Practical Steps Towards Fully Automated Biology

Lab automation is constantly evolving. While teams work hard to keep their labs running smoothly today, they must also evaluate and incorporate the emerging automation technologies of tomorrow. In particular, the rise of AI-led scientific discovery may very well turn the entire research process upside down. How can automation teams manage these dynamics and effectively plan for the future?In this webinar from Trilobio, Roya Amini-Naieni, CEO & Co-founder, presents a look at where lab automation is heading and what labs can do to build towards that future. Additionally, with a focus on the practical questions that matter most to the hardware, software, AI, and people of the biology lab.

  • Hardware: Making balanced investments that support today’s experiments while enabling long-term scale.
  • Software: Ensuring devices stay connected across a growing ecosystem of tools and data.
  • AI: Where do you let AI drive your lab, from data analysis to protocol design, and where human expertise remains essential
  • People: How roles will evolve, and how to free up time for training and higher-value work.

Key:

Complete
Failed
Available
Locked
Practical Steps Towards Fully Automated Biology
Open to view video.
Open to view video. Lab automation is constantly evolving. While teams work hard to keep their labs running smoothly today, they must also evaluate and incorporate the emerging automation technologies of tomorrow. In particular, the rise of AI-led scientific discovery may very well turn the entire research process upside down. How can automation teams manage these dynamics and effectively plan for the future? In this webinar from Trilobio, Roya Amini-Naieni, CEO & Co-founder, presents a look at where lab automation is heading and what labs can do to build towards that future. Additionally, with a focus on the practical questions that matter most to the hardware, software, AI, and people of the biology lab. Hardware: Making balanced investments that support today’s experiments while enabling long-term scale. Software: Ensuring devices stay connected across a growing ecosystem of tools and data. AI: Where do you let AI drive your lab, from data analysis to protocol design, and where human expertise remains essential People: How roles will evolve, and how to free up time for training and higher-value work.
You must register to access.