Galileo adds computer vision and image recognition

Galileo adds computer vision and image recognition

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Galileo looks to improve unstructured data for machine learning (ML), raises $18M

Galileo, a company that brings data intelligence to unstructured data for machine learning (ML) announced $18 million in new funding. The company plans to use the capital to expand its platform to use cases beyond its current capabilities into computer vision for image recognition.

“We’re bullish on the idea of ML data intelligence and in the next few years we’re going to see this becoming more commonplace as a core part of the stack for ML data practitioners,” said Vikram Chatterji, cofounder of Galileo.

Machine Learning (ML) requires data on which to train and iterate. Making use of data for ML also requires a basic understanding of what is in the training data, which isn’t always an easy problem to solve.

Notably, there is a real challenge with unstructured data, which by definition has no structure to help organize the data so that it can be useful for ML and business operations. It’s a dilemma that Vikram Chatterji saw, time and again, during his tenure working as a project management lead for cloud artificial intelligence (AI) at Google.?

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In large companies across multiple sectors including financial services and retail, Chatterji and his colleagues kept seeing vast volumes of unstructured data including text, images and audio that were just lying around. The companies kept asking him how they could leverage that unstructured data to get insights. The answer that Chatterji gave was they could just use ML, but the simple answer was never really that simple.

“We realized very quickly that the ML model itself was something we just picked up off the shelf and it was very easy,”?Chatterji told VentureBeat. “But the hardest part, comprising 80 to 90% of my data scientist job, was basically to kind of go in and look at the data and try to figure out what the erroneous data points are, how to clean it, how to make sure that it’s better the next time.”

That realization led Chatterji and his cofounders, Yash Sheth and Atindriyo Sanyal, to form a new startup in late 2021 they called Galileo to bring data intelligence to unstructured data for ML.

Today, Galileo announced that it has raised $18 million in a series A round of funding as the company continues to scale up its technology.

Data intelligence vs. data labeling

All data, be it structured or unstructured, tends to go through a data labeling process before it is used to train an ML model. Chatterji doesn’t see his firm’s technology as replacing data labeling, rather, he sees Galileo as providing a layer of intelligence on top of existing ML tools.

Chatterji said that at Google and at Uber, data labeling is widely employed, but that still isn’t enough to solve the challenge of effectively making sense of unstructured data. There are issues before data is labeled, including understanding the quality of the data, accuracy and duplication. After data is labeled and in production, they’re also areas of concern.

“After you label the data and you’ve trained a model, how do you figure out what the mislabeled samples are?”?Chatterji said. “It’s a needle in the haystack problem.”

What Galileo has done is developed a series of sophisticated algorithms, to be able to identify potentially mislabeled samples rapidly. The Galileo platform provides a series of different metrics that can also help data scientists to identify data issues for ML models. One such metric is the data error potential score, which provides a number that can help an organization understand the potential incidents of data errors and the impact on a model.

Overall, the approach that Galileo is taking is an attempt to ‘debug’ data, finding potential errors and remediate them.

“The different kinds of data errors that people are looking for are just so varied, and the problem is, sometimes you don’t even know what you’re trying to find, but you know that a model just isn’t performing well,” he said.

ML data intelligence helps solve the challenge of bias and explainability

Helping to reduce potential bias in AI models is another area where Galileo can play a role.

Chatterji said that Galileo has created a variety of tools within its platform to help organizations slice data in different ways to help better group entities to understand diversity in several categories, such as gender or geography.

?“We’ve definitely seen people adopt these data slices to try to incorporate bias detection in their organizations,” he said.

When attempting to mitigate bias in AI models, it’s also critical to be able to explain how a given model was able to reach a specific result, which is what AI explainability is all about. To that end, Galileo can explain to its users what words were indexed most often that led to a specific prediction.

To date, Galileo has focused on unstructured text data and natural language processing (NLP). Now with its new funding, the company will look to expand its platform to other use cases, including computer vision for image recognition.

“We’re bullish on the idea of ML data intelligence and in the next few years we’re going to see this becoming more commonplace as a core part of the stack for ML data practitioners,” Chatterji said.

Computer vision brings intelligence to retail tech

Cashierless checkout and inventory management tools powered by AI and computer vision are on the rise. A variety of companies, both big tech and startups, have taken different approaches over the past few years, using cameras and sensors to identify items and ringing them up — allowing the customer to quickly grab items off the shelf and leave without standing in line.

Even as the economy slows, investors show no signs of pulling back on investments in this sector. Big funding rounds are still making news, including the Tel Aviv-based Trigo, which last week announced a $100 million series C investment, bringing its total funding to around $199 million, according to Crunchbase

From entry to exit, the average time a consumer spends in a grocery store is about 41 minutes for one trip. But when checkout lines are long and shoppers spend time scouring shelves for out-of-stock items, that trip quickly gets much longer. Neither consumers, who may quickly lose patience — nor retailers, who are already dealing with post-pandemic staffing shortages, supply chain disruptions and reduced foot traffic, want that.?

That is where cashierless checkout and inventory management comes in, powered by artificial intelligence?(AI)?and computer vision. A variety of companies, both big tech and startups, have taken different approaches over the past few years, using cameras and sensors to identify items and ringing them up — allowing the customer to quickly grab items off the shelf and leave without standing in line.?

