In this episode, Colin explains how AgriSynth is “getting humans out of the way” with its closed-loop control system and offers some insight into the sheer volume of data required to train its AI models.
EXAMPLE: AgriSynth Synthetic Data– Weeds as Seen By AI
Data is the backbone of agricultural innovation when it comes to increasing yields, reducing pests, and improving overall efficiency, but generating high-quality real-world data is an expensive and time-consuming process. Today, we are joined by Colin Herbert, the CEO and Founder of AgriSynth, to find out how the advent of synthetic data will ultimately transform the industry for the better. AgriSynth is revolutionizing how AI can be trained for agricultural solutions using synthetic imagery.
He also gives us an overview of his non-linear career journey (from engineering to medical school to agriculture, then through clinical trials and back to agriculture with a detour in Deep Learning), shares the fascinating origin story of AgriSynth, and more.
Key Points From This Episode:
- Colin’s career trajectory and the surprising role that Star Wars plays in AgriSynth’s origin story.
- Reasons that the use of AI in agriculture is still limited, despite its vast potential.
- Ways that AgriSynth seeks to bridge these gaps in the industry using synthetic imagery.
- Insight into the vast amount of parameters and values required.
- What synthetic data looks like in AgriSynth’s “closed-loop train/test system.”
- Why photorealistic data is completely unnecessary for AI models.
- How AgriSynth is working towards eliminating human cognition from the process.
- Dispelling some of the criticism often directed at synthetic data.
- Just a few of the many applications for AgriSynth’s tech and how their output will evolve.
- Why real-world images aren’t necessarily superior to synthetic data!
“The complexity of biological images and agricultural images is way beyond driverless cars and most other applications [of AI].” — Colin Herbert [0:06:45]
“It’s parameter rich to represent the rules of growth of a plant.” — Colin Herbert [0:09:21]
“We know exactly where the edge cases are – we know the distribution of every parameter in that dataset, so we can design the dataset exactly how we want it and generate imagery accordingly. We could never collect such imagery in the real world.” — Colin Herbert [0:10:33]
“Ultimately, the way we look at an image is not the way AI looks at an image.” — Colin Herbert [0:21:11]
“It may not be a real-world image that we’re looking at, but it will be data from the real world. There is a crucial difference.” — Colin Herbert [0:32:01]