Intelligence Discusses Automatic Intelligence

According to a new report from AdExchanger by Ryan Joe, with contributions from Zach Rodger, artificial intelligence (AI) is surging in ad/mar tech land.



IBM continues to push Watson, says the report, and, in the run-up to their respective conferences, Salesforce and Oracle talked up their own AI initiatives. Also, Google, Facebook, IBM, Microsoft and Amazon banded together to create best practices around AI technologies.

“Unfortunately, AI has become an umbrella term in the marketing/advertising world… “ says Santanu Kolay, SVP of engineering at ad tech company Turn. “Despite the marketing hype and advancements, we are still well away from a true, fully automated AI system that requires no human assistance… “

Gartner research VP, Martin Kihn, says “… the trouble with AI is that it’s defined however anybody wants to; some people use it as a synonym for machine learning.”

 “… but machine learning, and other technology bundled under the AI banner, like deep learning, natural language processing and natural language generation, are actually just the ‘ingredients’,” said Stephen Gold, CMO of IBM’s Watson Group.

Joe Stanhope, VP and principal analyst at Forrester, says “The idea of using machine learning and AI is driven by the complexity of where we are right now… there’s too much data. Marketing departments can’t deliver the analytics and deploy that level of agility that customers require. We’re reaching the limits of human cognitive power…”

Marketing tech and ad tech vendors have two strategies when integrating AI into their offerings, says the report. There’s the purpose-driven approach, from companies like Rocket Fuel or Boomtrain, who use machine learning to power very specific marketing or advertising processes.

 Then there’s the platform approach, says the report, championed by tech giants like IBM and Salesforce, who want to use machine learning to power processes across a range of enterprise functions, many of which go well beyond marketing.

These applications tend to revolve around image or speech/text recognition, which were the early academic focus areas. When Salesforce introduced its Einstein platform in September, it showcased how the system could find product images in social media feeds. And IBM built Watson around the ability to comprehend unstructured human speech and debuted it on the game show “Jeopardy.”

What follows in the report is a partial list of vendors, in no particular order, commenting on Artificial Intelligence

  • “AI is out of reach for the vast majority of companies,” said John Ball, GM of Salesforce’s Einstein, during a September conference call. He said Einstein would “democratize” AI such that any business could build machine-learning applications
  • “Our strategy is simple,” said Maria Winans, CMO of IBM’s commerce unit. “It’s looking at this whole [marketing] space and asking what are the right set of capabilities we want to bring Watson in to augment.”
  • Salesforce is the other giant taking a broad, platform-based approach with Einstein. Like Watson, Einstein is a software upgrade. If a client wants to enhance its Salesforce Marketing Cloud applications, it can add Einstein’s intelligence
  • Gartner’s Kihn says that Salesforce is putting a stake in the ground and it has a grand vision,” said “The image recognition is pretty cool, and marketers could use it, though it’s hard to see how it would work in Journey Builder (Salesforce’s workflow product). It’s a vision and not a marketer-ready product."
  • Shashi Upadhyay, CEO of Lattice Engines, which uses machine learning to predict whether consumers will make a purchase, said there are limited marketing applications for a system like IBM’s Watson. “… it came from the angle of memorizing large quantities of text; You ask it a question, it gives an answer. But in sales and marketing, it’s never about memorization, it’s about predicting what someone will do in an uncertain environment. And Watson isn’t good at that kind of stuff…”
  • Rocket Fuel CTO Mark Torrance Torrance and his team built a proof-of-concept application that automatically found concepts negatively associated with a brand (airline travel and the Samsung Note 7, as a hypothetical) and avoided showing ads near those concepts
  • Babak Pahlavan, senior director of product management at Google, reveals that “… ‘Automated Insights’ is large-scale machine learning deployed to understand and showcase patterns that businesses should pay attention to, both in terms of anomalies and opportunities… “ Additionally, he says that Google is working on applications around what he calls ‘conversational analytics’ to build a system that, like Watson, can respond to specific business questions in real time
  • Boomtrain CEO Nick Edwards, says “We spend most of the focus on the brain,” Edwards said. “The execution layer is the necessary part of the platform because marketers don’t want to buy an AI platform that needs API access. They want an API platform that can drive real business results today… There are plenty of machine-learning algorithms out there,” Edwards said. “The hard part is understanding which algorithms are optimal for a given client.”

For additional information about Artificial Intelligence, please visit here.

Next story loading loading..