While major language models (LLMs) and generative AI has dominated business conversations on AI for the past year, there are other ways that businesses can benefit from AI.

An alternative is large quantitative models (LQMs). These models are trained to optimize for specific objectives and parameters related to the industry or application, such as material assets or financial risk metrics. This contrasts with the more general understanding of language and generational tasks of LLMs. Among the main promoters and commercial sellers of LQMs are SandboxAQwhich today announced that it has raised $300 million in a new round of funding. The company was originally part of Alphabet and was conducted as a separate business in 2022.

The funding is a testament to the company’s success, and more importantly, to the future development prospects as it looks to resolve. business AI use cases. SandboxAQ has established partnerships with major consulting firms including Accenture, Deloitte and EY to distribute its business solutions. The key advantage of LQMs is their ability to deal with complex, domain-specific problems in industries where the underlying physics and quantitative relationships are critical.

“It’s all about the core product development of companies using our AI,” SandboxAQ CEO Jack Hidary told VentureBeat. “And so whether you want to develop a drug, a diagnostic, a new material or you want to manage the risk of a large bank, that’s where quantitative models shine.”

Why LQM is important for business AI

LQMs have different goals and work in a different way than LLMs. Not the same LLMs processing textual data of internet originLQMs generate their own data from mathematical equations and physical principles. The goal is to deal with the quantitative challenges that a business may face.

“We generate data and get data from quantitative sources,” Hidary explained.

This method enables breakthroughs in areas where traditional methods have stalled. For example, in battery development, where lithium-ion technology has dominated for 45 years, LQMs can simulate millions of possible chemical combinations without physical prototyping.

Similarly, in pharmaceutical development, where traditional methods face a high failure rate in clinical trials, LQMs can analyze molecular structures and interactions at the electron level. . In financial services, meanwhile, LQMs address the limitations of traditional modeling methods.

“Monte Carlo simulation is no longer sufficient to handle the complexity of structured instruments,” Hidary said.

Monte Carlo simulation is a classic form of computational algorithm that uses random sampling to obtain results. With the SandboxAQ LQM method, a financial services company can be measured in a way that a Monte Carlo simulation cannot. Hidary noted that some financial portfolios can be very complex with all manner of structured instruments and options.

“If I have a portfolio and I want to know what the tail risk is given changes in this portfolio,” Hidary said. “What I want to do is I want to do 300 to 500 million versions of that portfolio with little change in it, and then I want to look at tail risk.”

How SandboxAQ uses LQMs to improve cybersecurity

Sandbox AQ’s LQM technology is focused on enabling businesses to create new products, materials and solutions, rather than simply optimizing existing processes.

Among the business verticals in which the company is innovating is cybersecurity. In 2023, the company first released it Sandwich cryptography management technology. That has since been extended to the company’s AQtive Guard business solution.

The software can analyze a business’s files, applications and network traffic to determine the encryption algorithms used. This includes identifying the use of old or broken encryption algorithms such as MD5 and SHA-1. SandboxAQ feeds this information into a management model that can alert the chief information security officer (CISO) and compliance teams about potential vulnerabilities.

While the The LLM can be used for the same purposethe LQM provides a different approach. LLMs are trained on vast, unstructured data on the internet, which can include information about encryption algorithms and vulnerabilities. In contrast, Sandbox AQ’s LQMs are built using targeted, quantitative data about encryption algorithms, their properties and known vulnerabilities. LQMs use this structured data to build models and knowledge graphs specifically for encryption analysis, rather than relying on general language understanding.

Looking ahead, Sandbox AQ is also working on a future remediation module that can automatically propose and implement updates to the encryption used.

Quantum measurements without quantum computers or transformers

The original idea behind SandboxAQ was to combine AI techniques with quantum computing.

Hidary and his team realized early on that real quantum computers would not be easy ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ or very fast in the short term. SandboxAQ uses quantum principles implemented through an improved GPU infrastructure. Through a partnership, SandboxAQ extends Nvidia’s CUDA capabilities to handle quantum techniques.

SandboxAQ also does not use transformers, which are the basis of almost all LLMs.

“The models we train are neural network models and knowledge graphs, but they are not transformers,” Hidary said. “You can generate from equations, but you can also have quantitative data that comes from sensors or other types of sources and networks.”

While the LQM is different from the LLMs, Hidary does not see it as one or the other status for businesses.

“Use the LLMs for what they’re good at, then take the LQMs for what they’re good at,” he said.