76 research outputs found
Statistical Basis for Predicting Technological Progress
Forecasting technological progress is of great interest to engineers, policy
makers, and private investors. Several models have been proposed for predicting
technological improvement, but how well do these models perform? An early
hypothesis made by Theodore Wright in 1936 is that cost decreases as a power
law of cumulative production. An alternative hypothesis is Moore's law, which
can be generalized to say that technologies improve exponentially with time.
Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus.
These hypotheses have not previously been rigorously tested. Using a new
database on the cost and production of 62 different technologies, which is the
most expansive of its kind, we test the ability of six different postulated
laws to predict future costs. Our approach involves hindcasting and developing
a statistical model to rank the performance of the postulated laws. Wright's
law produces the best forecasts, but Moore's law is not far behind. We discover
a previously unobserved regularity that production tends to increase
exponentially. A combination of an exponential decrease in cost and an
exponential increase in production would make Moore's law and Wright's law
indistinguishable, as originally pointed out by Sahal. We show for the first
time that these regularities are observed in data to such a degree that the
performance of these two laws is nearly tied. Our results show that
technological progress is forecastable, with the square root of the logarithmic
error growing linearly with the forecasting horizon at a typical rate of 2.5%
per year. These results have implications for theories of technological change,
and assessments of candidate technologies and policies for climate change
mitigation
Pan-cancer analysis of whole genomes
Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe
- …