How Social Media Killed Intelligent Dialogue

In an age long ago, when internet discussion forums were a dime a dozen, and before sites like Facebook and Twitter took off, you could actually find intelligent discussions on the internet. People would generally “network” by joining a discussion group that matched their interests, and take off from there.

Back then, the narcissism was of a very low calibre. You didn’t have to constantly sift through people’s vacation photos or re-postings from other sites to find relevant content. In those days, relevance was only a few clicks away. Granted, admins could often be jerks—as they still are today. But those jerks would at least give you a reason why they moderated your content.

Incidentally, even engine results were better, because they would let you actually find what you were looking for. Remember the “Cache” feature that Google used to have? The internet of 15-20 years ago may have had its limitations. But content was a lot more user-controlled than it is nowadays, when everything is managed by algorithms.

When social media took off around 2008-2009, the large discussion boards hunkered down and still did their thing nonchalantly. But by 2011 and 2012 they were in trouble. Intriguing user “walls” and ‘likeable’ photo galleries had replaced the pert avatars of the Yahoo chat era, and made them obsolescent.

At some point during the last decade, the public’s content preferences shifted from impersonal & data-based to opinion-based & personal. For users, the net result wasn’t entirely a bad thing. Now someone could have all of their interests consolidated under one big blue banner. And the discussion groups seemed a lot more dynamic. The limitations could be lived with because the benefits in other areas were greater.

But at that time, few who had lived through internet’s Golden Era could have reckoned on the toll that social media would ultimately take on content relevance. As things became more personalized, users insidiously lost their ability to customize content. This was effected chiefly through algorithms and the progressive streamlining of user controls and content settings.

The impossibility of locating that thing that you are looking for is now an accepted condition of the internet world in which we live, move, and breathe. But did it have to be? Users can still create content ad infinitum. But they can only manage content within very narrow parameters. The walls are closing in every day. As the interface gets more and more streamlined, users lose more control over data.

The worst factor of social media’s takeover of content, is that it cheapened the public’s definitions of “discussion.” It took everything from the Phil Donahue level to that of Jerry Springer. Sensationalism, emotionalism, name calling, and harmful stereotyping are now the norms within social media. Some of this is fueled by “press-driven” media. But a lot of it is fueled by how we approach information.

With the decline of the big, user-friendly, faceless, but well-regulated discussion boards, users have been re-programmed to approach data and discuss things only a certain way. Fact-checking is not done as much as it should be. Whereas thoughtful response is a relic of past ages. Knee-jerk reactionism is the preferred method; and the more spastically you react, the more your wheels will get greased.

Obviously, this defeats the whole purpose of social networking. But it is what it is. Social Media still has its selling points. But more users are becoming disenchanted with the environment in which they now find themselves. They sense that all is not as it should be. Not to even mention the selling of personal data, but REAL content is getting harder to come by. And REAL discussion is a thing of the past.

Where will it all end? We do not know. However, sometimes we long for old finger-tapping days of dialup delays and annoying McAfee updates. It was a time when people we spoke to were often usernames and handles. But it was also a time when content reigned supreme. It was a time when you had to think before you responded. Unfortunately, it was a time that is gone forever.

Why AI Isn’t Right Around The Corner

What started with maverick software developers has actually become, during the last few years, a growing reality. Software systems can now be implemented to function in lieu of humans. While this sounds great (or not) on its face, there are a few key reasons why AI—that is, Artificial Intelligence, won’t be taking over your job any time soon.

Well, maybe it already has. If you are a checkout clerk, you may have witnessed an exponential growth in customer self-service stations at your workplace in recent months. That is not what I am talking about. When I reference AI, I am specifically talking about phone interface systems which function on behalf of a user or a business.

Having made that clear, let’s take a look at some of the systems that are already out there. Google Duplex is perhaps the most well known, simply because it’s Google. In one video, Sundar Pichai gives a jaw-dropping demonstration of the system showing what it can do for customers and businesses.. although he seems to admit that some calls don’t go as planned.

In another demo available on YouTube, Gridspace shows us what they can do with AI technology to create a seamless customer experience. While we are prepared to stand and applaud at the way everything ties together, again it becomes obvious that the representations are a bit fanciful.

Of course, we know that demos are meant to sell product. We get that. You always put your best foot forward when you are showing a demo; and whatever problems or glitches may lie back of your system are usually not discussed at that initial stage of presentation.

But there are reasons why companies are still very hesitant in investing in these systems. And it’s not simply the high cost of implementation. If you are a B2B or B2C company, you know that contact between your company and its clients and/or customers is simply one phase of its day-to-day business paradigm. When we consider someone calling in to purchase a product or request a service, we must also reckon on things such as data capture and data entry. And that’s where AI runs into kinks.

Not to even speak of things like data security (that is a totally different discussion), we all know how essential it is for the information tendered by the end-user/requester to be accurately captured by a system which purports to replace a live user. The videos make it look so easy. But we suspect that developers are not telling the entire story.

If I have to provide information relative to a transaction, what guarantee is there that the information will be correctly captured? After all, it’s just me and a computer program, and no one else is watching. Siri still doesn’t understand me when I tell her to turn off my flashlight. Are we really there yet?

Also, have we fully fleshed out issues like data entry? It is one thing to accurately capture the information. But how exactly will the system transcribe that information to third party software systems so that it can be internalized? AI developers must face the fact that virtually every organization uses multiple software systems to record and store data. Will AI cooperate efficiently with those systems? If so, how??

Suppose that the B2C company is using Oracle to store information regarding customers’ orders, and Salesforce to store internal notes. Suppose there is yet another program needed for tracking loyalty points. Can the AI software access all these systems simultaneously, and do all that is needed in one phone session, without any IT hiccups? Remember, there may be calls holding.

Until software developers take it to the next level, human users will be needed to transcribe data, because systems are still unable to do all that a live user is accustomed to do. They can’t, and we know they can’t. Even in a hybrid environment which used only some AI, data would still need to be gathered from the actual calls themselves, if only for quality assurance purposes; i.e., to make sure that AI was doing its job correctly. Hence, you can keep your call centers busy. If I have to listen to the same call to get the information, I may as well just take the call.

One solution, of course, would be to build “all-in-one” systems developed not only to take calls, but capture and store data as well. That would obviate the difficulty of dealing with multiple programs, and bring everything related to the transaction under one banner. Due to the nature of things, there will always be some amount of human interaction needed, because problems occur. Systems fail, data must be retrieved, glitches must be worked out, and what have you. But comprehensive software systems would at least float AI out of the novelty phase and into the mainstream.

Naturally, the more complex a company’s business operations, the more distant are its prospects of accepting AI as an alternative solution to an ‘analog’ work environment. Will there be storm signals before actual implementation? Of course. From the business side of things, company policies and operations would probably have to streamline, if not simplify, in order to accommodate themselves to the software. Thus, you would likely see a ‘dumbing down’ of the organization’s internal processes preparatory to any wholesale (or retail) AI invasion.

The conclusion, then, is clear. AI is still a way away. It’s in the offing. But it’s not right around the corner. Andrew Yang is still ahead of his time. Despite all the hype, I believe that much of the AI talk equates to “The Martians are coming!”.. because, after all, when a company develops a software system, they need to get it out there. Their business is not to present a realistic, but an ideal, scenario. And that is where we must stop and bring the conversation back down to earth. While we, too, look forward to the revolutionary changes that AI will bring, we must admit that, currently, HAL is still somewhere in space.