It's the beginning of September (and no, I don't know how that happened either), and as the summer lull winds to a close and we prepare for a renewed frenzy of activity, it's a good moment to take stock of the year so far. Last month, two different articles were published looking back over some of the key trends we've seen in 2018 with regard to emerging technology and digital transformation, and comparing them to what was predicted. One was a piece by Forbes contributor Daniel Newman of CMO Network, who revisited his predictions for digital transformation in 2018 in light of the past eight months, to see where we are with some of 2018's most potentially transformative technologies. It takes a broadly optimistic view of the technologies that are meant to be shaking up the digital world in 2018, with a few caveats. The other was by The Register's Andrew Orlowski, written in response to the publication of Gartner's annual'Emerging Technologies Hype Cycle', which revealed that a number of the most "hyped" technologies from last year have vanished from this year's chart.

U of A Lecture – Demystifying Artificial Intelligence- Part 1 Part 1 I have split this blog into two parts, as it is over 2000 words in its entirety. The AMII Institute We went to a lecture the other week (Feb 20, 2018) about developments in artificial intelligence. The talk was put on by the University of Alberta Faculty of Extension, partnering with the Alberta Machine Intelligence Institute (AMII). The presentation was given by Geoff Kliza, one of the researchers at AMII. AMII has been around for 15 years or so, though there has been a name change.

Every once in a while, there appears a paper that is so good, so well-written and so packed with insights, that I would read it a few times… that I wished I had written it myself. That happened when I read this perspective paper by Jonathan Chen and Steven Asch. Normally I'd tweet a link to it, or may be even add a figure from it (if I expect people to learn from it without them having to spend time), and there ends the matter. Sometimes I might add few more tweets, if I couldn't pack it all in a single tweet. But this piece is so good, the two pages are well worth a read.

We just finished our Lean LaunchPad class at UC Berkeley's engineering school where many of the teams embedded machine learning technology into their products. It struck me as I watched the teams try to find how their technology would solve real customer problems, is that machine learning is following a similar pattern of previous technical infrastructure innovations. Early entrants get sold to corporate acquirers at inflated prices for their teams, their technology, and their tools. Later entrants who miss that wave have to build real products that people want to buy. I've lived through several technology infrastructure waves; the Unix business, the first AI and VR waves in the 1980's, the workstation wave, multimedia wave, the first internet wave.

We just finished our Lean LaunchPad class at UC Berkeley's engineering school where many of the teams embedded machine learning technology into their products. It struck me as I watched the teams try to find how their technology would solve real customer problems, is that machine learning is following a similar pattern of previous technical infrastructure innovations. Early entrants get sold to corporate acquirers at inflated prices for their teams, their technology, and their tools. Later entrants who miss that wave have to build real products that people want to buy. I've lived through several technology infrastructure waves; the Unix business, the first AI and VR waves in the 1980's, the workstation wave, multimedia wave, the first internet wave.

Research firm Gartner periodically releases a "Hype Cycle," in which it attempts to show which technologies are just emerging, and which have plateaued. This year, its hype cycle highlights three technologies in particular: "transparently immersive experiences" (i.e., virtual- and augmented-reality systems), "the perceptual smart machine age" (hello, Internet of Things), and "the platform revolution" (i.e., apps, lots of apps). Gartner releases the "Hype Cycle" as a way for tech pros--including chief innovation officers, startup founders, and developers--to keep an eye on those technologies that haven't quite reached the mainstream-adoption stage, but will nonetheless prove critical to many businesses over the next several years. The Innovation Trigger When technologies emerge from the lab and start becoming popular. The Peak of Inflated Expectations In other words, when companies want to adopt said technologies.

According to the 2016 Gartner Hype Cycle for Emerging Technologies, machine learning is at the very "peak of inflated expectations," the highest point in the S-curve that Gartner awards technologies in its Hype Cycle reports. Many machine learning advocates may feel betrayed to think that machine learning is at the peak of its hype cycle, thinking that analysts like Gartner believe machine learning to be all fluff and no substance. But that is not the case. Many emerging technologies never actually make it to the peak of the hype cycle, fizzling out long before they can make a true impact. In reality, technologies that make it to the peak of the hype cycle are almost ready for universal deployment.