What Lies Ahead for Machine Learning and AI in Advertising

Rob Schmetterer, who oversees machine learning and data at public relations firm Ketchum, says there’s a reason more brands aren’t using such platforms: This technology is still in its infancy. “If you look at it, then you’re like ‘Wow, all this stuff is happening,'” he says. “But on the other hand, if you think about how far we have to go and the reality of where things are currently, there’s still a long way to go.”
However new, artificial intelligence is here to stay. “It’s just understanding that this is a part of your marketing investment and strategy,” Schmetterer says. “It’s not just one-off campaigns or one-off events—this stuff is going to be there all the time. It’s hard to not think about it. You guys are probably the people that are thinking about this the most.”
But before you use the AI to craft content, you need to train it first. And that’s where things can get tricky and even potentially dangerous for brands. In fact, in some cases, some companies haven’t made many efforts to do this at all, which could be leading them down a dangerous path.

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How We Can Use AI to Help Materialize Our Dreams & Goals

So with that said, “How can this technology benefit us?” you might be wondering. The answer: Using AI and machine learning algorithms to help us achieve our goals. “We’re seeing this technology essentially being applied to all kinds of different things,” Schmetterer says. For example, we’ve seen the use of machine learning applied to determine what is top news while social media users are talking about on a daily basis. “What are the things that people care about during the day? What are the things that people want to be talking about?” Schmetterer continues. “So by using AI and also using machine learning technology, you can essentially quantify what’s happening. And then you can use that as a metric to identify the most relevant content and then determine what should be pushed out at the time.”
Which is why today Facebook, Google and Twitter are also pushing their algorithms to identify trending news stories. Schmetterer says those platforms would see a lot of attention if they were able to identify what people are discussing on social media that might become news in the coming days or weeks. “Can you identify something that’s emerging as a trending topic? Or can you classify stories that are being broken as news? And once they’re identified, then can you figure out how to push that out at the right time to the right people so they can see it and then potentially react to it?” he explains.

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What Is Machine Learning And Why Do We Need It?

The mechanics of artificial intelligence are changing rapidly. More and more computers are gaining the ability to learn autonomously, while a new subfield called “deep learning” is becoming increasingly popular. Deep learning is a specific implementation of machine learning that uses neural networks. In layman’s terms, a neural network is basically how our brain works, but it’s standardized across computer data. “What we’re seeing, which is this new movement of ‘deep learning,'” says Schmetterer, “What we’re seeing a lot of people doing is applying these deep neural networks to machine learning problems.”
Much like the brain, a deep neural network is made of different layers and neurons. The first layer receives data as input, the next layer processes that data, and so on. The final output is sent to the outside world. Feeding this neural network large amounts of data allows it to learn what works, which then produces more-accurate results in turn. Deep learning has already produced incredible results in image and speech recognition—which are two very important kinds of data for any computer to process.

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Machine Learning – A Comprehensive Review of Its History & Technology

This technology is being used in all kinds of industries, but public relations is at the forefront of this development. “What’s important to note,” says Schmetterer, “is that machine learning is not new. What is new with machine learning and artificial intelligence is our ability to actually apply that.” And this understanding poses a real threat to those who wish to remain relevant and competitive in the global digital advertising industry. “You’re seeing this technology being applied to all kinds of different things,” Schmetterer continues. “And companies are realizing that as we apply these machine learning [and deep learning] algorithms to our own particular problems, we’re going to be able to reach different audiences in different ways.”
This technology is already being used in the digital advertising industry through its application to identifying consumer behavior and preferences. The use of big data is a critical component in this process, as well as automation. “What we’re able to do,” Schmetterer says, “is we’re able to take all kinds of different data sets, whether they’re web browsing history, whether they’re consumer reviews or ratings—and you can tie it to consumers directly—or you could tie it to what they purchase. And then we’re able to use that in a very automated way to build our advertising campaigns.”

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Machine Learning – A Beginner’s Guide To Understanding The Fundamentals Of This Technology In Order To Get Started in it Today And Beyond!

The most basic description for machine learning is that it allows computers to “learn” without being explicitly programmed. “If you look at the way that humans learn,” Schmetterer says, “we start off as a baby and we’re just kind of figuring everything out. We’re seeing things and how the world works. We’re building on top of that knowledge.” That’s how machine learning works. “When we’ve got a problem to solve, we’re able to look at that problem, and instead of just looking at it as a problem to solve, we actually start applying some machine learning and deep learning algorithms on top of that,” Schmetterer continues. “And the computer is able to do things that the person who is trying to solve the problem couldn’t do. So, you’re teaching the computer how to do it and then it’s actually able to solve it for you.”
Data is a key ingredient in machine learning, and computers learn from lots of examples ideally all at the same time. Schmetterer says the number of examples is or can be extremely large, but we’re talking in petabytes. A petabyte is 1,000 terabytes or 1,000,000 gigabytes or a million megabytes. “It’s a lot of data,” he says. “And the problem with all of this data is that it’s messy.”

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