I have spent the past 20 years working in natural language processing and machine learning. My first project involved automatically summarizing news for mobile phones. The system was sophisticated for its time, but it amounted to a number of brittle heuristics and rules. Fast forward two decades and techniques in natural language processing and machine learning have become so powerful that we use them every day—often without realizing it.

After finishing my studies, I spent the bulk of these 20 years at Google Research. I was amazed at how machine learning went from a promising tool to one that dominates almost every consumer service. At first, progress was slow. A classifier here or there in some peripheral system. Then, progress came faster, machine learning became a first class citizen. Finally, end-to-end learning started to replace whole ecosystems that a mere 10 years before were largely based on graphs, simple statistics and rules-based systems.

After working almost exclusively on consumer facing technologies. I started shifting my interests towards enterprise. There were so many interesting challenges that arose in this space. The complexity of needs, the heterogeneity of data and often the lack of clean, large-scale training sets that are critical to machine learning and natural language processing. However, there were properties that made enterprise tractable. While the complexity of tasks was high, the set of tasks any specific enterprise engaged in was finite and manageable. The users of enterprise technology are often domain experts and can be trained. Most importantly, these consumers of enterprise technology were excited to interact with artificial intelligence in new ways— if it could deliver on its promise to improve the quality and efficiency of their efforts.

This led me to ASAPP.

I am firm in my belief that to take enterprise AI to the next level a holistic approach is required. Companies must focus on challenges with systemic inefficiencies and develop solutions that combine domain expertise, machine learning, data science and user experience (UX) in order to elevate the work of practitioners. The goal is to improve and augment sub-tasks that computers can solve with high precision in order to enable experts to spend more time on more complex tasks. The core mission of ASAPPis exactly in line with this, specifically directed towards customer service, sales and support.

To take enterprise AI—and customer experience—to the next level a holistic approach is required.

Ryan McDonald, PhD

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The customer experience is ripe for AI to elevate to the next level. Everyone has experienced bad customer service, but also amazing customer service. How do we understand choices that the best agents make? How do we recognize opportunities where AI can automate routine and monotonous tasks? Can AI help automate non deterministic tasks? How can AI improve the agent experience leading to less burn out, lower turnover and higher job satisfaction? This is in an industry that employs three million people in the United States alone but suffers from an average of 40 percent attrition—one of the highest rates of any industry.

ASAPP is focusing its efforts singularly on the Customer Experience and there are enough challenges here to last a lifetime. But, ASAPP also recognizes that this is the first major step on a longer journey. This is evident in the amazing research group that ASAPP has put together. They are not just AI in name, but also in practice. Our research group consists of machine learning and language technology leaders, many of whom publish multiple times a year. We also have some of the best advisors in the industry from universities like Cornell and MIT. This excites me about ASAPP. It is the perfect combination of challenges and commitment to advanced research that is needed in order to significantly move the needle in customer experience. I’m excited for our team and this journey.