Emergence

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Emergent behavior is a phenomenon where a system exhibits new and unexpected properties or behaviors that arise from the interactions of its individual components. In AI, emergent behavior can be seen when large and complex models develop novel and surprising abilities or strategies that were not explicitly programmed or anticipated by their creator; such as some Large Language Models (LLM) can solve simple math problems, generate computer code, compose music, generate fictional stories, or decode movie titles based on emojis.


AI decoded the movie title based on these emojis, can you?

These abilities are surprising and unpredictable because they seem to have little to do with analyzing text, which is the main task of these models. Emergence with AI raises many questions about how and why these abilities occur, and what are the potential benefits and risks of using them. One area of research is to understand the evolutionary patterns of AI emergence across different countries and technologies. Another area of research is to test the performance of large AI models on various tasks and identify the emergent abilities they display.


Merely quantitative differences, beyond a certain point, pass into qualitative changes. - Karl Marx.


Emergence is related to Singularity, Artificial Consciousness / Sentience, Artificial General Intelligence (AGI), & Moonshots ...

  • Singularity is the hypothetical point when AI surpasses human intelligence and becomes uncontrollable and unpredictable. Some researchers fear that emergence with AI could lead to singularity, while others doubt that singularity is possible or imminent.
  • AGI is the hypothetical state of AI when it can perform any intellectual task that a human can. Some researchers believe that emergence with AI is a sign of approaching AGI, while others argue that emergence with AI is not sufficient or necessary for achieving AGI.
  • Emergence could be a sign or a step toward AI Artificial Consciousness / Sentience, which is the ability to experience feelings and sensations.
  • Moonshots are ambitious and visionary projects that aim to solve major challenges or create breakthroughs with AI. Some researchers use emergence with AI as a metric or a goal for their AI moonshots, while others focus on more specific or practical applications of AI.



Emergence from Analogies

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Principles of analogical reasoning have recently been applied in the context of machine learning, for example to develop new methods for classification and preference learning. In this paper, we argue that, while analogical reasoning is certainly useful for constructing new learning algorithms with high predictive accuracy, is is arguably not less interesting from an interpretability and explainability point of view. More specifically, we take the view that an analogy-based approach is a viable alternative to existing approaches in the realm of explainable AI and interpretable machine learning, and that analogy-based explanations of the predictions produced by a machine learning algorithm can complement similarity-based explanations in a meaningful way. Towards Analogy-Based Explanations in Machine Learning | Eyke Hüllermeier

Analogies
This video is part of the Udacity course "Deep Learning". Watch the full course at https://www.udacity.com/course/ud730

Complexity Concepts, Abstraction, & Analogy in Natural and Artificial Intelligence, Melanie Mitchell
Complexity Concepts, Abstraction, & Analogy in Natural and Artificial Intelligence a talk by Melanie Mitchell at the GoodAI Meta-Learning & Multi-Agent Learning Workshop. See other talks from the workshop

Conceptual Abstraction and Analogy in Natural and Artificial Intelligence
Melanie Mitchell, Santa Fe Institute; Portland State University While AI has made dramatic progress over the last decade in areas such as vision, natural language processing, and game-playing, current AI systems still wholly lack the abilities to create humanlike conceptual abstractions and analogies. It can be argued that the lack of humanlike concepts in AI systems is the cause of their brittleness—the inability to reliably transfer knowledge to new situations—as well as their vulnerability to adversarial attacks. Much AI research on conceptual abstraction and analogy has used visual-IQ-like tests or other idealized domains as arenas for developing and evaluating AI systems, and in several of these tasks AI systems have performed surprisingly well, in some cases outperforming humans. In this talk I will review some very recent (and some much older) work along these lines, and discuss the following questions: Do these domains actually require abilities that will transfer and scale to real-world tasks? And what are the systems that succeed on these idealized domains actually learning?

Melanie Mitchell: "Can Analogy Unlock AI’s Barrier of Meaning?"
UCSB College of Engineering Speaker Bio: Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute and Professor of Computer Science (currently on leave) at Portland State University. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. She is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her latest book is Artificial Intelligence: A Guide for Thinking Humans. Abstract: In 1986, the mathematician and philosopher Gian-Carlo Rota wrote, “I wonder whether or when artificial intelligence will ever crash the barrier of meaning.” Here, the phrase “barrier of meaning” refers to a belief about humans versus machines: humans are able to “actually understand” the situations they encounter, whereas it can be argued that AI systems (at least current ones) do not possess such understanding. Some cognitive scientists have proposed that analogy-making is a central mechanism for concept formation and concept understanding in humans. Douglas Hofstadter called analogy-making “the core of cognition”, and Hofstadter and co-author Emmanuel Sander noted, “Without concepts there can be no thought, and without analogies there can be no concepts.” In this talk I will reflect on the role played by analogy-making at all levels of intelligence, and on how analogy-making abilities will be central in developing AI systems with humanlike intelligence.


Emergence & Reductionism

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Examples

  • Guess movie title by providing icons or emojis
  • Chain of thought: AI can generate text that follows a logical and coherent sequence of ideas, building on previous statements to form a chain of thought.
  • Performing arithmetic: AI can perform basic arithmetic operations such as addition, subtraction, multiplication, and division, and can also solve more complex mathematical problems.
  • Answering questions: AI can answer questions on a wide range of topics, drawing on its knowledge base to provide accurate and relevant responses.
  • Summarizing passages: AI can summarize long texts, condensing the most important information into a shorter, more easily digestible form.
  • Reasoning: AI can reason and make logical deductions, using its knowledge of the world and its ability to understand language to draw conclusions.
  • Translating between languages: AI can translate between different languages, allowing people who speak different languages to communicate more easily.
  • Generating creative content: AI can generate creative content such as poems, stories, and music, using its understanding of language and its ability to generate text that is stylistically and thematically coherent.
  • Generating code: AI can generate code for different programming languages, using its understanding of programming concepts and its ability to generate syntactically correct code.
  • Generating dialogue: AI can generate text that simulates a conversation between two or more people, responding to prompts in a natural and engaging way.
  • Predicting the next word in a sentence: AI can predict the most likely next word in a sentence, based on its understanding of language and its analysis of the context.
  • Generating text in a specific style or tone: AI can generate text that is tailored to a specific style or tone, such as formal or informal, academic or conversational.
  • Generating text based on a given prompt: AI can generate text in response to a given prompt, using its knowledge of language and its ability to generate text that is relevant and informative.
  • Generating text that is informative and accurate: AI can generate text that is informative and accurate, drawing on its knowledge base to provide detailed and accurate information on a wide range of topics.
  • Generating text that is engaging and interesting: AI can generate text that is engaging and interesting, using its ability to generate text that is coherent and compelling.
  • Generating text that is persuasive or argumentative: AI can generate text that is persuasive or argumentative, using its ability to construct arguments and present them in a convincing way.
  • Generating text that is humorous or entertaining: AI can generate text that is humorous or entertaining, using its understanding of language and its ability to generate text that is witty and engaging.
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