Much like human speech, bird song learning is social; perhaps we'll discover machine learning is social, too.
Mike Loukides is Vice President of Content Strategy for O'Reilly Media, Inc. He's edited many highly regarded books on technical subjects that don't involve Windows programming. He's particularly interested in programming languages, Unix and what passes for Unix these days, and system and network administration. Mike is the author of System Performance Tuning and a coauthor of Unix Power Tools. Most recently, he's been fooling around with data and data analysis, languages like R, Mathematica, and Octave, and thinking about how to make books social. Mike can be reached on Twitter @mikeloukides and on LinkedIn.
Consent is the first step toward the ethical use of data, but it's not the last.
We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.
Our bad AI could be the best tool we have for understanding how to be better people.
If we’re going to think about the ethics of data and how it’s used, then we have to take into account how data flows.
HTTPS "everywhere" means everywhere—not just the login page, or the page where you accept donations. Everything.
General intelligence or creativity can only be properly imagined if we peel away the layers of abstractions.
These studies provide a foundation for discussing ethical issues so we can better integrate data ethics in real life.
We can build a future we want to live in, or we can build a nightmare. The choice is up to us.
Five framing guidelines to help you think about building data products.
Oaths have their value, but checklists will help put principles into practice.
“Human in the loop” software development will be a big part of the future.
Data scientists, data engineers, AI and ML developers, and other data professionals need to live ethical values, not just talk about them.
It’s easy to imagine an AI winning a game of Go, but can you imagine an AI wanting to play a game of Go?
We need to build organizations that are self-critical and avoid corporate self-deception.
When we finally find the best use cases for blockchains, they may look like nothing we would have expected.
Successful projects will think seriously about what blockchains mean, and how to use them effectively.
Don’t pigeonhole blockchain as a technology that’s primarily useful for finance.
Unpacking the complexity of blockchain, term by term.
We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.