Imagine an AI application capable of engaging meaningfully with experts, synthesizing specialized literature, and contributing to research. This vision extends beyond the capabilities of current generative AI models like OpenAI’s GPT-4o. While these models are impressive, they lack the advanced reasoning and autonomy necessary for more complex applications, such as in medical research.
The Geopolitical AI Race
Despite the limitations of current AI models, the global race to dominate AI continues unabated. Countries like China, Australia, and India are exploring new AI paradigms that move beyond traditional machine learning. For instance, the Beijing Institute for General Artificial Intelligence (BIGAI) focuses on “small data, big task” AI models, seeking to overcome the constraints of data-intensive approaches.
BIGAI’s research is driven by the idea that current machine learning models, reliant on vast amounts of data, are not the pinnacle of AI development. Researchers at BIGAI, including American-educated Director Zhu Songchun, are disillusioned with the “big data” approach and are instead inspired by “brain-inspired” AI models. These models aim to replicate the cognitive functions of the human brain, making them more efficient and capable of performing complex tasks with less data.
Similarly, Australia’s Kingston AI Group is developing AI systems designed to work with smaller datasets, acknowledging their limited access to large-scale data. In a February 2023 statement, the Group emphasized the importance of creating a ‘small data capability’ to enable Australia to compete in AI research and development. By focusing on smaller, more manageable datasets, Australia aims to develop AI technologies that are not only more efficient but also more adaptable to various applications.
India’s Prime Minister Narendra Modi has also highlighted the country’s potential to lead the next phase of AI. In his June 2023 address to the U.S. Congress, Modi spoke about India’s collaboration with America through the Initiative on Critical and Emerging Technology (iCET). Modi’s vision extends to embracing Neuro-Symbolic AI, which combines symbolic AI and machine learning techniques to achieve greater contextual adaptation and reasoning. This approach is seen as the future of AI, capable of driving significant advancements in various fields.
America’s Focus on Second Wave AI
In contrast, much of American AI policy and research remains entrenched in the second wave of AI—statistical machine learning. President Biden’s 2023 Executive Order on AI reflects this focus, emphasizing the regulation of computational power as a measure of AI capability. The Executive Order mandates that companies developing “dual-use foundational models” report on their development and testing to the Department of Commerce. This approach is based on the belief that increasing the size of models, datasets, and computing power will continue to drive AI advancements.
The U.S. has also implemented strict export controls on advanced computing tools to prevent China from developing cutting-edge AI models. These controls, introduced in October 2022, aim to limit China’s access to American semiconductor designs and manufacturing equipment. The underlying assumption is that state-of-the-art AI will indefinitely rely on the massive data and computing power that characterize machine learning models today.
While these measures aim to maintain America’s AI leadership, they also risk limiting the country’s advancement to the next phase of AI. This entrenchment could prove detrimental as other nations explore hybrid AI approaches, including Neuro-Symbolic AI, which promise more robust and adaptable AI systems. The focus on “big data” AI might lead to algorithmic stagnation, where the U.S. falls behind in developing more advanced, contextually adaptive AI technologies.
Advancing American AI Research
To secure its AI dominance, the U.S. must harmonize domestic and international AI research efforts. For example, the Department of Defense’s partnership with the National Science Foundation to fund the AI Institute for Artificial and Natural Intelligence (ARNI) represents a step in the right direction. ARNI’s interdisciplinary approach seeks to merge AI advancements with our understanding of the brain, aiming to develop AI models that mimic human reasoning and adaptability.
ARNI’s research focuses on integrating techniques from both symbolic AI and machine learning to create Neuro-Symbolic AI systems. These systems are designed to reason, analogize, and engage in long-term planning, making them more versatile than current machine learning models. By drawing inspiration from the human mind’s ability to understand context and make decisions, ARNI aims to develop AI technologies that are both explainable and reliable.
