Thanks to the four authors and publisher for making this available as a PDF download.
I am definitely going to give this a read. After decades of more or less using GOFAI, the last eight years has mostly been machine learning and deep learning. Lately I have been scratching an itch to combine old fashioned symbolic AI with more modern deep learning (hybrid systems). I started a new job on Monday where I think this may happen.
From the table of contents, the four examples look appropriate for looking at ML in the context of large real world problems.
Are you sure? You sound like someone who’s interested in actually doing things; the book looks like endless rambling about high level ethical/conceptual/societal context for data science. It looks about as interesting as picking up a random volume of a random social sciences journal. I think Peter Norvig’s python code is beautiful and amazing though, and probably same for most things he’s done.
Funny, I was going to say it seems similar to that one - dense with words but light on technical theory (I was tempted to say 'content' but I'm sure that's unduly harsh, as you say this will suit some). Maybe I didn't spend enough time with it, my recollection is just of it waffling on and on about agents.
> This metaphorical braid shows the integration of the foundational fields, labeled S, OR, and C. Figure I.1 Integration of Statistics, Operations Research, and Computing
Brilliant.
The single biggest issue I see working in this area today are two things:
Lack of historical context. People have no clue about the history of quantitative management (rocky, lots of ups and downs)
And sales. Terrible, terrible salesmanship from nerds.
Anyone involved in this field professionally needs to read this book.
I was really looking forward to diving deeper into this, having enjoyed, and learnt, a lot from Peter Norvig's blog and writings in the past. I especially enjoyed the succinctness and density of knowledge in his writing.
Disappointingly, to me, this book seems to lack both of those properties. It seems to meander between talking about core data science concepts, and also about privacy, ethics etc. Given the title of the book perhaps this content makes sense, but it was not useful for me personally. Wish the authors the best.
I was excited at clicking the topic - quickly read/skimmed the PDF, and it reads more like an industry analysis than a code/technical book related to the application of data science. Quite disappointed.
They should change the title. It is not a book for programmers about implementation of data science techniques. It is a book for managers about concerns surrounding the application of data science in various domains. The title should be something like "Social Concerns in the Application of Data Science."
Social concerns are as a concern for engineers, as they are for their managers. However, I agree on changing the title somewhat, in order to indicate, that this is not a book about computer programming.
While data science is not principally about programming, I think that having Norvig as one of the authors will lead to false expectations of the book unless the title is made more informative.
This book should be geared for one of those introduction courses, like “Introduction to Engineering”, “AI 101”, etc. I do agree with the vision of the authors that the book is a holistic view of data science, as I personally believe data science is not all about maths, programming; but consider the principles that surround it as a science.
This is also quite practical for large consultancy firms. Most of the chapters, I’ve had clients discuss with me (such as Responsible AI). Personally, I think it could have went away from the applications as it was too high level.
Not a native speaker (never heard "clear box" as terminus technicus), but a mathematician: If it is "the (only) opposite", the relation ist symmetric. So the opposite of "clear box" is "black box" again. Skipping the injective part, "black box" would be still one possible opposite.
No, it makes no sense to change a well-known phrase to an unfamiliar and goofy sounding one just to appease a silly ideology that injects racism into everything.
> It has nothing to do with racism, but good on you for bringing racism into the argument though.
Nonsense. This is all part of the "oppressive language" concept peddled by the neo-Marxist authoritarian types.
> Some people use the term opaque box instead because it a more clear antonym to clear/open box.
This is the first time in my life that I head the term "opaque box" used instead of "black box" in this context. I couldn't even find this phrase using a Google search.
It appears they are just using an alternative phrasing without making comment, but if they were actually making a comment then wouldn’t the overall objection be “not to read too much into the wording”, but that is a catch 22. Objecting to someone taking wording too seriously to the point of changing the wording is in itself taking the wording too seriously.
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I am definitely going to give this a read. After decades of more or less using GOFAI, the last eight years has mostly been machine learning and deep learning. Lately I have been scratching an itch to combine old fashioned symbolic AI with more modern deep learning (hybrid systems). I started a new job on Monday where I think this may happen.
From the table of contents, the four examples look appropriate for looking at ML in the context of large real world problems.
Brilliant.
The single biggest issue I see working in this area today are two things:
Lack of historical context. People have no clue about the history of quantitative management (rocky, lots of ups and downs)
And sales. Terrible, terrible salesmanship from nerds.
Anyone involved in this field professionally needs to read this book.
Rapidly.
Disappointingly, to me, this book seems to lack both of those properties. It seems to meander between talking about core data science concepts, and also about privacy, ethics etc. Given the title of the book perhaps this content makes sense, but it was not useful for me personally. Wish the authors the best.
Data scientists are not software engineers.
They analyze data, they don't produce applications.
Not everything in the world is written for the benefit of software engineers.
But in the book, computation is one of the three braids comprising data science. And they include software engineering in computation.
See book sections 1.2, 1.2.4, 2.1.3 etc
—
What I am trying to untie is, should data engineering be distinguished from data science, or not?
This is better than yet another machine learning algorithm book.
This is also quite practical for large consultancy firms. Most of the chapters, I’ve had clients discuss with me (such as Responsible AI). Personally, I think it could have went away from the applications as it was too high level.
Not a native speaker (never heard "clear box" as terminus technicus), but a mathematician: If it is "the (only) opposite", the relation ist symmetric. So the opposite of "clear box" is "black box" again. Skipping the injective part, "black box" would be still one possible opposite.
I would not consider the attribute "rare" to be appropriate here.
For example: I regularly deal with adversarial networks, and both black- and white-box attacks are quite common there.
Black box vs clear/open box are very well known terms. Some people use the term opaque box instead because it a more clear antonym to clear/open box.
Nonsense. This is all part of the "oppressive language" concept peddled by the neo-Marxist authoritarian types.
> Some people use the term opaque box instead because it a more clear antonym to clear/open box.
This is the first time in my life that I head the term "opaque box" used instead of "black box" in this context. I couldn't even find this phrase using a Google search.