In the field of functional genomics, the analysis of gene expression profiles through Machine and Deep Learning is increasingly providing meaningful insight into a number of diseases. The paper proposes a novel algorithm to perform Feature Selection on genomic-scale data, which exploits the reconstruction capabilities of autoencoders and an ad-hoc defined Explainable Artificial Intelligence-based score in order to select the most informative genes for diagnosis, prognosis, and precision medicine.

I was just a youth when Evelyn Wood debuted her speed-reading course back in 1959. For years, I was fascinated with the prospect of getting my reading assignments over with as quickly as possible so that I could get on to the fun part of life. Fortunately, I massively turned that around. The Evelyn Wood Reading Dynamics course became a huge sensation. So much so that the Kennedy White House sent staff members to take the course.

Deep learning (DL) has transformed much of AI, and demonstrated how machine learning can make a difference in the real world. Its core technology is gradient descent, which has been used in neural networks since the 1980s. However, massive expansion of available training data and compute gave it a new instantiation that significantly increased its power. Evolutionary computation (EC) is on the verge of a similar breakthrough. Importantly, however, EC addresses a different but equally far-reaching problem.

This means that certain aspects of PyTorch are hidden for convenience. This makes certain routines easier and adds additional functionality but introduces an additional layer of abstraction. This series starts with PyTorch at the bottom and moves upward (bottom up approach), so it's really a matter of preference for both the course and the library. The general suggestion is to use both courses as learning resources, and to learn pure PyTorch as well as the fast.ai

At Sentient, we have an entire team dedicated to research and experimentation in AI. Over the past few years, the team has focused on developing new methods in Evolutionary Computation (EC), i.e. designing artificial neural network architectures, building commercial applications, and solving challenging computational problems using methods inspired by natural evolution. This research builds upon more than 25 years of research at UT Austin and other academic institutions, and coincides with related efforts recently at OpenAI, DeepMind, Google Brain, and Uber. There is significant momentum building in this area; indeed, we believe evolutionary computation may well be the next big thing in AI technology. Like Deep Learning (DL), EC was introduced decades ago, and it is currently experiencing a similar boost from the available big compute and big data.