Artificial Intelligence (original) (raw)
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Key Takeaways
• Artificial intelligence (AI) is a foundational technology that is supercharging other scientific fields and, like electricity and the internet, has the potential to transform societies, economies, and politics worldwide.
• Despite rapid progress in the past several years, even the most advanced AI models still have many failure modes and vulnerabilities to cyberattacks that are unpredictable, not widely appreciated or easily fixed, and capable of leading to unintended consequences.
• Nations are competing to shape the global rules and standards for AI, making interoperability, sizeable national compute resources, and international governance frameworks critical levers of geopolitical influence.
Overview
Artificial intelligence (AI) is the ability of computers to perform functions associated with the human brain, including perceiving, reasoning, learning, interacting, problem solving, and exercising creativity. AI promises to be a fundamental enabler of technological advancement and progress in many fields, arguably as important as electricity or the internet. In 2024, the Nobel Prizes for Physics and Chemistry were awarded for work intimately related to AI.
Three of the most important subfields of AI are computer vision, machine learning, and natural language processing. The boundaries between them are often fluid.
- Computer vision enables machines to recognize and understand visual information, convert pictures and videos into data, and make decisions based on the results.
- Machine learning (ML) enables computers to perform tasks without explicit instructions, often by generalizing from patterns in data. ML includes deep learning that relies on multilayered artificial neural networks to model and understand complex relationships within data.
- Natural language processing (NLP) equips machines with capabilities to understand, interpret, and produce spoken words and written texts.
Although AI draws on other subfields, it is mostly based on ML, which requires data and computing power, often on an enormous scale. Data can take various forms, including text, images, videos, sensor readings, and more. The quality and quantity of data play a crucial role in determining the performance and capabilities of AI models. Models may generate inaccurate or biased outcomes, especially in the absence of sufficient high-quality data. Furthermore, the hardware costs of training leading AI models are substantial. Currently, only a select number of large US companies have the resources to build cutting-edge models from scratch.
Key Developments
Dominating the AI conversation since late 2022 are foundation models, which are large-scale systems trained on very large volumes of diverse data. Such training endows them with broad capabilities, and they can apply knowledge learned in one context to a different context, making them more flexible and efficient than traditional task-specific models.
Large language models (LLMs) are the most familiar type of foundation model and are trained on very large amounts of text. LLMs are an example of generative AI, which can produce new material based on its training and the inputs it is given, which enable it to make statistical predictions about what other words are likely to be found immediately after the occurrence of certain words.
These models generate linguistic output surprisingly similar to that of humans across a wide range of subjects, including computer code, poetry, legal case summaries, and medical advice. Specialized foundation models have also been developed in other modalities such as audio, video, and images.
Taking full advantage of AI will require managing the risks associated with the technology, some of which include:
- Explainability Today’s AI is for the most part incapable of explaining how it arrives at a specific conclusion.
- Bias and fairness ML models are trained on existing datasets, which means that any bias in the data can skew results.
- Deepfakes AI provides the capability for generating highly realistic but entirely inauthentic audio and video, with concerning implications for courtroom evidence and political deception.
- Hallucinations AI models can generate results or answers that seem plausible but are completely made up, incorrect, or both.
Over the Horizon
AI agents are AI-based software entities that execute tasks, such as setting people’s daily agendas and coordinating software tools, with minimal human input and oversight. However, present-day AI agents face major limitations, such as reliability issues and their inability to communicate with each other.
Embodied AI means AI integrated into robots or other physical devices that are able to sense and act in the physical world, thus expanding the range of interactions robots have with that world. More advanced systems combining robots and AI could lead to applications in various fields such as logistics and domestic assistance.
POLICY ISSUES
AI and Jobs
A major challenge posed by AI involves the future of human work. AI models have already demonstrated how they can be used in a wide variety of fields, including law, customer support, coding, and journalism. This has led to concerns that AI’s impact on employment will be substantial, especially on jobs that involve knowledge work. In some cases, the technology will help workers to increase their productivity and job satisfaction; in others, AI will lead to job losses—and it is not yet clear what new jobs, if any, will arise to take their place.
Governance of AI
Over the past couple of years, nations have explored various possible regimes for governing the technology. In the United States, the Trump administration has taken executive action to promote innovation and leadership by eliminating previous executive restrictions and requirements that had been placed on AI. The administration also set forth America’s AI Action Plan to “accelerate innovation, build American AI infrastructure, and lead in international diplomacy and security.” This plan faces challenges, including its alignment with concurrent proposals to reduce broader scientific research funding. US states are also experimenting with their own AI legislation, often proposing requirements that go well beyond federal guidance.
AI Talent
Talent remains a critical policy issue as the number of graduates in AI who are joining industry, particularly start-ups, is increasing, taking away from the number contributing to foundational AI research. The United States is thus experiencing an AI “brain drain” that does not favor the future of the US research enterprise or its innovation capacity.
AI and Geopolitical Competition
The technological race between the United States and China regarding AI is intensifying. China is aggressively pushing existing AI capabilities into every sector—from education to manufacturing to government—aiming to lock in large-scale network advantages at home and abroad. In response to these and other efforts, the United States is seeking to contain China’s growing technological prowess by using tools such as export controls on technologies that would facilitate Chinese advancement.
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