The total of technology to the total of People. - Buckminster Fuller.

The next generation Lab Automation.

I don’t know you, but there is a high chance you would agree that in the past decade we (civilization) put more effort on digital gossiping (yes, social networks) than on Scientific tools. Hence 2020, where I could upload 3D videos with my face using real-time machine learning to make cute videos, running on my iPhone 12 4K camera in less than 100ms per frame.. but to produce simple mRNA therapy to save a couple million lives we took way more time than we had.

[This is not the company’s view, this is my personal perspective on things. You know.]

Science is structured skepticism, you can’t trust human memory (or brains in general), otherwise I wouldn’t have a job doing UX design for computing systems.

Our current scientific generation is brilliant, beautifully motivated minds. So was Newton and our boy Albert, the difference the later had better tools, thus better data. Maybe Newton even had a more brilliant mind (who knows?).. but worse-to-no data to work with. The relationship happens between Ptolemy and Copernicus: the lack of tools can put earth on the wrong place ;). Improve the tools and you get Galileo (or vice-versa on this case..).

Good! You got it: you want to advance Science? Advance the tools! I came to Artificial Inc. with this idea as my blank canvas. Why we need science? We can’t trust brains, we are just quite weird monkeys making complex sounds while doing recursive symbolic representation.

On my first month at Artificial I got to sit with quite creative people to figure out how to advance tools for LifeSciences Labs. Turns out, scientists had to coupe with a ton of convoluted tools, even when their labs were highly automated. And here is a point: the Hardware was actually great! #goLiquidHandlers. But the software sucked big time.

To get from a Protocol (“recipe” of how to process Biological samples) to a Layout of an automated lab used to take around 7weeks, and you (the scientist) had to talk with a handful of engineers. And, sometimes, you had to sit and code glue-code in python or C++ and interface with many diverse Drivers, APIs and User Interfaces. Does it sound efficient? No. The scientist NEEDS the data, not the drill to figure out how to make robot arm 1 go to point A to point B using C++. This is an Arbitrary Complexity.

It’s just a lab! shouldn’t be complicated for scientists to do science! (ha, you understand the name now: aLab).

aLab, one simple tool to help science meet scale, to enable scientists to spend less time pipetting liquids on plastic plates, and spend more time thinking! (I still don’t fully trust a human brain, neither should you. But Their brains happens to be the Best tool we have. So let’s use it!).
That’s all folks. All the rest of the UX will be walking you how we worked the details.

WIP.