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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).

Artificial Intelligence (AI) Stats News: 120 Million Workers Need To Be Retrained Because Of AI

Recent surveys, studies, forecasts and other quantitative assessments of the impact and progress of AI highlighted the need to retrain many workers, improving AI’s score from F to A on 8th-grade science exam, and the $97.9 billion the AI market will reach in 2023.

67% of organizations will look to AI to intelligently automate IT processes to some extent within their IT environments [ESG] Quantified business impact L’Oréal’s recruiters believe they saved 200 hours of time to hire 80 interns out of a pool of 12,000 candidates, using a chatbot that saves significant time in the early stages of the recruiting process by handling questions from candidates, and Seedlink, AI software that assesses their responses to open-ended interview questions [Forbes] Infusion Software, using a chatbot from LeadsDrift.com, has reduced the number of front-line salespeople who field customer inquiries from 25 to 9 since April and expects to save $1 million a year [Wall Street Journal] Business adoption 51% of sales organizations have already deployed or plan to deploy algorithmic-guided selling in the next five years.

Algorithmic-guided selling leverages emerging artificial intelligence technology and existing sales data to guide sellers through deals, automating manual sales actions while reducing the need for individual seller judgment in the sales process [Gartner survey of 250 sales leaders worldwide] AI research successes The Aristo System from the Allen Institute for Artificial Intelligence, designed to answer non-diagram, multiple choice questions, correctly answered more than 90% of the questions on an eighth-grade science exam and exceeded 83% on a 12th-grade science exam;

In 2018, almost 40 professors left academic positions for an industry job [University of Rochester] “I was at MIT for another fifteen years after I graduated…twenty years after I went and asked to do my bachelor’s thesis [with Victor Zue on speech recognition], Siri comes out… twenty years ago, we [wanted to] have a device where you can talk to it and it gives you answers and twenty years later there it was.

By the end of 2019, 4.8 billion endpoints are expected to be in use, up 21.5% from 2018 [Gartner] The AI in healthcare market worldwide is estimated to reach $19.25 billion by 2026, up from $0.95 billion in 2017 [Wiseguy Reports] The AI for telecommunications applications market worldwide is estimated to reach more than $11.2 billion in 2025, up from $419 million in 2018 [Tractica] AI quote of the week: “We face a choice.

AI Everything Summit for Gov Businesses | 10 - 11 March 2020, Dubai

Dr. Aisha Bint Butti Bin Bishr is the Director General of the Smart Dubai Office, the government entity entrusted with Dubai’s city-wide smart transformation by His Highness Sheikh Mohammed bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai.

Dr. Aisha also leads the creation of ‘The Smart City Index’ - the first-ever benchmark for smart city implementation across the globe in cooperation with International Telecommunication Union and the United Nations.

33 Pharma Companies Using Artificial Intelligence in Drug Discovery

If you read my list of startups using artificial intelligence to drug discovery, you may have wondered: how much traction do these companies actually have?

To help answer such questions, this post summarizes how pharmaceutical companies apply artificial intelligence in drug discovery, including through partnerships with AI startups.

MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

ProteinQure is a fellow Canadian AI drug discovery company that uses quantum computing, molecular simulations, and machine learning to design drugs.

MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

A Numerate press release states that it is 'leveraging the power of cloud computing and novel computational methods to transform the drug design process.'

MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

But there are startups out there that focusjust on doing this, like Winterlight Labs.) In February 2018,Bristol-Myers Squibb (BMS) announced entering into a partnership with Sirenas to apply the biotech company's technology to challenging therapeutic targets.

In March 2019,BMS announced another partnership, with Concerto HealthAI.Concerto specializes in using AI to analyze real-world oncology data in order to generate insights and real-world evidence.

Its partnership with BMS covers a range of data sources, cancers, and activities, including clinical trials, protocol design, and precision oncology treatments.

In June 2017, Genentech and precision medicine startupGNS Healthcare announced a partnership to find and validate potential cancer drug targets by analyzing data from sources such as electronic medical records and next generation sequencing.

The partnership with Excscientia, announced in July 2017, is to discover novel and selective small molecules for up to 10 disease-related targets across undisclosed therapeutic areas.

