It’s brutal out there for tech startups trying to raise money, particularly those looking to raise later-stage growth rounds. But innovative A.I. startups are still getting funded. Case in point: V7, a software platform that makes it much easier for companies to train computer vision algorithms and integrate them into their processes.
The company has helped companies such as Merck KGaA develop systems that can spot physical defects in pills in its manufacturing plants and GE Healthcare create algorithms to analyze the scans its medical imaging devices produce. “We are trying to capture every part of the life sciences and healthcare data that manifests itself in the form of images,” Alberto Rizzoli, V7’s co-founder and CEO, tells me. But the company has also expanded from healthcare—which still represents about 40% of V7’s business, according to Rizzoli—to all sorts of other industries too, helping to train algorithms to analyze satellite images, as well as those that can detect corroded parts from photographs or spot stock outages in retail stores.
Today, V7 announced it has raised $33 million in a Series A investment round to help the company expand into the U.S. market. The round was lead by Radical Ventures, a Canadian venture capital firm that invests in deep tech, and Temasek, the sovereign wealth fund of Singapore, with participation from Air Street Capital, Amadeus Capital, and Partech. The firm also has an impressive lineup of A.I. researchers who are investing as individuals, including Ashish Vaswami, who helped created the Transformer model while at Google Brain and has gone on to co-found Adept AI, Francois Chollet, the Google Brain researcher who created Python-based deep learning API Keras, and DeepMind researcher Oriol Vinyals.
These investors like V7 because its software is a key foundation piece for many companies that want to use A.I. as part of a digital transformation strategy. And despite concerns about inflation and looming recession, most companies are pushing ahead with those plans because they are seen as a strategic necessity. (And to the extent that these A.I.-driven solutions ultimately save labor, through automation, or capital costs, through better asset utilization, they are seen as offering a good return on investment.) “Our thesis for V7 is that the breadth of applications, and the speed at which new products are expected to be launched in the market, call for a centralized platform that connects AI models, code, and humans in a looped ecosystem,” Pierre Socha, a partner at Amadeus Capital Partners, said in a statement.
Part of what V7 provides is data labeling, much like larger, better known competitors such as Scale AI. But, Rizzoli says, the company has deliberately stayed away from the end of the market that just requires lots of relatively unskilled human eyeballs—a phenomenon that has driven labelling companies to seek out cheap labor in developing countries and led to charges of “Silicon Valley sweatshops.” Those kinds of labels are most needed for applications such as moderating social media content, surveillance and security technology, and labeling roadside scenes to help train self-driving cars. V7 has tried to sidestep this ethical quagmire by focusing on computer vision use cases that require highly skilled labelers—radiologists, structural engineers, metallurgists, manufacturing experts, intelligence analysts, and the like. “We don’t want to be associated with a low cost, low value set of tasks that you don’t need any specialized background or education to do,” Rizzoli says.
He also says that V7’s specialty is less about Big Data, and more about pinpointing the exact data a company most needs to improve the performance of its computer vision models. He says that this often requires a big shift in thinking from the way academic A.I. researchers often think about the performance of computer vision systems. Academics often focus on a metric called mAP, or mean average precision. Most computer vision benchmarks are based around trying to obtain the highest mAP for a task. But Rizzoli says that in many real world commercial uses, what actually matters is not a high mAP at all. The value is in the sub-set of data where the mAP is lowest but where failure has tremendous consequences. “You need to think about what is the worst possible disaster that could happen in a plant where A.I. could save the day,” he says. Most businesses want a model that can spot these rare but catastrophic failures 100% of the time, even if the model is slightly worse on average.
He says that this same logic helps explain why adoption of neural networks and deep learning in industry is continuing to lag. Many of V7’s customers, he says, are mostly deploying older kinds of machine learning such as support vector machines, decision trees, and good old linear regression. Why? Because deep learning, Rizzoli says, is often not reliable enough for engineering and manufacturing use cases where you need “five nines.” (In other words, 99.999% reliability.) “A lot of large companies, say chemical companies for instance, are happy to pour $1 million into a classifier model if it is 99% accurate”— and that is something he says neural nets generally can’t deliver. Plus, neural networks are still perceived of as “black boxes,” whose failure modes can’t be reliably predicted or understood. “A lot of these use cases require hard-core mechanical engineering-levels of accuracy and, until we get there, A.I. will be met with skepticism,” he says.
With that, here’s the rest of this week’s A.I. news.
Jeremy Kahn
@jeremyakahn
jeremy.kahn@ampressman
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Hope to see you all at Brainstorm A.I. next week!
I hope to see some of you at the best business A.I. conference on the planet next week. Just a reminder that Fortune’s Brainstorm A.I. conference is taking place in San Francisco on Monday, December 5th, and Tuesday, December 6th. We have an amazing lineup of big thinkers on A.I. and on how A.I. is impacting business. Attendees will hear from luminaries such as Stanford University’s Fei-Fei Li, Landing AI’s Andrew Ng, Meta’s Joelle Pineau, Google’s James Manyika, Microsoft’s Kevin Scott, Covariant co-founder and robotics expert Pieter Abbeel, Stable Diffusion’s founder Emad Mostaque, and Greylock partner, Paypal and LinkedIN co-founder, and A.I. investor Reid Hoffman. We will also hear from Intuit CEO Sasan Goodarzi and top executives from Sam’s Club, Land O Lakes, Capital One, and more. And there’s still a chance to join us. You can apply here to register. (And if you use the code EOAI you’ll get a special discount.) See you there!
