Deon Nicholas, Forethought Co-Founder CEO, grew up in inner city Toronto stocking shelves in a pharmacy before learning to code at an early age. He started Forethought in 2017 after learning the value of answering customer questions working for companies like Facebook and Pure Storage.
Deon has since raised $92M from an exceptional group of investors including funds like Steadfast Capital and NEA plus celebrities including Gwyneth Paltrow, Ashton Kutcher, and Robert Downey Jr. Deon won the TechCrunch Disrupt Battlefield startup competition in 2018 and is a member of the Forbes 30 under 30. He’s also a mentor and advisor to founders of color. Listen and learn...
1. How AI connects customers to the right agents then indicates the likelihood of a support interaction escalating 2. How to use historical data to help live agents fix problems faster 3. The evolution of chatbots from decision trees to AI 4. How to combine generic language models with domain-specific data to increase the accuracy of NLP 5. How to solve the problem of bias encoded in data 6. How GANs, generative adversarial networks, work 7. Why ML pipelines need to be monitored like web apps
References in this episode...
ForethoughtDeon on TwitterForward, the Forethought customer eventKrishna Gade from Fiddler on AI and the Future of WorkMonotonic selective risk may solve the AI bias problem
Deon Nicholas, Forethought Co-Founder CEO, grew up in inner city Toronto stocking shelves in a pharmacy before learning to code at an early age. He started Forethought in 2017 after learning the value of answering customer questions working for companies like Facebook and Pure Storage.
Deon has since raised $92M from an exceptional group of investors including funds like Steadfast Capital and NEA plus celebrities including Gwyneth Paltrow, Ashton Kutcher, and Robert Downey Jr. Deon won the TechCrunch Disrupt Battlefield startup competition in 2018 and is a member of the Forbes 30 under 30. He’s also a mentor and advisor to founders of color. Listen and learn...
1. How AI connects customers to the right agents then indicates the likelihood of a support interaction escalating 2. How to use historical data to help live agents fix problems faster 3. The evolution of chatbots from decision trees to AI 4. How to combine generic language models with domain-specific data to increase the accuracy of NLP 5. How to solve the problem of bias encoded in data 6. How GANs, generative adversarial networks, work 7. Why ML pipelines need to be monitored like web apps
References in this episode...
ForethoughtDeon on TwitterForward, the Forethought customer eventKrishna Gade from Fiddler on AI and the Future of WorkMonotonic selective risk may solve the AI bias problem
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