(For more information on how GSK became such a leader, check out my article “6 Steps to AI Leadership in Pharma: An Interview with John Baldoni of GSK.”) In May 2018, GSK announced a partnership to use AI for the design of novel small-molecule drugs with Cloud Pharmaceuticals.

In early April 2019, Exscientia announced that its partnership with GSK had produced its first tangible result:a 'highly potent'lead molecule targeting a novel pathway for chronic obstructive pulmonary disease.

MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

In November 2016, they announced that BenevolentAI would license the right to develop, manufacture, and commercialize clinical stage drug candidates from Janssen after using artificial intelligence to identify untapped potential in Janssen's portfolio.

This deal may already be bearing fruit, as BenevolentAI recently launched a phase 2b trial for a drug from the partnership to treat sleepiness in people with Parkinson's disease.

(In fact, Janssen issued a press release that was one of the few sources that mentioned all the initial members.) MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

In July 2019, Celsius Therapeutics announced a partnership with Janssen to use its single-cell genomics and machine learning platform to find predictive biomarkers of response in Janssen’s VEGA study of golimumab (Simponi) and guselkumab (Tremfya) in patients with ulcerative colitis.

In December 2018, Merck announced a partnership with Cyclica to use its AI-augmented proteome screening platform toelucidate mechanisms of action, evaluate safety profiles, and explore additional applications for investigational small molecules.

few months later, in March 2019, Merck announced a partnership with Iktos to useits generative AI to design novel molecules with desired properties for a specified disease.

MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

March 2018 report also describes a partnership with IBM Watson to improve clinical trial recruitment, and the use of a “digital cortex” to predict medication efficacy.

In June 2018,Business Insider published an interview with the outspokenJay Bradner, president of the Novartis Institutes for BioMedical Research (NIBR), about the company's progress with artificial intelligence.Bradner stated that 4% of the 6,000 scientists working at NIBR are data scientists.'We like to think of ourselves as the lead turtle in the race of the turtles,' he said, referring to pharma's conservative adoption of emerging technology.

In January 2019, the company announced a partnership with the University of Oxford’s Big Data Instituteto predict how patients respondto drugs.The work willcombine different types of data, such as clinical, imaging, and genomics data.

MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

few months later, in December 2018, Novo announceda deal with UK biotech e-Therapeutics to use its AI-based drug discovery technology to find newtreatments for type 2 diabetes.

There has been little announced since (at least, that I can find), but a string of negative reports about IBM Watson's capabilities (here, here, here, here, here, here, and I could go on), including in healthcare, call into question how fruitful the partnership might have been.

In previous work, the startup has discovered new cellular players in melanoma, new mechanisms of action in atopic dermatitis, and novel pretreatment biomarkers in inflammatory bowel disease anti-TNFα therapy.CytoReason will standardize and organize Pfizer's data and integrate it into aPfizer-specific immune system model.

In April 2019, Concerto HealthAI announced a partnership with Pfizer to use AI and real world data in oncology.The partnership aims tofindactionable insights for Pfizer's investigational and commercialized therapiesfor solid tumors and hematologic malignancies.

Their partnership, announced in May 2017, focuses on finding bispecific small molecule drugs for metabolic diseases such as diabetes and their comorbidities.

Through a new virtual 'Innovation Lab,' Sanofi and Google will analyze real world data to understand what treatments work for patients, and analyze manufacturing and commercial data to forecast sales and inform marketing and supply chain activities.

Another partner of the very active Numerate, Servier and the startup announced in June 2017 a collaboration to design small molecule modulators of ryanodine receptor 2 (RyR2), a target thought to be important in cardiovascular disease that has eluded drug-ability.

MELLODDY will train machine learning models on datasets from multiple partners while ensuring the privacy of each partner using federated learning.

In April 2019, SK Biopharmaceuticals, a Korean company that focuses on disorders of the central nervous system and cancer, announced an agreement with twoXAR to develop new treatments for non-small cell lung cancer.

Let me know in the comments.) One of Exscientia's early partners, Sumitomo Dainippon and the startup announced in September 2015 initial results of a collaboration to identify new treatments for psychiatric diseases.

Another Numerate partner, Takeda and the startup announced in June 2017 that they would collaborate on identifying candidates for oncology, gastroenterology, and central nervous system disorders.

In January 2019, Takeda partner Recursion announced an expanded partnership with the pharmaceutical company to evaluate and identify novel preclinical candidates for rare diseases.

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