Skip to main content

Before Time Began - Helmut Satz **

This is an odd little book. The aim seems to be to provide more detail about the most widely accepted cosmological theories than we usually get in a popular science title, which to some extent it does - but in a way that, for me, fails the Feynman test (more on that in a moment).

In his introduction, Helmut Satz tell us that not everyone agrees with some of the things he is going to describe, but I'm not sure that's good enough. For example, we are presented with the full current inflation theory as if it were fact, yet it seems to be going through a whole lot of uncertainty at the time of writing. It's fine to present the best accepted theory, but when there is significant concern about it, it's important to at least outline why it has problems and where we go from here.

In content terms, it's hard to fault what Satz covers - it gives us everything from a description of spontaneous symmetry breaking to the Higgs field, all with significantly more detail than you might normally expect. There's plenty too, for example, on nucleosynthesis and the cosmic microwave background. The problem I have with this book is the way this is presented.

There's one trivial issue. I hate the way the book is structured. It treats all the headings as if they were part of the body text. This totally misunderstands the point of headings, which is to provide an indicator of a clear break. What's more, readers don't always read the text of a heading, so end up with disjointed text. It's ironic that a book about the structure of the universe so messes up the structure of a book.

The bigger issue, though, is that Feynman test. The great American physicist Richard Feynman famously made the distinction between knowing something and knowing the name of something. Feynman pointed out that his dad taught him as a kid when looking at birds: 'You can know the name of that bird in all the languages of the world, but when you’re finished, you’ll know absolutely nothing whatever about the bird. You’ll only know about humans in different places, and what they call the bird. So let’s look at the bird and see what it’s doing—that’s what counts.'

I got exactly that feeling here - we're told the name of everything but don't get any feel for what's really happening or why it's happening. Take transitions and spontaneous symmetry breaking - there is a good example made using magnetisation (much clearer than some of the analogies I've seen) - but the phenomenon is just described. We get no idea why this is happening. Elsewhere analogies are used, but not necessarily very effectively. In describing the action of the Higgs field we are told it's a bit like the way a snowball gains mass by rolling through snow. But the snow it rolls through is the same material and itself has mass - the snowball is just accreting mass - so as an analogy it provides little benefit.

I don't think this book is a waste of time. It will fill in some gaps for those who only have a conventional popular science view of cosmology and may encourage some to move onto the more mathematical material. But I don't think it really achieves what it sets out to do.

Using these links earns us commission at no cost to you

Review by Brian Clegg


  1. Thanks. Yet another occasion where you have saved me a lot of time and frustration. Keep up the good work


Post a comment

Popular posts from this blog

Models of the Mind - Grace Lindsay *****

This is a remarkable book. When Ernest Rutherford made his infamous remark about science being either physics or stamp collecting, it was, of course, an exaggeration. Yet it was based on a point - biology in particular was primarily about collecting information on what happened rather than explaining at a fundamental level why it happened. This book shows how biologists, in collaboration with physicists, mathematicians and computer scientists, have moved on the science of the brain to model some of its underlying mechanisms. Grace Lindsay is careful to emphasise the very real difference between physical and biological problems. Most systems studied by physics are a lot simpler than biological systems, making it easier to make effective mathematical and computational models. But despite this, huge progress has been made drawing on tools and techniques developed for physics and computing to get a better picture of the mechanisms of the brain. In the book we see this from two directions

The Ten Equations that Rule the World - David Sumpter ****

David Sumpter makes it clear in this book that a couple of handfuls of equations have a huge influence on our everyday lives. I needed an equation too to give this book a star rating - I’ve never had one where there was such a divergence of feeling about it. I wanted to give it five stars for the exposition of the power and importance of these equations and just two stars for an aspect of the way that Sumpter did it. The fact that the outcome of applying my star balancing equation was four stars emphasises how good the content is. What we have here is ten key equations from applied mathematics. (Strictly, nine, as the tenth isn’t really an equation, it’s the programmer’s favourite ‘If… then…’ - though as a programmer I was always more an ‘If… then… else…’ fan.) Those equations range from the magnificent one behind Bayesian statistics and the predictive power of logistic regression to the method of determining confidence intervals and the kind of influencer matrix so beloved of social m

Grace Lindsay - Four Way Interview

Grace Lindsay is a computational neuroscientist currently based at University College, London. She completed her PhD at the Centre for Theoretical Neuroscience at Columbia University, where her research focused on building mathematical models of how the brain controls its own sensory processing. Before that, she earned a bachelor’s degree in Neuroscience from the University of Pittsburgh and received a research fellowship to study at the Bernstein Center for Computational Neuroscience in Freiburg, Germany. She was awarded a Google PhD Fellowship in Computational Neuroscience in 2016 and has spoken at several international conferences. She is also the producer and co-host of Unsupervised Thinking , a podcast covering topics in neuroscience and artificial intelligence. Her first book is Models of the Mind . Why science? I started my undergraduate degree as a neuroscience and philosophy double major and I think what drew me to both topics was the idea that if we just think rigorously enou