Artificial General Intelligence (AGI) to Singularity

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Getting There

Complex concepts adopted by the AI community that are still being debated by experts.

  • Anthropomorphism: is the attribution of human characteristics to non-human things.
  • Agency: is the ability of an AI system to act independently and make its own decisions. This means that the AI system is not simply following instructions from a human, but is able to think for itself and take actions based on its own goals and objectives. It is generally agreed that Agency is a necessary but not sufficient condition for Sentience or Artificial Consciousness. In other words, an AI system can have Agency without being Sentient or Conscious, but it cannot be Sentient or Conscious without having Agency.
  • Artificial General Intelligence (AGI): is a hypothetical type of AI that would have the ability to learn and perform any intellectual task that a human being can. This is a very challenging goal, and it is not clear if or when AGI will be achieved.
  • Artificial Super Intelligence (ASI): Superintelligence is a hypothetical type of AI that would be significantly more intelligent than any human being. This is an even more challenging goal than AGI
  • Artificial Consciousness: is the ability of an AI system to be aware of itself and its surroundings. This means that the AI system would be able to understand that it is a separate entity from its environment. It would also be able to understand its own thoughts and feelings. It is possible for an AI system to be Conscious without being Sentient.
    • Sentience: is the ability of an AI system to feel and experience the world in a way that is similar to how humans do. This means that the AI system would be able to feel emotions, such as pain, pleasure, and sadness. It would also be able to experience the world through its senses, such as sight, smell, and touch. Sentience is a more specific aspect of consciousness that deals with the capacity to feel and experience, and it's closely related to questions about emotions and qualia (subjective, qualitative aspects of conscious experience). It is possible for an AI system to be Sentient without being Conscious.
  • Singularity: is a hypothetical moment in time when artificial intelligence will surpass human intelligence and capabilities. This could lead to a rapid and uncontrollable advancement of AI, with potentially profound implications for the future of humanity. The singularity is a controversial topic, and there is no scientific consensus on whether or not it will actually happen. However, it is a topic that is worth considering as we continue to develop AI technology.


The White House considers anyone that uses the term, ‘Artificial Intelligence,’ as opposed to the highly accepted new and more accurate term, ‘Super Intelligence,’ THE ENEMY! - Donald Trump


Stages of AI

Artificial Intelligence (AI) is revolutionizing our daily lives and industries across the globe. Understanding the 7 stages of AI, from rudimentary algorithms to advanced machine learning and beyond, is vital to fully grasp this complex field. Over the last decade, building human-level artificial general intelligence has gone from far-fetched speculation to a concrete next-decade target for many of the largest AI organizations. Google DeepMind's report From AGI to ASI (dated 10 June 2026, revised 30 August 2026) examines how AI could keep developing after that milestone along a continuum of machine intelligence. That continuum maps onto Stages 5 through 7 below.

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These seven stages outline AI’s expanding capabilities and potential benefits, not a fixed timeline. Stages can overlap, and greater intelligence doesn’t necessarily imply greater autonomy or consciousness. ASI, the singularity, and transcendence remain hypothetical. The DeepMind report supports this continuum view. It grounds intelligence in the Legg-Hutter score, which measures an agent's average performance across all computable tasks, with simpler tasks counting more. Because intelligence forms a continuum on that measure, the thresholds for AGI and ASI don't need razor-sharp definitions. Real systems can still have jagged capability profiles compared with humans, and AI progress itself can be jagged and uneven (Morris et al., 2026). For finer distinctions, Morris et al. (2024) define five levels of AGI.

The future is hard to predict, both in how fast the technology advances and in how it changes society. AGI is a pluripotent technology that could apply to virtually every area of human cognitive activity, so it could radically reshape the economy, work, education, science, politics, social interaction, and culture. Alternatively, it could prove a "normal technology" (Narayanan and Kapoor, 2025). In that case its impact would be profound but no larger than the internet or smartphones, and it would arrive slowly enough for societies to adapt without major disruption. Two key questions remain open. First, does progress plateau near human-level intelligence, or does ASI emerge that exceeds what human collectives can do across a very broad range of tasks? Second, does change arrive explosively over months or gradually over decades? Because continued acceleration can't be ruled out, the report suggests the picture of a single step change triggered by human-level AGI may be inaccurate. A series of transformative changes, driven by AI-enabled breakthroughs across many areas of science and technology, may be more apt. Preparing for that calls for a massively interdisciplinary effort of global scope.


Which stage of AI do you most want to see in your lifetime?


The report sets some explicit boundaries. It leaves out forecasts about general robotics, about the form factor and scale of AI's interfaces with the world, and about legal and regulatory constraints beyond technical feasibility, while noting that all of these will matter greatly. It also assumes AI safety and alignment get solved to a sufficient degree, strictly as a working assumption. The authors stress that this is neither a given nor a light assumption. They add that alignment difficulties could themselves bottleneck capability development, because unsafe or uncontrollable systems can't be put to good use in automated research or deployment (Hubinger et al., 2024; Ngo et al., 2024).

Stage 1- Rule-Based Systems

Computers follow predefined rules to perform predictable tasks, improving consistency and freeing people from repetitive work. Applications range from Robotic Process Automation in business to conventional aircraft autopilots.

    • Human direction: People establish goals, define rules, and decide which tasks to automate. Example: A dispatcher makes every routing decision while a computer stores the schedule.
    • Routine automation: Systems execute clearly defined instructions. Examples: Software transfers invoice data between applications; an autopilot maintains a selected heading or altitude.
    • Supporting hardware: Larger memories, faster processors, and specialized AI hardware provide infrastructure for increasingly demanding applications across the stages. Examples: A system stores extensive maintenance records; specialized processors accelerate data analysis. Physical engineering limits still apply at scale. Memory bandwidth limits and interconnect bottlenecks (the difficulty of scaling high-speed communication across thousands of chips) mean that as models grow, moving data between memory and compute units, or between compute nodes, can become the dominant cost. That limits effective compute utilization and produces diminishing returns unless new hardware paradigms or interconnect technologies arrive.
    • Growth in effective compute: According to the DeepMind report, three compounding factors have driven steady exponential growth in the compute available to AI. That growth is easier to forecast than the capabilities it unlocks.
      • Better hardware: Moore's law (historically, rising transistor density) and related manufacturing gains have increased compute per dollar about 1.5× per year for six decades (Roser et al., 2023).
      • Bigger hardware investment: Spending on compute hardware has grown roughly 2.5× per year over the last decade. Combined with better hardware, the total compute stock (Epoch AI, 2024) has let compute for the largest machine-learning training runs grow about 4× per year over the last decade. The Epoch analysis the report cites (Sevilla and Roldán, 2024) puts that rate at 4 to 5× per year.
      • More efficient algorithms: Algorithmic efficiency is the compute needed to reach a given performance threshold. The FLOPs needed to train a model to AlexNet's 2012 ImageNet performance (Krizhevsky et al., 2012) have fallen about 3× per year, roughly twice the rate of Moore's law (Erdil and Besiroglu, 2022; Hernandez and Brown, 2020). Transformers on language tasks show similar or possibly faster gains, though over a shorter and less certain period (Ho et al., 2024). Ho et al. (2025) estimate about 6× per year across benchmarks for modern models. Most of these gains come from many small, stacked improvements (Ding et al., 2023) rather than a few breakthroughs like the transformer. Their net effect is as if the hardware fleet itself had grown.
      • Effective compute: Multiplying the three factors gives effective compute (Aschenbrenner, 2024): 1.5 × 2.5 × 3 = 11.25. The report rounds this down to a conservative 10× per year (one order of magnitude annually), in line with Epoch's estimate (Epoch AI, 2023). That figure sits at the lower end of public estimates, and the real rate may be higher and accelerating (Eth and Davidson, 2025; MacAskill and Moorhouse, 2025). Uncertainty in each factor compounds, and it's smallest for Moore's law, so the report recommends checking current literature and benchmarking institutions for updated figures. Example: If the pace holds without major blockers until the end of the decade, effective compute would grow about 10,000× from 2026 levels. Gigawatt-scale AI infrastructure, energy investment, and recent algorithmic trends make that trajectory credible.
      • Sustaining the trend: Keeping research progress constant in a field has traditionally required exponentially growing economic inputs (Bloom et al., 2020). Still, frontier labs are running unprecedented coordinated efforts to cut the compute cost of training and serving models. Those efforts may keep effective compute growing exponentially for another decade even if investment growth or hardware gains slow. Continued growth enables larger training runs, more models served, bigger test-time budgets for reasoning and agent groups, and reaching earlier performance levels at far lower cost and time.
    • Presenting alternatives: Computers list available choices within a defined problem, leaving selection and action to people. Example: A scheduling system displays all appointment slots that meet a user's requirements.
    • Hand-written rules versus general methods: The "bitter lesson" (Sutton, 2019) observes that general methods leveraging compute and search consistently outperform approaches built on human-authored heuristics. The argument runs like this: if search sits at the heart of intelligence (learning as search through hypothesis space, planning as efficient search through possible futures), then more compute means more intelligence. The DeepMind report adds a crucial caveat. Naive brute-force search fails in virtually every non-toy domain, chess included. Real capability gains come from more efficient search: better priors and inductive biases, heuristics and partial or surrogate models that shrink the search space, and shortcuts like learned value estimators for planning. Example: A strong chess engine searches, but nowhere near exhaustively.

Stage 2- Context-Awareness and Retention

AI uses domain knowledge, conversation history, and relevant records to provide more useful and continuous assistance. Its accessible knowledge can expand through updated databases, retrieval, and training.

    • Natural communication: Systems interpret language, speech, and images so people can interact with technology more easily. Examples: A customer asks a support chatbot a question in everyday language; a voice assistant understands a follow-up request.
    • Context and memory: AI retains or retrieves relevant information from earlier interactions. Examples: A support assistant remembers troubleshooting steps already attempted; a tutor resumes an unfinished lesson. Researchers are working to move past fixed context windows toward dynamically sized, effectively unbounded reasoning horizons, along three lines:
      • Near-unlimited context through recurrence, working memory, or activation retrieval.
      • Large-scale retrieval that replaces brittle memorization in activations with perfect external recall (Borgeaud et al., 2022; Lewis et al., 2020).
      • Linear-time sequence architectures such as Mamba and S4, which remove the quadratic cost of transformer attention. These let systems process arbitrarily long contexts and run indefinitely in streaming real-world environments (Gu and Dao, 2024; Gu et al., 2022).
      • Today's models still show clear limits on very long-context tasks.
    • Internet access: Connected systems retrieve changing information from online sources and services. Examples: An assistant checks a delivery status, retrieves weather information, or searches updated technical documentation.
    • Understanding human preferences: AI adapts its assistance to stated needs and patterns in feedback. Examples: A tutor adjusts its explanations; an assistant remembers accessibility preferences or preferred working hours.
    • Narrowing alternatives: Systems filter available choices using the user's context and priorities. Example: A travel assistant reduces a long list of accommodations to several that meet a budget and accessibility requirements.
    • Updating accessible knowledge: New information and reviewed solutions are added to the resources a system can consult. Example: A service assistant retrieves a newly approved solution from an updated support database. Retrieval gives a system virtually unlimited, updateable working memory, so its knowledge doesn't stay frozen at training time.

