Anonymity in Predicting the Future

Consider an arbitrary set $S$ and an arbitrary function $f : \mathbb{R} \to S$. We think of the domain of $f$ as representing time, and for each $x \in \mathbb{R}$, we think of $f(x)$ as the state of some system at time $x$. Imagine that, at each time $x$, there is an agent who can see $f \upharpoonright (-\infty, x)$ and is trying to guess $f(x)$--in other words, the agent is trying to guess the present state of the system from its past history. In a 2008 paper, Christopher Hardin and Alan Taylor use the axiom of choice to construct a strategy that the agents can use to guarantee that, for every function $f$, all but countably many of them will guess correctly. In a 2013 monograph they introduce the idea of anonymous guessing strategies, in which the agents can see the past but don't know where they are located in time. In this paper we consider a number of variations on anonymity. For instance, what if, in addition to not knowing where they are located in time, agents also do not know the rate at which time is progressing? What if they have no sense of how much time elapses between any two events? We show that in some cases agents can still guess successfully, while in others they perform very poorly.Comment: 12 pages, 1 figur

Predicting Deeper into the Future of Semantic Segmentation

The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g. in robotics or autonomous driving, which depend on visual scene understanding for decision making. While prediction of the raw RGB pixel values in future video frames has been studied in previous work, here we introduce the novel task of predicting semantic segmentations of future frames. Given a sequence of video frames, our goal is to predict segmentation maps of not yet observed video frames that lie up to a second or further in the future. We develop an autoregressive convolutional neural network that learns to iteratively generate multiple frames. Our results on the Cityscapes dataset show that directly predicting future segmentations is substantially better than predicting and then segmenting future RGB frames. Prediction results up to half a second in the future are visually convincing and are much more accurate than those of a baseline based on warping semantic segmentations using optical flow.Comment: Accepted to ICCV 2017. Supplementary material available on the authors' webpage

The Future of Family Philanthropy: Predicting and Preparing

There is little dispute that philanthropy is undergoing a profound change. Traditional foundation grantmaking, and giving from perpetually endowed advised funds, are now just two options among a growing array of methods that family donors and social entrepreneurs can use to create impact. New organizational forms, new types of social investment, and new collaborations are part of an ambitious, boundary-blurring experiment in innovation for good. While many family donors are wary of these new approaches, looking for more information before venturing into the new spaces, others have become pioneers and are eager to share their experiences

Predicting the socio-technical future (and other myths)

A snooker ball model implies that simple, linear and predictable social change follows from the introduction of new technologies. Unfortunately technology does not have and has never had simple linear predictable social impacts. In this chapter we show that in most measurable ways, the pervasiveness of modern information and communication technologies has had little discernable ?impact? on most human behaviours of sociological significance. Historians of technology remind us that human society co-evolves with the technology it invents and that the eventual social and economic uses of a technology often turn out to be far removed from those originally envisioned. Rather than using the snooker ball model to attempt to predict future ICT usage and revenue models that are inevitably wrong, we suggest that truly participatory, grounded innovation, open systems and adaptive revenue models can lead us to a more effective, flexible and responsive innovation process

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

Predicting the Future

Five factors will influence the online databases of the future: telecommunications developments, scanning and storage improvements, increasing database distribution options, user needs and demands, and changes in database production