Building on the prior discussion regarding the absence of a single, universally accepted definition for artificial intelligence (AI), this section elucidates a pivotal distinction within the field: the contrast between Narrow AI (also referred to as “Weak AI”) and General AI (often called “Strong AI”). The ensuing analysis explores the functionalities, constraints, and potential implications of each variant, establishing a framework for understanding the critical governance, legal, and ethical considerations that arise as AI technology continues to evolve.

Narrow AI: The Workhorse of Today’s AI Applications

Narrow AI, also referred to as Weak AI, represents the present state of AI development and deployment. These systems are designed to fulfill specific tasks—or a limited range of tasks—excelling within their assigned domains. See, e.g., Marcia Narine Weldon et al., Establishing a Future-Proof Framework for AI Regulation: Balancing Ethics, Transparency, and Regulation, 25 Transactions: Tenn. J. Bus. L. 253, 265 (2024) (“Traditional, narrow, or weak AI are what most consumers have been using for years … Traditional or weak AI identifies patterns but does not have the power to create anything new.”).

Narrow AI technologies are deeply embedded in everyday life, underpinning a variety of widely used consumer products. Examples include:

• Virtual Assistants (e.g., Siri and Alexa). These systems respond to user queries, offer information, play music, and manage smart home devices.

• Recommendation Systems (e.g., Netflix and Spotify). By meticulously analyzing user viewing and listening histories, these platforms propose content that aligns with individual preferences.

• Spam Filters. These tools sift through email inboxes, accurately identifying and redirecting unwanted messages to maintain cleanliness and efficiency.

• Industrial Robots. Operating on assembly lines, these robots carry out repetitive tasks with enhanced precision, thereby improving manufacturing processes.

See id.

The defining feature of Narrow AI lies in its specialization. These systems are trained on extensive datasets tailored to their respective tasks, enabling them to achieve remarkable performance—often surpassing human capabilities in their given spheres. Nevertheless, their “intelligence” is bounded by the scope of their programming. See, e.g., Kelly Carman, The Genie Is Out of the Bottle: What Do We Wish for the Future of AI?, 9 Penn St. J.L. & Int’l Aff. 180, 190 (2020) (explaining that Narrow AI systems are designed for specific tasks, as illustrated by Apple’s Siri, which can only operate within its programmed parameters); Gary Marchant, Lucille Tournas & Carlos Ignacio Gutierrez, Governing Emerging Technologies Through Soft Law: Lessons for Artificial Intelligence, 61 Jurimetrics J. 1, 2 (2020) (observing that although AI tools like IBM Watson can outperform humans in certain diagnostic tasks, they remain confined to narrowly defined applications).

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By way of illustration, a chess-playing AI may masterfully defeat grandmasters but lacks the ability to apply that expertise to solving mathematical equations or composing music. Similarly, an AI platform trained to detect fraudulent transactions might excel in financial risk assessment yet prove incapable of adapting its knowledge to diagnose medical conditions. This inability to generalize or transfer learning across multiple domains sets Narrow AI apart from human intelligence: while these systems can be highly proficient within their designated functions, they lack the cognitive flexibility and adaptability characteristic of human cognition.

Concerns surrounding Narrow AI frequently stem from its inherent limitations. As these systems become further integrated into critical infrastructure and decision-making processes, their inability to adapt to unanticipated circumstances raises important questions about possible risks:

• Overreliance and Excessive Responsibility. Relying on Narrow AI systems to perform functions exceeding their programmed parameters can yield unpredictable results and cause substantial harm. See Alicia Solow-Niederman, Administering Artificial Intelligence, 93 S. Cal. L. Rev. 633, 663 (2020) (noting that poorly designed AI systems can lead to unforeseeable and deleterious outcomes, and defining an “accident” as “a situation where a human designer had in mind a certain (perhaps informally specified) objective or task, but the system that was designed and deployed for that task produced harmful and unexpected results”). For instance, an autonomous vehicle that relies solely on Narrow AI may be ill-equipped to address novel road conditions or unexpected scenarios, potentially culminating in accidents.

