Toby Walsh in “The Singularity May Never Be Near” gives six arguments to support his point of
view that technological singularity may happen but that it is unlikely. In this paper, we provide
analysis of each one of his arguments and arrive at similar conclusions, but with more weight given
to the “likely to happen” probability.

http://www.thetechnocracy.com/2018/09/13/abstract-series-e6-the-singularity-may-be-near/

Abstract Series: E6 -The Singularity May Be Near

Abstract:

Purpose: This paper formalizes long-term trajectories of human civilization as a
scientific and ethical field of study. The long-term trajectory of human civilization can be
defined as the path that human civilization takes during the entire future time period in
which human civilization could continue to exist.

Approach: We focus on four types of trajectories: status quo trajectories, in which
human civilization persists in a state broadly similar to its current state into the distant
future; catastrophe trajectories, in which one or more events cause significant harm to
human civilization; technological transformation trajectories, in which radical
technological breakthroughs put human civilization on a fundamentally different course;
and astronomical trajectories, in which human civilization expands beyond its home
planet and into the accessible portions of the cosmos.

Findings: Status quo trajectories appear unlikely to persist into the distant future,
especially in light of long-term astronomical processes. Several catastrophe,
technological transformation, and astronomical trajectories appear possible.
Value: Some current actions may be able to affect the long-term trajectory. Whether
these actions should be pursued depends on a mix of empirical and ethical factors. For
some ethical frameworks, these actions may be especially important to pursue.

http://www.thetechnocracy.com/2018/09/14/ai-abstract-series-ep7-long-term-trajectories-of-human-civilization/

AI Abstract Series Ep7: Long-Term Trajectories of Human Civilization

Welcome to the Technocracy A.I. Abstract Series for Published Scientific Work in the A.I. and Artificial General Intelligence field.  

Todays paper is titled: Detecting Qualia in Natural and Artificial Agents


Authored By Roman V. Yampolskiy
Computer Engineering and Computer Science
Speed School of Engineering
University of Louisville

Abstract
The Hard Problem of consciousness has been dismissed as an illusion. By showing that computers
are capable of experiencing, we show that they are at least rudimentarily conscious with potential
to eventually reach superconsciousness. The main contribution of the paper is a test for confirming
certain subjective experiences in a tested agent. We follow with analysis of benefits and problems
with conscious machines and implications of such capability on future of computing, machine
rights and artificial intelligence safety.


As always thank you for listening to the Technocracy Abstract Series and a special thank you for our sponsors the Foundation, Transhumanity.net and the AGI Laboratory

http://www.thetechnocracy.com/2018/09/17/ai-abstract-series-e8-detecting-qualia-in-natural-and-artificial-agents/

AI Abstract Series E8:  Detecting Qualia in Natural and Artificial Agents

Todays paper is titled:  Ultimate Intelligence Part I: Physical Completeness and Objectivity of Induction.

Authored By Eray Özkural.

Abstract:  We propose that Solomonoff induction is complete in the physical sense via several strong physical arguments. We also argue that Solomonoff induction is fully applicable to quantum mechanics. We show how to choose an objective reference machine for universal induction by defining a physical message complexity and physical message probability, and argue that this choice dissolves some well-known objections to universal induction. We also introduce many more variants of physical message complexity based on energy and action, and discuss the ramifications of our proposals.

As always thank you for listening to the Technocracy Abstract Series and a special thank you for our sponsors the Foundation, Transhumanity.net and the AGI Laboratory.

http://www.thetechnocracy.com/2018/09/20/ai-abstract-series-ep9-ultimate-intelligence-part-i-physical-completeness-and-objectivity-of-induction/

AI Abstract Series Ep9 -Ultimate Intelligence Part I

Welcome to the Technocracy A.I. Abstract Series for Published Scientific Work in the A.I. and Artificial General Intelligence field.  

Todays paper is titled:  Zeta Distribution and Transfer Learning Problem.

Authored By Eray Özkural.
 
