I don’t see artificial intelligence improving in a way that suggest that it soon solves all our biological problems.
As far as effective robots go, you could call high-throughput sequencing machines effective robots. Cheaper gene sequencing then produced a lot of new knowledge. Fortunately databases like uniprot allow access towards the produced knowledge that goes well beyond what scientific papers can provide.
Imagine the mess if all the information about genetic sequences would be written in papers and textbooks.
We could have further success in biology if we manage to treat other biological problems the same we. Instead of focusing on insight, we focus on building better tools and infrastructure to organize the resulting knowledge in a useful fashion.
With smart watches that integrate constant heart rate monitoring, we have suddenly a lot of data but are still lacking in interpretation. Lack of use cases for data currently stalls development (see Apple).
It might be possible to develop early warning signals for the illnesses like the flu with pick up signals before the patient is consciously aware of the flu.
Obesity is also an interesting subject. To me BMI feels like a stone age diagnostic tool. There’s no way that the statistic is optimal but it still get’s used because obesity researchers generally don’t work on developing better scales for obesity but take the existing scores for granted and test interventions or go for molecular biology.
I think we should look more on how we structure biological knowledge then on testing individual molecular biological hypothesis.
These are all good points. And you’re right that A.I. and robotics will come (for a while presumably) in the form of incremental improvements, as they already are.
I guess it feels to me though, like the rate of improvement is basically independent of biology itself, and is determined by the rate at which other fields hand it technology. Sequencing being a good example. We’ve basically done it to death now, and are waiting for someone to give us better methods that will yield new insights. The major bottleneck at the moment is that we insist on using grad students for manual labor, and nobody has the vision to see that the initial problems in automating basic laboratory tasks would be more than compensated for in the long run, as methods improve, provided there’s a collective effort.
I’m also in general not enthusiastic about the many aspects of biology I see, because they seem aimed towards giving incremental life extensions to already affluent and long lived people, at great cost, or of helping a very small number of people at very high cost. Not that these aren’t good goals, I just think there’s lower hanging fruit.
nobody has the vision to see that the initial problems in automating basic laboratory tasks would be more than compensated for in the long run
That’s not true. Peter Thiel’s Founders Fund for example backs http://emeraldcloudlab.com/ which automate basic laboratory tasks. They seem to have enough customers to have a waiting list.
I don’t see a “Jobs” page on their homepage at first glance, but I would expect that they are a company that can make use of your skills.
It might very well be possible to get a meaningful happiness score out of high resolution GSR data and heart rate data. A few years ago I heard that they manage to do emotional detection with 80% accuracy.
Having an objective way to measure happiness on a daily basis would be huge for treating depression and measuring which drug works.
Today’s heart rate monitors might not be high accuracy enough, so there’s a need to provide applications for higher resolution monitoring to incentivise Apple, Samsung and Microsoft to develop the tech for smartwatches.
GSR data is very useless if you don’t have an algorithm interpreting it but once you interpret it you can pick up bodily events.
Besides developing the actual hardware developing statistics like that, that provide meaningful insights seems to be important to me. Developing better scales for obesity isn’t only about producing new technology but also about analysis of data.
The fact that we have an open access uniprot is also not just about technology. If you look at chemistry where the American Chemical Association claims ownership of CAS numbers, the state of affairs is worse.
Thinking about getting more knowledge into a format like uniprot, isn’t just about technology but requires thinking about ontology. I like Barry Smiths work in that area.
The fact that you can do your PHD without being aware where real innovation is happening is a good illustration of the poor state of academic biology.
I don’t think there are easy solutions for fixing academic biology, but it’s an important problem. The key is to step up a meta level. It might be harder to get grants to work on that level but the possible gain is much higher.
In some sense the biosafety issues are also up one meta level.
The replicability of papers is significantly hindered by lack of automation, to my mind.
I think the Cloud approach is here really great. In a laboratory you can do things implicitly. In a cloud experiment you have to specify everything explicitly.
Emerald Cloudlab allows free hosting of all the experimental data if a researcher makes the data publically available.