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These days, even as the economy slows, investors show no signs of pulling back on investments in this sector. Big funding rounds are still making news, including the Tel Aviv-based Trigo, which last week announced a $100 million series C investment, bringing its total funding to around $199 million, according to Crunchbase.

Amazon Go spawned many competitors

Six years ago, everyone thought Amazon, with its novel Amazon Go, had the cashierless grocery store model in the bag. But while many predicted the technology would scale massively, there are still only 28 Amazon Go stores worldwide — and that includes its recent expansion in India.?

In general, scaling is the biggest challenge, said Brad Jashinsky, marketing analyst at Gartner.?“The main technical challenge behind check-out computer vision is scaling up the systems to handle more customers and larger store formats,” he said. “There is also a need to more easily integrate them into existing store footprints.”

As Amazon has wavered, several startups have stepped in with their own computer vision-powered technology — aimed at solving for scale and ease of integration. Some startups, like Caper AI –?which was acquired by Instacart in 2021 – and Mashgin have focused on AI-powered plug-and-play smart carts or kiosks. Others, like AiFi and Trigo, are focused on ceiling cameras, shelf sensors and digital twin technology. Even though the economy is taking a downturn, it hasn’t seemed to phase-out opportunities in this niche sector.?

Bringing computer vision to the physical store

According to Michael Gabay, CEO of Trigo, the draw of computer vision for grocery retailers is gaining the capabilities currently available to them in an ecommerce space and bringing that intelligence into their physical store spaces.??

?“That, for grocery retailers, is by far a bigger market than the online market,” Gabay said. ”Their confidence [in technology] is much higher than it was last year or two years ago.”

Supporting that statement, a Gartner study released late last year found that 73% of retail respondents expect to increase store technology investments for 2022.

In addition, retailers have been forced to refocus on connecting stores to their entire ecosystem, Jashinsky explained in an email. “New attention and investment has been given to digitalization of store technology investments — including check-out computer vision,” he said.

Trigo was founded in 2017 by brothers Michael and Daniel Gabay, who grew up on a kibbutz in northern Israel and served in technological roles in elite Israel Defense Force units. The CEO and CTO, respectively, set out to be the antidote for the headache of waiting in long checkout lines.?

Its 3D store mapping and computer vision capabilities uses artificial intelligence (AI) and machine learning (ML) to keep track of a customer’s shopping tab as they go — even updating the total if they put an item back — and charges them accordingly when they walk out, no lines necessary. It also tracks item stock and customer body language so it can alert store employees if it suspects an item has been hidden in a jacket, for example. This is all without harvesting any biometric or facial recognition data.?

While that may sound like a lot for retailers to wrap their brains around, all they need to do is work with Trigo to set it up, and once the installation is complete — which the company claims is typically overnight — nothing more is needed and they have the green light to begin using it.?

Despite the swath of competitors and growing interest in the market as a whole, Gabay is confident in Trigo’s capacity to stand out among the crowd. The company is currently deployed in supermarket chains worldwide including the Wakefern Cooperative in the U.S., the U.K.’s Tesco chain, Israel’s Shufersal stores, Aldi Nord in the Netherlands and REWE, a chain in Germany.

“We’re also the only startup, that is not Amazon, currently focusing on supermarkets and not just convenience stores or small stores,” Gabay told VentureBeat.

Trigo’s technology can be implemented by stores that are 3,000 square feet to 5,000 square feet — and claims it is working toward use in 10,000-square-foot stores next. However, its competitor AiFi also claims it can be used in up to 10,000 square-foot spaces. It has similarly worked to refurbish stores into fully autonomous ones.

Retail-focused computer vision surge

Experts expect the computer vision market, specifically, to surge worldwide to $41 billion by 2030. Investing in technology like this is the logical “next step for the industry,” McKinsey analysts Tyler Harris, Alexandra Kuzmanovic, and Jaya Pandrangi recently wrote.?

?“Investments in technology used to feel optional for grocers — an opportunity to experiment or increase the ‘wow factor’ in stores rather than to support mission-critical operations,” the article reads. “Today, a wide range of affordable, field-tested technologies can help retailers reduce the cost structure of their stores while delivering a better experience for both consumers and employees.”

In contrast with the tightening of the current financial market, a survey from Battery Ventures found that 54% of C-suite executives have plans to increase their tech budgets next year — with 75% saying they have plans to at least increase it within the next five years.?

With its range of use cases from labor allocation, checkout ease, merchandising, inventory management, loss prevention and maintenance — Jashinsky doesn’t expect the boom in retail-focused computer-vision innovation for retail to phase out anytime soon.?

?“The ability to provide ambient customer transactions is only one use case for smart check-out computer vision technology,” he said.?

The real power, he explained, comes from additional real-time business insights: “Retailers using computer vision for smart check-out can leverage the real-time insights captured to improve decisions.”

PagerDuty expands incident response capabilities to build user trust and loyalty

PagerDuty today announced enhancements to PagerDuty Operations Cloud to help expand capabilities around incident workflows.

As chief product development officer at PagerDuty, Sean Scott, put it, organizations must move beyond the idea of “incident response” to a more comprehensive understanding of “incident management.”

“Incident response used to be all about ‘how quickly can we get back up’ when your digital operations are disrupted, but today it is much deeper than that,” he said.