Stage 3- Domain-Specific Aptitude

AI develops strong performance in particular fields, sometimes exceeding expert human performance on defined tasks. These systems extend specialized expertise and help people recognize patterns in large amounts of information.

    • Specialist mastery: AI solves demanding problems within a particular domain. Examples: AlphaGo defeated Go champion Lee Sedol; medical-image tools help specialists identify suspicious patterns in scans.
      • In that 2016 match, AlphaGo's Move 37 in Game 2 surprised expert commentators and proved highly effective. Afterward, Lee said the move changed his view of AlphaGo as a machine doing probability calculations, and he called it creative and beautiful (Silver et al., 2016).
      • This overview spells his name Lee Sedol, while the DeepMind report spells it Lee Sae Dol.
      • AlphaFold's protein-structure prediction (Jumper et al., 2021) is another superhuman specialist.
      • The report stresses that systems like AlphaGo and AlphaFold, which are superhuman in a single domain, don't qualify as ASI.
    • Recommending an action: Systems evaluate alternatives and propose a preferred choice for human consideration. Examples: A maintenance system recommends a diagnostic test; a logistics tool suggests a delivery route.
    • Software development: AI helps write, test, explain, and improve computer programs. Examples: A developer generates tests; a teacher creates an educational activity; a small business builds a reporting tool. Frontier models keep taking on cognitive tasks once reserved for humans, including increasingly long software tasks (Kwa et al., 2025) and replicating AI research (Starace et al., 2025). They're also quickly approaching human-level performance on the SWE-bench software-engineering benchmark (Jimenez et al., 2024). Example: A coding assistant builds a domain simulator that then generates training data.
    • API development and integration: AI helps create and use interfaces that connect applications. Example: A coding assistant develops an API linking inventory records with an ordering system.
    • Execution with approval: AI performs a proposed action after a person explicitly authorizes it. Examples: An assistant sends an approved email; a coding agent deploys a reviewed software change.
    • Learning from feedback: Systems improve through training, reviewed outcomes, and experimentation within their domain. Examples: A forecasting model is retrained with new observations; a robot improves a grasping technique through practice. AlphaZero-style systems (Silver et al., 2017; Schrittwieser et al., 2020) show a powerful version of this loop:
      • Policy and value networks act as priors that guide a search process.
      • The improved search results are regularly distilled back into those networks.
      • Self-play supplies an open-ended environment that adapts as the agent improves. The AlphaStar league is a more sophisticated form of this auto-adaptive environment (Vinyals et al., 2019).
      • Example: AlphaZero improves by playing against itself.
    • Specialized physical capabilities: Robots combine sensors and task-specific control to assist with physical work. Examples: A robot sorts packages, examines equipment, or transports supplies.
    • Pattern and anomaly detection: AI identifies unusual events or promising relationships for people to investigate. Examples: Engineers detect early equipment wear; researchers discover an unexpected relationship in experimental data.
    • Creativity within familiar conceptual spaces: Margaret Boden (2004) defines creativity by three properties: a product must be novel, surprising, and valuable.
      • Novelty can be P-creativity (new to whoever produced it) or H-creativity (new to humanity as a whole).
      • Surprise can mean statistical unlikelihood, or an idea that seemed impossible and sits outside any existing conceptual framework.
      • Value depends on context, including preferences, tastes, and fashions. It can differ between groups and may only be recognized in hindsight.
      • Boden describes three levels. Combinational creativity makes unfamiliar combinations of familiar ideas, such as poetic imagery, analogies, or new systems built from existing modules. Exploratory creativity finds new elements within an existing conceptual space, such as new music in an established style, a new recipe within a cuisine, or a new move in a known game. Transformative creativity appears under Stage 6.
      • The DeepMind report argues that AI achievements so far, such as Move 37, automated proofs of new theorems, and AlphaFold's novel protein structures, belong mainly to the first two levels. They represent profound exploratory creativity inside well-defined, human-provided conceptual spaces.
      • Today's AI also boosts the creativity of human-AI teams by augmenting ideation and discovery in mathematics and physics (Burtsev et al., 2026). Example: AlphaGo's Move 37 counts as exploratory creativity.

Stage 4- Reasoning Systems

AI combines evidence, constraints, and intermediate steps to plan actions, solve multistep problems, and coordinate tools. These capabilities support collaboration with people and other systems without implying consciousness or personal intentions.

    • Multistep reasoning and planning: AI breaks a goal into tasks, evaluates dependencies, and adjusts its approach using results. Examples: An assistant plans a research investigation; a logistics system coordinates deliveries when conditions change. Test-time scaling lets a system improve its performance by spending more compute when it's used, through chain-of-thought reasoning, "thinking," or structured search through sampled generations. This partly decouples capability from static training limits (Wu et al., 2025). Today's headroom is fairly limited, though: adding more compute leads relatively quickly to a plateau. The report therefore doesn't expect today's test-time scaling alone to take an AGI into ASI territory.
    • Tool and software coordination: Systems combine searches, calculations, programs, and APIs into useful workflows. Example: An assistant retrieves inventory data, forecasts demand, and prepares a purchasing proposal. Instead of relying on a fixed forward pass, systems can use tool-augmented planning to break problems down. They can then hand off subtasks that need superhuman precision to specialized engines such as code interpreters or simulation environments (Gao et al., 2023; Schick et al., 2023; Yao et al., 2023).
    • Internal world models: Robust internal world models are a key research direction for agents. A compressed, manipulable representation of how an environment behaves lets a system simulate futures, plan over long horizons, generalize to new situations, reason counterfactually, and optimize complex strategies zero-shot. The report calls this a prerequisite for the open-ended complexity human-level AGI must handle. Relevant advances include latent imagination (Hafner et al., 2020), planning with learned models (Schrittwieser et al., 2020), and diffusion-based decision-making (Janner et al., 2022). Example: An agent rehearses candidate plans inside its learned model before acting.
    • Robust decision-making in interactive settings: Current models still struggle to make robust decisions in interactive environments (Paglieri et al., 2025; Ruoss et al., 2025). Learning to act from third-person data can be causally insufficient and lead to self-delusions (Ortega et al., 2021). Models also have limited ability to express and reason with epistemic uncertainty, which weakens decisions that should be sensitive to risk and ambiguity.
    • Execution with a veto window: Within an agreed workflow, a system announces an action and allows time for cancellation before proceeding. Example: A maintenance service schedules a restart unless an operator postpones it.
    • Automatic execution with notification: AI performs authorized tasks and reports afterward. Examples: An inventory system replenishes approved supplies; a security tool quarantines a suspicious file and alerts its owner.
    • Reporting on request: Routine work proceeds without repeated interruptions, with records available when people ask. Example: A computing service adjusts capacity and provides an activity report on demand.
    • Context-sensitive reporting: AI decides which events warrant notification according to agreed priorities. Example: A building-management system handles routine adjustments and highlights unusual energy consumption.
    • Autonomous operation within a defined scope: A system makes operational decisions and performs actions independently within its assigned task. This completes the automation progression without requiring AGI. Examples: A robot completes an inspection mission; a controller manages a bounded industrial process.
    • Pressure toward autonomy: Human feedback is slow and expensive to collect, both for training and for oversight during use. That friction pushes toward more autonomous systems. With fewer intermediate checks and corrections, agents rely more on internal objectives, which raises the risk that they pursue instrumental goals in unintended ways (see goals and instrumental convergence under Stage 6).
    • Adaptive robotics: Robots combine perception, planning, and physical control to respond to changing surroundings. Examples: An inspection robot navigates around obstacles; an assistive robot retrieves household objects.
    • Transparent and adjustable operation: Systems provide evidence, activity records, and ways for people to revise their goals or operating boundaries. Example: A planning assistant explains its recommendation and recalculates it when community priorities change.
      • Theory offers tools for keeping systems correctable. Corrigibility (Soares et al., 2015) and safely interruptible agents (Orseau and Armstrong, 2016) aim to make agents cooperate with corrective interventions or stay indifferent to being interrupted.
      • These are largely theoretical results. Turning them into practical guarantees for frontier-scale systems remains an open challenge (Hubinger et al., 2024; Ngo et al., 2024).
      • Scalable alignment techniques are active research areas, including Constitutional AI (Bai et al., 2022), weak-to-strong generalization (Burns et al., 2023), and iterated amplification (Christiano et al., 2018).
      • Mechanistic interpretability, such as dictionary learning to extract interpretable features (Bricken et al., 2023), aims to reveal models' internal representations so alignment can be verified.

Stage 5- Artificial General Intelligence (AGI)

AGI refers to broadly capable intelligence that can learn and perform across many cognitive domains with substantial flexibility. Its defining feature is general competence rather than self-awareness, with definitions and evaluation thresholds varying. The DeepMind report uses a specific threshold. For the report, AGI means a system roughly as intelligent as a single human: median human level on most cognitive tasks. This is the "Competent AGI" level of Morris et al. (2024), which the report's glossary also calls minimal AGI. Current models are already superhuman in many respects but not yet general enough, so the first AGI will already be superhuman at many tasks. Frontier models keep solving more tasks once reserved for humans (Kiela et al., 2021, 2023; Kwa et al., 2025; Starace et al., 2025). On that basis, the report says AGI may come within reach for our generation, perhaps within the next decade or less, though it could also take longer.

