Can intelligence explode?

The technological singularity refers to a hypothetical scenario in which technological advances virtually explode. The most popular scenario is the creation of super-intelligent algorithms that recursively create ever higher intelligences. It took many decades for these ideas to spread from science fiction to popular science magazines and finally to attract the attention of serious philosophers. David Chalmers' (JCS, 2010) article is the first comprehensive philosophical analysis of the singularity in a respected philosophy journal. The motivation of my article is to augment Chalmers' and to discuss some issues not addressed by him, in particular what it could mean for intelligence to explode. In this course, I will (have to) provide a more careful treatment of what intelligence actually is, separate speed from intelligence explosion, compare what super-intelligent participants and classical human observers might experience and do, discuss immediate implications for the diversity and value of life, consider possible bounds on intelligence, and contemplate intelligences right at the singularity

Heterogeneous Stochastic Interactions for Multiple Agents in a Multi-armed Bandit Problem

We define and analyze a multi-agent multi-armed bandit problem in which decision-making agents can observe the choices and rewards of their neighbors. Neighbors are defined by a network graph with heterogeneous and stochastic interconnections. These interactions are determined by the sociability of each agent, which corresponds to the probability that the agent observes its neighbors. We design an algorithm for each agent to maximize its own expected cumulative reward and prove performance bounds that depend on the sociability of the agents and the network structure. We use the bounds to predict the rank ordering of agents according to their performance and verify the accuracy analytically and computationally

Evaluation of an Australian Solar Community : Implications for Education and Training

1.1 Background What is renewable energy education and training? A cursory exploration of the International Solar Energy Society website (www.ises.org) reveals numerous references to education and training, referring collectively to concepts of the transfer and exchange of information and good practices, awareness raising and skills development. The purposes of such education and training relate to changing policy, stimulating industry, improving quality control and promoting the wider use of renewable energy sources. The primary objective appears to be to accelerate a transition to a better world for everyone (ISEE), as the greater use of renewable energy is seen as key to climate recovery; world poverty alleviation; advances in energy security, access and equality; improved human and environmental health; and a stabilized society. The Solar Cities project – Habitats of Tomorrow – aims at promoting the greater use of renewable energy within the context of long term planning for sustainable urban development. The focus is on cities or communities as complete systems; each one a unique laboratory allowing for the study of urban sustainability within the context of a low carbon lifestyle. The purpose of this paper is to report on an evaluation of a Solar Community in Australia, focusing specifically on the implications (i) for our understandings and practices in renewable energy education and training and (ii) for sustainability outcomes. 1.2 Methodology The physical context is a residential Ecovillage (a Solar Community) in sub-tropical Queensland, Australia (latitude 28o south). An extensive Architectural and Landscape Code (A&LC) ‘premised on the interconnectedness of all things’ and embracing ‘both local and global concerns’ governs the design and construction of housing in the estate: all houses are constructed off-ground (i.e. on stumps or stilts) and incorporate a hybrid approach to the building envelope (mixed use of thermal mass and light-weight materials). Passive solar design, gas boosted solar water heaters and a minimum 1kWp photovoltaic system (grid connected) are all mandatory, whilst high energy use appliances such as air conditioners and clothes driers are not permitted. Eight families participated in an extended case study that encompassed both quantitative and qualitative approaches to better understand sustainable housing (perceived as a single complex technology) through its phases of design, construction and occupation. 1.3 Results The results revealed that the level of sustainability (i.e. the performance outcomes in terms of a low-carbon lifestyle) was impacted on by numerous ‘players’ in the supply chain, such as architects, engineers and subcontractors, the housing market, the developer, product manufacturers / suppliers / installers and regulators. Three key factors were complicit in the level of success: (i) systems thinking; (ii) informed decision making; and (iii) environmental ethics and business practices. 1.4 Discussion The experiences of these families bring into question our understandings and practices with regard to education and training. Whilst increasing and transferring knowledge and skills is essential, the results appear to indicate that there is a strong need for expanding our education efforts to incorporate foundational skills in complex systems and decision making processes, combined with an understanding of how our individual and collective values and beliefs impact on these systems and processes

On the tangled discourse between transaction costs economics and competence-based views of the firms: Some comments

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Low-Cost Learning via Active Data Procurement

We design mechanisms for online procurement of data held by strategic agents for machine learning tasks. The challenge is to use past data to actively price future data and give learning guarantees even when an agent's cost for revealing her data may depend arbitrarily on the data itself. We achieve this goal by showing how to convert a large class of no-regret algorithms into online posted-price and learning mechanisms. Our results in a sense parallel classic sample complexity guarantees, but with the key resource being money rather than quantity of data: With a budget constraint $B$, we give robust risk (predictive error) bounds on the order of $1/\sqrt{B}$. Because we use an active approach, we can often guarantee to do significantly better by leveraging correlations between costs and data. Our algorithms and analysis go through a model of no-regret learning with $T$ arriving pairs (cost, data) and a budget constraint of $B$. Our regret bounds for this model are on the order of $T/\sqrt{B}$ and we give lower bounds on the same order.Comment: Full version of EC 2015 paper. Color recommended for figures but nonessential. 36 pages, of which 12 appendi