Abstract: We explore the relations between the zeta distribution and algorithmic information theory via a new model of the transfer learning problem. The program distribution is approximated by a zeta distribution with parameter near 1. We model the training sequence as a stochastic process. We analyze the upper temporal bound for learning a training sequence and its entropy rates, assuming an oracle for the transfer learning problem. We argue from empirical evidence that power-law models are suitable for natural processes. Four sequence models are proposed. Random typing model is like no-free lunch where transfer learning does not work. Zeta process independently samples programs from the zeta distribution. A model of common sub-programs inspired by genetics uses a database of sub-programs. An evolutionary zeta process samples mutations from Zeta distribution. The analysis of stochastic processes inspired by evolution suggest that AI may be feasible in nature, countering no-free lunch sort of arguments.

As always thank you for listening to the Technocracy Abstract Series and a special thank you for our sponsors the Foundation, Transhumanity.net and the AGI Laboratory.

http://www.thetechnocracy.com/2018/09/24/ai-abstract-series-e10-zeta-distribution-and-transfer-learning-problem/

AI Abstract Series E10: Zeta Distribution and Transfer Learning Problem.

Welcome to the Technocracy A.I. Abstract Series for Published Scientific Work in the A.I. and Artificial General Intelligence field.  

Todays paper is titled:  Omega: An Architecture for AI Unification.

Authored By Eray Özkural.

Abstract:  We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an "AI kernel" for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages and six classes of neural networks, which are briefly introduced. The architecture is intended to initially address data science automation, hence it includes many problem solving methods for statistical tasks. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, intelligent agents, the process and memory hierarchy, hardware abstraction, peer-to-peer computing, and data abstraction facility.

As always thank you for listening to the Technocracy Abstract Series and a special thank you for our sponsors the Foundation, Transhumanity.net and the AGI Laboratory.

http://www.thetechnocracy.com/2018/09/25/ai-abstract-series-e11-omega-an-architecture-for-ai-unification/

AI Abstract Series E11 - Omega: An Architecture for AI Unification

Welcome to the Technocracy A.I. Abstract Series for Published Scientific Work in the A.I. and Artificial General Intelligence field.  

Todays paper is titled:  A RIGHT TO REASONABLE INFERENCES: RE-THINKING DATA PROTECTION LAW IN THE AGE OF BIG DATA AND AI
Big Data an

Authored By Sandra Wachter & Brent Mittelstadt 

Abstract:  Big Data analytics and artificial intelligence (AI) draw non-intuitive and unverifiable inferences and predictions about the behaviours, preferences, and private lives of individuals. These inferences draw on highly diverse and feature-rich data of unpredictable value, and create new opportunities for discriminatory, biased, and invasive decision-making. Concerns about algorithmic accountability are often actually concerns about the way in which these technologies draw privacy invasive and non-verifiable inferences about us that we cannot predict, understand, or refute. Data protection law is meant to protect people’s privacy, identity, reputation, and autonomy, but is currently failing to protect data subjects from the novel risks of inferential analytics. The broad concept of personal data in Europe could be interpreted to include inferences, predictions, and assumptions that refer to or impact on an individual. If seen as personal data, individuals are granted numerous rights under data protection law. However, the legal status of inferences is heavily disputed in legal scholarship, and marked by inconsistencies and contradictions within and between the views of the Article 29 Working Party and the European Court of Justice.

As we show in this paper, individuals...

http://www.thetechnocracy.com/2018/10/08/ai-abstract-series-e12-a-right-to-reasonable-inferences-re-thinking-data-protection-law-in-the-age-of-big-data-and-ai-big-data-an

AI Abstract Series E12 - A RIGHT TO REASONABLE INFERENCES...

