Not Another “AI is here” Blog Post

We originally wrote this post for the Silicon Valley Organization blog.

As the future unfolds, AI has become a ubiquitous and somewhat annoying buzzword that risks losing its meaning. So, what is AI anyway and why does your business need to know about it?

AI, ML, DL and other confusing acronyms

AI is any system that resembles, or aims to resemble, human intelligence. In 1950, English mathematician Alan Turing developed the Turing test to determine a machine’s ability to answer questions — and therefore “reason” — in the same way a human would.

The sophistication of a machine’s “intelligence” depends on the programming behind it. Machine Learning (ML) is one type of programming, and essentially the science of making machines learn from experience, much like humans do (our ML team lead, Matteo, explained more here).

When it comes to AI, experience comes from data, and algorithms are the rules that govern how machines learn from that data.

Deep Learning (DL) is a particular type of ML that’s built using neural networks — sophisticated sets of algorithms inspired by the human brain that allow AI to learn about context by providing consistent examples of a certain pattern.

It all boils down to training: AI is a relatively new technology and it needs to learn, much as children do as they grow up. AI training ultimately revolves around two important elements: data and the configuration of neural networks, aka the level of sophistication of the programming behind the AI.

Artificial General Intelligence (AGI) is the scary sci-fi AI often depicted in Hollywood movies. In the AGI scenario, machines become self-aware and are as smart as humans. We are quite far away from AGI however, so no need to worry about robots taking over the world just yet.

We need to talk

NLP — Natural Language Processing — is another acronym you might hear a lot. It indicates a machine’s ability to understand human language as it’s spoken, and basically the technology behind AI assistants such as Siri, Cortana and Alexa.

Conversational AI is changing the way we interact with our devices in an unprecedented way.

To put this in perspective, US advisory firm the Future Today Institute predicts that by the end of 2020 half of the interactions we’ll have with computers will be using our voice and not a keyboard.

According to UK analyst firm Juniper Research, there will be 8 billion digital voice assistants in use by 2023 — that means about the same amount of people as virtual assistants on the planet.

In short, we’ll progressively type less and talk more with our devices.

AI is here, but not to take your job

Here comes the inevitable cliché: AI is not the future, it’s the present. According to a 2018 survey by Pega, consumers around the world use AI more than they realize: though only 34% of respondents said they use technology with AI, the survey found that 84% of them actually use an AI-powered service or device. Some — well, a lot of them — simply didn’t know it.

A recent survey by PWC found that 27% of consumers could not tell whether their last customer service interaction was with a human or a chatbot.

From a business perspective, PWC also found that 72% of business execs believe AI is a business advantage, with companies making substantial investments in that direction.

Gartner has recently released the results of a survey carried out at the end of 2018 revealing a “substantial acceleration in AI adoption” by leading companies.

According to the report, the number of AI projects deployed by early AI adopters will double by 2020, with respondents naming customer experience and boosting internal decision-making as the main drivers behind the adoption of AI projects. “It is less about replacing human workers and more about augmenting and enabling them to make better decisions faster”, said Jim Hare, research vice president at Gartner.

Ok, but what does that mean?

It means the name of the game is not predicting whether AI will change our personal and work lives — that is already happening — it’s understanding how, and getting ready to reap the benefits.

Respondents to the Gartner survey said one of the main challenges to adopting AI was understanding AI use cases. This is not surprising, as the applications are potentially limitless.

From optimizing marketing campaigns with suggestions on the best time to post on social media channels, to helping insurance agents keep track of sales goals and product performance, to revolutionizing mobile banking customer experience, the list goes on.

At iGenius, we like to call it data democratization: empowering any team, regardless of their comfort level working with data and analytics, to access business data in real time anywhere — say, via an app with the help of a smart AI advisor — and extract insights that lead to better, more informed operational decisions.

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