Every major tech company is dedicating resources to breakthroughs in artificial intelligence. Personal assistants like Siri and Alexa have made AI a part of our daily lives. Meanwhile, revolutionary breakthroughs like self-driving cars may not be the norm, but are certainly within reach.
As the big guys scramble to infuse their products with artificial intelligence, other companies are hard at work developing their own intelligent technology and services. Here are 30 artificial intelligence companies, according to Forbes, you should know.
After working for more than five years each on Google’s self-driving project, Dave Ferguson and Jiajun Zhu were done trying to ferry people around in autonomous vehicles. So, they ditched humans for local goods. Nuro’s driverless delivery vehicles have completed thousands of trips to shoppers through a partnership with Kroger in Texas. Shifting from people to pasta and Poptarts eliminates safety and technical constraints. “You can drive more conservatively because you don’t have someone inside the vehicle that’s getting frustrated,” Ferguson says.
A trifecta of autonomy and transportation experts from Tesla, Uber, and Google came together to build Aurora, a self-driving car company that plans to sell its system to automakers instead of operating its own fleet (it currently has a deal with Hyundai to provide software for its future Kia models). A recent round of funding from Sequoia Capital, Amazon, and T. Rowe Price makes it one of the best-funded players in an increasingly crowded space.
Uptake CEO Brad Keywell says his company is in the business of making sure things work, “whether it’s the U.S. Army’s Bradley Fighting Vehicle, or the components that make up Rolls-Royce’s fleet of market-leading engines.” It’s brought in more than 100 industrial customers on its way to a $2.3 billion valuation. With a huge database of machine failures at its disposal, the five-year-old company leverages artificial intelligence to analyze how its customers’ machines can run better and avoid these failures. “There is no more guesswork or operating blindly involved,” says Keywell, who cofounded Groupon before founding Uptake.
Lemonade sells renters and homeowners insurance, though, unlike Hippo, it is actually a licensed policy carrier itself. It uses a chatbot to collect customer information and work through claims — 30% of which apparently don’t require human intervention to be resolved. It now has more than 500,000 customers, the majority of whom are first-time insurance buyers.
Dataminr ingests public internet data, like social media posts, and uses deep learning, natural language processing, and advanced statistical modeling to send users tailored alerts. The company has more than 500 clients paying its subscription fees, including Amazon, CNN, and The United Nations, which uses the system to find early signs of potential humanitarian crises around the world.
DataRobot wants to automate as much of a data scientist’s job as possible. The company just raised a new $206 million Series E round of funding as it develops the software that it says has helped customers like United Airlines, PNC Bank, and Deloitte build their own predictive models. The company boasts that users only need “curiosity and data,” and not coding skills, to use its platform to answer business questions with machine learning.
Icertis, which celebrated its ten-year anniversary earlier this year, manages nearly 6 million contracts. Its cloud-based platform helps companies analyze past contract negotiations and automate administrative tasks. These offerings have brought on clients from more than 90 countries, including Airbus (France), Daimler (Germany), and Microsoft, the company where CEO Samir Bodas was previously a director.
Hippo Insurance is one of a handful of companies trying to make the process of applying for home insurance faster and more Millennial-friendly. It pulls public data about a property to automatically answer many of the questions a typical insurer would ask, which means it can quickly dole out quotes, and pulls data from aerial images and smart home sensors to detect issues that could lead to claims in real-time. Hippo sells policies backed by established insurers, rather than underwriting them itself, and takes a commission off each one.
Alexandr Wang’s data labeling startup Scale AI has gained so much attention from customers — particularly autonomous transportation companies, which need gobs of well-labeled data to train their systems — that he’s running a unicorn company before his 23rd birthday. Scale works with tens of thousands of contractors and though Wang says that the company uses machine learning to help improve the accuracy of its labeling, those humans are core to its mission. “ML is very much garbage-in garbage out, so we focused on quality from day one,” he says.
Former Oculus cofounder Palmer Luckey is back after his dramatic exit from Facebook (he has hinted that the company fired him from the virtual reality unit for his political views, which it denies) with a defense technology startup called Anduril Industries, founded in 2017. The company makes a threat-detection system, using data from sensors mounted on towers, drones, and vehicles to create a real-time, 3D model of an area. It has contracts with the Marine Corps and UK’s Royal Navy, as well as with Customs and Border Protection for what has been described as a controversial “virtual border wall.” Following a report that it became a unicorn after a recent fundraise, the company confirmed to Forbes that it now has $180 million in total funding and a near-billion valuation.
Domino Data Lab
Domino Data Lab’s software-as-a-service platform provides data scientists with the “picks and shovels” they need to build, test and run their own AI models. CEO Elprin describes its as a sort of GitHub for experts and its 70-plus client lists include big enterprises and startups alike, including Allstate, Instacart, Dell, Gap and FabFitFun.
