What’s happening with start-ups in cognitive computing? It is hard to know where to begin. The combination of the real dramatic progress on the technology, the surge of creativity in conceiving new applications – and big improvements on existing ones – and the tsunami of public hype around AI all combine to inspire a vast array of cognitive computing startups. Over the past three months I have compiled a working list of cognitive computing startups, as a tool to understand the scope and trends of entrepreneurship in the field.

The current list has 185 entities that look like startups – generally, small, privately held organizations, with basic web presence and a stated and serious focus on technology and applications of AI, machine learning and neural-inspired computing. I have tried to omit companies that have been acquired or gone defunct, or are so stealthy that they have no meaningful Internet footprint.   There are many more companies using some form of big data analysis than shown here. Given the hype around cognitive computing, it is certainly popular for many companies to include some mention of AI or machine learning, even when it is fairly tangential to a companies core activities. Making the judgment to include a name on my list was often a close call – there was no bright line. So in rough terms, the criteria might be summarized as follows:

• Must be a company or independent organization, not an open-source project.
• Must have enough information on the Internet (company description on web site, LinkedIn, angel investing sites, job postings) to get at least a crude understanding of the degree of focus on cognitive computing
• Focused on developing or using sophisticated machine learning, especially deep learning methods, not just, for example, doing big data management and analytics as modest part of a general cloud application environment in business intelligence, marketing, CRM, or ecommerce.

I examined four of five hundred companies as candidates for the list, and whittled it down to about 190 that seemed most interesting, most innovative and most focused on advanced cognitive computing technology and applications. The list of candidates came from lots of sources. I have heard about a wide range of vision-centric cognitive computing companies from working intensively in the computer-vision field in the past five years, as well as most of the companies doing specialized processors, and basic neural network technology. I also worked from other teams excellent published lists. The most useful of these is the excellent “The State of Machine Intelligence, 2016”, a list almost 300 companies put together by Shivon Zilis and James Cham and published in the Harvard Business Review, November 2, 2016. I also used the Machine Learning Startup list from angel.co as a source of ideas. Finally, I have had scores of conversations with practitioners in the field and read hundreds articles about startup activity over these three months to put together my list.

Three trends stand out as lessons from this survey exercise, beyond the sheer numbers. First, the group represents tremendous diversity, covering novel ideas from robotics, health care, self-driving cars, enterprise operations, on-line commerce, agriculture and personal productivity. These entrepreneurs all believe they have an opportunity to understand and exploit complex patterns in the masses of available data to yield better insights into how to serve customers and users. The more overwhelming the data, the greater the enthusiasm for deep learning from it. (It remains to be seen, however, which of these teams will actually succeed in systematically uncovering dramatic patterns and in monetizing those insights.)

Second, cloud-based software applications dominate the list. I think this comes both from the relative ease of starting enterprise software companies in the current venture climate and from the remarkable breadth of applicability of the powerful pattern recognition and natural language capabilities of state-of-the-art learning algorithms. So every application niche has an available sub-niche in cognitive compute approaches to that application. On the other hand, hardware startups, especially silicon-intensive startups, are pretty scarce. This reflects the fact that many enterprise-centric uses of cognitive computing are not actually much limited by the cost, power or performance of their cognitive computing algorithms – they are initially more concerned with just getting any consistent insights from their data. There is a healthy number of real-time or embedded applications here, especially in robotics and automotive, but these may be content for a while to build at the systems level leveraging off-the-shelf sensors, memories, and CPU, GPU and FPGA silicon computing platforms.

Third, the list is dynamic. Since I started looking, a handful has been acquired, and many more have been created. Undoubtedly many will fail to meet their lofty objectives and others will shift focus in response to the market’s relentless education on what’s really wanted. I’m convinced that the cognitive computing trend is not close to peaking, so we’ll see many new rounds of startups, both going deeper into the underlying technology, as it evolves, and going wider into new application niches across all kinds of cloud and embedded systems.

In the future, I expect to see a huge variety of every-day devices sprout cameras, microphones and motion sensors, with sophisticated cognitive computing behind them to understand human interactions and their environment with astonishing detail and apparent sophistication. Similarly, it seems quite safe to forecast systematic cloud-based identification of trends in our health, habits, purchases, sentiment, and activities. At a minimum, this will uncover macroscopic trends of specific populations, but will often come down, for better or for worse, to individual tracking, diagnosis and commercial leveraging.

The current list:  http://www.cogniteventures.com/the-cognitive-computing-startup-list/