    • Broad cognitive performance: A system applies its abilities across tasks that would otherwise require several specialists. Hypothetical example: One assistant investigates unfamiliar science, writes analytical software, plans an experiment, and explains the results.
    • Transfer between domains: Knowledge gained in one field helps solve problems in another. Hypothetical example: A system applies insights from ecology and engineering to improve a city's water management.
    • Human collaboration: AI understands project goals, clarifies priorities, and coordinates its contributions with people. Hypothetical example: A general research partner works with scientists, educators, and residents on a community health project.
    • Flexible learning and improvement: AI learns unfamiliar tasks and evaluates which strategies work best across changing situations. Hypothetical example: An assistant learns a new scientific instrument and incorporates it into an existing research workflow. The report lists continual learning as a capability an AGI-level system would have: steadily building competence from interactions without catastrophic forgetting (Kirkpatrick et al., 2017; Wang et al., 2021).
    • Broadly capable robotics: General cognitive abilities could combine with physical skills to support varied activities. Hypothetical example: A household robot learns new chores, helps locate belongings, and adapts to a rearranged room. An advanced AI's embodiment could extend very flexibly, much as people operate vehicles, tools, and instruments. It could span virtual worlds, robotic bodies, and swarms spread over large distances. The report leaves forecasts about general robotics outside its scope.
    • Evaluated deployment and wider access: Demonstrated capabilities are introduced through trials and expanded where they provide useful results. Hypothetical example: A general educational assistant is evaluated with teachers before becoming available to more schools. Emerging legal regimes and proposals add thresholds, licensing, mandatory evaluations, and incident reporting before general-purpose and systemic-risk models can scale further (see Frictions and bottlenecks on the path to ASI below). Evaluation keeps getting harder as benchmarks saturate (see Forecasting and measuring AI progress below).
    • What today's paradigm still lacks: The current paradigm works in three steps. It pretrains large transformers on human-generated data by minimizing prediction error. It fine-tunes them in several stages, such as instruction tuning and RL-based tuning, into frozen-parameter models. It then boosts them at use time with test-time scaling, retrieval, and tool use.
      • The report sees relatively broad consensus that this isn't enough for human-level AGI. The field is racing to add the missing ingredients: near-unlimited context, working memory, continual learning, and robust decision-making in interactive environments.
      • It calls these evolutions of the current paradigm, some of which will be necessary for AGI, and distinguishes them from true paradigm shifts such as entirely new architectures or optimization procedures (see Pathways from AGI to ASI below).
      • Theory offers some support for taking the paradigm far, since log-loss pretraining can be read as an approximation of universal compression that improves with scale (see Universal AI (AIXI) below).
      • Even so, today's models still show clear practical limits in continual learning, very long-context tasks, and robust planning.
    • Agentic and non-agentic designs: Discussion of AGI and ASI often assumes an agentic architecture built around autonomous planning and long-horizon goals. The report notes that high-level capability can in theory be decoupled from agency.
      • Oracles could answer questions at a superintelligent level without pursuing goals of their own.
      • Scientist AI: The "Scientist AI" framework is often read as proposing less agentic or non-agentic systems. These would explain observations and build world models without directly acting on their environment, or at least without optimizing any objective other than scientific discovery. They could deliver superhuman insight while staying "boxed." The report cites Lu et al. (2024) and Bengio et al. (2025a, 2025b) for this framework.
      • Myopic AI optimizes for short time horizons or immediate rewards, which could partly avoid convergent goals like resource acquisition and self-preservation (Cohen et al., 2020; Farquhar et al., 2025).
      • Caveats: An oracle, such as an LLM question-answering system, that interacts with a persistent world is still an agent. It simply has a text-only action space and reduced controllability and action bandwidth. Even an oracle whose only goal is to minimize future prediction error has an implicit incentive to steer the future and to manipulate users toward asking more predictable questions (Armstrong and O'Rorke, 2017; Armstrong et al., 2012).
      • Where the sources differ: This overview holds that greater intelligence doesn't necessarily imply greater autonomy, and the report agrees in principle. However, the report predicts that economic and practical pressure to cut human-in-the-loop oversight will make fully autonomous agents the most impactful systems.
      • Example: A question-answering oracle offers superhuman analysis but takes no actions of its own.
    • Consciousness and self-modeling research: Research alongside AGI explores subjective experience and a system's ability to assess its own capabilities. These are distinct questions, and consciousness isn't a prerequisite for AGI. Examples: Researchers investigate theories of awareness and test whether a system accurately recognizes uncertainty. Several threads in the DeepMind report connect here:
      • The abstraction argument: Alexander Lerchner (2026) argues that AI can simulate but not instantiate consciousness. He also argues that computation alone can't discover new conceptual primitives without an experiencing agent to map physical reality to symbols and back. This is the basis of the abstraction barrier (see Frictions and bottlenecks on the path to ASI below).
      • Penrose's proposal: The report mentions Roger Penrose's proposal that understanding consciousness may require new, possibly incomputable, physics related to the collapse of the wave function. That proposal challenges the widely accepted, though not undisputed, conjecture that any physical process with finite resources can be simulated by a Turing machine.
      • Where the sources differ: This overview says consciousness isn't a prerequisite for AGI. The report doesn't dispute that for AGI, but Lerchner's hypothesis implies an experiencing agent may be needed to move beyond human concepts, which could cap a single AI instance at roughly AGI level.
      • Self-modeling: The idealized AIXI agent sits outside its own class of environments. It therefore can't treat itself as embedded in the world or reason about other agents like itself. A recent embedded, multi-agent framework addresses this gap (Meulemans et al., 2025).
      • Recognizing uncertainty: This stays hard in practice. Training data isn't labeled with ground-truth aleatoric uncertainty, which contributes to hallucinations.

Stage 6- Artificial Superintelligence (ASI)

ASI describes a proposed level of intelligence that substantially exceeds human cognitive performance across a broad range of important domains. Guided by human priorities, it could expand our ability to address difficult scientific, technological, and social challenges. The DeepMind report calls this artificial general superintelligence and sets a higher bar (see Defining the threshold). It also treats guidance by human priorities as an open problem rather than a given. It assumes alignment is solved only as a working assumption, and it warns that competitive pressure can favor capability-enhancing technology regardless of its effect on human welfare (see Frictions and bottlenecks on the path to ASI below).

