AI powers a plethora of real-world applications, ranging from facial recognition and image detection to language translators and assistants like Siri and Alexa. As more companies harness the power of AI, many are navigating unforeseen challenges and opportunities in commercializing AI.
In this session, Alessandra Sala, Senior Director of AI & Data Science at Shutterstock, Josh Hollin, VP of Engineering at AMP Robotics and Shyam Rajagopalan, Co-founder/CTO of Infinitus Systems, dive into overcoming the obstacles – good and bad – that enable the widespread adoption of their AI applications.
Here are 3 key takeaways from the session:
- Some of the most significant data challenges preventing the commercialization of AI are human bias in training data, edge cases, and a lack of high-quality data. Efficient data engineering and data labeling can advance the process significantly.
- Companies are realizing the need for end-to-end data pipeline management to develop and optimize their ML models.
- Human review is a very important process for companies building AI products to ensure that the training data is up to the standard and of high quality.