Emre Sevinç

“So the syntax of the regular-expression language is awful; there are various incompatible forms of the language; and the quotation conventions are baroquen [sic]. While regular expression languages are domain-specific languages, they are bad ones. Part of the value of examining regular expressions is to experience how bad things can be.”
― Chris Hanson, Software Design for Flexibility: How to Avoid Programming Yourself into a Corner

“Regular expressions are widely used for string matching. Although regular-expression systems are derived from a perfectly good mathematical formalism, the particular choices made by implementers to expand the formalism into useful software systems are often disastrous: the quotation conventions adopted are highly irregular; the egregious misuse of parentheses, both for grouping and for backward reference, is a miracle to behold. In addition, attempts to increase the expressive power and address shortcomings of earlier designs have led to a proliferation of incompatible derivative languages.”
― Chris Hanson, Software Design for Flexibility: How to Avoid Programming Yourself into a Corner

“A good program must be written many times. This is true of the programs we show. The first draft may not clearly separate out the concerns, but by making that draft the programmer learns the structure of the problem. We will show two different implementations, which will reveal the evolution of the program as we identify shortcomings in our draft.”
― Chris Hanson, Software Design for Flexibility: How to Avoid Programming Yourself into a Corner

“Yann LeCun's strategy provides a good example of a much more general notion: the exploitation of innate knowledge. Convolutional neural networks learn better and faster than other types of neural networks because they do not learn everything. They incorporate, in their very architecture, a strong hypothesis: what I learn in one place can be generalized everywhere else.

The main problem with image recognition is invariance: I have to recognize an object, whatever its position and size, even if it moves to the right or left, farther or closer. It is a challenge, but it is also a very strong constraint: I can expect the very same clues to help me recognize a face anywhere in space. By replicating the same algorithm everywhere, convolutional networks effectively exploit this constraint: they integrate it into their very structure. Innately, prior to any learning, the system already “knows” this key property of the visual world. It does not learn invariance, but assumes it a priori and uses it to reduce the learning space-clever indeed!”
― Stanislas Dehaene, How We Learn: Why Brains Learn Better Than Any Machine . . . for Now