• Algorithmic Bias. AI systems are informed by the data on which they are trained. Where such data lacks adequate representation—for example, in terms of skin tone, gender, or other demographic factors—the AI may perpetuate or amplify extant biases, thereby resulting in discriminatory or otherwise problematic outcomes. This issue is particularly salient in military contexts, where narrow AI systems with hidden biases could misidentify targets and cause unintended civilian harm. In addition, narrow AI systems can produce unpredictable or seemingly illogical outcomes—for example, misclassifying images after imperceptible distortions—that adversaries might exploit, raising considerable ethical and legal concerns should lethal decisions be made autonomously. See ARTIFICIAL INTELLIGENCE DISCOVERY AND ADMISSIBILITY CASE LAW AND OTHER RESOURCES, VCFI0530 ALI-CLE 17 (identifying the potential for algorithmic bias and unforeseen failures in narrow AI, documenting racial and gender bias in AI applications, and warning that flawed or easily manipulated AI could contribute to misidentification, civilian casualties, and possible infractions of the law of armed conflict).

Despite these issues, Narrow AI continues to spur progress across numerous industries. As these systems become increasingly advanced and embedded in daily life, a clear understanding of their capabilities and limitations is essential for maximizing their benefits while minimizing associated risks.

General AI: The Realm of Hypothetical Intelligence

General AI, also known as Strong AI, represents the holy grail of AI research. This hypothetical form of AI envisions systems that possess human-level or superhuman-level intelligence across a wide range of cognitive tasks. See id. (explaining that U.S. policymakers generally define AI as a computer system capable of human-level cognition, subdivide AI into narrow, general, and superintelligent categories, and noting that general AI and artificial superintelligence do not yet—and may never—exist).

A General AI system would be capable of:

• Learning and adapting to new information and environments, continuously expanding its knowledge and capabilities.

• Solving problems across diverse domains, drawing upon its knowledge and understanding to address complex challenges.

• Reasoning and making decisions based on logic, evidence, and contextual understanding.

• Communicating and interacting with humans and other AI systems in a natural and meaningful way.

See, e.g., Karl Manheim & Lyric Kaplan, Artificial Intelligence: Risks to Privacy and Democracy, 21 Yale J.L. & Tech. 106, 116 (2019) (explaining that the next generation of AI—known as Artificial General Intelligence—would transcend solving only a predefined set of problems and instead apply intelligence to virtually any problem, and suggesting that once computers outperform even the smartest humans, society will have reached Artificial Super Intelligence); Marcia Narine Weldon, Gabrielle Thomas & Lauren Skidmore, Establishing A Future-Proof Framework for AI Regulation: Balancing Ethics, Transparency, and Innovation, 25 Transactions: Tenn. J. Bus. L. 253, 266–67 (2024) (“Theoretically, AGI would reason like humans, learn from its mistakes, display emotional intelligence, communicate with the human it was interacting with, and would not need reprogramming.”).

Another perspective emerges from Google DeepMind’s newly proposed taxonomy of artificial general intelligence (“AGI”). In a paper, the team identified five ascending levels of AGI—emerging, competent, expert, virtuoso, and superhuman—based on an AI’s ability to learn, adapt, and outperform human capabilities across a broad range of tasks. Their approach focuses more on what an AGI can do than on how it does it, underscoring the importance of ongoing performance-based evaluation rather than a one-time threshold test. The researchers acknowledge that no level beyond “emerging” AGI has been achieved, and they caution that defining AGI also raises normative questions about whether—and why—society should pursue ever more powerful AI systems. See Will Douglas Heaven, Google DeepMind Wants to Define What Counts as Artificial General Intelligence, MIT Tech. Rev. (Nov. 16, 2023), https://www.technologyreview.com/2023/11/16/1083498/google-deepmind-what-is-artificial-general-intelligence-agi/ [https://perma.cc/T7EP-PS8B].

In a noteworthy deviation from traditional discussions of AGI, Microsoft and OpenAI have reportedly adopted a purely financial definition of the term in their partnership agreement. According to a recent report, the agreement stipulates that OpenAI will be deemed to have achieved AGI only once its AI systems generate at least $100 billion in profits—an internal metric that diverges markedly from conventional technical or philosophical conceptions of AGI. See Microsoft and OpenAI Wrangle Over Terms of Their Blockbuster Partnership, The Information (Dec. 2024), https://www.theinformation.com/articles/microsoft-and-openai-wrangle-over-terms-of-their-blockbuster-partnership?rc=dp0mql. This definition is further underscored by OpenAI’s current financial trajectory: the startup expects to incur substantial losses this year and does not anticipate turning a profit until 2029. Notably, if OpenAI reaches its profit-based threshold and thus triggers its AGI designation, Microsoft’s contractual right to access OpenAI’s technology would terminate. Observers have speculated that such a profit-centric approach could delay Microsoft’s loss of access to OpenAI’s cutting-edge models for a decade or more, even as questions persist about whether financial metrics alone can adequately capture the broader technical and societal implications of AGI.