Brain Corp. aims to upgrade dumb machinery with robotic software. It’s tackling the world of floor-cleaning equipment first, partnering with manufacturers to make their machines better at avoiding obstacles in busy environments. Walmart announced earlier this year that nearly 2,000 stores will be humming with BrainOS-powered cleaners by the end of 2019. “I have always dreamed of building artificial brains,” says neurobiology researcher and CEO Eugene Izhikevich. “Starting Brain Corp. gave me this opportunity.”
AEye wants to improve the “eyes” of autonomous cars, robots and drones by combining laser lidar— which stands for “light detection and ranging”—with a high-definition camera. That integrated system can increase the speed and decrease the power consumption of a self-driving car’s perception system, says CEO Luis Dussan, who worked at Northrop Grumman and NASA’s Jet Propulsion Lab before founding the company.
ClimaCell’s cofounders all had what CEO Shimon Elkabetz describes as “life-threatening experiences due to poor weather forecasts” while serving in the Israeli military, inspiring them to try to find a way to make predictions more accurate. The company uses vast quantities of nontraditional data—like signals from cellphones, internet-of-things devices and street cameras—to issue hyperlocal “street-by-street, minute-by-minute” weather forecasts. More than 150 corporate customers, including JetBlue, the New England Patriots and ride-sharing service Via, are shelling out for its real-time predictions.
The red-hot autonomous trucking space is full of well-funded competition, but that inherent risk isn’t deterring Kodiak Robotics cofounders Don Burnette and Paz Eshel (the two met on a sky-diving trip, after all). The idea is that autonomously driving cargo-laden trucks down a highway could be a nearer-term and even more attractive commercial opportunity than passenger vehicles. Kodiak recently started shipping household goods in between Dallas and Houston, Texas, but said that it can’t name clients just yet. Ultimately, it plans to build its own comprehensive logistics business instead of selling its technology to other carriers.
Stephen Pratt first saw machine learning in action while working as a consultant on a Department of Defense project in the early 1990s. Computers were too slow and data too expensive to make AI practical at the time, but roughly three decades later, Pratt teamed up with investment firm TPG to help it identify a data analytics company to buy or invest in. After a yearlong search he couldn’t find the right fit and joined IBM Watson, but only stayed for eight months before deciding to build something new with TPG’s backing. Pratt gathered a handful of industry vets as his cofounders and Noodle.ai launched on Pi Day 2016 (3.14.16). The startup charges a one-time fee to create AI software for industrial and transportation companies, with a monthly hosting fee on top. It’s booked $50 million in total contract value from the likes of XoJet and Big River Steel.
In June, transcription service Rev.com said that its tests show that its word error rate on podcast transcriptions was lower than what Google, Amazon or Microsoft’s tools produced. While developers can buy access to that completely automated speech recognition engine, its network of freelance transcribers also use it to make their client work easier and faster. CEO Jason Chicola says this hybrid approach leads to higher- quality, cheaper transcriptions. “Language is incredibly complex—think accents, mumbling, arcane terminology, bad microphones, background noise,” says Chicola. “Humans are far, far better at making judgment calls for these real-world factors.”
Moveworks wants to end the frustration of waiting around for corporate IT help–its natural language understanding engine can solve 25% to 35% of all employee IT issues autonomously. For example, if a worker sends a frantic message like, “Sorry, I was biking to work and dropped my laptop accidentally, and it won’t turn on now. What should I do?? Please help!” the system can both understand the problem and send the right form for a loaner laptop. Moveworks has a “deep, semantic understanding of the kinds of problems employees experience and how they express them,” CEO Bhavin Shah says. Big customers like Autodesk, Western Digital and Nutanix are on board.
K Health doesn’t think that everyday health concerns need to warrant a trip to the doctor’s office. “K was built by technologists and doctors because we felt frustrated with the ability to access relevant, personalized and affordable healthcare,” says cofounder and CEO Allon Bloch. The company’s consumer app draws on a data set of more than 2 billion anonymized medical records, finding subtle patterns in the data to give users personalized health advice. In July, K announced a partnership with insurance provider Anthem to let members see how doctors diagnose and treat similar people with similar symptoms for free (though they’ll be charged to chat with an actual doctor).
Online recruiting platform Pymetrics helps companies find the right hires by looking beyond experiences and skills on a résumé. Its more than 80 enterprise customers, including LinkedIn, Accenture, MasterCard and Unilever have current, top-performing employees complete the platform’s set of assessments. Pymetrics gleans key emotional and cognitive traits for different roles so when job seekers apply to work at one of those companies and complete the challenges themselves, they’re paired with jobs that are the best fit. Companies can also use the platform for internal career development. “It makes the process more efficient with better outcomes and increases diversity tremendously,” says CEO and neuropsychology Ph.D. Frida Polli. Pymetrics open-sources its algorithm auditing tool, aimed at preventing its systems from reinforcing gender or ethnic bias.