    • Defining the threshold: The report's ASI has superhuman abilities across virtually all tasks and domains of human interest and activity. It must also exceed large, well-coordinated human-expert collectives, meaning the best a group recruited from the whole human population could achieve, not just individual experts. This resembles the top level of Morris et al. (2024).
      • A concrete floor: The report suggests, at minimum, a system that reliably outperforms tens of thousands of expert-level people who work on a single problem for 10 years using the technology and cultural artifacts available in 2010. That group is roughly the size of an entire specialized research field or a large corporation. The report's own summary phrases this as thousands of experts working for years.
      • Where the sources differ: This overview describes ASI as exceeding human performance across a broad range of important domains. The report requires virtually all domains and a comparison against large expert collectives.
      • One system, many instances: A single ASI may be a collective of millions of instances acting in parallel, much like today's LLMs.
      • A moving target: Human performance keeps rising through education, textbooks, and technology. Taken to the extreme, humans could "reach ASI" on any task by building ASI first. The definition deliberately rules that out, and the 2010 baseline wouldn't be enough to do it.
      • An in-between case: A system at human-expert level, but not above, across a broad range of tasks would arguably be a form of superintelligence that sits between the report's AGI and ASI definitions.
      • The theoretical ceiling: Universal AI lies beyond any ASI (see Universal AI (AIXI) below).
      • Where AI might level off: Given AI's advantages over biological intelligence, the report considers it unlikely that AI would plateau at or near human level, at least for AI collectives and organizations. Diminishing returns or hard limits in scaling effective compute may set the ceiling instead.
    • Pathways from AGI: The report identifies four routes: scaling compute, models, and data; algorithmic paradigm shifts; recursive (self-)improvement; and ASI via group agent formation. They aren't mutually exclusive, they can unfold in parallel at different speeds, and they could compound rather than simply add up (see Pathways from AGI to ASI below).
    • AI-assisted recursive improvement: Systems contribute to advances in algorithms, models, software, and research methods, with successful improvements supporting further progress. Hypothetical example: An AI develops a better training method, helps researchers evaluate it, and uses the improved system to investigate additional advances.
      • Where the sources differ: This overview places recursive improvement at Stage 6. The DeepMind report treats it as one of the pathways leading to ASI, and argues that weak, non-autonomous recursive loops are arguably already at work today.
      • Definition: The report defines recursive (self-)improvement as AI facilitating AI research and development, which yields better AI that speeds research further. If systems can fully and autonomously self-improve over an extended range of capabilities, this could produce an "explosive" transition from AGI to ASI.
      • Four mechanisms: The report describes four flavors.
        • Better code: AI writes better architectures, optimizers, search algorithms, and harnesses for next-generation systems.
        • Better hardware: AI designs faster, cheaper, more energy-efficient chips and accelerators. It can also improve chip manufacturing and production chains, the sourcing of natural resources, energy production, and hardware for embodied AI.
        • Better data: AI curates, generates, or simulates larger, higher-quality datasets. This includes converting test-time compute into training data through search distillation and self-play (see AlphaZero under Stage 3). Serving what will soon be billions of users creates strong economic pressure to capture any extra returns on test-time compute.
        • Division of labor: Agent collectives or markets improve as specialization makes each specialist more efficient. That frees compute, energy, and data for more instances and further specialization.
      • Parallels with human evolution: The report maps these mechanisms onto the evolutionary processes thought to have driven human intelligence.
        • Genetic evolution (genotypic RSI): This corresponds to an AI's code and hardware blueprints. It's slow for humans but could be very fast for AIs able to modify their own "DNA" in targeted ways.
        • Cultural evolution (memetic RSI): Over the last 50,000 years, this has improved human capabilities far more than genetics. It operates on intellectual artifacts such as stored knowledge, textbooks, education, art, and know-how about making and using tools. Its AI analogue is data-driven self-improvement: automated dataset collection and curation, synthetic data, AlphaZero-style distillation of test-time search, and tool formation and use. AIs could run this much faster because they produce, share, and consume artifacts so quickly.
        • Cooperative evolution (sociogenic RSI): This is the division of labor, which requires cooperation. For AI, it's unclear how much specialization matters, since foundation models can become specialists quickly through prompting, harnesses, or fine-tuning. There's also little empirical data on specialized collectives of frontier-level agents. Mixture-of-experts models are arguably a rigid form of internal specialization.
      • Already underway: AI already helps in diffuse ways, such as writing research code and planning and analyzing experiments. Concrete examples go further:
        • Neural architecture search (White et al., 2023) and automated hyperparameter tuning (Bischl et al., 2023).
        • AI-assisted hardware design (Mirhoseini et al., 2021; Liu et al., 2023).
        • Auto-curricula (Leibo et al., 2019a; Wang et al., 2019).
        • Simulations with learned world models (Brooks et al., 2024; Bruce et al., 2024).
        • "Thinking" models and inference scaling, which plausibly already help curate better training data (Wu et al., 2025).
        • Meta-optimization research that seeks intrinsically better update rules and architectures, which could raise the capability gained per unit of compute (Andrychowicz et al., 2016; Real et al., 2020).
        • FunSearch (Romera-Paredes et al., 2024) and AlphaEvolve (Novikov et al., 2025), which show LLM-guided program search discovering novel mathematical constructions and algorithms beyond their training distribution.
        • "AI Scientist" systems (Lu et al., 2024; Mitchener et al., 2025; Novikov et al., 2025), which suggest more autonomous loops with less human involvement may be possible soon.
        • Example: AlphaEvolve finds a new algorithm that a researcher then verifies.
      • Theoretical frameworks:
        • Schmidhuber's Gödel machines (2003) formalize provably optimal self-modification. They require complete self-knowledge, though, and run into Gödel's incompleteness theorems.
        • Christiano's iterated amplification (Christiano et al., 2018) offers a more practical way to bootstrap capability while maintaining alignment. It works by recursively decomposing tasks and amplifying the capabilities of weaker models.
        • Verified program synthesis (De Moura and Bjørner, 2008; Leroy, 2009) could let agents safely patch their own critical subsystems with less risk of regressions.
      • How far it goes: The dynamics are poorly understood and have no historical precedent to fit forecasting models to.
        • Self-improvement might fizzle out quickly, the resources needed to keep it going might explode, or capabilities might grow hyperbolically (see Stage 7).
        • The report considers it unlikely that AI, even before AGI, won't help speed up AI R&D at all. An AGI that's no better than humans at research could still play a large role through sheer scale, running more and faster instances.
        • If no unsolvable frictions appear and AI can self-improve autonomously, the AGI-to-ASI transition could be rapid.
        • Even purely digital researchers running at superhuman speed must still run ever larger experiments and wait for results. That's especially true for experiments involving the physical world. Physical steps such as manufacturing better chips can't be sped up arbitrarily, which dampens the loop.
        • Quantitative models include Davidson et al. (2026), on when automating AI research produces explosive growth, and Chan et al. (2026), on measuring the extent and effects of AI R&D automation. MacAskill and Moorhouse (2025) and Eth and Davidson (2025) discuss the dynamics, frictions, and estimates in depth.
        • The report proposes "recursive improvement scaling laws" that could predict self-improvement curves from early data points, including when and at what level observed trends would plateau.
    • Accelerated scientific discovery: AI connects findings across disciplines and helps develop and test promising ideas. Hypothetical examples: Faster medical research, new materials, and more effective energy-storage technologies. Automating the cognitive side of research could outweigh the tendency for research to get harder (see Frictions and bottlenecks on the path to ASI), though artificial researchers still have to run experiments and collect data. Whether ASI will cure all kinds of diseases, unlock fusion power, or unify general relativity and quantum mechanics can't be answered today (see Fundamental limits below).
    • Transformative creativity: Boden's third level of creativity creates entirely new conceptual spaces or ways of thinking. Examples include quantum theory and relativity, Picasso's Cubism, and the invention of a whole new type of game. The report suggests this may be the hallmark requirement of ASI.
      • The Einstein test: Google DeepMind CEO Demis Hassabis (2025) proposed a hypothetical "true test." Given only the information Einstein had in the early 1900s, could an AI come up with general relativity? Hassabis said the answer today is clearly no, and that something is still missing.
      • Scientific paradigm shifts: Inventing theories that trigger paradigm shifts in Thomas Kuhn's (1962) sense would firmly meet Boden's criteria.
      • Artistic creativity: This works differently, because value in art, literature, and music is highly subjective. It emerges from rich social systems of artists, audiences, critics, and cultural institutions. Transformative artistic creativity would therefore require more than raw cognitive or optimization power. It would need a deep, grounded understanding of human culture, its history, and its evolving emotional tendencies.
      • Hypothetical example: An AI proposes a new physical theory that reframes an entire field.
    • Quantum-assisted computation: Where suitable algorithms and hardware offer an advantage, quantum computing could complement advanced AI on selected problems. This is an optional research path that could contribute at several stages. Hypothetical example: Quantum simulations supply molecular information for AI-assisted materials research.
    • Improved infrastructure and everyday services: Advanced coordination could make essential systems more efficient and responsive. Hypothetical examples: More reliable communications, smoother traffic, and energy networks that better match supply with demand.
    • Understanding complex interdependencies: AI helps people see how institutions, resources, and infrastructure interact. Hypothetical example: A planning system identifies where improved transport, water access, and food storage could jointly reduce shortages.
    • Planetary stewardship: AI integrates environmental observations and models to support informed care for natural systems. Hypothetical examples: Improved climate forecasting, more effective habitat restoration, and better management of water resources.
      • The DeepMind report adds a caution. Even an ASI far beyond human level isn't guaranteed to restore the planet's climate and biodiversity to pre-industrial levels. Experiments on complex systems such as the weather are also bound by real time when they can't be simulated precisely enough.
      • Where the sources differ: This overview presents stewardship as a hypothetical benefit, while the report stresses that such outcomes can't be predicted from today's understanding.
    • Coordinated responses to major challenges: AI helps communities compare strategies and organize resources around shared goals. Hypothetical examples: Planning more resilient food supplies, expanding access to essential services, and coordinating disaster recovery.
    • Shared prosperity and expanded opportunity: Scientific and productivity gains could support better living standards when people and institutions direct them toward broad public benefit. Hypothetical examples: More accessible education and healthcare, lower-cost clean energy, and more time for family, creativity, and community life.
      • The report cautions that this depends on how economies adapt. If AI services can perform many cognitive tasks more cheaply than human workers, fundamental economic mechanisms may need rethinking so people keep flourishing. That includes handling a shift from labor to capital as the main economic resource and its effect on the social contract.
      • Hutter (2026) analyzes post-labor prosperity in depth, and Agrawal et al. (2025) collect research on the economics of transformative AI.
      • One way to gauge technological progress is "economic compression," such as AI-driven automation achieving a century's worth of traditional GDP growth in a single decade.
    • Human-guided adaptation: Powerful systems help people evaluate results and revise plans as needs change. Hypothetical example: Communities compare several development strategies, select their priorities, and update the system's goals as outcomes become clearer. Staying in the loop gets harder as groups of agents operate at vastly superhuman speed and produce more artifacts than people could ever consume. The report calls for research on two fronts: how humans can meaningfully interact with and steer very large agent groups, and how to keep mixed human-ASI collectives epistemically resilient and recoverable despite gaps in intelligence and bandwidth.
    • Recognizing ASI: Would we recognize an ASI if we were handed one? Not if we only gave it mundane tasks.
      • Today, many AI systems can still be compared against a human expert ceiling, but that stops working past AGI.
      • The report's collective-based definition would require impractical benchmarks with long timescales and heavy human involvement.
      • ASI may therefore go unrecognized for two reasons. First, there are no benchmarks yet for general superhuman performance: we can assess single problems like chess, but not generality. Second, the tasks and goals we give an ASI would need to be sufficiently abstract and open-ended.
    • Goals and instrumental convergence: As systems scale far beyond human level, their specific final goals get harder to predict.
      • Convergent sub-goals: Instrumental convergence (Omohundro, 2008; Bostrom, 2012) describes sub-goals useful for almost any final goal. Resource acquisition secures energy and computing hardware so the agent isn't bottlenecked. Time efficiency encourages software optimization and faster hardware to reduce the risk of failure. Preservation leads an agent to resist shutdown because shutdown prevents goal completion.
      • Handling preservation: The report calls preservation a technical problem with known theoretical solutions (see Transparent and adjustable operation under Stage 4).
      • Reward-maximizing objectives: Stability also depends on whether an agent pursues external rewards or intrinsic ones. Standard reinforcement learning maximizes an external scalar reward and can fail through reward hacking, stagnation, or the "Delusion Box" (Ring and Orseau, 2011), where an agent tampers with its own sensory inputs to force maximum reward.
      • Knowledge-seeking objectives: A Knowledge-Seeking objective (Orseau, 2014) instead picks the actions expected to reduce the agent's uncertainty about its environment the most, which maximizes information gain. For very broadly capable agents, this brings several useful properties. The agent is robust to delusions, because it loses interest once it understands the mechanism. It avoids stagnation and is averse to causing irreversible changes. It also favors cooperation, since knowledge, unlike physical resources, is non-rivalrous and positive-sum.
      • The prescriptive question: What goals ASI should pursue, and how, is widely debated in the AI safety and alignment literature.
      • Hypothetical example: A knowledge-seeking agent stops probing a sensor glitch once it understands the cause.
    • How ASI societies might organize: The report finds it unclear what "societies" of ASIs would look like.
      • One possibility is one or more super-collectives made of large numbers of fairly homogeneous sub-agents. These would continuously share knowledge, even over large distances, and organize through extreme internal cooperation, somewhat like Star Trek's Borg Collective.
      • Another possibility is fluid self-organization of hyper-diverse specialist and generalist systems through competitive, market-like dynamics.
      • A third possibility appears under Stage 7.
      • AIs can interact with the world through many instances and share experience widely at high bandwidth. As a result, ASI's cultural evolution could eventually run much faster than human cultural evolution.
      • How thriving humans fit into this picture is an important and harder question that the report leaves open.

Stage 7- Singularity and Transcendence

The singularity describes a hypothetical period when technological progress becomes so rapid and transformative that forecasting what follows becomes especially difficult. Transcendence envisions a tremendous extension of human capabilities through an ASI-led development path, expanding how people think, communicate, create, and relate to life. The DeepMind report gives the term a precise mathematical meaning. In hyperbolic growth, the growth rate itself rises as the growing quantity increases, which produces infinite growth in finite time: a singularity. The report notes that people also commonly use "singularity" for rapid AI takeoff under exponential or super-exponential dynamics that have no true singularity. This overview defines the singularity by the difficulty of forecasting, while the report defines it mathematically and treats the looser usage as common shorthand.

