Imagine an artificial intelligence (AI) application that can communicate meaningfully during careful reflection. I don't mean imitating the communication popularized by large language model (LLM)-powered chatbots, recently embodied in OpenAI. GPT-4o. I envision an AI model that can productively utilize specialized literature, extract and reformulate important ideas, and engage in meaningful interactions with human experts. It is easy to imagine that such a model could be applied to fields such as medical research. However, machine learning systems (generative AI such as ChatGPT, Gemini, Claude, etc.) that are attracting worldwide attention lack intellectual resources. autonomy Required to support such applications. Our lofty AI visions remain the stuff of science fiction for now.
The drive to master AI in geopolitics is undaunted by this reality. Indeed, the geopoliticalscrambleAI activation in 2023――represented This effort by countries as diverse as the UK, France, Germany, India, Saudi Arabia, the United Arab Emirates, the US, and China was undoubtedly driven by broader generative AI and machine learning. However, some in the AI world believe that machine learning is just the current stage of cutting-edge AI, not the final stage.
Paradigms beyond learning strategies tied to machine learning are being explored, for example, by the state-backed Beijing Institute for General Artificial Intelligence (BIGAI), which was founded in 2020. As a Security and Emerging Technology Center in 2023 report As shown in the figure, BIGAI is working with researchers disillusioned with “big data” approaches, including American-educated director Zhu Songchun, to pursue “brain-inspired” AI models. was established by the people. BIGAI's research theme is “small data, big tasks.”
The strategic importance of “small data” AI is also recognized in Australia. Kingston AI Groupis made up of AI academics who aim to align Australia's national AI research and education strategy. in February 2023 statementThe group recognized that Australia was at a relative disadvantage in terms of economic size and access to large datasets used to train machine learning models. They therefore recognize the need to develop “small data capabilities” that will enable Australia to compete in “designing AI systems from small datasets”.
Additionally, India's commitment to embracing technological innovation was highlighted by Prime Minister Narendra Modi in a speech in June 2023. address He appealed to the U.S. Congress and for cooperation with the United States. Addressing important and emerging technologies (iCET). But equally worthy of mention is Prime Minister Modi. meeting with AI researchers Amit Sheth,director Artificial Intelligence Research Institute from the University of South Carolina.
In December 2023, Sheth announced the following AI. vision To India's 3rd Annual Chief Secretaries Conference: The US led the first two phases of AI. While “symbolic” AI dominated the first wave, the now-fashionable “statistical” AI (i.e. machine learning) dominated the second wave. India can and should, Sheth argues.Dominate AI Phase III” The third wave refers to AI models that can adapt according to the situation. One of his new paradigms in this field is Neurosymbol AI, combines the techniques of both waves to obtain new features. Generative AI is important for: IndiaSheth Said Indian officials have said that “neuro-symbolic AI…will drive the next third phase of AI.”
If this concept of AI development sounds familiar, it's worth remembering that its origins date back to the US Defense Advanced Research Projects Agency (DARPA). DARPA distinguishes between two waves of AI, where models are first governed by rules and then learn through statistical association of data. However, in both waves, the model lacks robust inference ability in new situations. DARPA'sthird waveWe assume a model that is capable of “''.contextual reasoningThis is an initiative that took shape in 2018. AI Next motion to “Beyond Second Wave Machine Learning Technologies” (and clear In 2022 Robust neurosymbolic learning and reasoning program).
DARPA's efforts are continually evolving. Still, the tripartite conceptualization of AI is a relic of a pre-ChatGPT era, and American policymakers risk losing sight of its strategic importance.
American trenches in the second wave
Machine learning will remain an essential element for some time to come for national institutions interested in taking a leading role in AI, but why are nations like China, Australia, and India encouraging such research? This is because cutting-edge machine learning techniques are not widely available. It provides the functionality needed to support applications such as virtual medical agents.
But much of U.S. policymakers’ focus on AI is rooted in the era of and content of “big data” AI.President Biden’s 2023 Executive Order on Safe, Secure, and Trustworthy AIFor example, the Defense Production Act could be invoked to require companies that plan to or are actively developing a “dual-use foundational model” whose training breaks the computational threshold of 1026 floating-point operations. (flop) to report such developments and their testing to the Department of Commerce; As Paul Schall says, computational ability is “crude proxySee for model features. This mandate reflects a widespread belief in the effectiveness of increasing the size of models and the datasets used to train them, as well as the computing power they require.
