Interview with Kevin Sun, Data Scientist, Deloitte - Speaker at Global Artificial Intelligence Conference April 2018 Posted on : Apr 20 - 2018

We feature speakers at Global Artificial Intelligence Conference - April 27 - 29 2018 – Seattle to catch up and find out what he or she is working on now and what's coming next. This week we're talking to Kevin Sun (Data Scientist, Deloitte) Topic - "Practical Data Science - An Overview Of Data Science Best Practices For Common Business Problems"

Interview with Kevin Sun

1. Tell us about yourself and your background.

I have a masters degree in Data Science from the University of Virginia and have a bachelors degree in economics from Washington University in Saint Louis. My masters capstone project was about developing a novel algorithm to identify gene sets that discriminate cell types. I have been working as a data scientist at Deloitte for about 9 months. Some of my data science side projects are stock investing, cryptocurrency investing, data visualization, and NBA analytics.

2.  What have you been working on recently?

I have been working on identifying what factors influences employee engagement and performance at Deloitte. I have also been learning how to best ensemble models and how to deal with missing data.

3. Tell me about the right tool you used recently to solve customer problem?

I use primarily R and Tableau.

4. Where are we now today in terms of the state of artificial intelligence, and where do you think we’ll go over the next five years?

I think we are at the cusp of a breakthrough. I wouldn't say artificial intelligence right now is really intelligent. They can learn a specific task well after learning from a lot of training data, but I wouldn't say that these systems are generally intelligent. However, with the research of reinforcement learning and successful systems, such as Alpha Go Zero, I will say artificial intelligence will be more intelligent in the next five years. I think in 5 years, we will begin to see artificial intelligence begin to have the capability to have general intelligence for areas with a lot of training data.

5. There is a negative perception around AI and even some leading technology folks have come out against it or saying that it’s actually potentially harmful to society. Where are you coming down on those discussions? How do you explain this in a way that maybe has a more positive beneficial impact for society?

I think we need to be careful about Artificial Intelligence because in the end, humans are making the algorithms and providing the training data, which may lead to bias. A very good example is Microsoft's racists chatbot that they deployed. However, I also do think that AI can provide a lot of benefits to society, such as allowing personalized medicine, preventing financial crashes, and having self-driving cars.

6. When you’re hiring, what types of people are you hiring?

The job market for traditional programmers, engineers is  very difficult to get into AI space. Are you hiring from that talent pool or is that a different talent pool? In terms of talent, how do you go about ensuring you get the best AI people at your company?I look for people who have a masters degree in data science, or has experience with analytic projects with a software engineering component. Our team is very young, so for some candidates, we value software engineering skills, while others, we value skills in statistics. I usually target specific data science programs and give technical questions during my interview to make sure their foundation is strong.

7. Will progress in AI and robotics take away the majority of jobs currently done by humans? Which jobs are most at risk?

I think it is hard to say. A lot of jobs will be replaced, but I think there will be jobs in making sure these systems are safe and are doing well. I think jobs that do not contain a human component will be at risk, and areas that have a lot of data and have a clear cut problem, such as stock traders. 

8. What can AI systems do now?

AI systems can detect fraud, trade on the market, self drive cars, and predict what you like to see, buy, and watch.

9. When will AI systems become more intelligent than people?

I think in about 2070. So far, they can optimize tasks, but I think it will be some time until they can be more intelligent than people.

10. You’ve already hired Y number of  people approximately. What would be your pitch to folks out there to join your Organization?

Why does your organization matter in the world? What makes Deloitte so special is it's deluge of domain experts in all industries and sectors. With such specialized knowledge, coupled with data science expertise, Deloitte can really help reform the world by helping leaders and game changing organizations in each industry.

11. What are some of the best takeaways that the attendees can have from your talk?

1) Ultimately data science is a tool to help people. It is not all about the correct theoretical algorithm but implementation, and answering the business question are the key priorities. 2) There isn't a "perfect" solution for each problem. Data Science is as much art and business as it is science. 3) Always check your biases. Your models and AI systems will affect people, you need to make sure that all groups are being influenced the way that you think they are being influenced. 4) You can never be too cautious when cleaning data. Model performance is influenced more by your features rather than your algorithm. 5) Always have a good validation methodology to check if your algorithm is performing correctly.

12. What are the top 5 AI Use cases in enterprises?

1) Automate rote work, 2) Use clustering to gain insights from unlabeled data, 3) Identify your customers, 4) Improve your workforce 5) Build a recommender system

13. Which company do you think is winning the global AI race?

Alibaba, Amazon, and Google

14 Any closing remarks

It is interesting to see how the world will change when AI becomes more prevalent and how quickly your models may be outdated with all of these influences.