If you want to get a jump on the latest developments in machine intelligence, head down to where Manhattan's Lower East Side borders Chinatown, a still-gritty but gentrifying neighborhood where hip coffee houses are springing up next to industrial-supply outlets and wholesale stores. The location, an area in the midst of transition and modernization, is a fitting spot for Fast Forward Labs, a startup founded to help companies innovate and compete using what founder and CEO Hilary Mason calls "recently possible" machine intelligence techniques and technology. Fast Forward Labs has an unusual business model. It produces quarterly reports on emerging (or what Mason calls "near future") technology, builds prototypes to demonstrate the technology and offers advisory services. The business is based on a subscription model: For an annual fee, clients get access to the reports and prototypes plus time with Mason and her colleagues, who offer advice on how to apply the technology.

The advent of audit analytics and cognitive technology does not mean the end of human auditors. It means an end to painstaking checking and crossfooting of debit and credit entries and the beginning of auditing careers that thrive on understanding, monitoring, and improving analytical and cognitive systems. I have worked for a couple decades with professional services firms that perform financial audits, but I have never done one--nor have I ever wanted to do one, to be honest. I'm not good with work that involves structured processes, details, and rigorous checking, and audits always seemed heavily infused with those kinds of tasks. Now, however, I am becoming quite interested in audits for two reasons.

Artificial intelligence (AI), one of 20 core technologies I identified back in 1983 as the drivers of exponential economic value creation, is rapidly working its way into our lives from Amazon's Alexa and Facebook's M, to Google's Now and Apple's Siri. An example of how far AI has come is the recent news that a Google supercomputer, using its advanced AI software, was able to win a stunning 3-0 victory in a man vs. machine face-off against Go grandmaster Lee Sedol, one of the game's all-time champions. For those who are not familiar with Go, it is a 3,000-year-old game that is widely considered to be the most complex game ever invented because it is reported to have more possible board configurations than there are atoms in the universe. Until just a few months ago, it was thought that a computer could not defeat a human grandmaster for at least another decade due to the game's complexity. How did Google's AlphaGo program advance so much faster than many expected?

In this paper, we present a prototype application — the Technology Trend Tracker — to measure the reality of technology trends of interest using information on the Web to inform decisions such as when to develop training, when to invest in expertise, and more. This prototype performs this task by integrating several artificial intelligence technologies in an innovative way. These technologies include rich semantic representations, a natural language understanding module, and a flexible semantic matcher. We use our system to augment Accenture's annual technology vision survey and show how our system performs well on measuring the reality of technology trends from this survey. We also show why our system performs well through an ablation study.