Engage 2017: Creating the future - Google

How are you creating the future?  

By developing engaging, feature-rich and contextual applications with the ultimate aim of making the world’s information accessible to everyone.

Google continues to make strides in using machine and deep learning technologies. Artificial Intelligence as a driver for new products, customer engagement and innovation is becoming the norm across the entire tech sector. Everyone wants to see what AI can bring to their organisation. We can compare AI adoption and research to the rise of PCs, the internet, cloud, or even mobile. At the start of all these game-changers, no one could have predicted where it would take us to today.

Google has been using deep learning (AI) for many years, across dozens of products and hundreds of teams - from classifying photos, enhancing search and creating better translations. AI has improved the Google products you use every day. That said, deep learning systems can be computationally expensive, so whether you are a small or large business, deploying them at scale can be an issue. We focus on this scalability in our own organisation and pass this knowledge on to our cloud customers.

If it could be invented tomorrow, what one thing would make your job easier?

A machine that could read and predict human emotions and teach other machines empathy and capture emotional signal. 

We have made huge advancements and breakthroughs in the last few years in Computer Vision (Google photos), Speech recognition (Google Assistant) and translation. Now if machines could understand and capture human emotions represented in media, that would be helpful. Imagine a world where computers can recommend music and other media content based on the emotions of the requestor.

What one thing would you like the industry to do as a result of Engage as we look ahead to creating the future for (digital) advertising?

"Follow the data" - see if you have some unique data that you can apply deep learning and complex analytics to. The data can be transactional, transportational traffic patterns, customer journey logs from an e-commerce web site. Anything that is relevant to your customers and your business.

Another dimension to keep in mind is the data pool needs to be large and varied with extensive computing resources available to analyse it. You need to plan, initiate, fund, and prioritise deep learning projects and continue to act as change agents in your respective organisations towards adopting machine learning and assistive technologies. As a starting point, make your success metric user happiness.