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Nevertheless, the concept of General AI remains controversial, with experts deeply divided on its feasibility and timeline. Some researchers view its emergence as only a matter of time, predicting “a 25% chance that we will have singularity as soon as 2030” and noting that “most believe that it will occur by the year 2060.” THE HITCHHIKER’S GUIDE TO ARTIFICIAL INTELLIGENCE, CG205 ALI-CLE 419 (explaining that existing algorithms “will not reach general intelligence anytime soon,” although predictions vary widely). Others contend that “the so-called general or strong AI that resembles human intelligence (developing general and abstract thinking to perform different tasks) has not yet been created and it will probably not be created in the near future,” in part because current AI can only solve narrowly defined problems. José Vida Fernández, Artificial Intelligence in Government: Risks and Challenges of Algorithmic Governance in the Administrative State, 30 Ind. J. Global Legal Stud. 65, 73–74 (2023). Still others highlight the “framing problem,” which posits that “formal programs are finite, and the number of possible social contexts is infinite,” thus preventing truly human-like adaptability. N. F. Sussman, A Behavioral Theory of Robot Rights, 32 S. Cal. Interdisc. L.J. 113, 145 (2022) (explaining Hubert Dreyfus’s a priori argument that machines cannot replicate the contextual awareness and improvisation integral to human behavior). The debate therefore hinges on whether these barriers—ranging from practical limitations to fundamental questions about the nature of human cognition—can ever be overcome.

The potential of General AI evokes both enthusiasm and apprehension among scholars and practitioners:

• Prospects for addressing complex challenges. General AI promises to transform diverse fields, including medicine, scientific research, environmental sustainability, and space exploration. By exhibiting reasoning, learning, and adaptive capabilities at human or superhuman levels, future systems could substantially accelerate progress in these areas, yielding breakthroughs with far-reaching societal benefits.

• Concerns regarding humanity’s future. The notion of AI surpassing human intelligence also raises fundamental questions about control, safety, and the long-term survival of our species. Some experts worry that uncontrolled or misaligned AI could pursue objectives at odds with human interests, thus posing significant or even existential risks.

While these debates continue, it is important to recognize that General AI remains a hypothetical construct at present. Contemporary AI research is firmly anchored in Narrow AI systems, and although advancements in the field are noteworthy, bridging the gap to General AI remains both a formidable challenge and an area of considerable uncertainty. Should General AI ever be realized, its implications would be profound, necessitating thorough ethical deliberation and the careful governance of such transformative technology.

The Spectrum Demands Responsible Innovation

The distinction between Narrow AI and General AI underscores the evolutionary trajectory of artificial intelligence. While Narrow AI systems already foster innovation and transform industries, the potential advent of General AI invites both optimism and apprehension. As AI continues to progress, a prudent and ethically informed approach to its development and deployment becomes imperative:

• Investing in Research and Development. Ongoing research is critical to refine and expand AI capabilities while anticipating and mitigating potential risks. Scholars and practitioners must investigate both Narrow AI and General AI, examining their respective benefits, limitations, and broader societal implications.

• Promoting Ethical Guidelines and Regulation. Establishing explicit ethical standards and regulatory frameworks is crucial for fostering responsible innovation. Such efforts should address algorithmic bias, data privacy, and the risk of misuse, ensuring that AI deployment aligns with the broader public interest.

• Fostering Public Understanding and Dialogue. Open and informed engagement with diverse stakeholders is vital to demystify AI and foster realistic expectations regarding its capabilities. Public discourse should clarify the technology’s possibilities and limitations, thereby promoting transparency and trust.

The future direction of AI is inextricably tied to present decisions. By integrating rigorous research, ethical awareness, and a commitment to societal well-being, artificial intelligence can be cultivated as a transformative force with the potential to contribute meaningfully to human progress.

AI has quickly permeated the general lexicon, but public understanding of what constitutes AI, and what its ethical implications are, lags behind its proliferation. This introductory blog post offers a primer on artificial intelligence, exploring its definition—or rather, the universal lack thereof.

