are being funneled into perfecting this platform. Those who can really lead the pack here will undoubtedly have a place at the international tech table and possibly take over. This is a big deal.

Patrick Roland, December 4, 2017

Next Page »

  • Archives

  • Recent Posts

  • Meta