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Beyond LLMs: How SandboxAQ’s large quantitative models could optimize enterprise AI

Dec 19, 2024 - venturebeat.com
The article discusses the rise of large quantitative models (LQMs) as an alternative to large language models (LLMs) in enterprise AI. Unlike LLMs, which focus on language tasks, LQMs are designed to optimize specific industry objectives using mathematical equations and physical principles. SandboxAQ, a leading advocate of LQMs, recently raised $300 million to expand its solutions across various sectors, including pharmaceuticals, financial services, and cybersecurity. The company partners with major consulting firms to distribute its technology, which can tackle complex, domain-specific problems like simulating chemical combinations or analyzing financial portfolios.

SandboxAQ's LQMs are also being used to enhance cybersecurity through its Sandwich cryptography management technology and AQtive Guard enterprise solution. These tools analyze encryption algorithms to identify vulnerabilities, offering a targeted approach compared to LLMs. While SandboxAQ initially aimed to integrate quantum computing, it now uses enhanced GPU infrastructure to implement quantum principles without relying on transformers. CEO Jack Hidary emphasizes that LQMs and LLMs can complement each other, with each being used for their respective strengths in enterprise applications.

Key takeaways:

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  • Large Quantitative Models (LQMs) offer enterprises a specialized AI approach, focusing on domain-specific challenges like material properties and financial risk metrics, unlike the general language tasks of Large Language Models (LLMs).
  • SandboxAQ, a leading advocate of LQMs, has raised $300 million to further its enterprise AI solutions, partnering with major consulting firms to tackle complex industry problems.
  • In cybersecurity, SandboxAQ's LQMs provide targeted encryption analysis, identifying vulnerabilities in encryption algorithms, and offering a structured approach compared to LLMs.
  • SandboxAQ combines AI with quantum principles using enhanced GPU infrastructure, avoiding transformers and focusing on neural network models and knowledge graphs for enterprise solutions.
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