My key observation for this discovery was that we humans are very bias to what kind of information (i.e. seeing, hearing, feeling, tasting and smelling) we pay attention but disregard the rest. That is why we don't get to see the whole picture and hence, remain stuck in a confusing state of mind - sometimes for many years - because we tend to neglect to consider almost all information outside the spectrum of our very subjective and limited range of sensory perception. Since we humans tend not to be systematic in selecting the data sources and dimensions of information (i.e. feature selection) without even being aware of it, we need the help of less bias artificial intelligence.
The fact that we cannot perceive this information does not make it any less important or relevant for our lives. That is what I realized when trying to get a better understanding of the regulation of the aging process. My problem was - or actually still is - that I could not find the kind of datasets, I'd need to test new hypotheses. This implies that nobody before me seemed to have felt that collecting transcriptome, proteome, metabolome and epi-genetic data every 5 minutes throughout the entire lifespan of the yeast, would be worth the effort. We are totally capable of generating the data needed to advance in our understanding of aging and many other complex and still obscure phenomena, but the wet-lab scientists, who design our biological and medical experimental studies, don't seem to be even aware of missing something very important.
A good example is the magnetic field. We humans tend to ignore it because we cannot feel it. But, nevertheless, it affects our lives. It can cure depression. It can make your thumbs move involuntarily. Some birds use it to navigate the globe on their seasonal flight migration.
I am worried about that there are other fundamental phenomena similar to the magnetic field, of which none of us is aware yet, because so far we have not tried to look for similar imperceptible information carrying dimensions. For example, spiders, ants and bets are blind. However, visible light affects their lives regardless whether or not they have a concept of vision, since they have never experienced it. There could be other information carrying dimensions that – like the light for the spiders, ants and bets – is an imperatively hidden object (IHO), although it affects our lives so profoundly that we cannot understand aging and many other complex phenomena without considering such kind of information as well. That is why I recommend using artificial intelligence to reduce our observational bias.
Often, scientific progress has been made by accident. The means that a mistake, which changed the otherwise constant experimental environment in such a way that an unexpected result or observation was the consequence, was what helped us unexpectedly to finally make some progress. That is why I am proposing to intentionally vary external experimental conditions, methods, measurements, study designs, etc., to discover new features, which affect the outcome, much sooner.
Dear Thomas,
very interesting observation and field of research. I think human attention, nowadays are widely used in computer applications such as machine vision, saliency extraction and other applications. you need to take a look at such topics in additional to the main problem with human attention which is known as singularity of attention.
best,
Mohammad
Dear Thomas,
very interesting observation and field of research. I think human attention, nowadays are widely used in computer applications such as machine vision, saliency extraction and other applications. you need to take a look at such topics in additional to the main problem with human attention which is known as singularity of attention.
best,
Mohammad
Hey Thomas Hahn,
Machine learning could be improve modeling and simulation designs by applying the challenging of Quantum Biology and Quantum Artificial Intelligence Inspired Strategies,…
Prof. Dr. SOLTANE Mohamed
MESRS
In summary you need thinkers out of the box when designing machine learning systems or data collection methods. They may help in doing things differently such that you may strike that deviation from the norm which you are looking for. For that you need to reach scientists that are unfamiliar with machine learning.
I think the answer lies in the training dataset (liken our experiences), If we were to ground ML to its fundamental core, it is essentially the probability of an occurrence of an experience or a combination of experiences The more rich (chronological and variable) the training dataset the better is your prediction and modelling. Applying any amount of sophistication in the ML algorithm can not substitute a rich dataset, on the contrary may introduce known/unknown mathematical bias.
See ACTIVE LEARNING (http://burrsettles.com/pub/settles.activelearning.pdf)
https://machinelearningmastery.com/improve-deep-learning-performance/
- Badges
- Science topic