Transform 2022: Announcing the nominees for VentureBeat’s 4th Annual AI Innovation Awards
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As the AI and data community prepares to come together at VentureBeat’s flagship event Transform – today’s leading event on applied AI for enterprise business and technology decision-makers – we’re pleased to present the nominees for the 4th annual AI Innovation Awards.
Winners will be honored on July 19 at Transform’s one-day in-person Executive Summit at San Francisco’s Palace Hotel.
During Transform, which begins with in-person, all-day programming on July 19 and continues with six days of virtual programming over two weeks, industry experts provide comprehensive coverage across the breadth and depth of the world of applied AI, as well as industry-focused vertical tracks in healthcare, finance, retail, manufacturing and security.
VentureBeat’s 4th AI Innovation Awards recognizes and awards noteworthy, compelling, innovative and successful AI initiatives in five categories: Conversational AI, Applied AI, AI on the Edge, AI for Good and AI Innovators (startups less than two years old and with no more than $30 million in funding).
Join us at the leading event on applied AI for enterprise business and technology decision makers in-person July 19 and virtually from July 20-28.
Drawn both from VentureBeat’s daily editorial coverage and the expertise, knowledge and experience of our nominating committee members, these awards give us a chance to shine a light on the people and companies making an impact in AI.
Thank you to our nominating committee members for their guidance, insights and recommendations:
Tonya Custis, director of AI research, Autodesk
Seth Dobrin, global chief AI officer, IBM
Andrea Huels, head of AI, Lenovo North America
Di Mayze, global head of data and AI, WPP
Stephanie Moyerman, senior director of risk and trust science, eBay
Shawn Wang, chief AI officer, Anthem
And the nominees are:
Built on natural language processing (NLP), natural language understanding and other machine learning (ML) technologies, today’s conversational AI applications go far beyond chatbots. This award will highlight the sophistication, scale and effectiveness of conversational AI, which is shaping the customer experience.
Germany-based Cognigy provides a low-code conversational AI platform that enables customers to create text and voice virtual agents. From a graphical conversation editor, users can manage the conversational flow of chatbots, developing experiences across a range of channels including the web, WhatsApp, Amazon Alexa, and more. According to Gartner’s January 2022 Magic Quadrant, “Cognigy’s capabilities fit well with the varied needs of large enterprises by offering flexible deployment options, strong integration capabilities and robust no-code tooling. This enables easier deployment into complex existing architectures.”
Fiserv, which provides financial services technology such as ecommerce solutions, ACH solutions, and point-of-sale technology to merchants, began using conversational AI for customer service and quickly scaled up to drive more efficiency. Fiserv started using natural language processing AI with a virtual agent, Mave, to help merchants keep the software in their legacy credit card terminals updated. Fiserv trained Mave so that the AI could recognize specific nouns that are unique to the context of Fiserv’s business, and built auditory models to help account for background noise and different dialects.
Hugging Face, the Brooklyn-based open-source and platform provider of machine learning technologies, enables conversational AI by democratizing NLP tools, benchmarks and datasets. Although they aren’t specifically targeted at conversational AI, the work they do and tools they make available have a huge impact on the community, putting technology and large foundational models into the hands of researchers and companies who may not have the resources to do this on their own.
Conversational AI platform LivePerson recently announced new AI capabilities that can assist companies in offering digital experiences that understand, connect and deliver outcomes for both brands and consumers. By using the nearly one billion human interactions that occur each month on LivePerson’s Conversational Cloud, the company’s AI interprets and simplifies intricate customer inquiries. LivePerson has a sizable enterprise presence, but is still innovating and doing interesting research at scale. They’re an example of a company that evolves and improves based on not only the needs of their customers, but by leveraging the data that their product collects and generates, and by investing in research and innovation.
McDonald’s is piloting automated order taking technology that uses conversational AI and advanced natural language processing models to streamline the process of taking customer orders in the drive-thru — a voice-based platform for “complex, multilingual, multi-accent and multi-item conversational ordering.” McDonald’s reports that the chain has cut the time it takes to serve drive-thru customers by thirty seconds and claims that this has resulted in increased customer satisfaction. McDonald’s has announced plans to expand its drive-thru innovations to more than 14,000 locations across the country and integrate additional languages, dialects and menu variations.
