This list gathers five of our most popular articles covering tools, technologies, and trends relevant to engineering leaders.
The web was never supposed to be a few walled gardens of concentrated content owned by a few major publishers; it was supposed to be a cacophony of different sites and voices.
I don't want to underestimate the difficulty of this project, or overestimate its chances of success. We'd certainly have to get used to sites that aren't as glossy or complex as the ones we have now. We might have to revisit some of the most hideous bits of the first-generation web, including those awful GeoCities pages. We would probably need to avoid fancy, dynamic websites; and, before you think this will be easy, remember that one of the first extensions to the static web was CGI Perl. We would be taking the risk that we'd re-invent the same mistakes that brought us to our current mess. Simplicity is a discipline, and not an easy one. However, by losing tons of bloat, we'd end up with a web that is much faster and more responsive than what we have now. And maybe we'd learn to prize that speed and that responsiveness. — Mike Loukides.
A look inside the quest to evolve neural networks through evolutionary algorithms.
Neuroevolution is making a comeback. Prominent artificial intelligence labs and researchers are experimenting with it, a string of new successes have bolstered enthusiasm, and new opportunities for impact in deep learning are emerging. Maybe you haven’t heard of neuroevolution in the midst of all the excitement over deep learning, but it’s been lurking just below the surface, the subject of study for a small, enthusiastic research community for decades. And it’s starting to gain more attention as people recognize its potential. — Kenneth O. Stanley
Data engineers and data scientists are not interchangeable—and misperceptions of their roles can hurt teams and compromise productivity.
When I work with organizations on their team structures, I don’t use a Venn diagram to illustrate the relationship between a data engineer and a data scientist. I draw the diagram as shown [in the figure below]. — Jesse Anderson
We must be prepared for the blockchain’s promise to become a new development environment.
For developers, the blockchain concept represents a paradigm shift in how software engineers will write software applications in the future, and it is one of the key concepts that needs to be well understood. We need to really understand five key concepts, and how they interrelate to one another in the context of this new computing paradigm that is unravelling in front of us: the blockchain, decentralized consensus, trusted computing, smart contracts, and proof of work/stake. This computing paradigm is important because it is a catalyst for the creation of decentralized applications, a next-step evolution from distributed computing architectural constructs. — William Mougayar
Design thinking helps organizations grow, innovate, and improve financial performance.
By introducing different ways of problem solving and methods for discovering what people truly need, design thinking helps organizations change their cultures to become more customer centric and collaborative. While every company is different, useful metrics for assessing the impact of design thinking include: cultural measures, such as employee satisfaction, internal engagement, and efficiency; financial measures, such as sales and productivity; and product quality measures, such as customer satisfaction. — Jonathan Follett and Mary Treseler