No Universal Definition

It might surprise people to know AI has no universally accepted definition. See U.S. GOV’T ACCOUNTABILITY OFF., GAO-18-142SP 15 (2018) (stating “[t]here is no single definition of AI, but rather differing definitions and taxonomies”); Forrest E. Morgan et al., Military Applications of Artificial Intelligence: Ethical Concerns in an Uncertain World, RAND CORP. 8-9 & 9 n.4 (2020), www.rand.org/pubs/research_reports/RR3139-1.html [https://perma.cc/4XUX-7PQZ] (emphasizing experts’ aversion to providing definitive definitions for AI). This lack of consensus is partly due to the field’s quick evolution and its interdisciplinary nature. See David S. Rubenstein, Acquiring Ethical AI, 73 Fla. L. Rev. 747, 758 (2021) (“‘Artificial intelligence’ has no ‘universally accepted definition.’ The lexical rifts are largely attributable to the field’s evolution and multi-disciplinarity, which spans computer science, mathematics, psychology, sociology, neuroscience, philosophy, linguistics, and intersects with countless more.”). John McCarthy has offered a classic definition of AI as the science and engineering of making intelligent machines, especially intelligent computer programs related to the similar tasks of understanding human intelligence using computers. IBM, What is Artificial Intelligence (AI)? https://www.ibm.com/topics/artificial-intelligence (last visited Nov. 26, 2023); see Yijun (Jenny) Ge, AI and Corporate Governance: How May AI Assist the Board in Informed Decision-Making?, 24 U.C. Davis Bus. L.J. 115, 120 (2024).

AI as a Simulation of Human Intelligence

One common approach is to define AI as the ability of machines to mimic human intelligence or cognitive functions. See Tabrez Y. Ebrahim, Artificial Intelligence Inventions & Patent Disclosure, 125 Penn St. L. Rev. 147, 165 (2020). This includes tasks like problem-solving, decision-making, learning, perception, reasoning, and language understanding. See Scott J. Shackelford & Rachel Dockery, Governing AI, 30 Cornell J.L. & Pub. Pol'y 279, 285 (2020). The U.S. Congress defines AI as a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations [,] or decisions influencing real or virtual environments. H.R. REP. NO. 116-617, at 1164 (2020) (Conf. Rep.); see Haley Giaramita, AI Assistance in the Drug Development Process: Reaching for A Regulatory Framework, 54 Seton Hall L. Rev. 1239, 1243 (2024). AI systems, as Congress defines them, employ a combination of machine-generated and human-provided inputs to (A) perceive real and virtual environments; (B) abstract such perceptions into models through analysis in an automated manner; and (C) use model inference to formulate options for information or action. Id.

AI as a Set of Techniques

Another perspective views AI as a collection of computational techniques. See Ryan Calo, Artificial Intelligence Policy: A Primer and Roadmap, 51 U.C. Davis. L. Rev. 399, 404 (2017) (“AI is best understood as a set of techniques aimed at approximating some aspect of human or animal cognition using machines.”); Claudia E. Haupt, Artificial Professional Advice, 21 Yale J. L. & Tech. 55, 77 (2019). These techniques are often centered around machine learning, including supervised, unsupervised, and reinforcement learning. See Amanda Levendowski, How Copyright Law Can Fix Artificial Intelligence's Implicit Bias Problem, 93 Wash. L. Rev. 579, 590 (2018) (“When journalists, researchers, and even engineers say ‘AI,’ they tend to be talking about machine learning, a field that blends mathematics, statistics, and computer science to create computer programs with the ability to improve through experience automatically.”); Urs Gasser & Virgilio A.F. Almeida, A Layered Model for AI Governance, 21 IEEE INTERNET COMPUTING 58, 59 (2017) (“From a technical perspective, [AI] is not a single technology, but rather a set of techniques and sub-disciplines ranging from areas such as speech recognition and computer vision to attention and memory, to name just a few.”).