The field of AI is rife with new ideas and compelling research, at an increasingly blistering pace, but the practical applications of AI matter to businesses right now, whether that’s robotic process automation (RPA) to reduce human toil, or streamlined processes, or more intelligent software and services or other solutions to real-world work and life problems.
Aible, which offers a cloud-based AI solution and AutoML platform, offers capabilities beyond just model building and tuning. It helps generate automated visualizations to help data scientists perform quick analysis, while it goes beyond optimizing model statistical parameters to optimizing the model for the actual business metric.
Bank of America
Bank of America has thousands of data scientists and citizen analysts developing, deploying and managing AI/ML models across their enterprise. With this amount of scale, governance is a time-consuming and complex challenge. Using AI responsibly is a critical priority for the bank, so they put in a place an AI governance solution that enables them to continuously monitor their AI models and, if a model falls below a certain performance threshold or is accessed in an unauthorized way, the model owner can be alerted quickly and take action to correct the issue.
London-based Signal’s “decision-augmentation” uses cutting-edge AI to enable businesses to cut through the volume of information noise. With over 700 clients using their platform and services, Signal’s technology is now the standard for news monitoring, reputation management and regulatory research.
Snorkel AI has been developing a solution to enable efficient labeling of training data for building AI models. In many cases, data labeling is the main bottleneck in building AI models at scale, and the automatic labeling offered by Snorkel AI enables the development of practical solutions that otherwise simply would not have been possible because of the prohibitive amount of required manual effort.
Sund & Baelt
Sund & Baelt operates some of the largest civil infrastructure in the world. They are using AI-powered visual inspection, along with drones and IoT sensors, to identify areas for maintenance, prolonging the lifespan of aging bridges, tunnels, highways and railways. In one project, Sund & Baelt expects to be able to extend the life of the Great Belt, an 11-mile bridge and tunnel combination, by 100 years, decreasing its C02 emissions by an estimated 750,000 tons.
AI on the Edge
Today’s AI applications and workflows go far beyond the cloud, moving to edge devices closer to human activity – even reaching as far as users’ mobile devices. This award goes to a company taking AI in distributed computing to the next level.
Boston Dynamics develops and deploys mobile robots equipped with advanced mobility, dexterity and intelligence, enabling automation in unstructured or hard-to-traverse spaces like manufacturing plants, construction sites, distribution centers and warehouses. One of the most interesting robots in their portfolio is Spot, an agile robot that navigates terrain with sensory-based mobility, allowing businesses to automate routine inspection tasks and data capture safely, consistently and at any time interval, from any location with a network connection. The Spot robot is activated with different types of IoT sensors connected to AI running at the edge of the network and collects a rich set of visual and acoustic data, and analyzes it while walking the factory floor.
Ford Motor Company
Every year, Ford Motor Company produces millions of vehicles across multiple models, each with a variety of different package options. Manufacturing at that scale makes it time-consuming and challenging to look at every facet of the production process. Ford is using computer vision AI at the edge in several of its manufacturing plants to help detect and correct defects in the body of the cars — identifying defects that are difficult to detect but can be costly and risk customer satisfaction. This is helping Ford lower repair and warranty costs, and helping them produce higher quality vehicles for their customers.
With Landing AI’s recently launched LandingEdge, manufacturers will more easily deploy deep learning visual inspection solutions to edge devices on the factory floor to better and more consistently detect product defects. With the new edge capabilities, customers will more easily integrate with factory infrastructure to communicate with cameras, apply models to images and make predictions to inform real-time decision-making on the factory floor.
Using existing security cameras, RadiusAI’s technology equips retailers with real-time actionable insights on queue analytics, customer counts, store layout, parking lot analytics, customer journey and employee metrics. They are currently deployed at many of the largest convenience store chains and retailers in North America. RadiusAI’s computer vision helps retailers take advantage of missed opportunities, such as knowing if someone left the store without purchasing, or if their product placement is ideal, to provide the insights to help them create more sales and satisfied customers. The focus of their human-centric AI is to inform staff in real time so they can make improvements.
Sibel Health develops wearable skin patches to monitor vital signals. The company has been developing AI approaches to identify biomarkers for specific health conditions. Sibel sensors have been approved for use in hospital settings, and the company extends the capability to monitor vital signs using mobile devices in the home environment.