I don’t see artificial intelligence improving in a way that suggest that it soon solves all our biological problems.
As far as effective robots go, you could call high-throughput sequencing machines effective robots. Cheaper gene sequencing then produced a lot of new knowledge. Fortunately databases like uniprot allow access towards the produced knowledge that goes well beyond what scientific papers can provide. Imagine the mess if all the information about genetic sequences would be written in papers and textbooks.
We could have further success in biology if we manage to treat other biological problems the same we. Instead of focusing on insight, we focus on building better tools and infrastructure to organize the resulting knowledge in a useful fashion.
With smart watches that integrate constant heart rate monitoring, we have suddenly a lot of data but are still lacking in interpretation. Lack of use cases for data currently stalls development (see Apple). It might be possible to develop early warning signals for the illnesses like the flu with pick up signals before the patient is consciously aware of the flu.
Obesity is also an interesting subject. To me BMI feels like a stone age diagnostic tool. There’s no way that the statistic is optimal but it still get’s used because obesity researchers generally don’t work on developing better scales for obesity but take the existing scores for granted and test interventions or go for molecular biology.
I think we should look more on how we structure biological knowledge then on testing individual molecular biological hypothesis.
These are all good points. And you’re right that A.I. and robotics will come (for a while presumably) in the form of incremental improvements, as they already are.
I guess it feels to me though, like the rate of improvement is basically independent of biology itself, and is determined by the rate at which other fields hand it technology. Sequencing being a good example. We’ve basically done it to death now, and are waiting for someone to give us better methods that will yield new insights. The major bottleneck at the moment is that we insist on using grad students for manual labor, and nobody has the vision to see that the initial problems in automating basic laboratory tasks would be more than compensated for in the long run, as methods improve, provided there’s a collective effort.
I’m also in general not enthusiastic about the many aspects of biology I see, because they seem aimed towards giving incremental life extensions to already affluent and long lived people, at great cost, or of helping a very small number of people at very high cost. Not that these aren’t good goals, I just think there’s lower hanging fruit.
That’s not true. Peter Thiel’s Founders Fund for example backs http://emeraldcloudlab.com/ which automate basic laboratory tasks. They seem to have enough customers to have a waiting list.
I don’t see a “Jobs” page on their homepage at first glance, but I would expect that they are a company that can make use of your skills.
It might very well be possible to get a meaningful happiness score out of high resolution GSR data and heart rate data. A few years ago I heard that they manage to do emotional detection with 80% accuracy. Having an objective way to measure happiness on a daily basis would be huge for treating depression and measuring which drug works.
Today’s heart rate monitors might not be high accuracy enough, so there’s a need to provide applications for higher resolution monitoring to incentivise Apple, Samsung and Microsoft to develop the tech for smartwatches. GSR data is very useless if you don’t have an algorithm interpreting it but once you interpret it you can pick up bodily events.
Besides developing the actual hardware developing statistics like that, that provide meaningful insights seems to be important to me. Developing better scales for obesity isn’t only about producing new technology but also about analysis of data.
The fact that we have an open access uniprot is also not just about technology. If you look at chemistry where the American Chemical Association claims ownership of CAS numbers, the state of affairs is worse. Thinking about getting more knowledge into a format like uniprot, isn’t just about technology but requires thinking about ontology. I like Barry Smiths work in that area.
Thank you, I’d never heard of Emerald Cloudlab. I guess I was speaking too much from my own observations and without enough research.
The fact that you can do your PHD without being aware where real innovation is happening is a good illustration of the poor state of academic biology.
I don’t think there are easy solutions for fixing academic biology, but it’s an important problem. The key is to step up a meta level. It might be harder to get grants to work on that level but the possible gain is much higher. In some sense the biosafety issues are also up one meta level.
I think the Cloud approach is here really great. In a laboratory you can do things implicitly. In a cloud experiment you have to specify everything explicitly. Emerald Cloudlab allows free hosting of all the experimental data if a researcher makes the data publically available.