There are several Dark Secrets at the heart of current artificial intelligence. MIT Technology Review in the issue of Nov./Dec. 2017 said, “No one really knows how most advanced algorithms do what they do. That could be a problem.” Jeff Hinton is deeply suspicious about the current approach to machine learning and thought that in his view to throw it all away and start again. Josh Tennenbaum at CNN2017 said, "All of these AI systems we see, none of them is ‘real’ AI". In Yann LeCun’s view, "The brain learns with an efficiency that none of our machine learning methods can match". It is the time to take a completely new approach to artificial intelligence and machine learning. This representation attempts to demystify intelligence in one slide with an approach completely departure from existing ones.

1. Copyright by Xiaofei Huang , Ph.D. of Tsinghua University, Beijing, China , HuangZeng@yahoo.com, 3/20/18
The Dark Secrets at the Heart of Current Artificial Intelligence
➢ MIT Technology Review in the issue of Nov./Dec. 2017 said, “No one really knows how most
advanced algorithms do what they do. That could be a problem.”
➢ Jeff Hinton is deeply suspicious about the current approach to machine learning and
thought that in his view to throw it all away and start again.
➢ Josh Tennenbaum at CNN2017 said, "All of these AI systems we see, none of them is ‘real’ AI".
➢ In Yann LeCun’s view, "The brain learns with an efficiency that none of our machine learning
methods can match".

2. The Essence of Learning is Generalization
Let 𝑝 𝑥 be the original probability density function and 𝑝+
(𝑥) be an approximation to it.
Let the entropy of the former be 𝐻(𝑝) and 𝐻(𝑝+
) for the latter. 𝑝+
(𝑥) is a generalization
to 𝑝 𝑥 if it is an underfitting function to the latter satisfying 𝐻(𝑝+
) ≥ 𝐻(𝑝) in any
subspace or the original space and the equality holds true if and only if 𝑝+
𝑥 = 𝑝(𝑥).
 Learning: The ultimate objective of learning is to use existing observations and
experiences to find a mapping from observables Y to another set of variables X and
a generalization 𝑝+
(𝑥) on X such that its degree of freedom 𝑯(𝒑+
) is minimized. That
is, the objective can be described as the following minimization problem:
𝐦𝐢𝐧
𝒀→𝑿, 𝒑+ 𝒙
𝑯(𝒑+
)
 Prediction: The model 𝑝+
(𝑥) can be used to make predictions on any subset of
variables 𝑋𝑗 given the observations 𝑝(𝑋𝑖) on another subset of variables 𝑋𝑖 by the
following generalized Bayesian law:
𝒑+
𝑿𝒋/𝒑(𝑿𝒊) = ෍
𝑿−𝑿 𝒋
𝒑+
𝒙 𝒑(𝑿𝒊)
σ 𝑿−𝑿 𝒊
𝒑+(𝒙)
This information-theoretic approach not only can unlock the mystery of human visual
perception and explain the great discovery of 𝑬 = 𝒎𝒄 𝟎
𝟐
, but also can lead to a new
discovery 𝑬 = 𝒎𝒄𝒄 𝟎 when gravity is presented. Here, 𝑐0 is the speed of light in free space
and 𝑐/𝑐0 is the time-dilation factor. Consequently, we have 𝒈 = −𝒄𝜵𝒄.