To imitate the teaching-learning phenomenon in the real world, the HDTLM is composed of three phases, i.e. discrete teaching phase based on probabilistic model, discrete learning phase based on hierarchical structure, and reinforcement learning.
A startup out of Cambridge called Prowler.io is developing a new kind of decision-making platform based on probabilistic modelling, reinforcement learning and game theory that it believes may help the AI community answer that question with a "yes".
The first one is based on probabilistic estimation [9].
There exist several algorithms for unsupervised learning that are based on probabilistic reasoning, such as Bayes networks, graphical models, multiple eigenspaces, and different variants of HMMs.
Other machine learning and physics approaches are based on probabilistic graphical models such as Latent Dirichlet Allocation (LDA, Airoldi et al., 2008; Ball et al., 2011; Blei et al., 2003).
The third method abandons the biophysical model in favor of machine learning algorithms that empirically infer structure based on probabilistic graphical models [e.g. CONTRAfold (Do et al., 2006)] or non-parametric methods [e.g. KNETfold (Bindewald et al., 2006)].
Will we toss the embryo, "start all over again and try for a better one?" Or change the offending genes based on probabilistic outcomes?
In particular, supervised machine learning algorithms are used to find information in semantic annotations, based on probabilistic reasoning.
