Three Big Lessons
Successful teams are multifaceted, combining machine learning experts with software engineers, product managers, and subject matter experts.
Many organizations expect to hire "unicorn" data scientists with a range of expertise that doesn’t exist, leading to failed AI projects. Instead, successful teams are multifaceted, combining machine learning experts with software engineers, product managers, and subject matter experts. Additionally, AI projects should start with clear business goals rather than data, as most data is often inadequate. Finally, rather than investing in moonshot projects, a series of smaller, iterative projects is more likely to succeed and bring real ROI.
Other Highlights
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My Favorite Is Health
There will be many companies focused on vertical applications, leveraging GenAI and LLMs with a deep understanding of industry-specific workflows and data.
You Shouldn't Try to Take on AGI
Narrow AI is where most machine learning applications excel, solving specific problems. AGI requires massive resources that only a few companies can pursue.
Three Big Lessons
Successful teams are multifaceted, combining machine learning experts with software engineers, product managers, and subject matter experts.
The Next Generation of Opportunities
The next big opportunities in AI lie in the application layer, where innovations can build on the existing infrastructure.