Princeton researchers, in collaboration with Analog Devices Inc., have fabricated a chip that markedly boosts the performance and efficiency of neural networks
—
computer algorithms modeled on the workings of the human brain. [28] "It will be interesting to see if this collection is used to train future generations of computer models," Sturm says. [27] Now, a team of A*STAR researchers and colleagues has developed a detector that can successfully pick out where human actions will occur in videos, in almost real-time. [26]  A team of researchers affiliated with several institutions in Germany and the U.S. has
 
developed a deep learning algorithm that can be used for motion capture of animals of any kind. [25] In 2016, when we inaugurated our new IBM Research lab in Johannesburg, we took on
 
this challenge and are reporting our first promising results at Health Day at the KDD
 
Data Science Conference in London this month. [24] The research group took advantage of a system at SLAC's Stanford Synchrotron Radiation
 
Lightsource (SSRL) that combines machine learning... 

http://www.thetechnocracy.com/2018/10/08/ai-abstract-series-e13-chip-up-ai-performance

AI Abstract Series E13 - Chip Up AI Performance

Abstract: This paper presents a multi-agent distributed framework for Swarm Intelligence (SI) based on our previous work ACODA(Ant Colony Optimization on a Distributed Architecture). Our framework can be used to distribute SI algorithms for solving graph search problems on a computer network. Examples and experimental results are given for SI algorithms of: Ant Colony System (ACS) and Bee Colony Optimization (BCO). In order to use the framework, the SI algorithms must be conceptualized to take advantage of the inherent parallelism determined by their analogy with natural phenomena (biological, chemical, physical, etc.): (i) the physical environment of the swarm entities is represented as a distributed multi-agent system and (ii)entities’ movement in the physical environment is represented as messages exchanged asynchronously between the agents of the problem environment. We present initial experimental results that show that our framework is scalable. We then compare the results of the distributed implementations of BCO and ACS algorithms using our framework. The conclusion was that our approach scales better when implementing the ACS algorithm but is faster when implementing BCO.


to learn more: http://www.thetechnocracy.com/2018/10/15/ai-abstract-series-e14-multi-agent-distributed-framework-for-swarm-intelligence/

AI Abstract Series E14 - Multi-agent distributed framework for swarm intelligence

Todays paper is titled:
Hybridization Concepts of Artificial Human Optimization Field Algorithms Incorporated into Particle Swarm Optimization

Authored By Hassan M. H. Mustafa and Satish Gajawada

Abstract: This piece of research presents the Particle Swarm Optimization (PSO) as a biologically inspired computational paradigm searches for problem optimization technique. Specifically (PSO) consists of a swarm of particles, where particle represent a potential solution. More precisely it is a population-based, stochastic algorithm modeled on the social behaviors observed in flocking birds. Over the past quarter century, Global Optimization techniques like Genetic and PSO algorithms has attracted many researchers attention at engineering and industry. In December 2016, a new field titled
“Artificial Human Optimization” was introduced in
literature. Referring to that innovative field it is clear that the agents in Artificial Human Optimization are Artificial Humans. Recently, a new algorithm titled Multiple Strategy Human Optimization (MSHO) is designed based on Artificial Humans. This paper adopted an interesting novel experimental idea which incorporated Hybridization of perspective concepts of Artificial Human Optimization into some experimental illustrations of PSO algorithms. Additionally, Human Inspired Differential Evolution (HIDE) is recently proposed method which is based on Differential Evolution and MSHO. For particular parameters settings HIDE performed approximately as good as Differential Evolution. In the experiment in this paper, a new algorithm
titled “Hassan Satish Particle Swarm Optimization (HSPSO)”
is proposed. HSPSO is tested by applying it on a complex benchmark function. Interesting Hybridization results have been obtained. Results obtained by HSPSO are compared with Particle Swarm Optimization.

As always thank you for listening to the Technocracy Abstract Series and a special thank you for our sponsors the Foundation, Transhumanity.net and the AGI Laboratory

http://www.thetechnocracy.com/2018/10/24/ai-abstract-series-e15-hybridization-concepts-of-artificial-human-optimization-field-algorithms/

AI Abstract Series E15 - Hybridization Concepts of Artificial Human Optimization Field Algorithms...

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AI Abstract Series E10: Zeta Distribution and Transfer Learning Problem.

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