Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

This video clip is from the Creative Destruction Lab's third annual conference, "Machine Learning and the Market for Intelligence", hosted at the University of Toronto's Rotman School of Management on October 26, 2017. The Creative Destruction Lab is a seed-stage program for massively-scalable, science-based companies. Graduates include companies such as Atomwise (San Francisco), Thalmic Labs (Waterloo), Deep Genomics (Toronto), Kyndi (Palo Alto), Nymi (Toronto), Automat (Montreal), Ada (Toronto), and Heuritech (Paris). This year, the program admitted 125 AI-oriented startups in Toronto and another 40 at other CDL locations across Canada. To our knowledge, this is the third year in a row that the CDL is home to the greatest concentration of AI startups of any program on Earth.

Some of the world's wealthiest and most influential leaders came to California this week for the Milken Institute Global Conference, a wide-ranging review of issues permeating economics and politics, with topics ranging from agriculture to mortgage markets to international trade and alliances, plus a long look at what the future will hold. Of the 4,000 VIPs who attended -- invitations are highly selective, and tickets topped out as high as $50,000 -- one of the most intriguing questions under discussion was one that almost no one could readily answer: What effect will robotics and artificial intelligence have on our lives and on the world's business, and how rapidly will this next technological revolution take place? The Milken Institute Global Conference, an annual event for the past 20 years, has grown steadily into a unique gathering: individuals with the capital, power and influence to move the world forward meet face-to-face with those whose expertise and creativity are reinventing industry, philanthropy and media. This year's meeting in Beverly Hills, California, amounted to a peer review of President Donald Trump's first 100 days in office. Four members of Trump's Cabinet took part.

Artificial Intelligence (AI) has permeated many industries from finance to healthcare, and sales is no exception. Within sales tech, there are a number of startups using AI and machine learning capabilities to help facilitate and streamline the sales process, particularly in the area of voice call and speech analytics. We used the CB Insights platform to surface 10 notable, early- to mid-stage (Series C or earlier) sales tech startups developing software platforms that specifically incorporate AI and machine learning. We selected these companies based on several factors including the recency and size of the companies' disclosed funding, as well as Mosaic scores, CB Insights' algorithm that uses financial and non-financial signals to predict private company health. After evaluating the companies on the list, we determined that the 10 companies below are some of the top companies to pay attention to.

Cobalt Robotics has launched their stylish security robot. The robot was designed by Yves Behar and as a fabric covered robot, it's putting a new spin on soft robotics! Behar's goal was to create a robot that didn't conform to Hollywood stereotypes but instead as an augmentation of human ability and an enhancement to the human environment. "Creating the right form for Cobalt is crucial to its success. As a service for security and concierge, it becomes part of an office culture. This balance between approachability and discretion became a thematic challenge throughout the design process. We decided that the robot should not adopt a humanoid personality. Instead, it should aesthetically align with the furniture and décor of the office environment. The Cobalt robot's semi-cylindrical self-driving mechanism, sensors and cameras are covered by a tensile fabric skirt. This helps maximize the access and usability of the internal technologies, creates airflow to prevent overheating, and conveys a soft and friendly persona." said Behar.

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

Shivon Zilis will participate in a panel discussion at Strata Hadoop World New York 2016, "Where's the puck headed?," considering the big trends in big data and explaining what the field will look like down the road. A year ago today, 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.

When Bloomberg first built the terminal system, back in the early 1980s, most of its customers -- mainly finance professionals -- didn't have computers on their desks. The internet was not yet a commonly-accepted technical protocol for networking and hardware of the terminal's kind hadn't been seen before. So Bloomberg's engineers had to go about inventing the tech themselves -- from the set of instructions to carry data across a network, to custom-built hardware so traders could use a keyboard, and monitors you could stack. It created a great culture of invention at Bloomberg, which has more software engineers than journalists. But cultivating that culture to create new products within came at a small cost.

A year ago today, 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. Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways.

Machine intelligence is your firm's primary area of interest. Can you talk about how machine intelligence will make organizations "smarter" and what it'll do to the traditional employer-employee relationship? The intersection of machine intelligence and organizational life is a complex subject about which, today, we know very little. I generally try to avoid predictions, because I think they're mostly lullabies -- stories we tell ourselves to make ourselves feel better. I prefer to focus on the unknowns, so we can work to make them known.