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Palo Alto, California-based Landing AI, the AI startup led by Andrew Ng — the cofounder of Google Brain, one of Google’s AI research divisions — today announced that it raised $57 million in a series A funding round led by McRock Capital. In addition, Insight Partners, Taiwania Capital, Canadian Pension Plan Investment Board, Intel Capital, Samsung Catalyst Fund, Far Eastern Group’s DRIVE Catalyst, Walsin Lihwa, and AI Fund participated, bringing Landing AI’s total raised to around $100 million.
The increased use of AI in manufacturing is dovetailing with the broader corporate sector’s embrace of digitization. According to Google Cloud, 76% of manufacturing companies turned to data and analytics, cloud, and AI technologies due to the pandemic. As pandemic-induced challenges snarl the supply chain, including skilled labor shortages and transportation disruptions, the adoption of AI is likely to accelerate. Deloitte reports that 93% of companies believe that AI will be a pivotal component in driving growth and innovation in manufacturing.
Landing AI was founded in 2o17 by Ng, an adjunct professor at Stanford, formerly an associate professor and director of the university’s Stanford AI Lab. Landing AI’s flagship product is LandingLens, a platform that allows companies to build, iterate, and deploy AI-powered visual inspection solutions for manufacturing.
“AI will transform industries, but that means it needs to work with all kinds of companies, not just those with millions of data points to feed into AI engines. Manufacturing problems often have dozens or hundreds of data points. LandingLens is designed to work even on these small data problems,” Ng told VentureBeat via email. “In consumer internet, a single, monolithic AI system can serve billions of users. But in manufacturing, each manufacturing plant might need its own AI model. By enabling domain experts, rather than only AI experts, to build these AI systems, LandingLens is democratizing access to cutting-edge AI.”
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Deep background in AI
Ng, who previously served as chief scientist at Baidu, is an active entrepreneur in the AI industry. After leaving Baidu, he launched an online curriculum of classes centered around machine learning called DeepLearning.ai, and soon after incorporated the company Landing AI.
While at Stanford, Ng started the Stanford Engineering Everywhere, a compendium of freely available online courses, which served as the foundation for Coursera. Ng is currently the chairman of AI cognitive behavioral therapy startup Woebot; sat on the board of Apple-owned driverless car company Drive.ai, and has written several guides and online training courses that aim to demystify AI for business executives.
Three years ago, Ng unveiled the AI Fund, a $175 million incubator that backs small teams of experts looking to solve key problems using AI. In a Medium post announcing the fund, which was an early investor in Landing AI, Ng wrote that he wants to “develop systematic and repeatable processes to initiate and pursue new AI opportunities.”
Landing AI focuses on MLOps, the discipline involving collaboration between data scientists and IT professionals with the aim of productizing AI systems. A compound of “machine learning” and “information technology operations,” the market for such solutions could grow from a nascent $350 million to $4 billion by 2025, according to Cognilytica.
LandingLens provides low-code and no-code visual inspection tools that enable computer vision engineers to train, test, and deploy AI systems to edge devices like laptops. Users create a “defect book” and upload their media. After labeling the data, they can divide it into “training” and “validation” subsets to create and evaluate a model before deploying it into production.
Labeled datasets, such as pictures annotated with captions, expose patterns to AI systems, in effect telling machines what to look for in future datasets. Training datasets are the samples used to create the model, while test datasets are used to measure their performance and accuracy.
“For instance … [Landing AI] can help manufacturers more readily identify defects by working with the small data sets the companies have … or spot patterns in a smattering of health care diagnoses,” a spokesperson from Landing AI explained to VentureBeat via email. “Overcoming the ‘big data’ bias to instead concentrate on ‘good data’ — the food for AI — will be critical to unlocking the power of AI in ever more industries.”
On its website, Landing AI touts LandingLens as a tailored solution for OEMs, system integrators, and distributors to evaluate model efficacy for a single app or as part of a hybrid solution, combined with traditional systems. In manufacturing, Landing AI supports uses cases like assembly inspection, processing monitoring, and root cause analysis. But the platform can also be used to develop models in industries like automotive, electronics, agriculture, retail — particularly for tasks involving glass and weld inspection, wafer and die inspection, automated picking and weeding, identifying patterns and trends to generate customer insights.
“A data-centric AI approach [like Landing AI’s] involves building AI systems with quality data — with a focus on ensuring that the data clearly conveys what the AI must learn,” Landing AI writes on its website. “Quality managers, subject-matter experts, and developers can work together during the development process to reach a consensus on defects and labels build a model to analyze results to make further optimizations … Additional benefits of data-centric AI include the ability for teams to develop consistent methods for collecting and labeling images and for training, optimizing, and updating the models … Landing AI’s AI deep learning workflow simplifies the development of automated machine solutions that identify, classify, and categorize defects while improving production yield.”
With upwards of 82% of firms saying that custom app development outside of IT is important, Gartner predicts that 65% of all apps — including AI-powered apps — will be created using low-code platforms by 2024. Another study reports that 85% of 500 engineering leads think that low-code will be commonplace within their organizations as soon as the end of this year, while one-third anticipates that the market for low- and no-code will climb to between $58.8 billion and $125.4 billion in 2027.
Landing AI competes with Iterative.ai, Comet, Domino Data Lab, and others in the burgeoning MLOps and machine learning lifecycle management segment. But investors like Insight Partners’ George Mathew believe that the startup’s platform offers enough to differentiate it from the rest of the pack. Landing AI’s customers include battery developer QuantumScape and life sciences company Ligand Pharmaceuticals, which says it’s using LandingLens to improve its cell screening technologies. Manufacturing giant Foxconn is another client — Ng says that Landing AI has been working with since June 2017 to “develop AI technologies, talent, and systems that build on the core competencies of the two companies.”
“Digital modernization of manufacturing is rapidly growing and is expected to reach $300 billion by 2023,” Mathew explained in a press release. “The opportunity and need for Landing AI is only exploding. It will unlock the untapped segment of targeted machine vision projects addressing quality, efficiency, and output. We’re looking forward to playing a role in the next phase of Landing AI’s exciting journey.”
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