Philosophy Mondays: Human-AI Collaboration
Today's Philosophy Monday is an important interlude. I want to reveal that I have not been writing the posts in this series entirely by myself. Instead I have been working with Claude, not just for the graphic illustrations, but also for the text. My method has been to write a rough draft and then ask Claude for improvement suggestions. I will expand this collaboration to other intelligences going forward, including open source models such as Llama and DeepSeek. I will also explore other moda...
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Web3/Crypto: Why Bother?
One thing that keeps surprising me is how quite a few people see absolutely nothing redeeming in web3 (née crypto). Maybe this is their genuine belief. Maybe it is a reaction to the extreme boosterism of some proponents who present web3 as bringing about a libertarian nirvana. From early on I have tried to provide a more rounded perspective, pointing to both the good and the bad that can come from it as in my talks at the Blockstack Summits. Today, however, I want to attempt to provide a coge...
Philosophy Mondays: Human-AI Collaboration
Today's Philosophy Monday is an important interlude. I want to reveal that I have not been writing the posts in this series entirely by myself. Instead I have been working with Claude, not just for the graphic illustrations, but also for the text. My method has been to write a rough draft and then ask Claude for improvement suggestions. I will expand this collaboration to other intelligences going forward, including open source models such as Llama and DeepSeek. I will also explore other moda...
Intent-based Collaboration Environments
AI Native IDEs for Code, Engineering, Science
Web3/Crypto: Why Bother?
One thing that keeps surprising me is how quite a few people see absolutely nothing redeeming in web3 (née crypto). Maybe this is their genuine belief. Maybe it is a reaction to the extreme boosterism of some proponents who present web3 as bringing about a libertarian nirvana. From early on I have tried to provide a more rounded perspective, pointing to both the good and the bad that can come from it as in my talks at the Blockstack Summits. Today, however, I want to attempt to provide a coge...
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Last Uncertainty Wednesday, I introduced the topic of spurious correlation. Since then I have discovered a site that gives some fantastic examples of (potentially) spurious correlations. Here is one:
The coefficient of correlation is 0.9926, i.e. almost 1 (which would be perfectly correlated).
Let’s remind ourselves what finding correlation in a sample of data means. It is simply a numerical measure that can be computed for any paired data. The formula produces a result that has nothing to with the labels on the data. This may seem like stating the obvious, but it is really important to keep in mind. The numerical result for correlation here is the same whether the labels read “Divorce rate in Maine” and “Per capita consumption of margarine” or if they were simply “Series 1″ and “Series 2.″
Why am I emphasizing this? Because whether or not we think sample correlation is indicative of real correlation is something we need to decide based on our explanations about the relationship between the two random variables. I don’t have an explanation relating “Per capita consumption of margarine” to the “Divorce rate in Maine.” Saying that I don’t have an explanation, importantly, though is not the same as saying that they are definitely independent (you may recall that independence is actually quite a strong assumption). Margarine consumption and divorce rates are both household behaviors and so it is quite possible for them to be dependent!
Next Uncertainty Wednesday we will take a deeper look into how much of a signal of real correlation we are getting depends on our prior believes (based on explanations) of the actual correlation.
Last Uncertainty Wednesday, I introduced the topic of spurious correlation. Since then I have discovered a site that gives some fantastic examples of (potentially) spurious correlations. Here is one:
The coefficient of correlation is 0.9926, i.e. almost 1 (which would be perfectly correlated).
Let’s remind ourselves what finding correlation in a sample of data means. It is simply a numerical measure that can be computed for any paired data. The formula produces a result that has nothing to with the labels on the data. This may seem like stating the obvious, but it is really important to keep in mind. The numerical result for correlation here is the same whether the labels read “Divorce rate in Maine” and “Per capita consumption of margarine” or if they were simply “Series 1″ and “Series 2.″
Why am I emphasizing this? Because whether or not we think sample correlation is indicative of real correlation is something we need to decide based on our explanations about the relationship between the two random variables. I don’t have an explanation relating “Per capita consumption of margarine” to the “Divorce rate in Maine.” Saying that I don’t have an explanation, importantly, though is not the same as saying that they are definitely independent (you may recall that independence is actually quite a strong assumption). Margarine consumption and divorce rates are both household behaviors and so it is quite possible for them to be dependent!
Next Uncertainty Wednesday we will take a deeper look into how much of a signal of real correlation we are getting depends on our prior believes (based on explanations) of the actual correlation.
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