Month: February 2017

Nature Insight: Frontiers in biology

This year’s ‘Frontiers in biology’ Insight features Reviews on how genomics is helping to uncover the peopling of the world, the interplay between morphogens and morphogenesis in determining organismal shape, the factors that influence the immune response to cancer, advances in single-cell genomics, and the effects of base modifications in messenger RNA.

Source: www.nature.com

Complexity is becoming mainstream…

A theoretical foundation for multi-scale regular vegetation patterns

Empirically validated mathematical models show that a combination of intraspecific competition between subterranean social-insect colonies and scale-dependent feedbacks between plants can explain the spatially periodic vegetation patterns observed in many landscapes, such as the Namib Desert ‘fairy circles’.

 

A theoretical foundation for multi-scale regular vegetation patterns

Corina E. Tarnita, Juan A. Bonachela, Efrat Sheffer, Jennifer A. Guyton, Tyler C. Coverdale, Ryan A. Long & Robert M. Pringle

Nature 541, 398–401 (19 January 2017) doi:10.1038/nature20801

Source: www.nature.com

A solution to the single-question crowd wisdom problem

Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard ‘most popular’ or ‘most confident’ principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions.

 

A solution to the single-question crowd wisdom problem

Dražen Prelec, H. Sebastian Seung & John McCoy

Nature 541, 532–535 (26 January 2017) doi:10.1038/nature21054

 

Source: www.nature.com

Shoppers like what they know

Faced with ever-changing products, consumers can benefit from trying new items. But data collected over almost five years show that, the longer shoppers have been buying a favourite product, the more likely they are to stick with it.

 

Human behaviour: Shoppers like what they know
Peter M. Todd
Nature 541, 294–295 (19 January 2017) doi:10.1038/nature21114

Source: www.nature.com

Brains, Minds & Machines Summer Course 2017

The basis of intelligence – how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines – is arguably the greatest problem in science and technology. To solve it, we will need to understand how human intelligence emerges from computations in neural circuits, with rigor sufficient to reproduce similar intelligent behavior in machines. Success in this endeavor ultimately will enable us to understand ourselves better, to produce smarter machines, and perhaps even to make ourselves smarter. Today’s AI technologies, such as Watson and Siri, are impressive, but their domain specificity and reliance on vast numbers of labeled examples are obvious limitations; few view this as brain-like or human intelligence. The synergistic combination of cognitive science, neurobiology, engineering, mathematics, and computer science holds the promise to build much more robust and sophisticated algorithms implemented in intelligent machines. The goal of this course is to help produce a community of leaders that is equally knowledgeable in neuroscience, cognitive science, and computer science and will lead the development of true biologically inspired AI.

 

Brains, Minds and Machines
Directors: Gabriel Kreiman, Children’s Hospital, Harvard Medical School; and Tomaso Poggio, Massachusetts Institute of Technology
Location: Marine Biological Laboratory, in Woods Hole, MA.
Course Dates: Aug. 13 – Sept. 3, 2017
Deadline: March 14, 2017

Source: cbmm.mit.edu