Superintelligence by Bostrom

Ref: Nick Bostrom (2015). Super Intelligence: Paths, Dangers, Strategies. ISBN: 978-1501227745.

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Summary­

  • The jury is out on whether machine intelligence will be like flight, which humans achieved through an artificial mechanism, or like combustion, which we initially mastered by copying naturally occurring fires.

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Human Intelligence

  • The rate-limiting step in human intelligence is not how fast raw data can be fed into the brain but rather how quickly the brain can extract meaning and make sense of the data.

  • In order for the thoughts of one brain to be intelligible to another, the thoughts need to be decomposed and packaged into symbols according to some shared convention that allows the symbols to be correctly interpreted by the receiving brain. This is the job of language.

  • Firms, work teams, gossip networks, advocacy groups, academic communities, countries, even humankind as a whole, can—if we adopt a somewhat abstract perspective—be viewed as loosely defined “systems” capable of solving classes of intellectual problems.

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Brains v. Computers

  • Information Speed Potential

    • Humans

      • Axons: Carry action potentials at speeds of 120 m/s or less.

      • Neurons: Operate at a peak speed of ~200Hz; the brain relies on massive parallelization and is incapable of rapidly performing any computation that requires a large number of sequential operations.

      • Human Retina: Transmits data at ~10M bits per second; comes pre-packaged with a massive amount of dedicated wetware, the visual cortex, that is highly adapted to extracting meaning from this information torrent and to interfacing with other brain areas for further processing.

    • Computers

      • Microprocessor: Operate at a speed of ~2GHz.

      • Electronic Processing Cores: Communicate optically at the speed of light (3e8 m/s).

  • Computational Elements

    • Brain: ~100B neurons; limited by cranial volume, metabolic constraints, cooling, development time, and signal-conductance delays.

    • Computer: Infinitely scalable nearly without limit.

  • The sluggishness of neural signals limits how big a biological brain can be while functioning as a single processing unit. For example, to achieve a round-trip latency of less than 10 ms between any two elements in a system, biological brains must be smaller than 0.11 m3. An electronic system, on the other hand, could be 6.1×1017 m3, about the size of a dwarf planet: 18 orders of magnitude larger.

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Superintelligence (SI)

  • Biotechnological Enhancement: Cognitively enhanced humans.

  • Quality Superintelligence: A system that is at least as fast as a human mind and vastly qualitatively smarter.

  • Seed AI: Sophisticated AI capable of improving its own architecture. In the early stages of a seed AI, such improvements might occur mainly through trial and error, information acquisition, or assistance from the programmers. At its later stages, however, a seed AI should be able to understand its own workings sufficiently to engineer new algorithms and computational structures to bootstrap its cognitive performance.

    • Recursive Self-Improvement Phase: A rapid cascade of recursive self-improvement cycles that cause an AI’s capability to soar; at the end of this phase, the system is strongly superintelligent.

  • Speed Superintelligence: A system that can do all that a human intellect can do, but much faster.

  • Whole Brain Emulation (aka uploading): Intelligent software would be produced by scanning and closely modeling the computational structure of a biological brain. Requires three key pre-requisites:

    • Scanning: High-throughput microscopy with sufficient resolution and detection of relevant properties.

    • Translation: Automated image analysis to turn raw scanning data into an interpreted three-dimensional model of relevant neurocomputational elements.

    • Simulation: Hardware powerful enough to implement the resultant computational structure.

  • Genetic Enhancement: Enhanced humans through selective breeding and gene-editing.

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SI Challenges

  • Analyzing visual scenes, recognizing objects, or controlling a robot’s behavior as it interacts with a natural environment has proved challenging.

  • Common sense and natural language understanding have also turned out to be difficult. It is now often thought that achieving a fully human-level performance on these tasks is an “AI-complete” problem, meaning that the difficulty of solving these problems is essentially equivalent to the difficulty of building generally human-level intelligent machines.

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Machine Learning

  • Speech Recognition: Based on statistical techniques such as hidden Markov models, has become sufficiently accurate for practical use. Personal digital assistants, such as Apple’s Siri, respond to spoken commands and can answer simple questions and execute commands. Optical character recognition of handwritten and typewritten text is routinely used in applications such as mail sorting and digitization of old documents.

  • Face Recognition: Used at automated border crossings in Europe and Australia. The US DoS operates a face recognition system with over 75M photographs for visa processing. Surveillance systems employ increasingly sophisticated AI and data-mining technologies to analyze voice, video, or text, large quantities of which are trawled from the world’s electronic communications media and stored in giant data centers.

  • Theorem-Proving & Equation-Solving: Equation solvers are included in scientific computing programs such as Mathematica. Formal verification methods, including automated theorem provers, are routinely used by chip manufacturers to verify the behavior of circuit designs prior to production.

  • Drones: The US military and intelligence establishments have been leading the way to the large-scale deployment of bomb-disposing robots, surveillance and attack drones, and other unmanned vehicles. These still depend mainly on remote control by human operators, but work is underway to extend their autonomous capabilities.

  • Intelligent Scheduling: The DART tool for automated logistics planning and scheduling was used in Operation Desert Storm in 1991 to such effect that DARPA claims that this single application more than paid back their 30-year investment in AI.

  • Reservations: Airline’s and businesses use sophisticated scheduling and pricing systems and helplines connected to speech recognition software to usher their hapless customers through labyrinths of interlocking menu options. AI technologies underlie many Internet services.

  • Inventory: Businesses make wide use of AI techniques in inventory control systems.

  • Bayesian Spam Filters: Hold the spam tide at bay.

  • Banking: Software using AI components is responsible for automatically approving or declining credit card transactions, and continuously monitors account activity for signs of fraudulent use.

  • Information Retrieval: Make extensive use of machine learning. The Google search engine is, arguably, the greatest AI system that has yet been built.

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Misc Quotes

  • Algorithmic high-frequency trading accounts for more than half of equity shares traded on US markets.

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Terminology

  • Moore’s Law: The observation that the number of transistors on integrated circuits (IC) have for several decades doubled approximately every 2-years.

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