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"The Coming of Evolution" by John W. Judd. Published by Good Press. Good Press publishes a wide range of titles that encompasses every genre. From well-known classics & literary fiction and non-fiction to forgotten−or yet undiscovered gems−of.
Table of contents
- The Fourth Industrial Revolution: what it means, how to respond
- German addresses are blocked - leondumoulin.nl
- Roots of the Scientific Revolution
- The rise of modern science
British Wildlife is the leading natural history magazine in the UK, providing essential reading for both enthusiast and professional naturalists and wildlife conservationists. Published six times a year, British Wildlife bridges the gap between popular writing and scientific literature through a combination of long-form articles, regular columns and reports, book reviews and letters. Conservation Land Management CLM is a quarterly magazine that is widely regarded as essential reading for all who are involved in land management for nature conservation, across the British Isles.
CLM includes long-form articles, events listings, publication reviews, new product information and updates, reports of conferences and letters. Exceptional customer service Get specialist help and advice. A reprint of a classical work in the Cambridge Library Collection. Before his retirement as Professor of Geology from Imperial College, he wrote this concise and accessible review of the beginnings of evolutionary theory.
Judd skilfully examined the roots of an idea that, already by , had profoundly influenced every branch of science and permeated the work of historians, politicians and theologians. His lively narrative introduces the key individuals, including Darwin and Lyell, who brought about this intellectual revolution.
The Fourth Industrial Revolution: what it means, how to respond
Judd analyses the principal influences that worked upon these scientists as well as the factors that permitted them to remain open to radical new views. His appreciation of the vision, courage and far-reaching impact of the work of both Lyell and Darwin, and the interplay between their ideas, is persuasively and eloquently expressed. Introductory 2. Origin of the idea of evolution 3. The development of the idea of evolution to the inorganic world 4.
The triumph of catastrophism over evolution 5. The revolt of Scrope and Lyell against catastrophism 6. The Principles of Geology 7.
The influence of Lyell's works 8. Early attempts to establish the doctrine of evolution for the organic world 9. Darwin and Wallace The Origin of Species The influence of Darwin's works The place of Lyell and Darwin in history. Newsletter Google 4. Help pages. Prothero Michael J. Benton Richard Fortey View All. British Wildlife. Go to British Wildlife. Conservation Land Management.
We believe that a new generation of powerful software tools, which support collaboration and data exploration on an unprecedented scale, are about to enable revolutionary discoveries in these fields. For decades computer scientists have tried to teach computers to think like human experts by embedding in them complex rules of linguistics and reasoning.
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Up to now, most of those efforts have failed to come close to generating the creative insights and solutions that come naturally to the best scientists, physicians, engineers, and marketers. We have reached a point, however, where even the experts are drowning in data. Digital information is streaming in from all sorts of sensors, instruments, and simulations, overwhelming our capacity to organize, analyze, and store it. To increase performance today, we must program multiple processors on multicore chips and exploit parallelism.
The multicore revolution has arrived just as we face an exponential increase in data. That increase is not a challenge we can address with patches and upgrades; we must rethink our whole approach to data-intensive science. Beginning in ancient Greece and China, people tried to explain their observations through natural laws instead of supernatural causes.
By the 17th century, scientists like Isaac Newton tried to make predictions for new phenomena and would verify hypotheses by conducting experiments. The advent of high-performance computers in the latter half of the 20th century allowed scientists to explore regimes inaccessible to experiment and theory, such as climate modeling or galaxy formation, by numerically solving systems of equations on a large scale and in fine detail.
Using more-powerful computers, scientists begin with the data and direct programs to mine enormous databases for relationships. In essence, they use computers to discover the rules by studying the data. The first two paradigms for scientific exploration and discovery, experiment and theory, have a long history. The experimental method can be traced back to ancient Greece and China, when people tried to explain their observations through natural rather than supernatural causes.
Modern theoretical science originated with Isaac Newton in the 17th century. After high-performance computers were developed in the latter half of the 20th century, Nobel Prize winner Ken Wilson identified computation and simulation as a third paradigm for scientific exploration. Detailed computer simulations capable of solving equations on a massive scale allowed scientists to explore fields of inquiry that were inaccessible to experiment and theory, such as climate modeling or galaxy formation.
