The recent work on SAEBER, which applies sparse autoencoders (SAEs) to the screening of dna synthesis printers marks a big step towards effective function based screening.
This allows for printers to be monitored just as a lab technician uses computational gel electrophoresis to separate a messy mixture into clear, readable bands through the use of a specialized gel. SAEs happen to do the exact same thing by taking the muddied activation results of a neural network and projecting them out onto a higher dimensional space until the individual viral motifs can be seen clearly. This allows for the motifs to be tracked as they move through the system in real-time, rather than waiting for a final product.
However, while SAEBER is undoubtedly an effective method, can we say for a fact that it is the best tool for function based screening? Would it be better to scan the digital thoughts of the AI responsible for guiding the system generating the product, or monitoring the stability of the system itself, given that we can model the printer's physical state at any given time step during the printer's run?
While scanning the digital motifs helps provide an understanding of the AI's intent, it would be interesting to see if monitoring the physical state of the printer might provide a more resilient safety net. My intuition is that modelling the printer’s state as a physical landscape and understanding the implications of changes in the landscape might be more prone to false positives from natural noise, but it also has the potential to be better at detecting divergence much earlier than waiting to interpret a complex digital signal. Has there been much discussion on combining these—using the physics of the machine to flag a problem, and the AI’s internal motifs to figure out exactly what that problem is?
The recent work on SAEBER, which applies sparse autoencoders (SAEs) to the screening of dna synthesis printers marks a big step towards effective function based screening.
This allows for printers to be monitored just as a lab technician uses computational gel electrophoresis to separate a messy mixture into clear, readable bands through the use of a specialized gel. SAEs happen to do the exact same thing by taking the muddied activation results of a neural network and projecting them out onto a higher dimensional space until the individual viral motifs can be seen clearly. This allows for the motifs to be tracked as they move through the system in real-time, rather than waiting for a final product.
However, while SAEBER is undoubtedly an effective method, can we say for a fact that it is the best tool for function based screening? Would it be better to scan the digital thoughts of the AI responsible for guiding the system generating the product, or monitoring the stability of the system itself, given that we can model the printer's physical state at any given time step during the printer's run?
While scanning the digital motifs helps provide an understanding of the AI's intent, it would be interesting to see if monitoring the physical state of the printer might provide a more resilient safety net. My intuition is that modelling the printer’s state as a physical landscape and understanding the implications of changes in the landscape might be more prone to false positives from natural noise, but it also has the potential to be better at detecting divergence much earlier than waiting to interpret a complex digital signal. Has there been much discussion on combining these—using the physics of the machine to flag a problem, and the AI’s internal motifs to figure out exactly what that problem is?