Home / Latest Headlines / The data science life: Intuit's Ashok Srivastava on AI, machine learning, and diversity of thought

The data science life: Intuit's Ashok Srivastava on AI, machine learning, and diversity of thought

Intuit is betting big on artificial intelligence and machine learning as it looks to infuse the technology into its products and customer experiences. As a result, Intuit is hiring flexible thinkers as well as data scientists to deploy AI broadly.

We caught up with Ashok Srivastava, senior vice president and chief data officer at Intuit, to talk shop, where machine learning ends and AI begins, and managing a data science team.

Here are some of the highlights.

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Ashok Srivastava

How does Intuit utilize AI and machine learning? Srivastava said Intuit looks at AI through a portfolio approach. The areas break down like this:

  • Building AI and machine learning into products.
  • Customer care to help employees get better information to solve problems. “We are creating augmented intelligence to help get the consumer the right information at the right time and context,” Srivastava said. “For something like tax, answering questions requires access to information beyond Intuit.”
  • Security, risk, and fraud.
  • Central data services for HR, sales, and marketing.
  • And engineering optimization to make code and systems more robust. “This one is new for us,” Srivastava said.

What AI platforms are built versus bought: Intuit is standardizing its machine learning on Amazon Web Services and building a data lake, Srivastava said. “That technology takes you to a certain point, but all the algorithm deployment is done by all team,” he said.

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(Image: Intuit)

The difference between machine learning and AI: Srivastava said he sees machine learning and AI as an overlapping Venn diagram. AI covers areas of research that can take many years. Machine learning is inclusive of that but mostly covers expert and rule based systems. “ML is related, but not identical, but there are intersections. Deep learning is something I view as an intersection,” Srivastava said. “We’re seeing a renaissance of AI and the idea that neural networks can solve complex problems.”

“We’re also in a situation where the emphasis is on the technology (AI) without understanding of that technology. There are unreasonable expectations on a reasonable technology,” he added.

Managing AI and data science teams: Srivastava said he is looking for people who can think about the end-to-end customer pipeline and experience. He needs people who can think from Turbo Tax back to the algorithm that informs the customer. “We no longer can say ‘oh that’s the algorithm guy,” he explained. “We have to think about customer driven innovation and that forces us to think about the end customer and value. It has to be end to end and requires a product manager, data scientist, engineers and designers.”

Read also: Death and data science: How machine learning can improve end-of-life care | The great data science hope: Machine learning can cure your terrible data hygiene | A day in the data science life: Salesforce’s Dr. Shrestha Basu Mallick

Hiring liberal arts majors: Srivastava said his team has doubled and he’s looking for people with “flexibility of thought.” Yes, new hires need the right technical background, but the end-to-end customer approach requires creativity. “It’s not a cookie-cutter type of thing,” he said. As a result, Srivastava is looking for hires with liberal arts backgrounds, too, and is establishing outreach efforts to universities. “We have emphasized that we need a pipeline from liberal arts to technology,” he said. “They are storytellers and we are actively recruiting them.”

“Liberal arts has a critical role in AI and it is critical for success in technology deployments,” Srivastava said. “We have hired people in the past with degrees in political science, art history and English. These people have good perspective and bring diverse thought to the table.”

And why is diverse thought important in AI? “You can’t have conversational technology and approach comprehension without general knowledge,” Srivastava said. “Humans have evolved over millions of years. Human expression is far more than adding up numbers and doing queries and searches.”

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