I built my own motion activated stairwell lights using an ESP32 and Home Assistant. Works pretty well.
So then I decided to set up some LED strip lights for my office with really nice wireless buttons from IKEA.
I confirmed that the buttons and the lights both were zigbee compatible, but I missed that one supported only Thread and the other only Matter. So now I have two identical antennas, each handling a different protocol.
Cool stuff. I first worked on ML for exploratory synthesis in 2012, and am still in related areas.
Once you have the experimental loop running, I suspect it will be quite difficult to hill climb on this task.
There will be some improvements you can make to the harness, but I suspect you'll be doing a lot of human in the loop review and providing feedback that goes back into the harness instructions.
I know it's fashionable to imagine automating the whole process, but everything I've seen is that the only systems that succeed are the ones that are augmenting an expert.
yeah, the tricky thing about the experimental loop is that:
1. its very difficult to do it in a reproducible manner (the same experiment done twice often gives different results due to small undocumented changes)
2. its expensive to do at scale.
Both of these properties make it hard to hill climb on experiment. What's worked for us so far is precisely what you said - having human experts review and provide feedback. we distil their reviews into rubrics, and have LLMs act as proxy experts using these rubrics. We expect the models will hill climb using this approach, and will reach (close to) human expert level by doing this.
Many years ago (13?), I was around when Amazon moved SABLE from RAM to SSDs. A whole rack came from a single batch, and something like 128 disks went out at once.
I was an intern but everyone seemed very stressed.
Shameless self promotion: I wrote one of the more cited papers in the field [0], back in 2016.
A key challenge: very few labs have enough data.
Something I view as a key insight: a lot of labs are doing absurdly labor intensive exploratory synthesis without clear hypotheses guiding their work. One of our more useful tasks turned out to be interactively helping scientists refine their experiments before running them.
Another was helping scientists develop hypotheses for _why_ reactions were occuring, because they hadn't been able to build principled models that predicted which properties were predictive of reaction formation.
Going all the way to synthesis is nice, but there's a lot of lower hanging fruit involved in making scientists more effective.
This is true. Getting datasets with the necessary quality and scale for molecular ML is hard and uncommon. Experimental design is also a huge value add, especially given the enormous search space (estimates suggest there are more possible drug-like structures than there are stars in the universe). The challenge is figuring out how to do computational work in a tight marriage with the lab work to support and rapidly explore the hypotheses generated by the computational predictions. Getting compute and lab to mesh productively is hard. Teams and projects have to be designed to do so from the start to derive maximum benefit.
Also shameless plug: I started a company to do just that, anchored to generating custom million-to-billion point datasets and using ML to interpret and design new experiments at scale.
> A key challenge: very few labs have enough data.
It is also getting harder, not easier, to get.
I am working right now on a retro synthesis project. Our external data provider is raising prices while removing functionality, and no one bats an eye. At the same time our own data is considered a business secret and therefore impossible to share.
As someone who does NLP research where the code, data and papers are typically free, this drives me insane.
Experienced chemists can look at molecule diagrams and have an intuition as to its activity and similarity to other known molecules. It’s like most of science and math: most discoveries begin with intuition and are demonstrated rigorously afterwards. I believe Poincare said something to this end.
I was implying that you still need a human to make the final decision. AI can be a valuable aid in both fields. Doctors can't just let the AI do all the work in the same way synthetic chemists can't blindly trust the AI to spit out correct and feasible results. Research time is expensive and thus the effort needs to be evaluated, and usually the intuition of said chemists trump that of the AI.
True. But perhaps you can eliminate 9 out of 10 chemists, and replace them by an AI that generates ideas. Then use the 1 chemist to validate those ideas.
Not to generate ideas, there's always more ideas than resources in chemistry.
Mainly to do more automated routines than ever.
9 out of 10 chemists aren't that great at the bench anyway.
Everyone would probably benefit from getting them in front of a computer full-time to leverage their training in a way, and freeing up the bench space to those who can really make the most of it.
Not the focus of the article, but analytical chemists need to do a lot of proper detecting themselves to be high-performing just like the radiologists do.
The brain is incredibly good at pattern matching while not necessarily being able to articulate why they came to that decision. Organic chemistry has these types of relations in spades. Say for example crystallization. You can kinda brute force it; there's only a few dozen realistic solvents to try, but that's a single solvent system. Then there's binary and ternary solvent systems. Then there's heat/cooling profiles, antisolvent addition, all kinds of things. Hundreds or thousands of possible experiments.
You might just decide that a compound "needs" isopropanol/acetone, plus a bit of water, cause something vaguely similar you encountered years ago crystallized well. You often start with some educated guesses and refine based on what you see.
But there's often no clear hypothesis, no single physical law the system obeys.
> Something I view as a key insight: a lot of labs are doing absurdly labor intensive exploratory synthesis without clear hypotheses guiding their work.
This lets you stumble over unknown unknowns. Taylor et al discovered high-speed steel by ignoring the common wisdom and doing a huge number of trials, arriving at a material and treatment protocol that improved over the then-state-of-the-art tool steels by an order of magnitude or more. The treatment mechanism was only understood 50-60 years later.
So then I decided to set up some LED strip lights for my office with really nice wireless buttons from IKEA.
I confirmed that the buttons and the lights both were zigbee compatible, but I missed that one supported only Thread and the other only Matter. So now I have two identical antennas, each handling a different protocol.
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