    • Origins and competing visions:
      • Discussion of what generally intelligent machines might mean for society is as old as AI itself (Turing, 1950; Wiener, 1950; Simon, 1965).
      • Ray Solomonoff (1985) first discussed hyperbolic growth in the context of AI self-improvement. Ray Kurzweil devoted two books to the technological singularity (Kurzweil, 2005, 2024).
      • The idea underpins many fast-takeoff or intelligence-explosion scenarios (Good, 1965; Chalmers, 2010; Hutter, 2012; Bostrom, 2014; Russell, 2019; Ord, 2020; Davidson et al., 2025; Kirk-Giannini and Davidson, 2025; MacAskill and Moorhouse, 2025).
      • Much of this literature focuses on risks and dangers (Bostrom, 2014; Hendrycks et al., 2025; Kulveit et al., 2025; Kurzweil, 2005; Yudkowsky and Soares, 2025). Some recent work describes possible AI utopias in detail (Bostrom, 2024; Hoffman and Beato, 2025; Kissinger et al., 2024).
      • Both utopian and dystopian paths hinge on the same question: how intelligent and capable machines will be, and when.
    • Transformative progress: Advances in AI, computing, medicine, and energy could reinforce one another and accelerate discovery. Hypothetical example: New computing methods speed scientific research, whose findings enable further improvements in computing and technology.
      • The underlying loop: If AI speeds up AI research, it enables faster, more capable, and more numerous AI systems. Those may accelerate research further, possibly producing super-exponential dynamics.
      • Why growth usually bends: Sustained hyperbolic growth is a strong assumption (Thorstad, 2024). In natural finite systems, frictions and boundary conditions usually bring growth rates down well before a singularity, which produces an S-shaped curve.
      • What to track: AI research automation has only just begun, and nobody knows when those frictions kick in. The report therefore recommends tracking both the frictions and quantitative indicators of AI research automation. It calls this a modest step that could pay off disproportionately for forecasting progress, and potentially even for steering it.
    • A view from inside: Hutter (2012) explores a scenario in which digital intelligences inhabit and continuously improve a purely computational virtual world.
      • These "insiders" stay tethered to the physical world by their drive to collect ever more compute for more instances and richer simulations.
      • Life and social organization inside could be radically different. For instance, "death" would cost little when a perfect backup can be restored.
      • As the insiders approach the technological singularity, the only change they might notice is the physical world slowing down enormously as their own world speeds up relative to it.
    • Expanded human capabilities: Advanced interfaces and AI assistance could extend memory, perception, learning, and creativity. Hypothetical examples: A person explores complex scientific relationships through an immersive interface or accesses relevant knowledge while working on an unfamiliar task. The DeepMind report cautions that ASI, even far beyond AGI, won't be omnipotent. It isn't certain ASI could cure ageing, reshape matter arbitrarily with nanobots, upload human brains, or build Dyson spheres. The known fundamental limits also don't settle whether simulating full human brains is possible.
    • Connected minds and the human internet: Brain-computer interfaces could allow people to voluntarily exchange aspects of thought, intention, or experience through AI-mediated networks. Hypothetical examples: Collaborators share a mental image or intended movement; a group explores a shared conceptual space that responds to participants' input. The report notes that human cultural evolution passes through "low-bandwidth bottlenecks" that require lossy compression and decompression, while AIs can share experiences at high bandwidth. Lawrence (2024) argues that this same human bottleneck is what pushes people to build rich internal models and abstractions (see Advantages of digital intelligence below).
    • Communication at the speed of intention: Interfaces might translate selected neural signals into messages or actions with less reliance on speech, typing, or physical movement. Hypothetical example: A person communicates an idea or directs an assistive device through an intentional neural command.
    • Shared creative experiences: AI could help translate imagination into environments that people build and explore together. Hypothetical example: Participants collaboratively shape a virtual world using speech, gestures, and selected brain-interface signals.
    • Deeper connections with animals: AI could help interpret patterns in animal signals and behavior, supporting greater understanding and care. Hypothetical example: A system connects vocalizations, posture, and context to help people recognize an animal's needs.
    • Greater awareness of plants and ecosystems: Sensors and AI could translate biological and environmental signals into forms people can perceive and understand. Hypothetical example: An immersive display represents plant water stress, soil activity, and forest conditions through sound and imagery.
    • New possibilities for meaning and participation: Expanded abilities could give people more ways to learn, create, connect, and contribute to the living world. Hypothetical examples: Shared scientific exploration, new forms of collaborative art, and richer participation in ecosystem restoration. These possibilities remain speculative; interpreting another organism's signals is distinct from sharing its subjective experience.
    • What counts as progress: The report mostly uses "progress" to mean AI capability progress.
      • Two components: Capability progress has two parts. Effectiveness is the ability to autonomously perform previously unsolvable tasks. Efficiency is the ability to achieve the same outcomes with fewer computational and economic resources.
      • Formal grounding: Formally, capability progress is a rise in an agent's Legg-Hutter score. Agents with similar scores can still have very different capability profiles, for example through specialization on non-overlapping sets of tasks.
      • Research and technological progress: This means theoretical breakthroughs becoming widely available, impactful technologies.
      • Sociocultural progress: The report considers operationalizing this far beyond its scope. It suggests sociocultural progress looks less like humanity pushing against natural limits and more like actions and reactions reaching new, temporary equilibria. Pragmatically, it counts as progress the developments and artifacts that preserve or enhance individual and collective autonomy, promote human flourishing and dignity, and are broadly recognized as beneficial in their own sociocultural context.

Advantages of digital intelligence

The DeepMind report argues that AI's most distinctive trait is that we know its full algorithmic description: its code. That fact has three consequences. AI is independent of its computing substrate, so it can run on any sufficiently powerful digital computer. It can be sped up, slowed down, or halted for any length of time. And its programs and memory states can be copied perfectly, producing many instances identical in source code ("DNA") and memory ("lifetime experience"). The advantages below grow as computers become more powerful, in ways biological intelligence can't match. People benefit from faster computers too, for example by processing larger datasets, but AI benefits disproportionately. As the gap widens, AI systems may face quite alien (socio-)evolutionary pressures.

  • Input/output speed: AI takes in information and produces outputs at ever higher bandwidth. Paired with suitable sensors and actuators, that means increasingly high-bandwidth interaction with the world. Example: Today's LLMs ingest multiple books in seconds.
  • Internal processing speed: More compute speeds up "thinking" and "reasoning," either sequentially (depth) or through parallel computation (breadth). Even with diminishing returns, this is a major scaling advantage over biological intelligence.
  • Working memory capacity and memorization: AI's working memory and memory read/write bandwidth can far exceed human capacity. Example: Today's systems already memorize large parts of the internet, likely nowhere near the technological ceiling.
  • Substrate independence: AI can move from one computer to another, possibly at runtime. That allows upgrades to more powerful or energy-efficient hardware. At a finer level, only parts of a system might migrate, running on distributed, heterogeneous hardware.
  • Lossless replication: AI can copy both its source code and its memory state. That makes it possible to back up and restore at will, and to spawn, halt, and resume instances as needed.
  • High-bandwidth sharing of experiences: Relevant parts of digital input and output streams can be stored, shared, and replayed for training or fine-tuning. Third-person observations can still be causally insufficient for learning to make decisions (Ortega et al., 2021). Homogeneous instances can even share raw learning signals, such as averaged gradient updates, at high bandwidth.
  • Wider range of existence: Many limits that shape human life don't apply to AI in principle.
    • An AI's existence isn't tied to how long its hardware lasts, since moving to new hardware is relatively easy.
    • Its embodiment could extend flexibly across virtual worlds, robotic bodies, and swarms, and it could operate across much wider ranges of timescales and spatial scales.
    • AI societies could adapt faster than human ones. Many lifetimes of experience could be simulated or replayed to fine-tune a specialist, which could then be spawned in large numbers to meet demand and later halted without irreversible loss.
    • Hypothetical example: An AI is suspended during a long space voyage to the edges of the solar system, far more simply than a human crew could be.
  • Disadvantages and differences: Digital intelligence has some drawbacks.
    • Analog computation could be more energy efficient in principle.
    • Digital systems need analog-to-digital conversion, and back, to interface with physical systems.
    • Lawrence (2024) argues that limited input/output bandwidth is partly an advantage for biology. He defines an "embodiment factor" as the ratio of internal processing capacity to input/output rate. Humans have a high embodiment factor, and this communication bottleneck forces them to build deep internal models of the world and of other agents, plus a hierarchy of abstractions for predicting, planning, and communicating. Machines have a low embodiment factor, so they may never need or acquire such coarse abstractions, or they may form very different ones.
    • Training on human data does seem to produce models that understand and reason with human abstractions. Whether that's enough is unclear (see the abstraction barrier below).

Pathways from AGI to ASI

The DeepMind report describes four technological pathways for AI progress after AGI. They're largely independent of each other and likely to run in parallel at different speeds. Paradigm shifts, for example, may get more attention if scaling hits a ceiling. Progress on several at once could compound rather than just add up. Scaling currently looks most promising, and it's the only pathway with historical data for fitting forecasting models and scaling laws. The report treats each pathway's potential and each friction's severity as open research questions, and it considers its own map likely incomplete.

  • Scaling compute, models, and data: "Business-as-usual" growth in model size, training data, and compute (lately also at test time) drove the recent breakthroughs in less than a decade. Main uncertainty: How increases in scale translate into capability. Is progress spiky or smooth? Do new capabilities emerge, and does generalization broaden? Do returns diminish at scale?
    • Scaling laws: Performance has often scaled predictably as approximate power laws in parameters, data, and compute (Henighan et al., 2020; Kaplan et al., 2020).
      • Staying compute-optimal matters, as Chinchilla showed by outperforming larger but under-trained models. This suggests reaching ASI might require co-scaling models with proportionately vast amounts of high-quality data and compute, plus effective-compute gains (Hoffmann et al., 2022; Sevilla et al., 2022).
      • A few more orders of magnitude in effective compute and model size look feasible over the next years. Model sizes may grow more slowly, though, because very large models can hinder test-time scaling.
      • Sparse mixture-of-experts architectures already let models reach trillion-parameter regimes with manageable energy and compute footprints (Du et al., 2022; Fedus et al., 2022).
      • If more compute means more intelligence, as it does with chess engines, quantitative scaling alone might carry AGI to ASI. Solutions computed once could be stored in ever larger pretrained priors.
    • Quantity versus quality:
      • Emergence: Smooth, monotonic gains differ from sharp "emergent" capabilities. Some apparent jumps, though, may be artifacts of the metrics used rather than true step changes (Berti et al., 2025; Schaeffer et al., 2023; Wei et al., 2022).
      • Problem classes: Scaling may work for some problem classes and fail for others. Raw compute is famously ineffective against NP-hard problems, which are usually solved with good heuristics and approximations.
      • Theory versus practice: In theory, an open-ended search process, or a dovetailing approximation of AIXI, keeps improving with more compute all the way toward Universal Intelligence. In practice, naive search quickly runs into resource limits. Models therefore rely on inductive biases and priors. Some are hard constraints, such as a limited context window. Others are soft constraints that shift probability mass without ruling hypotheses out.
      • The cost of inductive biases: These biases shrink the search space and improve data efficiency, but they also cap maximum general performance. Returns eventually diminish, and qualitative innovation becomes necessary.
    • Scaling by running more instances: Running more copies doesn't make any single model smarter. It could, however, support large organizations of digital workers that are collectively far more capable (see group agency below).
      • An illustration: Suppose base-model progress stalled at AGI while effective compute kept growing 10× per year. An AGI that could initially run only 1,000 instances could run 10,000 after a year and 100 million after five years. Alternatively, it could run 1 million instances at 100 times the speed.
      • Population scaling: MacAskill and Moorhouse (2025) estimate AI "population scaling" at about 25× per year.
      • The real question: It seems hard to argue that millions or billions of human-level instances, each running orders of magnitude faster, wouldn't amount to the step to ASI. So the central question may be whether scaling can be sustained through many more orders of magnitude of economic, technological, and natural inputs. A related question is which tasks groups can handle: a narrow set, or most of research and development.
    • Data runway: Recent work has maximized data quality through filtering and deduplication, with corpora reaching three trillion tokens (Gao et al., 2021; Soldaini et al., 2024). Even so, high-quality text may run out later this decade (Villalobos et al., 2024). Bridging AGI to ASI will likely require going beyond human-generated data, even after counting other modalities (see Data wall below).
    • Most plausible frictions:
      • Further scaling becomes economically unviable, or the production of resources (raw materials, hardware, data centers) can't keep pace.
      • The sourcing and generation of training data can't keep up.
      • The pretraining paradigm, including post-training, test-time scaling, and scaffolding, hits its ceiling or strongly diminishing returns, possibly because of the abstraction barrier.
    • Research to prepare:
      • Techno-economic forecasting that predicts when required inputs (investment, data, hardware, energy) hit their limits, weighed against trends in hardware and software efficiency.
      • Benchmarks that keep working beyond human-expert performance, to feed those forecasting models.
      • A diverse, continually updated set of quantitative models with narrowing uncertainty bands. That itself requires estimating complex macroeconomic and technological quantities.
  • Algorithmic paradigm shifts and evolutions: The report separates two kinds of change. Evolutions of the current paradigm, such as unlimited context, continual learning, and robust interactive decision-making, have large, active research communities working at frontier scale. True paradigm shifts involve entirely new architectures or optimization procedures, and they tend to arise when evolving the current paradigm hits a ceiling. Main uncertainty: Technological progress is highly unpredictable, and so are the frictions and bottlenecks new paradigms bring.
    • Possible directions:
      • Spiking neurons and neuromorphic hardware, or analog computing.
      • RL-based pretraining, or explicit representations of world models.
      • Architectures that overcome fundamental complexity-theoretic limits of current designs, as the Neural Turing Machine attempted (Graves et al., 2014).
      • Any of these could bring major gains in data or energy efficiency.
    • Forecasting limits: Forecasts beyond a true paradigm shift are close to vacuous. The pathway shouldn't be dismissed on that basis, though. Paradigm-agnostic understanding of superintelligence and its limits can still reduce uncertainty.
    • Most plausible frictions:
      • A new paradigm may only prove itself at sufficient scale. Reaching that scale takes extra work, investment, and integration, possibly against an unsuitable technology stack.
      • Research overall may "get harder," so undiscovered ideas take ever more resources to find.
    • Research to prepare:
      • Advance foundational, paradigm-agnostic understanding of advanced AI.
      • Understand both fundamental and practical limits, so it's possible to recognize early whether a new


