Is Anonymity the Future of the Internet?

Sep 08, 2014

Right now we're in a world that sees transparency as the new form of integrity. Right now we're in a world that understands that reputation is everything. Loyalty is somewhat fleeting as consumers, armoured with this incessant flow of knowledge from the web, have the ability to make swift judgements and decisions about individuals, companies and governments, often times to the detriment of the target.

The emergence of social media has forced companies to stop hiding from behind that veil of corporate spin and address the very things that the web has thrown at them. Nothing is secret any longer. Even secrets that were once held secure behind invulnerable fortresses now have a strong probability of materializing today.

Is transparency as a norm working? Or, are the results of transparency surfacing a new order that will create yet another tier of acceptance from the masses?

"Anonymity is Authenticity"

Following the death of Rahteah Parsons, who, after being assaulted by 4 boys, was tormented relentlessly by classmates and other kids on social networks; and also following the suicide of Hannah Smith, who experienced the same torment, it's clear the internet has evolved to an era that has given free reign to voice an opinion and use like-minded affiliations to express and further spread that opinion. In these cases, anonymous profiles proliferated the incessant stream of hateful attacks that eventually wore down both girls' defences.

And while I originally argue that anonymity was a cowardice state that allowed people to be and feel comfortable being the anti-self that runs away from accountability, my stance has seen another side of this coin.

Anonymity is Safe

It becomes clear that humans, while inherently social, are discriminating of the things we disclose and to those to whom we share.

If transparency breeds contempt, then anonymity should build acceptance

The freedom to express opinion and judgement without feeling guarded, or without fearing others linking you to a statement is indeed liberating. And while this free reign may take the form of a soapbox soliloquy or criticisms (and perhaps bullying attacks) against opposing views, there is a large segment of users who want the ability to share a secret, or have a place to vent their frustrations or challenges -- without the fear of reprisal.

Despite revelations from Snowden and the NSA that nothing on the net is private, this does not stop the wave of user adoption for applications like SnapChat, Whisper or Secret.

Here are some recent stats for Snapchat from Mashable

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I've recently downloaded Whisper and my experience has been more than liberating. It has allowed me an outlet to record my hopes, desires and more importantly, my anger and not-for-public emotions. Being judged in real life or on social takes its toll. If my reputation precedes me, then I will be discriminating about what I say in places where my content and identity are linked.

Popular opinion just doesn't matter. It's irrelevant. But I want to track progress in my life: my emotions, my dark moments, my personal observations, my milestones -- all in my own digital diary.

Why shouldn't users have the option to keep part of their identities secret and separate?

It's up to the next generation

This new medium has created is an endless volatile loop of positive and negative reinforcement. While transparency has extreme benefits, there are just as many negative consequences that have come as a result of creating this honesty within social channels. Society continues to send the wrong message to Millennials and GenZers, warning them to be more discerning and to suppress who they really are as individuals... warning them of the potential consequences should they venture down the wrong path.

How we communicate today poses tremendous issues for this younger generation. Their experiences are grounded in the fear of being vulnerable... fear of being misjudged... fear of not being accepted... fear of being punished. When the next generation grows up, it'll be up to them to shape the landscape and determine how to balance the impacts of transparency and anonymity.

Interested in reading more? Check out our other blogs:

Social selling for businesses

Social selling is one of the hottest buzzwords in the technology market. The popularity of social networks made the customer interaction and buyers hunting easier than before. More and more consumers are using social media to find deals, research products and make recommendations.

From the seller’s perspective the efficient use of social media is based on the mastery of following two major steps:

1. Finding the relevant audience,

2. Engaging with that audience.

The first step should be automated. This is exactly where the promise of Big Data, or Smart Data, as they now begin to call it, is supposed to come into fruition. Finding relevant information in the ocean of social data is the poster example of how Smart data can help businesses in the new world defined by computerized systems and networks. The companies should be able to use programs and solutions that accurately and efficiently deliver relevant data. If the company is spending time to sift through the ever increasing informational stream without automating the process, it is wasting precious time thus compromising its business growth and eventually losing competitive edge.

The second step however is inherently manual. it is not a good idea to automate the engagement process. Social networks are designed to build trust, and trust cannot be won automatically. So it requires time and effort and knowledge. It also requires patience - trust cannot be built in minutes.

It is important that businesses looking to add social media into their arsenal of revenue channels, and we believe that all businesses should do just that, grasp this two-steps process. A clear understanding of the nature and requirements for each of the steps helps to plan strategically, manage the resources properly and avoid costly mistakes.

What Is AI Engine and Do I Need It?

Chatbots and assistant programs designed to support conversations with human users rely on natural language processing (NLP). This is a field of scientific research that aims at making computers understand the meaning of sentences in natural language. The algorithms developed by NLP researchers helped power first generation of virtual assistants such as Siri or Cortana. Now the same algorithms are made available to the developer community to help companies build their own specialized virtual assistants. Industry products that offer NLP capabilities based on these algorithms are often called AI engines.

The most powerful and advanced AI engines currently available on the market are (in no particular order): IBM Watson, Google DialogFlow, Microsoft LUIS, Amazon Lex.

All these engines use intents and entities as primary pnguistic identifies to convey the meaning of incoming sentences. All of them offer conversation flow capability. In other words, intents and entities help to understand what the incoming sentence is about. Once the incoming sentence is correctly identified you can use the engine to provide a reply. You can repeat these two steps a large number of times, thus creating a conversation, or dialog.

In terms of language processing ability and simplicity of user experience IBM Watson and Google DialogFlow are currently above the pack. Microsoft LUIS is okay too; still, keeping in mind that Microsoft are aggressively territorial and like when users stay within their ecosystem, it is most efficient to use LUIS together with other Microsoft products such as MS Bot Framework.

Using AI engine conversation flow to create dialogs makes building conversations a simple, almost intuitive, task, with no coding involved. On the flip side, using AI engine conversation flow limits your natural tendency to make conversations natural. The alternative, delegating the conversation flow to the business layer of your chatbot, adds richness and flexibility to your dialog but makes the process more comppcated as it now requires coding. Cannot sell a cow and drink the milk at the same time, can you?

Amazon Lex lacks the semantic sophistication of their competitors. One can say (somewhat metaphorically) that IBM Watson was created by linguists and computer scientists while Amazon Lex was created by sales people. As a product it is well packaged and initially looks pleasing on the eye, but once you start digging deeper you notice the limitations. Also, Amazon traditionally excelled in voice recognition component (Amazon Alexa) and not necessarily in actual language processing.

The space of conversational AI is fluid and changes happen rapidly. The existing products are evolving continuously and a new generation of AI engines is in the process of being developed.