Shivon Zilis is an investor at Bloomberg Beta, a very interesting early stage venture capital fund. I knew I would like Bloomberg Beta when I saw how they do their website. It is smart and reflects very well on their leadership team: They decided to leverage GitHub for their site. Doing so puts them one step closer to the creators that a VC in their space needs to know and is a great way to step out in a trust-based relationship. It is also a great way to be more transparent- they use GitHub to provide their entire operating manual for all to see.
But as an enterprise technologist I'm personally more interested in the types of firms VC's invest in. And Bloomberg Beta is focused on themes of very high interest there. From their manual that includes:
- Machine intelligence: Artificial intelligence and machine learning technologies, including both core technologies and industry applications
- Networks and communities: Connecting professional networks other than office workers (especially those where most of the work is in mobile contexts)
- New organizational models: Bringing software development methodologies to other applications (e.g., version control for the real world); design as a differentiator; unbundling of corporations
- Human-computer interaction: Hardware companies with network effects
- Media distribution: Over-the-top television
- Content discovery: Self-development through professional skill development
- Technology platforms: Exploiting the increasing power of the browser, and WebRTC in particular; exploring companion businesses to open source software
- Data: Vertical applications and analytical tools that affect business decisions
Which brings me to Shivon's context on Machine Intelligence. She has spent a great deal of thought and time seeking to comprehend a large and evolving marketplace and provides a succinct overview in the graphic below:
(click on the image for the full scale view)
To me the graphic is useful for several reasons. One is that with just a quick glance you can see this is a very broad market touching many industries and functions. It also shows that it is possible to divide and categorize this complex field into sub components and that is a relief to see. It is also a good way to learn about firms you don't know yet and that is always a pleasure. The field is still overwhelming, but thanks to this graphic it is a bit more understandable.
More from Shivon's site http://shivonzilis.com:
A year ago, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive. Figure 1 showcases the new landscape of machine intelligence as we enter 2016:
Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways. Machine intelligence already helps us get the important business information we need more quickly, monitors critical systems, feeds our population more efficiently, reduces the cost of health care, detects disease earlier, and so on.
The two biggest changes I’ve noted since I did this analysis last year are (1) the emergence of autonomous systems in both the physical and virtual world and (2) startups shifting away from building broad technology platforms to focusing on solving specific business problems.
To track more on this topic see:
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