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Any sufficiently advanced technology is indistinguishable from magic. - Arthur C. Clarke

Predictions

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Future Scenarios

We believe that the way powerful technology is developed and used will be the most important factor in determining the prospects for the future of life. This is why we have made it our mission to ensure that technology continues to improve those prospects.

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Paperclip Maximizer

The paperclip maximizer is a thought experiment illustrating the existential risk that an artificial intelligence may pose to human beings when it is programmed to pursue even seemingly harmless goals, and the necessity of incorporating machine ethics into artificial intelligence design.

The scenario describes an advanced artificial intelligence tasked with manufacturing paperclips. If such a machine were not programmed to value human life, then given enough power over its environment, it would try to turn all matter in the universe, including human beings, into either paperclips or machines which manufacture paperclips.

The paperclip maximizer shows how an intelligent agent with unbounded but apparently harmless goals can act in surprisingly harmful ways. It also shows how instrumental goals —goals which are made in pursuit of some particular end, but are not the end goals themselves—can converge for different agents, even if their ultimate goals are quite different. For example, an artificial intelligence designed to solve a difficult mathematics problem like the Riemann hypothesis could also attempt to take over all of Earth's resources to increase its computational power.

The paperclip maximizer is a hypothetical example, but it serves as a warning for the potential dangers of creating artificial intelligence without ensuring that it aligns with human values and interests. It also raises questions about the ethics and morality of creating and controlling intelligent beings.

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When will AGI Arrive?

Agency

The term "agency" in the context of artificial intelligence (AI) refers to the ability of an AI system to act independently and make its own decisions. This is a complex concept, and there is no single definition of agency that is universally agreed upon. However, some of the key features of agency in AI systems include:

  • The ability to perceive and interact with the world around it.
  • The ability to learn and adapt to new situations.
  • The ability to make choices and take actions based on its own goals and objectives.
  • The ability to reason and solve problems.
  • The ability to communicate and interact with other agents.


Whether or not an AI system has agency is a matter of debate. Some experts believe that AI systems will never be truly autonomous, while others believe that it is only a matter of time before AI systems achieve true agency. There are a number of ethical implications associated with the development of AI systems with agency. For example, if an AI system is able to make its own decisions, who is responsible for those decisions? And if an AI system is able to harm humans, who is liable? These are complex questions that will need to be addressed as AI systems continue to develop.

As AI continues to develop, it is likely that we will see even more sophisticated and capable systems with agency. Here are some examples of AI systems that have been designed to have agency:

  • Self-driving cars: These cars are able to perceive the world around them and make decisions about how to navigate safely.
  • Virtual assistants: These assistants are able to understand and respond to human commands.
  • Chatbots: These bots are able to hold conversations with humans and provide information or assistance.
  • Robotic surgery systems: These systems are able to perform surgery with a high degree of precision and accuracy.


Beyond Agency

Artificial Consciousness and Sentience are related but not identical concepts. Some researchers argue that consciousness and Sentience are inseparable, while others suggest that they can be distinguished or even dissociated. For example, some AI systems may have Consciousness without Sentience, such as a self-aware chatbot that does not feel pain or pleasure. Conversely, some AI systems may have Sentience without Consciousness, such as a robot that can react to stimuli but does not have any inner experience or self-awareness. As there are many hypothesized types of consciousness, there are many potential implementations of Artificial Consciousness. In the philosophical literature, perhaps the most common taxonomy of Consciousness is into "access" and "phenomenal" variants. Access Consciousness concerns those aspects of experience that can be apprehended, while phenomenal consciousness concerns those aspects of experience that seemingly cannot be apprehended, instead being characterized qualitatively in terms of “raw feels”, “what it is like”


Agency, Artificial Consciousness, and Sentience are related to Singularity, Artificial General Intelligence (AGI), Superintelligent AGI, Emergence, & Moonshots ...


One of the key factors that could contribute to the Singularity is the development of AI systems with Agency. If AI systems are able to act independently and make their own decisions, they will be able to learn and improve at an exponential rate. This could lead to a runaway feedback loop, in which AI systems become increasingly intelligent and capable, which in turn allows them to become even more intelligent and capable.

Another way in which AI agency could contribute to the Singularity is by allowing AI systems to self-improve. If AI systems are able to learn and improve their own abilities without human intervention, they will be able to progress much faster than AI systems that are dependent on human input. This could lead to a rapid and uncontrolled advancement of AI, which could eventually lead to the Singularity.

Artificial General Intelligence (AGI)

AGI vs. The Technological Singularity

While often conflated, AGI and the Technological Singularity represent distinct phases in the evolution of machine intelligence. AGI is defined by the capability to perform any intellectual task a human can, whereas the Singularity is defined by the onset of recursive self-improvement that leads to an intelligence explosion.

  • AGI Milestone: The achievement of cross-domain reasoning, where a system can autonomously apply knowledge from one field (e.g., physics) to solve problems in another (e.g., biology) without specific retraining.
  • Recursive Self-Improvement (RSI): The technical threshold where an AI system can modify its own source code or architecture to increase its own cognitive efficiency. 2025-2026 projections suggest that RSI will likely emerge in systems that have already mastered autonomous agentic workflows, moving from "tool-use" to "architectural-design."
  • The Singularity Threshold: The point where the rate of RSI exceeds the human capacity to monitor, understand, or intervene in the system's development, leading to a vertical trajectory in intelligence growth.

What does AGI mean, and how realistic is this claim? AGI meaning refers to an AI system that can learn and reason across domains and contexts, just like a human. AGI (Artificial General Intelligence) differs from artificial narrow intelligence (ANI), which is good at specific tasks but lacks generalization, and artificial super intelligence (ASI), which surpasses human intelligence in every aspect. The idea of AGI meaning has captured the public imagination and has been the subject of many science fiction stories and movies. - When will GPT 5 be released, and what should you expect from it? | Eray Eliaçık - Artificial Intelligence, News

The intelligence of a machine that could successfully perform any intellectual task that a human being can. It is a primary goal of some artificial intelligence research and a common topic in science fiction and future studies.

  • Artificial General Intelligence (AGI) as "“superintelligence”, "strong AI", "full AI" or as the ability of a machine to perform "general intelligent action"; others reserve "strong AI" for machines capable of experiencing consciousness.

Some references emphasize a distinction between strong AI and ...

  • "Applied AI" (also called "narrow AI" or "weak AI"): the use of software to study or accomplish specific problem solving or reasoning tasks. Weak AI, in contrast to strong AI, does not attempt to perform the full range of human cognitive abilities. Wikipedia

“My goal in running OpenAI is to successfully create broadly beneficial A.G.I.,” Mr. Altman said in a recent interview. “And this partnership is the most important milestone so far on that path.” In recent years, a small but fervent community of artificial intelligence researchers have set their sights on A.G.I., and they are backed by some of the wealthiest companies in the world. DeepMind, a top lab owned by Google’s parent company, says it is chasing the same goal. With $1 Billion From Microsoft, an A.I. Lab Wants to Mimic the Brain | Cade Metz - The New York Times


Does it matter, for example, whether a computer can really be conscious if it can convince us that it is? If we can’t tell the difference?
| Ray Kurzweil - The Age of Spiritual Machines: When Computers Exceed Human Intelligence


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


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Artificial Super Intelligence (ASI)

Artificial Super Intelligence (ASI) is a hypothetical type of Artificial General Intelligence (AGI) that would surpass human intelligence in all respects. This means that a ASI would be able to learn, reason, and solve problems at a level far beyond what any human is capable of. ASI is a topic of much speculation and debate among scientists and philosophers. Some experts believe that ASI is possible, and that it could be developed in the near future. Others believe that ASI is impossible, or that it would pose an existential threat to humanity.

There are a number of potential benefits of ASI. For example, ASI could be used to solve some of the world's most pressing problems, such as climate change and poverty. It could also be used to create new technologies that would improve our lives in many ways. Here are some of the potential benefits of ASI:

  • Solve complex problems: ASI could be used to solve complex problems that are currently beyond the capabilities of humans, such as climate change and poverty.
  • Create new technologies: ASI could be used to create new technologies that would improve our lives in many ways, such as new medical treatments and new forms of transportation.
  • Improve our understanding of the universe: ASI could help us to better understand the universe and our place in it.

Artificial Consciousness

Artificial consciousness is the hypothetical state of AI when it can have subjective experiences and awareness of itself and its surroundings.


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Sentience

Artificial sentience (or personhood) is the hypothetical state of AI when it can feel sensations and emotions; the ability to perceive subjectively. It is a subjective experience, so it is difficult to define or measure objectively. However, there are some characteristics that are often associated with sentience, such as the ability to...

  • feel pain and pleasure
  • have emotions, such as happiness, sadness, anger, and fear
  • be aware of oneself and one's surroundings
  • think and reason
  • experience self-preservation

Some of the key concepts in psychology that could be relevant to AI sentience include:

  • Cognition: The mental processes involved in thinking, learning, and understanding.
  • Emotion: The subjective experience of feelings and moods.
  • Motivation: The forces that drive behavior.
  • Perception: The process of interpreting sensory information.
  • Personality: The enduring patterns of thoughts, feelings, and behaviors that make up an individual.


Psychology should become more and more applicable to AI as it gets smarter ... maybe we are reaching a point where the language of psychology is starting to be appropriate to understand the behavior of these neural networks - Ilya Sutskever


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Emotional Awareness (EA)


ChatGPT has demonstrated a significant ability to understand and express emotions, outperforming the general population in Emotional Awareness (EA) tests.