Coordinating and analyzing the data they generate can make water management more effective. The amount one health care provider discovered in underpayments within the first six months of using deep-data-mining tools. The cost of monitoring a patient that data-driven techniques predict will be rehospitalized, versus the potential cost of treating a readmitted patient.
The fourth paradigm also involves powerful computers. But instead of developing programs based on known rules, scientists begin with the data. They direct programs to mine enormous databases looking for relationships and correlations, in essence using the programs to discover the rules. We consider big data part of the solution, not the problem. Without the ability to harness sophisticated computer tools that manipulate data, even the most highly trained expert would never manage to unearth the insights that are now starting to come into focus.
In the s my colleague Eric Horvitz, while training at a Veterans Administration hospital as part of his medical education, observed a disturbing phenomenon. During the holiday season, the hospital experienced a surge in admissions for congestive heart failure. Each year, some patients who had otherwise successfully managed their health despite a weakened heart would reach a tipping point after a salty holiday meal. That extra salt caused their bodies to retain additional fluids, which would lead to lung congestion and labored breathing—and often to a visit to the emergency room.
Those post-turkey collapses were expensive in every sense of the word.
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They could be fatal for some patients—sometimes quite rapidly, sometimes by causing a downward spiral of failing physiological systems that took days to weeks. Today those bills would be far higher. More than two decades later, Eric and his colleagues at Microsoft Research have developed analyses that can predict with impressive accuracy whether a patient with congestive heart failure who is released from the hospital will be readmitted within 30 days.
Roots of the Scientific Revolution
This feat is not based on programming a computer to run through the queries a given diagnostician would ask or on an overall estimate of how many patients return. In one sense we owe this project to a human expert spotting a nonobvious connection: Eric not only earned his MD but also has a PhD in computer science, and he realized that machine-learning techniques similar to the ones he and his team had used to analyze Seattle traffic patterns could work for this important health care challenge.
In they had developed methods of predicting traffic jams by analyzing massive quantities of data, which included information on the flow of traffic over highways, weather reports, accidents, local events, and other variables that had been gathered over several years. The economic impact of this prediction tool could be huge. Hedge funds and large money managers are placing millions of dollars in bets every day based on these data-discovered relationships. On the operational side of business, the possibilities are endless. Companies will be able to do massive analyses of customers and business opportunities using programs that unearth patterns in price, buying habits, geographic region, household income, or myriad other data points.
The large quantities of available data on advertising effectiveness, customer retention, employee retention, customer satisfaction, and supply chain management will allow firms to make meaningful predictions about the behavior of any given customer or employee and the likelihood of gaps in service or supply.
And more and more, we find companies using data techniques to spot irregularities in payments and receivables. These programs can predict, for example, the revenues that should be collected for a given list of delivered services. An electronic entertainment company in the Philippines is using Microsoft data-mining technology to customize its sales pitches to individual customers, based on extensive analysis of such factors as past buying patterns, age, gender, financial profile, and location.
Almost immediately after implementing this technique, the company saw its response rate for offers for ringtones and other products double. With all those business opportunities, some ask why Microsoft Research is working on so many global health and environmental projects.
The rise of modern science
Yes, but the reason Microsoft Research has several dozen computer scientists working on them is that they involve some of the most enormous data stores imaginable and constitute an invaluable testing ground. We need to expand our own thinking and the capabilities of our tools by working on the biggest problems out there, which happen to be of immense importance to humanity. Tackling these problems also opens more opportunities for collaboration and experiments.
As Jim Gray used to say, astronomy data are valuable precisely because they have no commercial value. One such ambitious environmental project involves ocean science and is now under construction beneath the cool Pacific waters west of Washington State and British Columbia. The oceans drive weather systems; are the source of powerful, still largely unpredictable hazards such as tsunamis and hurricanes; store much more carbon than the atmosphere, vegetation, and soil; and are a critical food source.
And yet, in many ways we understand more about the surfaces of Mars and Venus than about the seafloors. That is about to change. The project aims to involve scientists from all over the world. The ability to measure and analyze natural processes—such as silt buildup or changes in the density of microscopic organisms—is unprecedented. The team is working out the data standards that will allow analysis programs to combine findings from different experiments into one larger analysis.