For many, the notion of artificial intelligence conjures fears of being put out of work by machines. It is likely that many jobs which involve solving specific tasks that don’t change much will eventually become automated.
The risk of replacement is higher for some careers than others. To appreciate whether or not AI is likely to replace you, it’s important to understand the difference between artificial general intelligence and narrow ai.
Google, Amazon and Netflix all use AI to help you find what you’re looking for. But is that AIG or narrow AI? The answer is the difference between keeping your job and being automated.
If Google knows what I want to search, Amazon knows what I want to buy and Netflix knows what I want to watch, why can’t news and social media monitoring platforms find me relevant news and online conversations?
It’s a fair question.
But recommendation engines perform a very simple task. They analyze specific patterns for a narrow outcome, such as a search term or product recommendation.
Recommendation engines deal with structured data and provide concrete recommendations.
As an aside. The New York Times has a new podcast called Rabbit Hole and in the very first episode, Kevin Roose and Andy Mills did a fascinating interview with Guillaume Chaslot (a former Google engineer who worked on the YouTube recommendation engine) about the social and political dangers of what he called “bubble filters” that us AI to increase total viewing time.
But unlike YouTube videos, if you’re trying to separate the true from the false, it’s not the same as recommending a cat video to someone who likes watching cat videos.
You have to actually understand natural language. News and social media posts are unstructured which means information isn’t parsed consistently which makes it even tougher for machines to understand it. Instead, what you have is a broad domain of sources with no consistent standards, complicated by slang, sarcasm and emotion.
So while recommendation engines are no doubt impressive — don’t get me wrong — they are incomparable to the level human understanding required to comprehend written language. They are examples of narrow AI.
A common goal of media monitoring for public relations is tracking message penetration. In other words, you have a message and you have targets. Rather than just counting keyword mentions in articles the more important goal of media monitoring is determining if your targets are repeating in your messages.
Tracking message penetration is less about aggregating articles than it is about understanding and identifying concepts and ideas occurring in natural language. But this requires a level of natural language processing specification and common Sense reasoning that automated Solutions still cannot deliver.
This task requires artificial general intelligence because you have to be able to actually understand natural language and machine still can’t do that. I wrote about all this in greater detail in my 2020 Media Monitoring Buyers Guide, which is a vendor neutral report I wrote to make it easy for business owners, marketers and public relations professional to easily assess which platforms can do what without having to demo every product themselves.
Comparing Apples to Oranges
They can compare apples to apples, but they can’t compare apples to oranges and this is what’s required or a true analysis of concepts and ideas.
These aren’t just my opinions by the way, these are the opinions of scientists and academics I interviewed in the report who are leading the natural language processing in AI charge.
I spoke with the chief scientist at Pinterest who is also a professor of computer science at Stanford about why AI can’t solve the fake news problem. And what he said is that in order to debunk fake news, basically what you have to do is build a machine that knows all the truth in the world so that then you can say what is truthful and what is not.
And what he said is that in order to debunk fake news, basically what you have to do is build a machine that knows all the truth in the world so that then you can say what is truthful and what is not.
A Bridge Too Far
Essentially you have to build a machine that knows everything that’s the truth, because only then, when you understand everything that’s true, can you say whether something is fake. And at this stage in the game, this is still a bridge too far for AI.
In his book AI Superpowers, the former president of Google China says that in order to build machines that can think as well as humans would require multi-domain learning, domain independent learning, natural language processing, common Sense reasoning, planning and learning from a small number of examples.
Taking the next step to emotionally intelligent robots may require self-awareness, humor, love, empathy and an appreciation for beauty. These are the key hurdles that separate what AI does today spotting correlations and data making predictions and what he calls artificial general intelligence.
So how in the world can you rely on artificial intelligence to determine relevance or analyze the sentiment of traditional and social media if you can’t build machines that understand natural language?
At the same time, there are some impressive AI features in many of the media monitoring platforms available today and if you reduce the number of results with Boolean filters before introducing AI algorithms there is a lot you can do to parse and interpret the data. But if accuracy is mission critical, you’re going to need to allocate human resources to analyze the data and make sure the machines get it right.
I monitored conversations about global climate change for the US Dept. of State to see which issues resonated on a country-by-country basis and in that case, automated sentiment analysis and AI was not good enough to handle relevancy. The results had to be sampled and checked manually.
A lot of organizations might be tempted to say their problems are incomparable to global warming or World Finance, but if you look at what’s happening today with the coronavirus pandemic the ability to monitor media and get advance warning about how these trends will affect future sales or product demand is be the difference between bankruptcy and survival.
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