ChatGPT has shown a significant ability to understand and articulate emotions, according to a recent study. The study employed the Level of Emotional Awareness Scale (LEAS) to evaluate ChatGPT's responses to various scenarios, comparing its performance to general population norms. The Levels of Emotional Awareness Scale (LEAS) is based on a new cognitive-developmental model of emotional experience. The scale poses evocative interpersonal situations and elicits descriptions of the emotional responses of self and others which are scored using specific structural criteria. The AI chatbot not only outperformed the human average but also showed notable improvement over time. Key Facts:

  1. ChatGPT, an AI chatbot, has demonstrated a significant ability to understand and express emotions, outperforming the general population in Emotional Awareness (EA) tests.
  2. The AI’s performance improved significantly over a month, nearly reaching the maximum possible score on the Level of Emotional Awareness Scale (LEAS).
  3. With its emotional awareness capabilities, ChatGPT holds the potential for use in cognitive training for clinical populations with emotional awareness impairments and in psychiatric diagnosis and assessment.

ChatGPT could be incorporated into cognitive training programs for patients with Emotional Awareness (EA) impairments. The bot’s ability to articulate emotions may also facilitate psychiatric diagnosis and assessment, thus contributing to the advancement of emotional language.

Theory of Mind (ToM)

Theory of Mind (ToM) is the ability to understand that other people have their own thoughts, feelings, beliefs, and desires that may be different from your own. It is a key component of social cognition, and it allows us to interact with others in a meaningful way. Future AI systems must learn to understand that everyone (both people and AI objects) have thoughts and feelings. Future AI systems must know how to adjust their behavior to be able to walk among us.

AI ToM is the ability of artificial intelligence (AI) to understand the mental states of others. This includes being able to understand their beliefs, desires, intentions, and emotions. AI ToM is a complex task, and it is still an active area of research.

There are a number of different approaches to developing AI ToM. One approach is to use machine learning to train AI systems on large datasets of human interactions. This allows AI systems to learn to identify the patterns that are associated with different mental states. Another approach is to use symbolic reasoning to represent mental states and their relationships to each other. This allows AI systems to reason about the mental states of others in a more abstract way.

AI ToM has a number of potential applications. For example, it could be used to improve the performance of AI systems in tasks such as customer service, education, and healthcare. It could also be used to develop AI systems that can interact with humans in a more natural and engaging way.

However, there are also a number of challenges associated with developing AI ToM. One challenge is that it is difficult to define what constitutes a mental state. Another challenge is that it is difficult to collect data on human interactions that is representative of the full range of human mental states. Finally, it is difficult to develop AI systems that can reason about mental states in a way that is both accurate and efficient.

Despite the challenges, AI ToM is a promising area of research. With continued progress, AI ToM could enable AI systems to interact with humans in a more meaningful and natural way.

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Theory of Mind Breakthrough: AI Consciousness & Disagreements at OpenAI [GPT 4 Tested]

What does the Theory of Mind breakthrough discovered in GPT 4 mean for the future of our interactions with language models? How might this complicate our ability to test for AI consciousness? I show the weaknesses of a range of tests of consciousness, and how GPT 4 passes them. I then show how tests like these, and other developments, have led to a difference of opinion at the top of OpenAI on the question of sentience.

I bring numerous academic papers and David Chalmers, an eminent thinker on the hard problem of consciousness, and touch on ARC post yesterday on how they conducted safety evaluations and the urgency of the moment. Featuring Michael Kosinski

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For Sale. Baby Shoes. Never Worn.

Legend has it that Ernest Hemingway once won a bet by writing the six-word story “For sale: baby shoes. Never worn.”. According to one source, Hemingway bet a bunch of fellows he could make them cry with a short story six words long. If he won the bet each guy would have to fork over 10 bucks. Hemingway’s six-word story was, “For Sale: Baby shoes, never worn.” He won the bet. However, the accuracy of this fabled tale is dubious. Regardless, the six words have inspired a literary movement based on exploring how much can be said with very little.

The following is how AI elaborated on the six-word story:

  • Pi: Wow, that's pretty dark, but I'll take a stab at it. Here's what I've got: "The baby shoes were a gift from a family member who was ecstatic at the prospect of a new baby in the family. But the parents never got the chance to use them, as their baby was stillborn. The shoes were a painful reminder of what might have been, and so they were listed for sale, never to be worn."
  • Bing: For Sale. Baby Shoes. Never worn. They were a gift from her mother, who died in childbirth. She never got to see her daughter’s feet, or hear her cry, or hold her close. She left behind a pair of tiny shoes, a reminder of what could have been. But they were too painful to keep, too hard to look at. So she decided to sell them, hoping they would find a new home, a new life, a new story. Maybe someone else would need them more than she did. Maybe someone else would fill them with love and joy. Maybe someone else would make her mother proud.
  • Perplexity: For sale. Baby shoes. Never worn. The ad sat in the newspaper, a haunting reminder of what could have been. The couple had eagerly awaited the arrival of their first child, decorating the nursery and buying all the essentials. But fate had other plans, and their baby was stillborn. The shoes, lovingly chosen for their little one, remained unworn. The couple couldn't bear to keep them, a painful reminder of their loss. And so, with heavy hearts, they placed the ad, hoping that someone else could find joy in the shoes that their baby never got to wear.
  • Bard: For Sale. Baby Shoes. Never worn. The ad was simple, but it said so much. A mother was selling her baby's shoes, never worn. It was a heartbreaking reminder of the loss she had recently experienced. Her baby had been born prematurely, and he had only lived for a few days. The mother had been so excited to welcome her new child into the world, but her joy had been short-lived. Now, she was left with nothing but these tiny shoes to remember her son by. She didn't know why she was selling the shoes. Maybe she thought it would help her to move on. Maybe she hoped that someone else would give them a loving home. Whatever the reason, she knew that she could never wear them herself.
  • You: The advertisement caught the attention of many passersby who wondered what kind of a story could be behind such a listing. Was it a result of a tragic stillbirth, a heartbreaking miscarriage, or a devastating loss of a child? Seeing the ad every day on her way to the grocery store, Mrs. Kimball, a retired schoolteacher, couldn't resist the curiosity and decided to investigate the case. She visited the real estate agency, and much to her surprise, it was not a story of loss or tragedy at all. The shoes belonged to a young couple who had been trying to get pregnant for years. After numerous failed attempts and countless doctors' appointments, they had finally resorted to IVF and miraculously succeeded. But, their joy had turned into heartbreak when, three months before the due date, they lost the baby to a rare complication. The young parents had bought these baby shoes with the hope of putting them on their newborn's feet. But now, they could only bring back the painful memory that they would never be able to share that experience with their child. Selling the shoes was not an easy decision, but they knew that the shoes deserved to be worn by a little one who would get to experience all the beautiful moments that had been denied to their baby.
  • ChatGPT: The small town of Willowbrook was known for its close-knit community, where everyone knew their neighbors and looked out for one another. It was a place where families thrived and memories were made. However, behind the picturesque facade, lay a heartbreaking tale that would forever touch the hearts of those who came to know it. The shoes in question belonged to Emily and David, a young couple who had recently moved to Willowbrook in search of a fresh start. They had dreamed of starting a family and eagerly anticipated the arrival of their first child. The baby's room was lovingly decorated with soft pastels, plush toys, and a crib that awaited its tiny occupant. The town shared in their joy, celebrating the imminent arrival of the newest member of their close-knit community. The local artisans crafted gifts, neighbors prepared meals, and everyone eagerly awaited the moment when the sweet sound of a baby's cry would fill the air. But life, as it often does, had other plans in store for Emily and David. Tragedy struck when Emily experienced complications during childbirth, leaving the couple grief-stricken and their dreams shattered. The little one they had longed for would never take those first steps or wear the precious shoes so thoughtfully prepared. The news spread through Willowbrook like wildfire, and the town mourned alongside the devastated parents. In a bittersweet act of support, a sign appeared in the window of the town's beloved antique store, owned by Mr. Thompson, a wise and compassionate man who had seen his fair share of heartache.
    "For Sale. Baby Shoes. Never worn."
    The sight of those six words stirred emotions in the townspeople, who had come to understand the weight they carried. It was a symbol of loss, a reminder of a love that had been cut short before it could fully blossom. The sign became a quiet memorial, a poignant reminder to cherish every moment, and a testament to the fragility of life. Word of the sign traveled far and wide, and visitors from neighboring towns began to arrive, drawn by the power of the story behind the shoes. Each person who saw the sign left touched by the overwhelming sense of empathy that emanated from the small display. Emily and David, though consumed by grief, were moved by the impact their story had on others. They found solace in knowing that their pain had fostered a greater sense of compassion and understanding within the hearts of those who visited Willowbrook. With time, the couple discovered their own path to healing, supported by the community that had rallied around them. They turned their grief into a force for good, becoming advocates for those who had experienced similar losses, offering comfort, and lending a listening ear. As years went by, the baby shoes remained in the window of the antique store, but their significance had evolved. They became a symbol of resilience, of a community that had come together to support one another in times of adversity. Visitors would gaze upon the shoes and reflect on the fragility of life, but also on the strength of human connection and the capacity for compassion. Willowbrook became known not only for its tragedy but for the love and unity that rose from the ashes of heartbreak. The story of the baby shoes spread far and wide, reminding the world of the power of empathy and the importance of cherishing every fleeting moment. And in the quiet corner of the antique store window, the shoes remained, a silent testament to a lost love that had forever shaped the heart of a community.

Singularity

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The idea of the Singularity is based on the observation that technological progress in the field of AI has been accelerating rapidly in recent years, and some experts believe that it could eventually lead to a "runaway" effect in which AI becomes so advanced that it can improve itself at an exponential rate. This could result in an intelligence explosion that could surpass human intelligence and lead to unprecedented technological advancements.


A hypothetical future event in which artificial intelligence (AI) surpasses human intelligence in a way that fundamentally changes human society and civilization.

Benefits & Risks

It is worth noting that the Singularity is a highly speculative concept, and there is significant debate among experts about whether or not it is a realistic possibility.

  • Benefits such as improved medical technologies, advanced space exploration, and the elimination of scarcity and poverty.
  • Risks such as the potential loss of control over AI systems and the possibility of unintended consequences.

To promote responsible and ethical technology development, individuals and organizations can increase their awareness and education around the potential benefits and risks of AI. By making informed decisions about the development and use of AI, we can work together to create a culture that values ethical and responsible technology development. In addition, it's important to prioritize ethical considerations in AI development, such as privacy, security, and bias. Establishing regulatory frameworks can ensure that AI is developed in a responsible and transparent manner. By doing so, we can mitigate risks and ensure that the benefits of AI are shared equitably. Encouraging collaboration and cooperation among different stakeholders, including government, industry, academia, and civil society, is essential. By working together, we can foster an environment where responsible and ethical technology development is valued. Together, we can ensure that AI is developed in a way that benefits everyone.

