“What seems natural to us is probably just something familiar in a long tradition that has forgotten the unfamiliar source from which it arose. And yet this unfamiliar source once struck man as strange and caused him to think and to wonder.” —Martin Heidegger

In Gartner’s hype cycle, it’s called the trough of disillusionment. In venture capital or technologist parlance, we call it deep tech.

Machine learning is not magic; it’s just multidimensional curve fitting.

Six years later, I built my own similar workshop for a few hundred dollars, with a staff of zero. I bought an Oculus Quest from Best Buy, downloaded some CAD software, bought a 3D printer from Amazon, and built an IoT service connecting the two by following a YouTube video. I have no experience in CAD, no training in electronics, nor fabrication. I

logistic regression, K-nearest neighbors, support vector machine (SVM), decision trees, random forests, and so on. How can a data scientist possibly know which

Sometimes we want to analyze clusters of data (K-means) or reduce the dimensions or features in play (manifold learning) or convert from one type of data to another (autoencoding).

Author Kurt Vonnegut once said, “We are what we pretend to be, so we must be careful about what we pretend to be.”

Tech isn’t what you do; it’s the underlying foundation to how you do what you do.

A ‘Deep’ Technology was impossible yesterday, is barely feasible today, and may soon become so pervasive and impactful that it is difficult to remember life without. Deep Tech solutions are reimaginations of fundamental capabilities that are faithful to real and significant problems or opportunities, rather than to any one discipline.