Despite what Stephen Hawking or Elon Musk say, hostile Artificial Intelligence is not going to destroy the world anytime soon. What is certain to happen, however, is the continued ascent of the practical applications of AI, namely deep learning and machine intelligence. The word is spreading in all corners of the tech industry that the biggest part of big data, the unstructured part, possesses learnable patterns that we now have the computing power and algorithmic leverage to discernand in short order.
The effects of this technology will change the economics of virtually every industry. And although the market value of machine learning and data science talent is climbing rapidly, the value of most human labor will precipitously fall. This change marks a true disruption, and there are fortunes to be made. There are also tremendous social consequences to consider that require as much creativity and investment as the more immediately lucrative deep learning startups that are popping up all over (but particularly in San Francisco.)
Shivon Zilis, an investor at BloombergBETA in San Francisco, put together the graphic below to show what she calls the Machine Intelligence Landscape. The fund specifically focuses on companies that change the world of work, so these sorts of automation are a large area of concern. Zilis explains, I created this landscape to start to put startups into context. Im a thesis-oriented investor and its much easier to identify crowded areas and see white space once the landscape has some sort of taxonomy.
Shivon Zilis, Machine Intelligence Landscape
What is striking in this landscape is how filled-in it is. At the top are core technologies that power the applications below. Big American companies like Google, IBM, Microsoft, Facebook and Chinas Baidu are well-represented in the core technologies themselves. These companies, particularly Google, are also the prime bidders for core startups as well. Many of the companies that describe themselves as engaging in artificial intelligence, deep learning or machine learning have some claim to general algorithms that work across multiple types of applications. Others specialize in the areas of natural language processing, prediction, image recognitionand speech recognition.
For the companies that are rethinking enterprise processes like sales, marketing, security or recruitment, or for others that are remaking industry verticals, the choices of technologies to license are dizzying. As Pete Warden, creator of the open source Data Science Toolkit, wrote in a recent post on deep learning, I dont see any reason why the tools we use to developand train networks, should be used to execute them in production. Entering 2015 we see all of this research finding its way into actual applications that relatively ordinary humans will use. I also think well end up with small numbers of research-oriented folks who develop models, Warden continues, and a wider group of developers who apply them with less understanding of whats going on inside the black box.
These companies will need more people who can create, iterate and debug deep learning and other kinds of machine learning models. They will also need an even larger cohort of developers and designers who can create usable experiences on screens that make all of this intelligence actionable. Big companies are poised to be the big winners here. Obviously they have the resources to attract or acquihirethis talent. Even more crucial, big companies have big data and ongoing relationships with large numbers of customers. In machine learning, it is most often the quality and quantity of data available that is the limiting factor, not the cleverness of the algorithms.
And what most concerns the big tech companies from Apple to Google to Microsoft and IBM? Yep, mobile, and as Zilis points out, Winning mobile will require lots of machine intelligence. Siri and Google Now are responses to the need for highly contextual voice interaction in mobile. Visual search like Amazons FireFly involves location-basedpattern recognition to create a pleasing experience. The reason for the current great enthusiasm for deep learning is that these kinds of problems can be solved now in minutes or days instead of years.
Original post:
Tech 2015: Deep Learning And Machine Intelligence Will Eat The World