While chat bots burst onto the scene with a lot of promise (remember how they were going to take over Facebook Messenger?), they never quite reached mainstream adoption, due in part to disenchantment with their limited scope and conversational rigidity. Rulai says its virtual assistants are different. While most bots run into trouble when users switch context or add tasks, cofounder Yi Zhang says that Rulai’s dialog manager models don’t get tripped up. “Virtual assistants need to handle the variation of natural language and the variation of conversation flows,” she says. Rulai has won over the likes of Lyft, Sanofi and Fidelity with its customer support, sales and employee productivity bots.
Aira helps blind and low-vision people better “see” the world by combining real human beings with an AI-powered agent through its app or custom smart glasses. The company admits that its AI agent, Chloe, is still in its infant stage right now—it can complete simple tasks like reading the instructions on a pill bottle — but it has big ambitions for more robust computer-vision-based navigation. The product is free for sessions under five minutes, and for all sessions in the 25,000+ locations where Aira has partnerships, like JFK Airport and the grocery chain Wegmans.
Suki is built around the idea that administrative tasks are a significant burden for doctors, cutting into their time to focus on patients. To relieve that strain, the startup makes a voice-enabled digital assistant that doctors can use to take notes and fill in electronic records in real time. It has signed on several large health systems and provider groups, including Unified Physician Management and Ascension Health, and says that users average a 76% reduction in time spent completing clinical notes. CEO Punit Soni says that its digital assistant goes “far beyond” voice-to-text software, recognizing context and becoming more personalized the more doctors use it. “Suki was born with a mission to bring joy back to medicine,” he says.
While researchers at the Allen Institute for Artificial Intelligence, Ali Farhadi and Mohammad Rastegari identified a problem: “Most of the time, AI researchers (including ourselves) tend to make better, newer algorithms with more demand for compute, memory and power,” Farhadi says. “However, the real-world use cases tend to move in the opposite direction—demanding solutions with less compute, memory and power.” They set about trying to create a system where complex algorithms could run on simple hardware and spun out of the Allen Institute, which was cofounded by the late Paul Allen of Microsoft, in 2016. Earlier this year, the company hit a technology breakthrough when it managed to run a simple computer vision system on a solar-powered computer chip.
May Mobility is taking on the self-driving challenge with form factor that’s more predictable than cars: autonomous shuttles. The company’s software has powered shuttle services in Providence, Rhode Island, and Columbus, Ohio, where passengers get scenic tours of the city.
DefinedCrowd taps human contributors to build bespoke datasets for a client list that includes Mastercard and BMW. The startup recruits freelancers through a platform called Neevo and assigns them tasks like labeling images or recording audio, hastening their work with machine learning-powered automation where possible. All the data created or checked by people gets compiled into a format that customers can use to train their own algorithms. DefinedCrowd is currently in the process of raising a big Series B round of funding it expects to complete by the end of the year.
CEO and cofounder Dhananjay Sampath launched Armorblox into the saturated cybersecurity market two years ago with the aim of protecting customers from socially engineered attacks, like phishing emails, that take advantage of human missteps. Sampath hopes it will stand out from the competition by using natural language processing, which allows machines to learn and understand language. Its software analyzes a customer’s communication styles to get a sense context and then automatically flags possible phishing attempts, insider threats or accidental data disclosures.
Lilt makes human translators better at their job. Cofounder John DeNero spent several years as a senior research scientist for Google Translate, learning the strengths and limitations of autonomous translation. Instead of relying solely on machines, Lilt can churn out better translations, faster, for the likes of HBC and Zendesk by equipping freelancers with machine translations and predictive typing tools.
When pharmaceutical research teams embark on a new clinical trial, one of the biggest bottlenecks can be finding the right cohort of patients to work with. That’s where Deep 6 comes in. CEO Wout Brusselaers says the company’s software can pull data from electronic medical records to create patient graphs that allow researchers to filter for specific conditions and traits, leading to matches in “minutes, instead of months.” The system’s language understanding engine has been trained so that it can infer some conditions even if they’re not explicitly mentioned in notes, and Deep 6 says it has more than 20 health system or pharmaceutical customers.
Viz.ai aims to reduce the number of stroke victims who don’t receive the right treatment in time. Its software cross-references CT images of a patient’s brain with its database of scans and can alert specialists in minutes to early signs of large vessel occlusion strokes that they may have otherwise missed or taken too long to spot. It sells its suite of products to hospital networks and medical institutions, including Mount Sinai in New York and Swedish Health System in Denver.