Data is the dynamic wireframe for machine-learning products. So, it’s critical to identify and collect real-world data and observations to train the algorithms that will drive decision making, actions and ultimately experiences. For example, Tesla asked permission from its customers to collect short video clips from cars to improve its Autopilot self-driving feature. 25 By synthesizing this real-world customer data, a better algorithm can be identified, or developed and turned into an inference model-so the car makes the correct turns on the road.
Typically, thousands of labeled examples are needed to train a model such as recognizing a car, a sign or a sidewalk using a machine-vision model. Disney Research has been working on a different way to recognize objects even if models have never seen a labeled example as part of the training. Disney’s approach comes in handy when the model is trying to figure out what’s happening in, say, a movie or surveillance video when an object in the frame is unknown and difficult to categorize.
To compete in the future algorithm economy, companies need to learn to fluently engage with data. A data-driven organization needs a product management process that is fine tuned for machinelearning products. The starting point is letting the development team research and experiment with trainable and pre-trained AI services. Innovation will then start to happen as the team gains experience with data acquisition, feature selection and algorithm development. New skills will be needed to add value. For example there are numerous algorithm frameworks to choose from, such as Google TensorFlow, Microsoft AzureML, Caffe MXNet and others. And data integration and management tools help filter the mass of data they ingest by clearly defining and classifying the data, so they process only the comprehensible data.
The race is on to bring intelligence to the customer experience.