Beyond LLMs: How SandboxAQ’s large quantitative models can improve enterprise AI

While large language models (LLMs) and Generative artificial intelligence AI has dominated enterprise AI conversations over the past year, and there are other ways companies can benefit from AI.

One alternative is large quantitative models (LQMs). These models are trained to optimize specific objectives and parameters relevant to an industry or application, such as material properties or financial risk metrics. This is in contrast to the more general language comprehension and generation tasks in LLMs. Among the leading advocates and commercial sellers of TQM is SandboxAQwhich announced today that it has raised $300 million in a new financing round. The company was originally part of Alphabet and was It emerged as a separate business In 2022.

The funding is a testament to the company’s success and, more importantly, to its future growth prospects as it looks to find a solution Cases of using artificial intelligence in the organization. SandboxAQ has established partnerships with major consulting firms including Accenture, Deloitte and EY to distribute its enterprise solutions. The main advantage of TQM is its ability to address complex domain-specific problems in industries where fundamental physics and quantitative relationships are critical.

“It’s all about creating the core products in companies that use our AI,” Jack Hydari, CEO of SandboxAQ, told VentureBeat. “So, if you want to create a new drug, or diagnostic, or substance, or you want to manage risk in a big bank, this is where quantitative models shine.”

Why TQM is important for enterprise AI

LLMs have different goals and operate in a different way than LLMs. Unlike LLMs that process textual data from Internet sourcesLQMs generate their data from mathematical equations and physical principles. The goal is to address quantitative challenges that an organization may face.

“We generate data and obtain data from quantitative sources,” Haidari explained.

This approach allows breakthroughs in areas where traditional methods have stalled. For example, in battery development, where lithium-ion technology has dominated for 45 years, QMs can simulate millions of potential chemical combinations without the need for physical prototypes.

Likewise, in pharmaceutical development, where traditional methods face a high failure rate in clinical trials, QM models can analyze molecular structures and interactions at the electron level. Meanwhile, in financial services, TQM addresses the limitations of traditional modeling approaches.

“Monte Carlo simulation is no longer sufficient to deal with the complexity of structured tools,” Heydari said.

Monte Carlo simulation is a classic form of computational algorithm that uses random sampling to obtain results. Using the SandboxAQ LQM approach, a financial services company can scale in a way that Monte Carlo simulation cannot enable. Haidari pointed out that some financial portfolios can be very complex with all kinds of structured instruments and options.

“If I have a portfolio and I want to know what risks might occur given the changes in this portfolio,” Haidari said. “What I would like to do is create 300 to 500 million copies of that wallet with minor changes to it, and then I want to look at what risks might occur next.”

How SandboxAQ uses TQM to improve cybersecurity

Sandbox AQ’s LQM technology focuses on enabling organizations to create new products, materials and solutions, rather than simply improving existing processes.

Among the enterprise sectors where the company has innovated is cybersecurity. In 2023, the company released its debut Sandwich crypto management technology. This has since been expanded with the company’s AQtive Guard enterprise solution.

The software can analyze an organization’s files, applications, and network traffic to determine which encryption algorithms are used. This includes detecting the use of outdated 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.

Instead LLM can be used for the same purposeTotal Quality Management provides a different approach. LLMs are trained on vast, unstructured Internet data, which can include information about encryption algorithms and vulnerabilities. In contrast, Sandbox AQ’s comprehensive quality models are built using targeted quantitative data about encryption algorithms, their properties, and known vulnerabilities. These QMs use structured data to build models and knowledge graphs specifically for coding analysis, rather than relying on general understanding of language.

Looking to the future, Sandbox AQ is also working on a future processing unit that can automatically suggest and implement updates to the encryption in use.

Quantum dimensions without quantum computers or transformers

The original idea behind SandboxAQ was to combine artificial intelligence and quantum computing techniques.

Heidry and his team realized early on that true quantum computers would not be easy to obtain or would not be powerful enough in the short term. SandboxAQ uses quantum principles implemented through GPU-optimized infrastructure. Through the partnership, SandboxAQ expands Nvidia’s CUDA capabilities to handle quantum technologies.

SandboxAQ also does not use compilers, which are the basis of almost all LLM software.

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

Although TQM is different from LLMs, Haidari does not see it as an either-or situation for organizations.

“They used LLMs for what they were good at, and then brought in LLMs for what they were good at,” he said.

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