Furthermore, the Biden administration Killed Advanced Computing October 2022 export regulation Chinese companies and beyond continue to evolve limit—The premise is that U.S. semiconductor design and manufacturing equipment cannot enable the development of China's advanced AI models. The implicit assumption is that cutting-edge AI will rely indefinitely on the large amounts of data and computing power that characterize today's machine learning models.
Among the early criticisms of the October 2022 export ban: Martin Lasser and Kevin Wolfe— making the case in their favor — citing an article in Neuro that such control could be a “potential breakthrough in AI that addresses some of the shortcomings of deep learning…by pursuing so-called hybrid AI.” He pointed out that it was a “calculated risk'' in that it had the potential to increase the Iconic AI.
The criticism was appropriate, but late. BIGAI was founded beyond machine learning in his 2020, long before the Biden administration expanded export controls. Australia and India, while enjoying relatively harmonious relations with the United States, recognize the importance of hybrid AI research.
Using export controls to strengthen America's AI lead in certain areas subfield Advances in AI, most notably natural language processing, can effectively anchor the United States in the second wave of AI (statistical machine learning) that is now mainstream. Even though the pitfalls of this lock-in may benefit American industry and national defense in the short and medium term, the long-term future of AI may take a more proactive and deliberate path beyond machine learning. may belong to countries that do. Therefore, relying on limited access to advanced computing tools and workers will not be enough for the United States to maintain its AI advantage.
A concerted effort is needed to harmonize research both nationally and with selected international partners in emerging paradigms such as neurosemiotic AI.
Maintaining and expanding America’s AI superiority
There is preliminary evidence that U.S. policymakers understand the need to engage partner nations in their efforts toward indigenous AI. Link Relations with China have become closer than acceptable.A good example is Microsoft's recent agreements It plans to invest $1.5 billion in Abu Dhabi-based AI conglomerate G42, ahead of negotiations with the Biden administration.Mohamed Soliman of the Middle East Institute, April 2024 testimony to U.S.-China Economic Security Review Commissionargues that this is, in part, a frank recognition that countries like the United Arab Emirates are poised to become leaders in AI.
But this recognition is only part of the effort needed by American policymakers. Much of the second wave of basic research in AI is occurring in the private sector. Google and OpenAI A new milestone in natural language processing has been achieved. Microsoft is partnership OpenAI, and now G42, cannot be expected to take the necessary steps to secure emerging third-wave AI technologies to support high-stakes applications in the corporate AI arms race for Generative AI. .
Therefore, concerted action by the U.S. government must be taken to balance the scales, including harmonizing and expanding existing efforts.
A useful model is the 2023 partnership between the Department of Defense and the National Science Foundation. AI Artificial Intelligence/Natural Intelligence Research Institute (ARNI).of partnership It helps fund efforts that connect “the great progress made in…” [AI] A system that will revolutionize our understanding of the brain. ” ARNI's interdisciplinarity echoes the chorus of voices about the potential achievements of neurosemiotic AI. inspired By the human mind's ability to reason, reason, and engage in long-term planning, with an emphasis on building algorithms that support explainable applications. It potentially means “Performance guaranteeIt doesn't exist in deep learning.and it provides Capacity to adapt This is not seen in deep learning. Policy makers may therefore pay attention to ARNI policies. interdisciplinary research and a funding scheme as an example of future research tailored to the needs of third-wave AI.
Additionally, a small but forward-looking industry stakeholder needs to be involved. These include companies such as: Symbolica Since the team aims to leverage applied areas, Math Build explainable models capable of structured inference with less training data and computational power. Birth AI Its Chief Scientist Karl Friston To tell The company says it “aims to offer models that are 99% smaller” without sacrificing quality. Such research could contribute to the foundations of third-wave AI.
Finally, the United States should selectively embrace partnerships to foster hybrid AI research that targets flaws in modern AI models. What is noteworthy is the rise of “.''mini lateral” Like the Quadrilateral Security Dialogue and AUKUS, New technology. While we need to exercise restraint to keep cutting-edge technologies out of the hands of our adversaries, the United States should consider targeting efforts in specific areas of hybrid AI research—particularly partnerships like AUKUS, which involve many countries. Because of the participation of… Japan (at least) in Pillar 2 activities, and South Korea It places emphasis on sharing advanced military technology.
The United States must take these steps not just to remain competitive in the second wave of AI, but to create and take advantage of the third.
Vincent J. Carchidi is an adjunct scholar in the Strategic Technology and Cyber Security Program at the Middle East Institute. He is also a member of the American Foreign Policy 2024 NextGen Initiative. His opinions are his own.you can follow him linkedin and X.
The views expressed in this article are solely those of the author and do not necessarily reflect the views of Geopoliticalmonitor.com.
