That sounds good enough for a start, considering you can massively parallelize the AI co-scientist workflow, compared to the timescale and physical scale it would take to do the same thing with human high school sophomores.
And every now and then, you get something exciting and really beneficial coming from even inexperienced people, so if you can increase the frequency of that, that sounds good too.
We have a couple automation systems that are semi-custom - the robot can handle operation of highly specific, non-standard instruments that 99.9% of labs aren't running. Systems have to handle very accurate pipetting of small volumes (microliters), moving plates to different stations, heating, shaking, tracking barcodes, dispensing and racking fresh pipette tips, etc. Different protocols/experiments and workflows can require vastly different setups.
See something like:
[1] https://www.hamiltoncompany.com/automated-liquid-handling/pl...
[2] https://www.revvity.com/product/fontus-lh-standard-8-96-ruo-...
I've been interested in this kind of stuff watching it from afar and now I may need to buy / build a machine that does this kind of stuff for work.
When I demo'd my scope (which is similar to a 3d printer, using low-cost steppers and other hobbyist-grade components) the CEO gave me feedback which was very educational. They couldn't build a system that used my style of components because a failure due to a component would bring the whole system down and require an expensive service call (along with expensive downtime for the user). Instead, their mech engineer would select extremely high quality components that had a very low probability of failure to minimize service calls and other expensive outages.
Unfortunately, the cost curve for reliability not pretty, to reduce mechanical failures to close to zero costs close to infinity dollars.
One of the reasons Google's book scanning was so scalable was their choice to build fairly simple, cheap, easy to maintain machines, and then build a lot of them, and train the scanning individuals to work with those machines quirks. Just like their clusters, they tolerate a much higher failure rate and build all sorts of engineering solutions where other groups would just buy 1 expensive device with a service contract.
Maybe the next startup idea is biochemistry as a service, centralised to a large lab facility with hundreds of each device, maintained by a dedicated team of on-site professionals.
Could they not make the scope easily replaceable by the user and just supply a couple of spares?
Just thinking of how cars are complex machines but a huge variety of parts could be replaced by someone willing to spend a couple of hours learning how.
Regulator is not only there to protect the public, it also protects VC from responsibility
Regulations around clinical trials represent the floor of what's ethically permissible, not the ceiling. As in, these guidelines represent the absolute bare minimum required when performing drug trials to prevent gross ethical violations. Not sure what corners you think are ripe for cutting there.
Disagree. The US FDA especially is overcautious to the point of doing more harm than good - they'd rather ban hundreds of lifesaving drugs than allow one thalidomide to slip through.
But off-label use is legal, so it's ok to use a drug that's safe but not proven effective (to the FDA's high standards) for that ailment... but only if it's been proven effective for some other random ailment. That makes no sense.
> What's your level of exposure to the pharma industry?
Just an interested outsider who read e.g. the Omegaven story on https://www.astralcodexten.com/p/adumbrations-of-aducanumab .
It is trained on human prose; human prose is primarily a representation of ideas; it synthesizes ideas.
There are very few uses for a machine to create ideas. We have a wealth of ideas and people enjoy coming up with ideas. It’s a solution built for a problem that does not exist.
Or imagine this one - computer maps the whole world, suggests a route how to get to any destination?!
You just described a basic search engine.
LLM is kind of a search engine for language
A child learns how to eat solid food and how to walk. That a square peg fits into a square hole. This has nothing to do with language.
people who deaf and mute and cannot read can still reason and solve problems.
big if true
And that their generation of impressive high school sophomore ideas is faster, more reliable, communicated better, and can continue 24/7 (given matching collaboration), relative to their bio high school sophomore counterparts.
I don’t believe any natural high school sophomore as impressive on those terms, has ever existed. Not close.
We humans (I include myself) are awful at judging things or people accurately (in even a loose sense) across more than one or two dimensions.
This is especially true when the mix of ability across several dimensions is novel.
(I also think people under estimate the degree that we, as users and “commanders” of AI, bottleneck their potential. I don’t suggest they are ready to operate without us. But that our relative lack of energy, persistence & focus all limit what we get from them in those dimensions, hiding significant value.
We famously do this with each other, so not surprising. But worth keeping in mind when judging limits: whose limits are we really seeing.)
We all have our idiosyncratically distributed areas of high intuition, expertise and fluency.
None of us need apprentice level help there, except to delegate something routine.
Lower quality ideas there would just gum things up.
And then we all have vast areas of increasingly lesser familiarity.
I find, that the more we grow our strong areas, the more those areas benefit with as efficient contact as possible with as many more other areas as possible. In both trivial and deeper ways.
The better developer I am, in terms of development skill, tool span, novel problem recognition and solution vision, the more often and valuable I find quick AI tutelage on other topics, trivial or non-trivial.
If you know a bright high school student highly familiar with a domain that you are not, but have reason to think that area might be helpful, don’t you think instant access to talk things over with that high schooler would be valuable?
Instant non-trivial answers, perspective and suggestions? With your context and motivations taken into account?
Multiplied by a million bright high school students over a million domains.
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We can project the capability vector of these models onto one dimension, like “school level idea quality”. But lower dimension projections are literally shadows of the whole.
It if we use them in the direction of their total ability vector (and given they can iterate, it is actually a compounding eigenvector!) and their value goes way beyond “a human high schooler with ideas”.
It does take time to get the most out of a differently calibrated tool.
I don't quite understand the argument here. The future hasn't happened yet. What does it mean to demonstrate the future developments now?
In this case they actually tested a drug probably because Google is paying for them to test whatever the AI came up with.
On the level of suggesting suitable alternative ingredients in fruit salad.
We should really stop insulting the intelligence of people to sell AI.