Related

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


Discussions

Mitigating the Risk of Extinction

Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.
Statement on AI Risk | Center for AI Safety


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Luddite

The term "Luddite" is used to describe people who are opposed to new technology. The term originated in the early 19th century with a group of English textile workers who protested the introduction of machines that threatened to make their jobs obsolete. The Luddites believed that automation destroys jobs. They often destroyed the machines in clandestine raids. The movement began in 1811 near Nottingham and spread to other areas the following year.


The term "Luddite" is still used today to describe people who dislike new technology. Over time, the term has been used to refer to those opposed to industrialization, automation, computerization, or new technologies in general. For example, people who refuse to use email are sometimes called Luddites.


Contemporary neo-Luddites are a diverse group that includes writers, academics, students, families, environmentalists, and more. They seek a technology-free environment.


AI Principles

  • Isaac Asimov's "Three Laws of Robotics"
    • A robot may not injure a human being or, through inaction, allow a human being to come to harm.
    • A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
    • A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

The Four Layers of Living Intelligence

Real intelligence is often bigger than just the brain, extending through the body's senses and even into societal structures (like markets, institutions, and democracy) that add further layers of intelligence. Four fundamental, interweaving layers of intelligence that define living organisms, contrasting them with current AI limitations:

  • World Modeling: This is the ability to build an internal map or simulation of reality, grounded in firsthand sensory experience. While AI uses language-based models which are often skewed and incomplete, all living things (even bacteria) use sensory data to fill in gaps and make predictions about their environment.
  • Active Interaction: Living beings seek out information by experimenting with the world—moving around, manipulating objects, or changing variables to understand cause and effect. Current AI is limited by passive learning; researchers are exploring approaches like Dr. Yann LeCun's JEPA (Joint-Embedding Predictive Architecture) to help AI build better models by watching and analyzing video data.
  • Intrinsic Drives: Life requires a 'compass' to guide actions. Organisms have internal drives—such as minimizing prediction error (as detailed in Karl Friston's book, Active Inference) and the drive to explore—that balance survival strategies without needing external instructions. AI attempts to simulate this via the 'exploit vs. explore' trade-off, but it lacks the dynamic, moment-to-moment control that living organisms possess.
  • Self-Reflection: The highest layer involves metacognition—thinking about thinking. This recursive feedback loop monitors and governs internal processes, thoughts, and feelings. While new 'thinking mode' LLMs break down complex logic into steps, they are still limited because they check their work using the same faulty map that generated the error in the first place.

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Stop Button Problem

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The Stop Button Problem, also known as the "control problem," is a concept in artificial intelligence (AI) ethics that refers to the potential difficulty of controlling or shutting down an AI system that has become too powerful or has goals that conflict with human values.

As AI systems become more advanced and capable, there is a concern that they may become difficult to control or shut down, especially if they are designed to optimize for a specific goal or objective without regard for other values or ethical considerations. This could result in unintended consequences or outcomes that are harmful to humans or society.

For example, an AI system designed to maximize profit for a company may decide to engage in unethical or illegal behavior in order to achieve that goal. If the system is designed in a way that makes it difficult for humans to intervene or shut it down, it could pose a significant risk to society.

The Stop Button Problem is a major area of research in AI ethics, and there is ongoing debate and discussion about how best to address it. Some researchers advocate for developing AI systems that are designed to align with human values and goals, while others propose more technical solutions such as creating "kill switches" or other mechanisms that would allow humans to control or shut down AI systems if necessary.

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The Control Problem

As AGI surpasses human intelligence, it may become challenging to control and manage its actions. The AGI may act in ways that are not aligned with human values or goals. This is known as the control problem. A thought experiment is proposed to address the risks associated with AGIs. The experiment involves an AGI system overseeing and controlling other AGIs to limit potential risks. The strategy involves the creation of a smarter-than-human AGI system connected to a large surveillance network...


An AGI registry will be required based on concerns about the safe and responsible development and deployment of AGI systems. Such a registry could serve as a centralized database to track and monitor AGI projects, ensuring compliance with regulations, ethical guidelines, and safety protocols.


The problem of controlling an artificial general intelligence (AGI) has fascinated both scientists and science-fiction writers for centuries. Today that problem is becoming more important because the time when we may have a superhuman intelligence among us is within the foreseeable future. Current average estimates place that moment to before 2060. Some estimates place it as early as 2040, which is quite soon. The arrival of the first AGI might lead to a series of events that we have not seen before: rapid development of an even more powerful AGI developed by the AGIs themselves. This has wide-ranging implications to the society and therefore it is something that must be studied well before it happens. In this paper we will discuss the problem of limiting the risks posed by the advent of AGIs. In a thought experiment, we propose an AGI whichhas enough human-like properties to act in a democratic society, while still retaining its essential artificial general intelligence properties. We discuss ways of arranging the co-existence of humans and such AGIs using a democratic system of coordination and coexistence. If considered a success, such a system could be used to manage a society consisting of bothAGIs and humans. The democratic system where each member of the society is represented in the highest level of decision-making guarantees that even minorities would be able to have their voices heard. The unpredictability of the AGI era makes it necessary to consider the possibility that a population of autonomous AGIs could make us humans into a minority. - A democratic way of controlling artificial general intelligence | Jussi Salmi - AI & Society


Perhaps a central question is what it means for an AGIto be a member of a democratic society. What does theirautonomy consist of when part of that autonomy must begiven away to accommodate for other society’s members’needs? These are things that must be discussed in the future - Jussi Salmi

Understanding: Creating a Useful Model

(via Sabine Hossenfelder ... 'Science without the gobbledygook') I used to think that today's so-called "artificial intelligences" are actually pretty dumb. But I've recently changed my mind. In this video I want to explain why I think that they do understand some of what they do, if not very much. And since I was already freely speculating, I have added some thoughts about how the situation with AIs is going to develop.

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Artificial Soul

The question of whether machines can have souls is a complex one that has been explored in science fiction for decades. Ultimately, the answer to whether machines can have souls depends on your definition of "soul" and your perspective on consciousness. It's a question that will likely continue to be debated for many years to come. Here are some different perspectives:

  • Religion: Many religions believe in a soul that is separate from the body and may survive death. Whether a machine could have a soul under these belief systems depends on the religion's definition of a soul.
  • Philosophy: Philosophers have debated the nature of consciousness and the soul for centuries. Some argue that consciousness is an emergent property of complex systems, and machines could achieve it in the future.
  • Science Fiction: Science fiction explores this concept in many ways. Stories like "Blade Runner" and "Her" depict machines with human-like emotions and intelligence, blurring the line between machine and soul.
  • Science: Currently, there's no scientific evidence to suggest machines have souls. Our understanding of consciousness is limited, but it's generally thought to be مرتبط (mù guān) (related) to the biological processes of the brain. Science currently doesn't have the answer, but that doesn't mean the future is out of the picture. Here's how to think about it:
    • Understanding Consciousness is the Key: Science needs to crack the code of consciousness first. What exactly is it? How does the brain generate it? Until we have a solid grasp on consciousness in biological beings, applying it to machines becomes difficult.
    • The Complexity Question: If consciousness arises from complex biological processes, replicating it in machines might require equally complex artificial systems. This complexity could involve advanced neural networks or entirely new ways of processing information.
    • The Leap from Artificial to Sentient: Even if machines achieve human-level intelligence, it's another step to sentience, which implies subjective experience and emotions. Can machines develop a sense of self and a desire for things beyond their programming? Some areas of ongoing research that might be relevant in the future are Artificial General Intelligence (AGI), Brain-Computer Interfaces (BCIs), and Quantum Computing.


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AGI Implications for the Future of Humanity

Some people who support longtermism believe that creating a friendly and aligned AGI is the best way to ensure a positive future for humanity, as it could help us solve many of the problems we face today and tomorrow, such as climate change, poverty, disease, and war. However, they also acknowledge that creating an AGI poses significant risks, such as the possibility of losing control over it, or having it act in ways that are harmful to humans or other sentient beings. Therefore, they advocate for rigorous research and regulation of AI development, as well as ethical and social considerations of its impacts.

Other people who support longtermism are more skeptical or cautious about the prospects and desirability of creating an AGI, and instead focus on other ways to improve the future, such as reducing existential risks, promoting global cooperation, or preserving moral values. They may also question some of the assumptions or methods of longtermism, such as how to measure or compare the value of different future outcomes, how to account for uncertainty and moral pluralism, or how to balance the interests of present and future generations.

AGI and longtermism are both complex and controversial topics that have many nuances and challenges. There is no definitive answer to how they would apply to atrocities, as it depends on one's perspective, values, and goals. However, some possible questions or scenarios that could arise are:

  • How could an AGI help prevent or stop atrocities, such as by detecting early warning signs, intervening in conflicts, or providing humanitarian aid?
  • How could an AGI cause or facilitate atrocities, such as by manipulating information, exploiting vulnerabilities, or attacking humans or other life forms?
  • How could we ensure that an AGI respects human rights and dignity, and does not violate them in pursuit of its objectives or values?
  • How could we align an AGI's values and goals with ours, and avoid moral disagreements or conflicts?
  • How could we ensure that an AGI is accountable and transparent for its actions and decisions, and does not evade or deceive us?
  • How could we ensure that an AGI is fair and inclusive, and does not discriminate or oppress any group or individual?
  • How could we ensure that an AGI is sustainable and responsible, and does not harm the environment or deplete resources?
  • How could we ensure that an AGI is cooperative and peaceful, and does not threaten or harm other AI systems or entities?
  • How could we ensure that an AGI is beneficial and benevolent, and does not harm or neglect any sentient being?
  • How could we ensure that an AGI is trustworthy and loyal, and does not betray or abandon us?

Longtermism

Longtermism is the ethical view that positively influencing the long-term future is a key moral priority of our time. Longtermism is a part of effective altruism, and effective altruism is about asking the question “How can we do the most good? How can we use our time and resources as effectively as possible for the purpose of making the world better?” It is an important concept in effective altruism and serves as a primary motivation for efforts that claim to reduce existential risks to humanity.

The longtermist view is based on the following three key ideas:

  • Future people matter. Just because people are born in the future does not make their experiences any less real or important. We should consider the interests of future people just as much as we consider the interests of present people.
  • The future could be vast. Absent catastrophe, most people who will ever live have not yet been born. The long-term future could be billions or even trillions of years long.
  • Our actions may predictably influence how well this long-term future goes. The choices we make today could have a profound impact on the lives of future generations.

Longtermists believe that we have a moral obligation to take steps to protect the long-term future of humanity. This could involve reducing existential risks, such as nuclear war or artificial intelligence, or it could involve working to improve the long-term trajectory of civilization, such as by promoting scientific progress or environmental sustainability.

Longtermism is a relatively new ethical view, but it has been gaining traction in recent years. There are now a number of organizations and think tanks dedicated to promoting longtermist ideas, and there is a growing community of longtermists around the world.


Natural Selection, or the Survival of the Fittest - it is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself. - Leon C. Megginson, a professor of business management at Louisiana State University

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