aiTech Trend Interview with Ryan McDonald, Chief Scientist at Asapp

aiTech Trend Interview with Ryan McDonald, Chief Scientist at Asapp

Can you tell us about ASAPP and your role as Chief Scientist?

ASAPP is a research-based artificial intelligence software provider that solves large, complex, data-rich problems with AI Native® technology. Large enterprises use ASAPP to make customer experience teams highly productive and effective by augmenting human activity and automating the world’s workflows.

As Chief Scientist, I’m responsible for setting the direction of the research and data science groups in order to achieve ASAPP’s vision to augment human activity positively through the advancement of AI. The group is currently focused on advancing the field of task-oriented dialog in real-world situations like customer care. 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.

Can you tell us about your journey into this market?

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 the 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 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 its efforts.

This led me to ASAPP. The work we’re doing in augmenting the customer service agent experience and performance is immensely rewarding. How can AI improve the agent experience leading to less burnout, 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.

How AI is elevating human performance?

Our central hypothesis at ASAPP is that AI should not replace humans, but augment them in positive and productive ways. This vision is broad and we have ambitions to apply it to all relevant human activity. However, as this is a broad mandate, the first area we’ve chosen to focus on is the customer experience domain.

The customer experience domain embodies all the challenges and rewards that come with augmenting human activity. Agents are engaged in complicated problem-solving tasks that require them to follow workflows, retrieve relevant information from customer and knowledge bases, and adapt to nuanced situations that a customer might find themselves in.

This gives rise to a huge number of opportunities for AI to improve that process. However, we think it is important to do this in a positive way, by which we mean:

  • Augmentation happens at points that are natural and fluid during the course of the agent’s job. This is critical. If AI is interfering or interjecting at awkward moments or with poor latency, this will actually have a negative effect on the agent’s experience as they will need to consciously ignore the AI.
  • More critically, we want the AI to achieve positive outcomes for all humans involved. In this case it is the customer, the agent and the organization. Customers want their issues handled efficiently and effectively. Agents want to do that for customers. Additionally, agents are doing a hard job, often dealing with difficult unsatisfied customers. AI should help them balance work and cognitive load in order to decrease fatigue and burnout and increase job satisfaction. Afterall, agents at call centers have one of the worst attrition rates (as high as 100% annually in some call centers) of any job in America. Finally, we want positive business outcomes for the company who runs the call center. This can be customer satisfaction, the throughput of issues that can be handled in a day or even the amount of sales.

For call centers, we often think of the positive outcomes between the customer, agent, and company as being in conflict with each other. But good AI will help to optimize these outcomes for all three.

How AI is being used to help train new workers?

The emergence of AI technologies to augment their performance during a call or digital customer interaction is becoming more commonplace, but AI to train workers is presently less conventional. Today, many agents train on new issues or procedures ‘live’. That is, they get a description of the procedure, but then only see it in practice when they take a call with a real customer. Imagine we gave pilots the manual of the plane and then told them to fly 300 passengers to Denver? Because of this, we are focusing on using AI to help build tools for agents to practice procedures and handle difficult situations before they deal with live customers. When this is coupled with targeted feedback (either by a supervisor or automatically) this will allow the agent to grow their skills in a less stressful environment.

How ASAPP is using AI to reduce turnover and augment this new generation of workers?

Large companies offering consumer goods and services spend millions, and sometimes billions of dollars each year on contact centers that serve their customers, with the labor cost representing 80-90% of total costs. It’s a big problem driving agent turnover to be 40%—and sometimes 100% or more—every year.

There is often a misconception that agents are indifferent to your problems and are going through the motions. In the worst case, even obstructs your ability to solve a problem. Nothing can be further from the truth. Agents, as with all people, derive satisfaction from helping customers solve their problems. How would you rather spend your day, hearing robust ‘thank you’s or screaming customers? In a recent study we conducted, we found that 90% of agents reported that calls with customers made their day, and the majority say they are happy with their jobs. But, agents want the tools and training required in order to make customers happy. Unhappy customers lead to frustrated, fatigued, and stressed agents. This is the primary driver of turnover.