AI for Good
This award is for AI technology, the application of AI, or advocacy or activism in the field of AI that protects or improves human lives or operates to fight injustice and bias, improve equality and better serve humanity.
Change Machine is a nonprofit organization working to build financial security for people in low-income communities through people-powered technology. Its SaaS platform provides financial coaches a way to connect low-income individuals, especially Black and Brown women, to financial products that can help them achieve their financial goals. Change Machine used AI to develop a recommendation engine to help ensure that appropriate, safe and nonpredatory financial products are offered to customers in low-income neighborhoods.
The DAIR Institute
DAIR (Distributed AI Research) Institute, founded by artificial intelligence computer scientist Timnit Gebru, is an interdisciplinary and globally distributed AI research institute “rooted in the belief that AI is not inevitable, its harms are preventable, and when its production and deployment include diverse perspectives and deliberate processes it can be beneficial.”
Responsible AI Institute
The Responsible AI Institute is a nonprofit focused on advancing responsible AI from principles to practice through community-driven, independent AI assessments and certifications. The work they do is critical because without a thoughtful approach to trustworthy AI, the AI systems that organizations develop can wind up perpetuating biases, jeopardizing personal data and risking noncompliance with existing laws. The Responsible AI Institute is also leading the way in working with government and standards bodies around the world, including a recently announced pilot with the Standards Council of Canada to help define requirements for establishing, implementing, maintaining and continually improving AI management systems in organizations.
SeedAI brings together the components of AI development and packages them so that any community can grow an AI ecosystem — democratizing and scaling AI through education, robustness and inclusion. The work that SeedAI is doing both at a grassroots level and in their AI Across America program with the U.S. Congressional AI Caucus is having a positive impact on how AI can help improve the quality of life across the U.S., across walks of life and across different communities.
The think tank Urban Institute developed a new method using AI to measure and predict neighborhood change and the impacts of gentrification, in order to develop better policy actions and support more inclusive growth initiatives. Their innovative new approach started by defining four types of neighborhood change: gentrifying, declining, inclusively growing and unchanging. Using data from the U.S. Census, Zillow and the Housing Choice Voucher, researchers were able to train individual AI models across eight different metropolitan core-based statistical areas, using model explainability techniques to describe the driving factors for gentrification.
Focused on companies that have raised $30 million or less in funding and have been in operation for two years or less, this award spotlights a startup that holds great promise for making an impact with its AI innovation.
Axelera is developing a chipset to accelerate AI and machine learning algorithms at the edge. The company claims that its product will achieve a fraction of the power consumption and price of competing hardware. It takes a memory cell and modifies it so it can compute inside the cell. This way, you can make millions of computations within one computational cycle. Axelera AI aims to produce the first samples next year and provide open access to the software in 2023.
Known Medicine provides a 3D cell culture dataset that predicts the drug for every cancer patient and creates the drug for every cancer. Through machine learning-based image analysis, it determines tumor sensitivity and the potential for drug resistance. Its dataset grows by thousands of images with each patient sample, allowing the company to make biological, computational and clinical insights. Data can be provided to oncologists for use as a decision-support tool or via partnership with biopharmaceutical companies for clinical trial or companion diagnostic patient selection.
Read AI, a startup developing an AI-powered platform for meeting metrics, was cofounded by CEO David Shim, former CEO of Foursquare. Read leverages AI, computer vision and natural language processing models to power its meeting analytics backend. The models, which Shim says were trained on an “internationally and demographically diverse set of training examples,” deliver real-time sentiment, engagement and participation metrics encouraging collaboration among attendees, and aggregate metrics across meetings.
Vistry is revolutionizing Quick Service Restaurant AI, with technology that uses vision, voice and IoT data analytics to help restaurants measure and improve speed and quality of service. Vistry’s use cases include order tracing, automated curbside check-in, drive-thru optimization, predict order make-time, and voice bots for automated order taking.
Voxel, a San Francisco-based AI-powered workplace-safety company, uses computer vision and AI to identify hazards, risky behaviors and operational inefficiencies across workplaces. The company has grown quickly by decreasing onsite injuries by upwards of 80% and increasing operational productivity by more than 20% using existing cameras.
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