As we move toward focusing on reader revenue, I admit that I do have concerns about creating a world of haves and have-nots. If we are creating an exclusive model for participation, there must be other solutions offered that allow for wider access to information. Our economy is already so widely divided, and our industry needs to serve all people, regardless of their ability to support us. We cannot have only one revenue stream if we wish to truly serve our communities. At the same time,…
The 2.0 stack can fail in unintuitive and embarrassing ways ,or worse, they can “silently fail”, e.g., by silently adopting biases in their training data, which are very difficult to properly analyze and examine when their sizes are easily in the millions in most cases.
The 2.0 stack also has some of its own disadvantages. At the end of the optimization we’re left with large networks that work well, but it’s very hard to tell how. Across many applications areas, we’ll be left with a choice of using a 90% accurate model we understand, or 99% accurate model we don’t.
It turns out that a large portion of real-world problems have the property that it is significantly easier to collect the data than to explicitly write the program. A large portion of programmers of tomorrow do not maintain complex software repositories, write intricate programs, or analyze their running times. They collect, clean, manipulate, label, analyze and visualize data that feeds neural networks.
… and behaviour, and filter your approach to new technologies based on what you know about people. A great deal of the work involved in predicting the future is really just understanding people and systems, and especially systems made up of people.
…ns and behaviour, and filter your approach to new technologies based on what you know about people. A great deal of the work involved in predicting the future is really just understanding people and systems, and especially systems made up of people.
You can’t predict the future, nor understand what scientific innovations might become dramatically important in the coming decades. You can maybe make some educated guesses about the next 18 months, but even that could be thrown out of the window by a major news event or a Zuckerbergian whim.
…ption about it is that it’s just a data display. It’s never been that: it’s a cultural change tool. It’s not just about putting numbers into the hands of editorial people — it’s explicitly about getting them to change the way they make decisions, and to make them better. It’s a tool for enhancing journalistic instinct, and one of the reasons why we can be so cavalier a…