The Singularity

1. The Singularity Amber Guo

2. Transcendence (2014) • https://www.youtube.com/watch?v=280qnrHpuc8

3. Technology Singularity • A hypothetical moment in time when artificial intelligence will have progressed to the point of a greater-than-human intelligence

4. The singularity in history • The first use of the term “singularity” was by mathematician John von Neumann (1958) • He stated: “[the] ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.” –John von Neumann

5. Possible causes of the singularity • VernorVinge (1993): • The development of computers that are “awake” and superhumanly intelligent • Large computer networks (and their associated users) may “wake up” as a superhumanly intelligent entity • Computer/human interfaces may become so intimate that users may reasonably be considered superhumanly intelligent • Biological science may find ways to improve upon the natural human intellect

6. Possible causes of the singularity (cont.) • Whole Brain Emulation (WBE): • The hypothetical process of copying mental content (including long-term memory and "self") from a particular brain substrate and copying it to another computational device, such as a digital, analog, quantum-based or software based artificial neural network • What the movie Transcendence is based off of • The topic of my presentation

7. Falsifiable Design • WBE only exists in theory • A falsifiable design is the an “experimental” map that not only incorporates the design of potential technologies, but also accounts for uncertainties and edge cases, as well as a series of potential tests and experiments that would progressively reduce these uncertainties

8. Emulation • The term emulation originates in computer science, where it denotes mimicking the function of a program or computer program by having its low-level functions simulated by another program • (In a non-math world) the emulation is regarded as successful if the emulated system produces the same outward behavior and results as the original (possibly with a speed difference)

9. Mathematical Definition of Emulation • A system S consists of a state x(t) evolving by a particular dynamics f, influenced by inputs and producing outputs: x(t+1)=f(I, x(t)), O(t)=g(x(t)). • Another system T simulates S if it produces the same output (within a tolerance) for the same input time series starting with a given state (within a tolerance): X(t+1)=F(I, X(t)), O(t)=G(X(t)) where |x(t)-X(t)|<e_1 and X(t)=x(0)+e_2. • The simulation is an emulation if F=f (up to a bijective transformation of X(t)), that is, the internal dynamics is identical and similar outputs are not due to the form of G(X(t)).

10. Simulation v. Emulation Simulation: • Simulation refers to a model where only some of the qualities exist • Mimics outward results Emulation: • Emulation refers to a 1-to-1 model where all relevant properties od a system exist • Mimics the internal causal dynamics (at some suitable level of description)

11. Approaches to Achieving WBE • Artificial Neural Networks (ANN) • Can be used to qualitatively model, explain and analyze the functions of brain systems • End point: a model that encompasses a full understanding of the function of all brain systems • This may not even result in consciousness or intelligence • Biologically Realistic Models • Compartment models of neurons and synapses multiple realistic neurons formed into networks (possibly accounting for chemical transmissions) • Quantitative understanding of the brain • Can lead to the creation of increasing large and complex simulations of neural systems • Most likely to result in a 1-to-1 model of brain function

12. Details v. Function

13. Feasibility of WBE • Philosophical Feasibility: • WBE assumes that everything that matters in brains supervenes on the physical • If there is something (a matter of “being” or “conscious”) that cannot be obtained, then WBE is not feasibly (though strong AI may still be achievable)

14. Feasibility of WBE • Chaos: • The brain contains chaotic dynamics (even a 3 neuron system can become chaotic) • The predictive validity of a simulation of a chaotic system is very low • As there is a range of acceptable behaviors, noise in emulated brains would not deter a WBE from completing predictable behavior • For example, if a WBE were to encounter life-changing events, a change in “character” proportional to the original mind is expected

15. Feasibility of WBE • Level of Understanding: • Scale separation • A higher level of detail requires gaining more information on smaller scales (which would require more developments to measure the information) • WBE tries to fulfill the brute force requirements of brain function • General opinion is that the electrophysiological (cellular compartmental) level of understanding is needed

16. Feasibility of WBE • Finding Biological Modalities: • Known unknowns: number of neuron types, neurotransmitters, relevant metabolites • Estimating number of species on earth • Do there exist entirely new forms of interactions in the brain?

17. Feasibility of WBE • Computability: • Church-Turing Thesis: computable functions are exactly the functions that can be calculated using a mechanical calculation device given unlimited amounts of time and storage space • WBE assumes that brain activity in large is Turing-computable • Component tractability: if hypothesis such as the quantum-mind proposal are true, then relevant components cannot even be measures in principle (even if their dynamics were known and implementable on suitable computer software) • Randomness

18. Feasibility of WBE • Scale Separation: • Butterfly effect (scales influence each other) • Other systems where scales move independently • The problems: does there exist a scale in the brain where finer interactions average out, or are each scale strongly linked to larger and smaller scales? • Neural Modeling: mnemonic equations (permanent or quasi-permanent changes in neural activities, such as memory) v. neuronic equations (the instantaneous behavior of the system) • These are largely decoupled as they occur at different time scales

19. Feasibility of WBE • Brain-centeredness: • A WBE would need to include at least some body and environment simulation • Bodily states influence brain states (hunger and hormones) • Some proponents of the extended mind hypothesis insist that the whole social and physical world must also be simulated • Simulations of bodily functions are likely to arrive before WBE

20. Technological Feasibility • Simulation Tractability: • Fundamental (brain operations are incomputable) • Practical (not enough computing power) • Scanning Tractability: • There exist methods of imaging volumes of neural tissue at resolutions enough to discern the finest fibers and chemical content at a slightly lower resolution • Scanning brains to produce emulations will likely be a destructive process • May be ethically impermissible due to risk of excessive suffering

21. Sources • http://shanghailectures.org/sites/default/files/uploads/2013_Sandberg_Brain-Simulation_34.pdf • http://www.fhi.ox.ac.uk/brain-emulation-roadmap-report.pdf