AI to augment the agents during a call already helps. If the agent has the tools and guidance on how to effectively and quickly solve a problem for a customer, then the odds that the customer is happy can only be higher, which in turn should lead to higher job satisfaction.

Better AI to improve customer satisfaction in dynamic situations as well as AI for grounded training — that is how ASAPP puts focus on the agent with the ultimate goal of reducing turnover.

After GPT-4, what will the future of large language models look like for the enterprise?

Every time we reach a new peak in what GPU/TPUs are able to handle, we see leading technology companies put out new, larger, pre-trained language models. These large, pre-trained models can be foundational for a number of downstream tasks and applications in NLP. While we’ll continue to use these pre-trained language models for the foreseeable future, one always needs to consider the value-added of a more powerful model vs. the costs that come with training and deploying it, especially in the enterprise.

For enterprise uses and applications, the future of innovation is now centered around the fine-tuned and specialized models created from these large language models to be the world’s best for a specific application or domain. It’s great to see how Hugging Face has democratized access to these large language models, but now there are more interesting questions in how we can adapt and control them for the specific workflows or problems for a given domain.

What are the best areas to scale automation in human-centered AI?

AI is already pretty prevalent in the workplace. As I write this spelling and grammar checkers as well as text autocomplete are helping me. I have spam filters and message classifiers on my email/messaging tools. I use AI-powered search to find the relevant information I need to execute. This will grow as well as my adoption as the number of AI-powered features and their quality increases.

However, I would call this kind of AI augmentation atomic. It is certainly assisting me, but in very precise moments that allow for high-precision predictions. I certainly cannot ask an AI to answer these questions for instance — yet!

More seriously, my vision is to see the adoption of end-to-end AI throughout the workspace. I don’t mean end-to-end in the machine learning modeling sense. What I mean is that the AI will power holistically large and complex tasks being optimized for the overall goal and not just atomic points during the process. ASAPP is already bringing this to bear in call centers. For instance, we optimize what the agent will say next based on a holistic set of factors about where the agent is in the conversation and what the ultimate goal is. But beyond that, imagine a scientist trying to write a systematic review of an important topic, a software engineer building a platform or integrating complex systems, a lawyer writing a legal brief, etc. In the future, each of these professionals will rely on AI to rapidly increase their effectiveness at these tasks and optimize desired outcomes, freeing them up for more critical challenges.

Do you have some final thoughts?

Our research team at ASAPP has a clear focus: we’re advancing AI to augment human activity to address real-world problems for enterprises. Researchers at ASAPP work to fundamentally advance the science of NLP and ML toward our goal of deploying domain-specific real-world AI solutions, and to apply those advances to our products. They leverage the massive amounts of data generated by our products, and our ability to deploy AI features into real-world use to ask and address fundamental research questions in novel ways.

Discover our recent papers at https://www.asapp.com/ai-research/

About Ryan McDonald

Ryan McDonald is the Chief Scientist at ASAPP. He is responsible for setting the direction of the research and data science groups in order to achieve ASAPP’s vision to augment human activity positively through the advancement of AI. The group is currently focused on advancing the field of task-oriented dialog in real world situations like customer care. Ryan has been working on language understanding and machine learning for over 20 years. He has published over 100 research papers in top tier journals and conferences which have been cited thousands of times. He has won best paper awards at premier international conferences (EMNLP, NAACL) for his work on multilingual syntactic analysis. His book ‘Dependency Parsing’ has served as one of the main pedagogical resources in syntactic parsing for over a decade.

About ASAPP

ASAPP is a research-based artificial intelligence software provider that solves large, complex, data-rich problems with AI Native® technology. Large enterprises use ASAPP to make customer experience teams highly productive and effective by augmenting human activity and automating the world’s workflows. The company has offices in New York, Silicon Valley, Buenos Aires, London, and Bozeman. Visit www.asapp.com for more information.

AITECHNEWS

Related post

Exit mobile version