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AI

Multiverse Computing utilizes quantum tools for finance apps

Despite great efforts to unseat it, Microsoft Excel remains the go-to analytics interface in most industries — even in the relatively tech-advanced area of finance.

Could this familiar spreadsheet be the portal to futuristic quantum computing in finance? The answer is “yes,” according to the principals at Multiverse Computing.

This San Sebastián, Spain-based quantum software startup is dedicated to forging forward with finance applications of the quantum kind, and its leadership sees the Excel spreadsheet as a logical means to begin to make this happen.

“In finance, everybody uses Excel; even Bloomberg has connections for Excel tools,” said Enrique Lizaso Olmos, CEO of Multiverse Computing, which recently gained $11.5 million in a funding round headed by JME Ventures.

Excel is a key entry point for emerging quantum methods, Lizaso Olmos said, as he described how users can drag and drop data sets from Excel columns and rows into Multiverse’s Singularity SDK, which then launches quantum computing jobs on available hardware.

From their desks, for example, Excel-oriented quants can analyze portfolio positions of growing complexity. The Singularity SDK can assign their calculations to the best quantum structure, whether it’s based on ion traps, superconducting circuits, tensor networks, or something else. Jobs can run on dedicated classical high-performance computers as well.

Quantum computing for finance

Multiverse’s recently closed seed funding round, led by JME, also included Quantonation, EASO Ventures, CLAVE Capital, and others. Multiverse principals have backgrounds in quantum physics, computational physics, mechatronics engineering, and related fields. On the business side, Lizaso Olmos can point to more than 20 years in banking and finance.

The push to find ways to immediately start work in quantum applications is a differentiator for Multiverse, claims Lizaso. The focus is to work with available quantum devices that can solve today’s problems in the financial sector.

Viewers see quantum computing as generally slow in developing, but the finance sector shows specific early promise, just as it has in the past with a host of emerging technologies. Finance apps drive investments like JME’s in Multiverse.

In a recent report, “What Happens When ‘If’ Turns to ‘When’ in Quantum Computing,” Boston Consulting Group (BCG) estimated equity investments in quantum computing nearly tripled in 2020, with further uptick seen for 2021. BCG states “a rapid rise in practical financial-services applications is well within reason.”

It’s not surprising, then, that Multiverse worked with both BBVA (Banco Bilbao Vizcaya Argentaria) to showcase both quantum computing in finance and Singularity’s potential to optimize investment portfolio management, as well as Crédit Agricole CIB to implement algorithms for risk management.

“We have been working on real problems, using real quantum computers, not just theoretical things,” Lizaso Olmos said.

Why quantum-inspired work matters

Multiverse pursues both quantum and quantum-inspired solutions for open problems in finance, according to Román Orús, cofounder and chief scientific officer at the company. Such efforts create algorithms that mimic some techniques used in quantum physics, and they can run on classical computers.

“It’s important to support quantum-inspired algorithm development because it can be deployed right away, and it’s educating clients about the formalism that they need for moving to the purely quantum,” Orús said.

The quantum-inspired work is finding some footing in quantum machine learning applications, he explained. There, financial applications that could benefit include credit scoring in lending, credit card fraud, and instant transfer fraud detection.

“These methods come from physics, and they can be applied to speed up and improve machine learning algorithms, and also optimization techniques,” Orús said. “The first ones to plug into finance are super successful.”

Being specific about applications is very important, as both Orús and Lizaso Olmos emphasize.

Whether the tools are quantum or quantum-inspired, the applications users in finance pursue must be selected wisely, Orús and Lizaso Olmos said. In other words, this is not your parents’ general-purpose computing.

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Categories
AI

Zillow utilizes explainer AI, data, to revolutionize how people sell houses

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Zillow has been a big name for online home seekers. There have been more than 135 million homes listed on the platform, and the company has streamlined the real estate transaction process from home loans, title, and buying. Its success in providing customized search functions, product offerings, and accurate home valuations, with a median error rate of less than 2%, have been thanks to the power of AI.

Zillow’s initial forays in AI in 2005 centered around blackbox models for prediction and accuracy, Stan Humphries, chief analytics officer at Zillow, said at VentureBeat’s virtual Transform 2021 conference on Tuesday. Over the past three or four years, as Zillow started purchasing homes directly from sellers, the company shifted towards explainable frameworks to add context that while still getting the same levels of accuracy from blackbox models. “That’s been a kind of a fun Odyssey,” Humphries said, noting that the results needed to be “understandable and intelligible” to a consumer in the same way as if the conversation was with a real estate agent. Zillow took inspiration from Comparative Market Assessments (CMAs), which are estimated appraisals of the property provided by realtors, to create an algorithm analyzing three to five similar homes.

“Humans can wrap their heads around [that], and say, ‘Okay, I see that home’s pretty similar, but it’s got an extra bedroom, and now, there’s been some adjustment for that extra bedroom,’ [compared to] a fully ensemble model approach using a ton of different methodologies,” Humphries said.

The move into explainable models was helpful for consumers to understand the value of their home, but also to inspect it and get a “gut check” relative to their own intuition, Humphries said. Now that he’s seen that it was possible to have “the best of both worlds” with accuracy from blackbox models and intuitiveness from explainable models, Humphries said that he wished Zillow had shifted approaches sooner.

Improving the appraisal model

Zestimate, the AI tool which allows Zillow to estimate market value for homes, has largely improved thanks to utilizing the information provided by its progress.

“We think about our gains that we’re going to make as being data-related, [which include] getting new data features out of that data or their algorithm, or algorithm-related, which is new ways to combine and utilize the features that we’re doing,” Humphries said.

Zillow only used public record data for Zestimate in the past but now incorporates information associated with previous sales of comparable homes. By utilizing natural language processing, Zestimate can pull information about what people wrote and said about the property when interacting with Zillow’s representatives. Another rich source of data has come from computer vision, to mine data out of all the images associated with the homes. It makes sense. People look at the appraisals and then look at homes and make judgements about which house looks nicer. Zillow had to teach computers to do that same type of work, Humphries said.

In February 2006, Zestimate required 35,000 statistical models to estimate the market value of 2.4 million homes. Now, the tool generates 7 million machine learning models to estimate 110 million homes nationwide.

“There’s been a lot of algorithmic advances in what we’re doing. But behind the scenes, there’s also been a huge amount of additional data that we take in now that we just didn’t back then,” Humphries said.

Zillow recently announced a new release of the Zestimate algorithm, version 6.7. This update introduces a new framework that leverages neural networking within the ensemble approach, making the algorithm much more accurate, decreasing Zillow’s median absolute percent error of 7.6% to 6.9%.

Zillow’s AI journey

The company’s technological innovation has to strike a balance between consumer interest and technological limitations. The team thinks about the pain points for consumers and the products to solve those challenges, but also has to consider what can actually be built. In the case of Zestimate, the context behind the appraisal was what the customer was asking for. The representatives using the tool didn’t ask for insights generated by natural language processing and computer vision because they didn’t even know that would be possible, Humphries said.

Currently, the company is working on having users close their own deals with a human agents. The goal is to have this evaluation eventually be completely machine-generated.

“The customer is kind of our North Star,” Humphries said.

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