AI is experiencing an astounding resurrection. After so many broken promises, the term “artificial intelligence” had become almost a dirty word in technology circles. The field is now rising from the ashes. Researchers who had been toiling away in semi-obscurity over the last few decades have suddenly become superstars and have been aggressively recruited by the largest Internet companies: Yann LeCun (see his recent talk at our Data Driven NYC event here
) by Facebook; Geoff Hinton by Google; Andrew Ng by Baidu. Google spent over $400 million to acquire DeepMind, a 2 year old secretive UK AI startup. The press and social media are awash with thoughts on AI. Elon Musk cautions us against its perils.
What’s different this time? As Irving Wladawsky-Berger pointed out in a Wall Street Journal article
, “a different AI paradigm emerged. Instead of trying to program computers to act intelligently–an approach that hadn’t worked because we don’t really know what intelligence is– AI now embraced a statistical, brute force approach based on analyzing vast amounts of information with powerful computers and sophisticated algorithms.”
In other words, the resurgence of AI is partly a child of Big Data, as better algorithms (in particular, what’s known as “deep learning”, pioneered by LeCun and others) have been enabled by larger than ever datasets and the ability to process those datasets at scale at reasonable cost.
Continue reading “x.ai and the emergence of the AI-powered application”
Note: This post appeared on VentureBeat, here.
It’s been almost two years since I took a first stab at charting the booming Big Data ecosystem, and it’s been a period of incredible activity in the space. An updated chart was long overdue, and here it is:
(click on the arrows at the bottom right of the screen to expand)
A few thoughts on this revised chart, and the Big Data market in general, largely from a VC perspective:
Continue reading “The State Of Big Data in 2014: a Chart”