Every day the ways we connect with technology are becoming more interactive, intelligent, and relevant. As technology becomes more cognitive and engaging, marketers need to be aware of the analytics behind this technology and how they can use it to better connect with their customers.
As consumers, all of us are engaging with technology in new ways. We have surely been able to talk to our phone, TV, or car for some time and give simple directions. But now these devices talk back and can engage in an iterative dialogue to perform more complex tasks, identify patterns in our behavior, and even make recommendations. And devices are taking in information from and making decisions based on their environment and performing autonomous activities like self-driving.
This innovation is driving new capabilities like the robot delivery services in San Francisco, Redwood City, and later this year in London, Düsseldorf, and Bern.
Robots delivering daily essentials and treats to your front door is one use case that demonstrates why Forrester predicts investment in AI and machine learning will triple in 2017.
But what is machine learning, why now, and how will it improve marketing and how brands can connect to their customers?
Simply put machine learning enables computers to learn autonomously without being explicitly programmed.
The recent explosion AI and machine learning is driven by a bunch of cool new hardware and software— better sensors, better data structures (Hadoop with distributed computing) and better processors (GPU’s) running more complex analytics. One of the key breakthroughs in machine learning that greatly improves how computers process and learn is deep learning. Deep learning in turn is machine learning using neural networks.
So, what are neural networks? Simply put, neural networks are learning systems modeled on the human brain comprised of multiple “neurons”. Each network contains neurons, often lots and lots of neurons connected in layers. These neurons, like those in the human brain, take information, process that information, and then make a decision to fire (pass information to other neurons) or not fire. Information is passed through the network of neurons and along the way patterns can emerge. The combination of multiple neurons, working together, eventually leads to a decision. And like the human brain neural networks also change the importance or weight given to an input or pattern to make a decision as the network learns.