AI News, 32 Artificial Intelligence Companies You Should Know

AI 50: America’s Most Promising Artificial Intelligence Companies

Artificial intelligence is infiltrating every industry, allowing vehicles to navigate without drivers, assisting doctors with medical diagnoses, and mimicking the way humans speak.

As Jeremy Achin, CEO of newly minted unicorn DataRobot, puts it: “Everyone knows you have to have machine learning in your story or you’re not sexy.” The inherently broad term gets bandied about so often that it can start to feel meaningless and gets trotted out by companies to gussy up even simple data analysis.

To help cut through the noise, Forbes and data partner Meritech Capital put together a list of private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so.

To be included on the list, companies needed to show that techniques like machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to “understand” written or spoken language), or computer vision (which relates to how machines “see”) are a core part of their business model and future success.

Only eight startups were founded or cofounded by women, reflecting trends in venture funding, where software startups run by men have received the lion’s share of investment dollars.

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.

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.

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.

“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.

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.

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.

it just happens to be stuck in a research paper somewhere.” He and longtime Microsoft employee Diego Oppenheimer banded together to devise an easier way for data scientists to discover and work with machine learning models.

The app can build a 3D map of a space in roughly 30 seconds and uses computer vision to recognize real-world objects, so that objects created in AR can interact with them like they would in the real world.

The company recently raised a fresh round of funding led by automotive company Aptiv with the hope that its technology could one day be integrated into smart cars (imagine a vehicle that could issue a warning to a drowsy-looking driver).In the meantime, it’s also being used to test consumer feedback on ads and TV programming.

Blue Hexagon, led by longtime Qualcomm executive Nayeem Islam, spent more than a year and a half building a deep learning system to analyze network traffic that it says can detect and block threats in under a second.

It evaluates data from hundreds of online and offline data sources including credit bureaus, carrier phone records, IP addresses, social networks and more, to monitor for any suspicious behavior.

Chief technology officer Sarjoun Skaff says it has taken iteration after iteration since 2013 to figure out how to let its robots maneuver safely around shoppers and interpret billions of images in a way that was accurate, timely and reliable.

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.

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).

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.

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.

Its image processing technology, called Cortex, works with its own 3D camera, as well as a selection ofcheaper360-degree cameras, to let users create virtual versions of their space.The company’s leaning into the real estate market, showcasing how agents can use it to give 3D tours.

Beck, who spent more than five years as a pathologist at Harvard, wants to make it easier for other pathologists to diagnose diseases like cancer by using machine learning to more quickly and accurately analyze images of cells.

Its overhead cameras track individuals and items continuously (notably, its so-called entity cohesion doesn’t rely on facial recognition, which it says gives shoppers more privacy).

A lineup of cloud-connected security cameras equipped with AI-driven features like object and movement detection has driven growth at Verkada, whose cofounders are three Stanford computer science graduates and the cofounder of enterprise cloud company Meraki, which sold to Cisco for more than $1 billion.

Among the company’s wide-ranging list of clients are fitness club Equinox, the $1.1 billion Vancouver Mall and more than 500 school districts, which use the cameras for anything from monitoring student safety to tracking food deliveries.

SentinelOne CEO Tomer Weingarten says he and his cofounder started the company in 2013 because antivirus software at the time was “some flavor of bad, incomplete, ineffective, and /or painful to deploy and operate.” They spent the past six years figuring out how to make endpoint security (which focuses on data coming from laptops, phones, and other network-connected devices) smarter, training machine learning models to detect malware in files and running in applications.

“Until now, the most complex operations in manufacturing have been too difficult for blind and dumb robots to perform with the same precision and fidelity as humans,” says CEO Amar Hanspal, adding that advances in computer vision and machine learning have changed the game.

Upstart CEO Dave Girouard admits that most of the early team of former Google employees had no history in financial services when they came up with the idea to apply advanced data science to the credit process in 2012: Only the belief that the current system was antiquated and exclusionary.

By using data not typically found in a person’s credit history to find more nuanced risk patterns, Girouard says Upstart’s lending model has higher approval rates and lower interest rates than traditional methods, with loss rates that are “less than half” of those of peer platforms.

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.

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.

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.

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.

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.

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.

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).

Why do tech companies tend to use AI and ML interchangeably?

October 15, 2018, by Roberto Iriondo — Last updated: August 23, 2019 Recently, a report was released regarding the misuse from companies claiming to use artificial intelligence [29] [30] on their products and services.

Last year, TechTalks, also stumbled upon such misuse by companies claiming to use machine learning and advanced artificial intelligence to gather and examine thousands of users’ data to enhance user experience in their products and services [2] [33].

Often the terms are being used as synonyms, in other cases, these are being used as discrete, parallel advancements, while others are taking advantage of the trend to create hype and excitement, as to increase sales and revenue [2] [31] [32] [45].

For instance, if you provide a machine learning model with a lot of songs that you enjoy, along their corresponding audio statistics (dance-ability, instrumentality, tempo or genre), it will be able to automate (depending of the supervised machine learning model used) and generate a recommender system [43] as to suggest you with music in the future that (with a high percentage of probability rate) you’ll enjoy, similarly as to what Netflix, Spotify, and other companies do [20] [21] [22].

In a simple example, if you load a machine learning program with a considerable large data-set of x-ray pictures along with their description (symptoms, items to consider, etc.), it will have the capacity to assist (or perhaps automatize) the data analysis of x-ray pictures later on.

The type of machine learning from our previous example is called “supervised learning,” where supervised learning algorithms try to model relationship and dependencies between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships, which it has learned from previous data-sets [15] fed.

According to Andrew Moore [6] [36] [47], Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.” That is a great way to define AI in a single sentence;

Prior works of AI utilized different techniques, for instance, Deep Blue, the AI that defeated the world’s chess champion in 1997, used a method called tree search algorithms [8] to evaluate millions of moves at every turn [2] [37] [52] [53].

Fields such as speech and face recognition, image classification and natural language processing, which were at early stages, suddenly took great leaps [2] [24] [49], and on March 2019–three the most recognized deep learning pioneers won a Turing award thanks to their contributions and breakthroughs that have made deep neural networks a critical component to nowadays computing [42].

For those who had been used to the limits of old-fashioned software, the effects of deep learning almost seemed like “magic” [16], especially since a fraction of the fields that neural networks and deep learning are entering were considered off-limits for computers.

Cerebras Systems Unveils 1.2 Trillion Transistor Wafer-Scale Processor for AI

The Cerebras WSE contains 400,000 sparse linear algebra cores, 18GB of total on-die memory, 9PB/sec worth of memory bandwidth across the chip, and separate fabric bandwidth of up to 100Pbit/sec.

Because the chip is built from (most) of a single wafer, the company has implemented methods of routing around bad cores on-die and can keep its arrays connected even if it has bad cores in a section of the wafer.

is cooled using a massive cold plate sitting above the silicon, with vertically mounted water pipes used for direct cooling.

fully functional wafer-scale processor, commercialized at scale, would be an exciting demonstration of whether this technological approach has any relevance to the wider market.

While we’re never going to see consumer components sold this way, there’s been interest in using wafer-scale processing to improve performance and power consumption in a range of markets.

If consumers continue to move workloads to the cloud, especially high-performance workloads like gaming, it’s not crazy to think we might one day see GPU manufacturers taking advantage of this idea —

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