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Palo Alto, California-based Landing AI, the AI startup led by Andrew Ng — the cofounder of Google Mind, one among Google’s AI analysis divisions — at the moment introduced that it raised $57 million in a sequence A funding spherical led by McRock Capital. As well as, Perception Companions, Taiwania Capital, Canadian Pension Plan Funding Board, Intel Capital, Samsung Catalyst Fund, Far Japanese Group’s DRIVE Catalyst, Walsin Lihwa, and AI Fund participated, bringing Touchdown AI’s whole raised to round $100 million.
The elevated use of AI in manufacturing is dovetailing with the broader company sector’s embrace of digitization. According to Google Cloud, 76% of producing firms turned to knowledge and analytics, cloud, and AI applied sciences as a result of pandemic. As pandemic-induced challenges snarl the availability chain, together with skilled labor shortages and transportation disruptions, the adoption of AI is more likely to speed up. Deloitte reports that 93% of firms imagine that AI will likely be a pivotal element in driving progress and innovation in manufacturing.
Touchdown AI was based in 2o17 by Ng, an adjunct professor at Stanford, previously an affiliate professor and director of the college’s Stanford AI Lab. Touchdown AI’s flagship product is LandingLens, a platform that permits firms to construct, iterate, and deploy AI-powered visible inspection options for manufacturing.
“AI will remodel industries, however which means it must work with every kind of firms, not simply these with thousands and thousands of information factors to feed into AI engines. Manufacturing issues usually have dozens or tons of of information factors. LandingLens is designed to work even on these small knowledge issues,” Ng advised VentureBeat through e mail. “In shopper web, a single, monolithic AI system can serve billions of customers. However in manufacturing, every manufacturing plant would possibly want its personal AI mannequin. By enabling area specialists, relatively than solely AI specialists, to construct these AI techniques, LandingLens is democratizing entry to cutting-edge AI.”
Deep background in AI
Ng, who beforehand served as chief scientist at Baidu, is an energetic entrepreneur within the AI business. After leaving Baidu, he launched an online curriculum of classes centered round machine studying referred to as DeepLearning.ai, and shortly after incorporated the company Landing AI.
Whereas at Stanford, Ng began the Stanford Engineering All over the place, a compendium of freely obtainable on-line programs, which served as the inspiration for Coursera. Ng is at the moment 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 on-line coaching programs that intention to demystify AI for enterprise executives.
Three years in the past, Ng unveiled the AI Fund, a $175 million incubator that backs small groups of specialists seeking to remedy key issues utilizing AI. In a Medium post saying the fund, which was an early investor in Touchdown AI, Ng wrote that he desires to “develop systematic and repeatable processes to provoke and pursue new AI alternatives.”
Touchdown AI focuses on MLOps, the self-discipline involving collaboration between knowledge scientists and IT professionals with the intention of productizing AI techniques. A compound of “machine studying” and “data know-how operations,” the marketplace for such options might develop from a nascent $350 million to $4 billion by 2025, according to Cognilytica.
LandingLens supplies low-code and no-code visible inspection instruments that allow pc imaginative and prescient engineers to coach, take a look at, and deploy AI techniques to edge units like laptops. Customers create a “defect e book” and add their media. After labeling the information, they will divide it into “coaching” and “validation” subsets to create and consider a mannequin earlier than deploying it into manufacturing.
Labeled datasets, corresponding to photos annotated with captions, expose patterns to AI techniques, in impact telling machines what to search for in future datasets. Coaching datasets are the samples used to create the mannequin, whereas take a look at datasets are used to measure their efficiency and accuracy.
“For example … [Landing AI] will help producers extra readily establish defects by working with the small knowledge units the businesses have … or spot patterns in a smattering of well being care diagnoses,” a spokesperson from Touchdown AI defined to VentureBeat through e mail. “Overcoming the ‘large knowledge’ bias to as an alternative focus on ‘good knowledge’ — the meals for AI — will likely be essential to unlocking the facility of AI in ever extra industries.”
On its web site, Touchdown AI touts LandingLens as a tailor-made resolution for OEMs, system integrators, and distributors to judge mannequin efficacy for a single app or as a part of a hybrid resolution, mixed with conventional techniques. In manufacturing, Touchdown AI helps makes use of circumstances like meeting inspection, processing monitoring, and root trigger evaluation. However the platform can be used to develop fashions in industries like automotive, electronics, agriculture, retail — notably for duties involving glass and weld inspection, wafer and die inspection, automated selecting and weeding, figuring out patterns and developments to generate buyer insights.
“An information-centric AI method [like Landing AI’s] entails constructing AI techniques with high quality knowledge — with a deal with making certain that the information clearly conveys what the AI should study,” Touchdown AI writes on its web site. “High quality managers, subject-matter specialists, and builders can work collectively throughout the growth course of to achieve a consensus on defects and labels construct a mannequin to investigate outcomes to make additional optimizations … Extra advantages of data-centric AI embrace the power for groups to develop constant strategies for gathering and labeling photos and for coaching, optimizing, and updating the fashions … Touchdown AI’s AI deep studying workflow simplifies the event of automated machine options that establish, classify, and categorize defects whereas enhancing manufacturing yield.”
With upwards of 82% of companies saying that customized app growth outdoors of IT is necessary, Gartner predicts that 65% of all apps — together with AI-powered apps — will likely be created utilizing low-code platforms by 2024. One other research reviews that 85% of 500 engineering leads assume that low-code will likely be commonplace inside their organizations as quickly as the tip of this 12 months, whereas one-third anticipates that the marketplace for low- and no-code will climb to between $58.8 billion and $125.4 billion in 2027.
Touchdown AI competes with Iterative.ai, Comet, Domino Information Lab, and others within the burgeoning MLOps and machine studying lifecycle administration phase. However traders like Perception Companions’ George Mathew imagine that the startup’s platform provides sufficient to distinguish it from the remainder of the pack. Touchdown AI’s prospects embrace battery developer QuantumScape and life sciences firm Ligand Prescribed drugs, which says it’s utilizing LandingLens to enhance its cell screening applied sciences. Manufacturing big Foxconn is one other shopper — Ng says that Touchdown AI has been working with since June 2017 to “develop AI applied sciences, expertise, and techniques that construct on the core competencies of the 2 firms.”
“Digital modernization of producing is quickly rising and is anticipated to achieve $300 billion by 2023,” Mathew defined in a press launch. “The chance and wish for Touchdown AI is just exploding. It’ll unlock the untapped phase of focused machine imaginative and prescient initiatives addressing high quality, effectivity, and output. We’re wanting ahead to enjoying a task within the subsequent section of Touchdown AI’s thrilling journey.”
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