There is a revolution in how marketers are using artificial intelligence (AI) and machine learning (ML) to help execute intelligent strategies and campaigns at scale. One important area where AI and ML can be put to good use is in market data management. "This is basically turning AI and ML into a useful tool for marketing itself," said Theresa Kushner, head of North American Innovation Center, NTT DATA Services, at The MarTech Conference. In this way, businesses can better understand all the data streaming in that relates to what's being done in markets, including who is buying products and other important buying trends. "AI and ML can help you sort through, organize that information and present it to you in a way that makes it more digestible within your marketing program," Kushner said.

Ever walked around exploring an area you have never been to and then you somehow end up in a street you know? I'm sure you know that feeling. That's exactly how I think about theoretical concepts you've heard before or read in passing and then one day someone explains it to you and relates it to a practical use case and you have that satisfying "Aha" moment. As a software engineer you would have worked with user-facing or backend applications and at most had some interaction with a database, maybe you've sent events/messages to a stream or an API but in general, the whole data pipeline and how data is used is a black box to you, an abstraction you don't need to understand. You might have heard about warehouses, lakes, Kafka etc, but there are enough languages, frameworks, and user requirements to think about so why dig deeper into something that is someone else's responsibility.

Under the hood of every cryptocurrency protocol, we will always find that Blockchain Technology is the engine that allows it to keep running. If we trace back the technologies that made its application possible, we will find that the science behind it has been around for decades, and only just recently became ubiquitous. Gradual changes over the last couple of decades contributed to the recent uptake of cryptocurrencies. More and more companies are now able to collect increasingly larger amounts of data. All that data was just lying there, like gasoline waiting for the spark that would transform it into valuable, usable information with real-world applications.

As it turned out, a neighbor of Caspari's in the International House, in the Morningside Heights neighborhood of Manhattan, had a solution. The neighbor, Pablo Crespo, at the time a graduate student in economics at City University of New York who was working with artificial intelligence to estimate volatility in commodity prices, told Caspari that what he needed was a convolutional neural network to search his satellite images for him. The two bonded over a shared academic philosophy, of making their work openly available for the benefit of the greater scholarly community, and a love of heavy metal music. Over beers in the International House bar, they began a collaboration that put them at the forefront of a new type of archaeological analysis.

A convolutional neural network, or C.N.N., is a type of artificial intelligence that is designed to analyze information that can be processed as a grid; it is especially well suited to analyzing photographs and other images. The network sees an image as a grid of pixels. The C.N.N. that Dr. Crespo designed starts by giving each pixel a rating based on how red it is, then another for green and for blue. After rating each pixel according to a variety of additional parameters, the network begins to analyze small groups of pixels, then successively larger ones, looking for matches or near-matches to the data it has been trained to spot. Working in their spare time, the two researchers ran 1,212 satellite images through the network for months, asking it to look for circular stone tombs and to overlook other circular, tomblike things such as piles of construction debris and irrigation ponds.

Every company is overflowing with data. They look around and see innovation is happening in the industry. Executives hear from their customers about their AI strategy. Management sees competitors with AI solution and make critical moves that bite into their addressable market. With all this background noise, the immediate reaction for the management is to conclude that we got to do something with our data and let us go and hire some data scientists.

The AI revolution has begun. Artificial intelligence will begin to completely transform every industry and every company within the next few years. And if we dig deeper, we can see that within each company there are many areas this will impact. AI will transform the way business does business. This is both an incredible opportunity and a huge risk.

Several years ago I was working as the CTO of a major tech company with significant operations around the world. One morning while reviewing our daily collected figures I noticed and determined that we were facing a significant sales drop in one of our most important target regions that my team simply couldn't explain. We immediately scrambled all the relevant people in our department and began combing through our data to try and identify what the problem was and how to fix it. In a matter of days, we had identified the source and created an effective way to counter the problem, yet the scars of the experience were lasting and deep. In the weeks that followed we pored over our protocol to identify what we could have done better to address the issue faster.