Engaging smaller, innovative companies is also crucial. Firms like Symbolica and Verses AI are pioneering efforts to create more efficient and explainable AI models that require less data and computing power. Symbolica, for example, is leveraging applied mathematics to build models capable of structured reasoning, while Verses AI focuses on delivering smaller models without sacrificing quality. Supporting such initiatives can help lay the groundwork for third-wave AI and ensure that the U.S. remains at the forefront of AI innovation.
Collaborating for a Competitive Edge
The U.S. should also consider strategic international collaborations. Partnerships like the Quadrilateral Security Dialogue and AUKUS can facilitate cooperative research on emerging AI technologies. By focusing on hybrid AI research, the U.S. can address the limitations of current AI models and ensure it remains at the forefront of AI innovation.
The recent agreement between Microsoft and Abu Dhabi-based AI conglomerate G42 exemplifies the potential benefits of international collaboration. This partnership, negotiated with the Biden administration’s input, acknowledges the ambitions of states like the United Arab Emirates to become AI leaders. By collaborating with such entities, the U.S. can gain access to new research avenues and innovative approaches that might otherwise be overlooked.
To truly lead in AI, America must look beyond the current wave of machine learning and invest in the future of AI technology. By fostering interdisciplinary research, supporting innovative startups, and building strategic international partnerships, the U.S. can pave the way for the next generation of AI. This includes not only enhancing current AI capabilities but also exploring new paradigms that could revolutionize the field.
Preserving and Expanding American AI Dominance
There is tentative evidence that American policymakers understand the need to engage with the indigenous AI efforts of partner states, including those whose links with China have hedged closer than comfort allows. A case-in-point is Microsoft’s recent agreement to invest $1.5 billion in Abu Dhabi-based AI conglomerate G42, preceded by negotiations with the Biden administration. The Middle East Institute’s Mohammed Soliman, in an April 2024 testimony to the U.S.-China Economic and Security Review Commission, argues that this is in part a frank recognition that states like the United Arab Emirates intend to become AI leaders.
This recognition is only part of the necessary effort by American policymakers, however. Much of the second wave of AI’s basic research is occurring in the private sector, where companies like Google and OpenAI achieved new milestones in Natural Language Processing. Microsoft, in its partnership with OpenAI—and now also with G42—cannot be expected to take the steps necessary to secure new techniques for third wave AI that support high-stakes applications during a corporate AI arms race over Generative AI.
Cohesive U.S. government action must therefore be undertaken to balance the scales, including harmonizing and expanding upon existing initiatives.
A useful model is the Department of Defense’s 2023 partnership with the National Science Foundation to fund the AI Institute for Artificial and Natural Intelligence (ARNI). The partnership helps fund an effort to link “the major progress made in [AI] systems to the revolution in our understanding of the brain.” ARNI’s interdisciplinarity echoes a chorus of voices on the potential fruits of Neuro-Symbolic AI: it is inspired by the human mind’s ability to reason, analogize, and engage in long-term planning, with an emphasis on constructing algorithms that support explainable applications; it potentially offers “performance guarantees” absent in deep learning; and it offers adaptability not seen in deep learning. Policymakers may thus look to ARNI’s interdisciplinary research and funding scheme as an example for future research tailored the needs of third wave AI.
Additionally, smaller yet forward-looking industry actors should be engaged. These include companies like Symbolica as its team aims to leverage an applied branch of mathematics to build an explainable model capable of structured reasoning on less training data and computing power and Verses AI whose Chief Scientist Karl Friston says the company “aims to deliver 99% smaller models” without sacrificing quality.” Such work may contribute to the foundations of third wave AI.
Finally, the U.S. should selectively recruit its partnerships to promote hybrid AI research that targets deficiencies in contemporary AI models. Notably, the rise of “minilaterals” like the Quadrilateral Security Dialogue and AUKUS are facilitating cooperation on emerging technologies. While restraint must be exercised to prevent advanced technology from falling into adversarial hands, the U.S. should consider initiatives that target specified areas of hybrid AI research—especially as partnerships like AUKUS entertain the participation of Japan in (at least) Pillar II activities and as South Korea weighs sharing its advanced military technology.
America, should it wish to do more than simply retain its competitive edge in the second wave of AI, must take these steps to create and harness its third wave
