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Nature Communications Publishes Zapata AI Research on Generative AI for Optimization

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Zapata Computing Holdings Inc. announces research on Generator-Enhanced Optimization (GEO) published in Nature Communications, showcasing how generative AI can revolutionize optimization problems in industrial settings. GEO proves competitive in financial portfolio optimization, outperforming existing algorithms. Zapata AI's CEO highlights the potential of generative AI beyond language tasks, emphasizing its impact on business analytics and optimization solutions.
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The paper demonstrates how generative AI can improve upon existing techniques for solving optimization problems common in industrial settings.

BOSTON--(BUSINESS WIRE)-- Zapata Computing Holdings Inc. (“Zapata AI”) (Nasdaq: ZPTA), the Industrial Generative AI company, today announced that its foundational research on generator-enhanced optimization (GEO) has been published in the esteemed Nature Communications journal. The research, titled “Enhancing Combinatorial Optimization with Classical and Quantum Generative Models,” introduces Generator-Enhanced Optimization (GEO), a novel optimization method that leverages the power of generative modeling to suggest high-quality candidate solutions to complex optimization problems. It is the second Zapata AI paper on generative AI to be published in Nature Communications since December 2023.

The research was published online on March 29th and can be accessed here.

The paper discusses our findings when we have tested GEO for financial portfolio optimization, finding that GEO performs competitively and often outperforms existing state-of-the-art optimization algorithms, which have been fine-tuned for decades. Portfolio optimization is a common problem among investors who aim to allocate their capital to maximize their returns for a given level of risk (or minimize their risk for a desired level of returns). Despite years of study, this problem remains a computational challenge for financial institutions that only becomes more challenging the more assets are involved. The GEO paper reflects the results of a pioneering effort to apply generative AI to portfolio optimization and other optimization problems.

“When a lot of business leaders think of Generative AI, they think of LLMs, but this research demonstrates one of the many ways generative AI can be applied to industrial problems beyond language tasks” said Christopher Savoie, CEO and co-founder of Zapata AI. “We believe generative AI is the next frontier in business analytics, whether that’s generating data for variables that couldn’t otherwise be measured or recommending better ways to solve optimization problems, as in this paper. It’s very exciting to see this continued validation of our work in generative AI and we’re immensely proud of the researchers involved.”

GEO has been applied to real-world industrial problems since the research paper was initially submitted to ArXiv in 2021. In 2022, GEO was used in work with BMW and the Center for Quantum Engineering at MIT to find more efficient manufacturing plant operating schedules, minimizing idle time between steps in the manufacturing process while meeting production targets. That research found that GEO tied or outperformed state-of-the-art optimization algorithms in 71% of problem configurations. More information on GEO can be found here.

Since GEO was first developed, Zapata AI has established a growing portfolio of quantum techniques for generative AI. For instance, Zapata AI researchers recently leveraged quantum-enhanced generative AI to generate viable cancer drug candidates for the first time. Quantum science could offer several advantages for enterprise problems, including compressing large, computationally expensive models; speeding up time-consuming and costly calculations; and generating more diverse, higher quality outputs for generative AI. More details on how quantum science can enhance generative AI can be found in a recent Zapata AI blog post.

“Our Nature Communications article reflects an early demonstration of how generative AI techniques inspired by quantum physics can be applied to solve optimization problems” said Mohammad Ghazi Vakili, a former post doc at Zapata AI who authored the paper along with Javier Alcazar, Can B. Kalayci, and Alejandro Perdomo-Ortiz. “It was impressive to see GEO go toe-to-toe or outperform algorithms that have been fine-tuned for decades. We expect to see more impressive results as quantum generative AI matures.”

About Zapata AI

Zapata AI is an Industrial Generative AI company, revolutionizing how enterprises solve complex problems with its powerful suite of Generative AI software. By combining numerical and text-based solutions, Zapata AI empowers industrial-scale enterprises and government entities to leverage large language models and numerical generative models better, faster, and more efficiently delivering solutions to drive growth, cost savings and operational insight. With proprietary data science and engineering techniques and the Orquestra® platform, Zapata AI is accelerating Generative AI’s impact across industries. The Company was founded in 2017 and is headquartered in Boston, Massachusetts.

Forward-Looking Statements

Certain statements made herein are not historical facts but are forward-looking statements for purposes of the safe harbor provisions under The Private Securities Litigation Reform Act of 1995. Forward-looking statements generally are accompanied by words such as “believe,” “may,” “will,” “intend,” “expect,” “should,” “would,” “plan,” “predict,” “potential,” “seem,” “seek,” “future,” “outlook,” and similar expressions that predict or indicate future events or trends or that are not statements of historical matters. These forward-looking statements include, but are not limited to, statements regarding future events and other statements that are not historical facts. These statements are based on the current expectations of Zapata AI’s management and are not predictions of actual performance. These forward-looking statements are provided for illustrative purposes only and are not intended to serve as, and must not be relied on, by any investor as a guarantee, an assurance, a prediction, or a definitive statement of fact or probability. These statements are subject to a number of risks and uncertainties regarding Zapata AI’s business, and actual results may differ materially. These risks and uncertainties include, but are not limited to, failure to realize the benefits expected from the business combination; a decline in the price of the combined company’s securities if it fails to meet the expectations of investors or securities analysts; Zapata AI’s ability to attract new customers, retain existing customers, and grow; competition in the generative AI industry; Zapata AI’s ability to raise additional capital on non-dilutive terms or at all; Zapata AI’s ability to improve its operational, financial and management controls; Zapata AI’s failure to maintain and enhance awareness of its brand; increased costs associated with operating as a public company; protection of proprietary rights; intellectual property infringement, data protection and other losses; and other factors discussed in Zapata AI’s definitive proxy statement/prospectus, filed with the Securities and Exchange Commission (the “SEC”) on January 29, 2024, and other documents of Zapata AI filed, or to be filed, with the SEC.

If any of these risks materialize or if assumptions prove incorrect, actual results could differ materially from the results implied by these forward-looking statements. There may be additional risks that Zapata AI presently does not know or that Zapata AI currently believes are immaterial that could also cause actual results to differ from those contained in the forward-looking statements. While Zapata AI may elect to update these forward-looking statements at some point in the future, Zapata AI specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing Zapata AI’s assessments as of any date subsequent to the date of this communication. Accordingly, undue reliance should not be placed upon the forward-looking statements.

Media: press@zapata.ai

Investors: investors@zapata.ai

Source: Zapata Computing Holdings Inc.

The research paper focuses on Generator-Enhanced Optimization (GEO), introducing a novel optimization method leveraging generative modeling to solve complex industrial optimization problems.

GEO competes well in financial portfolio optimization, often surpassing existing state-of-the-art optimization algorithms, proving its effectiveness in maximizing returns while managing risk.

GEO has been applied to real-world industrial problems, including optimizing manufacturing plant operating schedules for efficiency and performance improvement.

Quantum science can provide benefits such as compressing large models, speeding up calculations, and generating higher quality outputs for generative AI applications.

The research paper was authored by Mohammad Ghazi Vakili, Javier Alcazar, Can B. Kalayci, and Alejandro Perdomo-Ortiz.

:ZPTA

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