Artificial neural networks (ANNs) are one powerful computing technique often used in AI systems—inspired by the structure and function of the human brain, they use interconnected nodes or "neurons" to process information. See, e.g., N. F. Sussman, A Behavioral Theory of Robot Rights, 32 S. Cal. Interdisc. L.J. 113, 117 (2022) (“[M]y working definition of AI would include systems that execute via symbolic machine learning algorithms or ‘connectionist’ neural nets, which operate by means of programs that self-update according to the external ‘feedback such programs may receive on their prior outputs.). These networks "learn" by adjusting the weights of the connections between these neurons as they are exposed to input data. See David W. Opderbeck, Copyright in AI Training Data: A Human-Centered Approach, 76 Okla. L. Rev. 951, 958–59 (2024) (“Like the human brain, the algorithms include parameters that allow these systems to ‘learn’ as more and more data is processed, adjusting the algorithmic weights in various nodes[.]”); Walter A. Mostowy, Explaining Opaque AI Decisions, Legally, 35 Berkeley Tech. L.J. 1291, 1299 (2020) (“The neural network [] compares [incorrect] output to the correct output … and makes small adjustments to all of the neurons' parameters—the weights and the thresholds—to achieve a slightly better outcome. This process is then repeated for the entire training set, again and again, until overall accuracy no longer improves.”). ANNs, particularly deep learning networks, which consist of multiple layers of neurons, excel at recognizing patterns within datasets and can even make predictions based on those patterns. This capability to learn from data and make decisions based on recognized patterns aligns with a common understanding of AI as the simulation of human cognitive abilities using machines.

AI as a Practice

Some have proposed that AI is best understood as a practice—an applied science and engineering discipline that aims to instill qualities we consider intelligent into computer software. See Michael Veale et al, AI and Global Governance: Modalities, Rationales, Tensions, 19 Ann. Rev. L. & Soc. Sci. 255, 256 (2023). This definition emphasizes the human element: (1) Humans define the goals and applications of AI technologies; (2) humans design the tools and processes used in AI; thus, (3) the governance of AI must consider its human practitioners, their organizations, and the broader social, economic, and political structures surrounding its use. Id.

A Complex Endeavor

Ultimately, defining AI remains a complex and evolving endeavor. The lack of a single, universally accepted definition underscores the need to engage with AI's multifaceted nature when considering its ethical and legal implications. Future posts will move beyond these definitional explorations. My next post will delve into key distinctions within the field of AI, such as narrow (or weak) AI versus general (or strong) AI, providing a framework for understanding the varying capabilities and limitations of different AI systems. The subsequent post will then offer an initial survey of the inherent challenges in governing and defining AI, setting the stage for deeper explorations of specific ethical and societal implications.

My journey into the world of AI has been a constant process of learning and questioning. I've been fascinated by its potential, but also troubled by the lack of deep ethical reflection. I invite you to join me on this journey of exploration through ARTIFICIAL ETHICS, a SubStack dedicated to uncovering AI’s increasingly complex moral landscape.

My background as a lawyer trained at Stanford Law, combined with a deep interest in tech, philosophy, political science, and theology, has given me a unique perspective on the societal implications of emerging technologies. My experience as Editorial Director for FII, the world's largest investment conference, further exposed me to the transformative potential of AI across industries – and the urgent need for ethical frameworks to guide its development and deployment.

I've spent countless hours poring over academic articles, legal journals, and tech industry reports, and one thing has become abundantly clear: while many talk about AI ethics, there's a surprising lack of deep, rigorous analysis. There's no shortage of high-level principles and aspirational declarations, but the practical application of these principles remains a significant challenge. We're facing a critical gap between the rapid advancement of AI technology and our understanding of its ethical implications. This gap is what ARTIFICIAL ETHICS aims to bridge.

This hopefully will not be just an academic exercise. The ethical choices we make today about AI will shape the future of our society. Will AI exacerbate existing inequalities, or can we harness its power to create a more just and equitable world? Will AI enhance human autonomy, or will it erode our ability to make meaningful decisions? These are not abstract questions; they are urgent challenges that demand our attention.

ARTIFICIAL ETHICS intends to be a space for rigorous, insightful analysis of the most pressing ethical issues related to AI. I'll draw on my legal expertise, academic research, and real-world experience to provide a nuanced perspective on the complex interplay of law, technology, and ethics. I'll explore topics ranging from algorithmic bias and data privacy to the future of work and the existential risks of advanced AI.

My goal is to make ARTIFICIAL ETHICS the most comprehensive and insightful resource for anyone seeking to understand and navigate the ethical challenges of our increasingly automated world. I'll be sharing original analysis, summaries of key research, and commentary on current events related to AI ethics. I also hope to foster a community of engaged readers who can contribute their own perspectives and insights.

This is a journey of exploration, and I invite you to join me. Thank you for taking the time and see you in future posts.