How Aurora Solar expanded its business with Machine Learning | Real-Life AI Podcast

How Machine Learning powers noise cancelation software - Krisp | Real-Life AI Podcast

The guest on the 3rd episode of the Real-Life AI podcast is Davit Baghdasaryan - the co-founder and CEO of Krisp - the world’s leading noise cancelation software powered by Machine Learning. Before Krisp, Davit used to lead product security for Twilio product lines. Here, he shared the founding story of Krisp, how they turned their AI innovation into a product that is now loved by millions of users and businesses. Davit also introduced the latest Krisp 2.0 release and how its transcription technology allows users to easily generate summaries of their conversations and take quick actions following them.

How Aurora Solar expanded its business with Machine Learning | Real-Life AI Podcast

How to build a $4 billion valuation business starting from a pilot project with a $159 paycheck? How to extend business with machine learning? When 80% of ML projects fail, what’s the secret behind getting your ML efforts successfully on track? In the second episode of the Real-Life AI podcast Christopher Hopper, the co-founder and CEO of Aurora Solar, shared the story of founding the company, answered these questions, and highlighted the lessons learned. Tigran Petrosyan, the co-founder and CEO of SuperAnnotate hosted this episode and interviewed Christopher Hopper about the journey of building Aurora Solar and the AI application they brought to market.

AI talks with Trevor Darrell from UC Berkeley | Real-Life AI Podcast

In this episode, Professor Trevor Darrell from UC Berkeley shared his experience in working on exciting AI initiatives that emerged as a result of close collaboration between industry and academia. He covered different challenges that computer vision currently faces and solves, such as autonomous driving, SAR imagery, cashier-less checkout systems, and more. In addition, he shared great advice for fellow data scientists who are just getting started in the field.