Transfer Learning & Solving Unstructured Data with Indico Data CTO Slater Victoroff

Irrespective of the application or the technology, a common problem among AI professionals always seems to be data. Is there enough of it? What do we prioritize? Is it clean? How do we annotate it? Today’s guest, however, believes that AI is not data-limited but compute-limited. Joining us to share some very interesting insights on the subject matter is Slater Victoroff, Founder and Chief Technology Officer at Indico, an unstructured data platform that enables users to build innovative, mission-critical enterprise workflows that maximize opportunity, reduce risk, and accelerate revenue. Slater explains how he came to co-found Indico Data despite a previous admission that he believed that deep learning was dead. He explains what happened that unlocked deep learning, how he was influenced by the AlexNet paper, and how Indico goes about solving the problem of unstructured data.

Slater Victoroff, Founder and Chief Technology Officer at Indico Data, explains his company's approach to transfer learning, how they are solving the problem of unstructured data, and the current limitations in the field of AI.

Key Points From This Episode:

• How Slater Victoroff came to found Indico and how he came to understand the value of deep learning.
• How Indico’s approach has evolved over time.
• What happened that unlocked deep learning and what inspired Slater to incorrectly believe it was over before it began.
• The event of the AlexNet paper published in 2012 and its influence on deep learning.
• Insight into the application of deep learning at Indico and their focus on human-machine interaction.
• What is meant by “solving the problem of unstructured data”.
• How Indico is reducing the price of building unstructured use cases.
• Thoughts on whether or not the downsizing of investment and hardware requirements of AI technology is a necessary outcome.
• The surprisingly low percentage of projects that succeed.
• Why Slater believes that AI today is not data-limited but compute-limited.
• Why resolving compute won’t remove the need for clean annotated data.
• Whether or not determining which data to prioritize is still a computer vision problem.
• How the refocus into transfer learning has affected Indico’s approach.
• What Slater is really excited about in the short and medium-term future of AI.

Stream the full episode below, or head here to select your favorite listening app and view the full transcript.

Tweetables:

“Deep learning is particularly useful for these sorts of unstructured use-cases, image, text, audio. And it’s an incredibly powerful tool that allows us to attack these use cases in a way that we fundamentally weren’t able to otherwise.” — @sl8rv

“By and large, AI today is not data-limited, it is compute limited. It is the only field in software that you can say that.” — @sl8rv

“That’s really this next frontier though: This is where transfer learning is going next, this idea ‘Can I take visual information and language information? Can I understand that together in a comprehensive way, and then give you one interface to learn on top of that consolidated understanding of the world?’” — @sl8rv

“We have gone from asking the question ‘Is transfer learning possible?’ to asking the question ‘What does it take to be the best in the world at transfer learning?’”. — @sl8rv

Links Mentioned in Today’s Episode:

"Visualizing and Understanding Convolutional Networks"

Slater Victoroff

Slater Victoroff on Twitter

Indico Data