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Five insights about harnessing data and AI from leaders at the frontier

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What was once unknowable can now be quickly discovered with a few queries. Decision makers no longer have to rely on gut instinct; today they have more extensive and precise evidence at their fingertips. New sources of data, fed into systems powered by machine learning and AI, are at the heart of this transformation. The information flowing through the physical world and the global economy is staggering in scope. It comes from thousands of sources: sensors, satellite imagery, web traffic, digital apps, videos, and credit card transactions, just to name a few. These types of data can transform decision making.


Computer vision development platform CrowdAI raises $6.25M

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Register for a free or VIP pass today. CrowdAI, a computer vision development platform, today announced that it closed a $6.25 million series A financing round led by Threshold Ventures. The fundraising coincides with the launch of the startup's new solution that allows customers to create AI that analyzes images and videos. The AI skills gap remains a significant impediment to adoption in most enterprises, a 2020 O'Reilly survey found. Slightly more than one-sixth of respondents cited difficulty in hiring experts as a barrier to AI deployment in their organizations.


DoD's Joint AI Center to open-source natural disaster satellite imagery data set

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As climate change escalates, the impact of natural disasters is likely to become less predictable. To encourage the use of machine learning for building damage assessment this week, Carnegie Mellon University's Software Engineering Institute and CrowdAI -- the U.S. Department of Defense's Joint AI Center (JAIC) and Defense Innovation Unit -- open-sourced a labeled data set of some of the largest natural disasters in the past decade. Called xBD, it covers the impact of disasters around the globe, like the 2010 earthquake that hit Haiti. "Although large-scale disasters bring catastrophic damage, they are relatively infrequent, so the availability of relevant satellite imagery is low. Furthermore, building design differs depending on where a structure is located in the world. As a result, damage of the same severity can look different from place to place, and data must exist to reflect this phenomenon," reads a research paper detailing the creation of xBD.


How Startups Are Grappling With the Artificial Intelligence Talent Hiring Frenzy

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When Devaki Raj, the founder of the Mountain View-based deep learning startup CrowdAI, and Inc. 30 under 30 alum, learned that an artificial intelligence engineer she was trying to recruit was in the hospital, she didn't think twice about what she did next: Raj showed up at the hospital with flowers and balloons. She wanted to make sure the candidate got the message that her startup would be a very different atmosphere than what he'd get at a larger company. There's a talent war brewing between startups and big companies as both scramble to find top-notch artificial intelligence experts in a relatively small labor pool. "There's just not enough talent out there," Raj says. The number of experts in this field is not clear--Montreal startup Element A.I., which helps businesses build machine learning teams, estimates that there are some 20,000 PhD-level scientists around the world capable of building A.I. systems.


CrowdAI sells artificial-intelligence-as-a-service

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Computers aren't blind, but they can't see. They can detect objects, but must be trained to understand what those objects are. That takes two things: a ton of example data collected and organized by artificial intelligence and humans to accurately label and verify that data. Done right, this process can teach self-driving cars to avoid pedestrians, detect parking lot usage by counting cars in satellite photos or assess roof damage from drone footage. Businesses send CrowdAI their autonomous vehicle, satellite or drone imagery.


CrowdAI sells artificial-intelligence-as-a-service

#artificialintelligence

Computers aren't blind, but they can't see. They can detect objects, but must be trained to understand what those objects are. That takes two things: a ton of example data collected and organized by artificial intelligence and humans to accurately label and verify that data. Done right, this process can teach self-driving cars to avoid pedestrians, detect parking lot usage by counting cars in satellite photos or assess roof damage from drone footage. Now the Y Combinator startup has raised $2 million from Susa Ventures, Box Group, SV Angel, Metamorphic and Yahoo founder Jerry Yang's AME Cloud.