cowanon22

This isn't "statistical whataboutery", it is basic ethical reporting. The French media got it mostly right; The US media got it mostly wrong.

We must have a zero tolerance policy for abuse, but bad reporting and bad extrapolation serve no one.

I'm referring to the US media - they covered it very differently from the French media.

Should we also shut down schools, scouting programs, day cares, adoption, camping, etc? All of these have had almost exactly the same size and scope of scandal.

I'm not doubting major abuse occurred, and the church needs major reforms. But we should hold the numbers up to the same standard that we hold all other numbers to. There should be a detailed description of the sampling and data, a detailed description or the model used for the projection, and a detailed description and justification for any bias adjustments. Any results should have an error band. Otherwise, this is likely published with an agenda, not to find the truth.

We don't know yet if there is anything special about human intelligence, or what the limitations of general intelligence might be. Are animal and plants a slower form of the same intelligence, or is there something qualitatively different? Can rocks and liquids be considered intelligent since it led to life, which eventually led to us?

Current AI/ML does not appear to have any properties of "life intelligence" - for example you can put an animal or even plant into an unfamiliar situation and it will often figure out a way to survive. AI/ML in the evaluation phase is often pretty dumb, and needs new training if anything changes. Reinforcement learning is probably the closest but still seem pretty limited.

I don't think the current approaches will lead to general intelligence. However, I do suspect that when the right theoretical breakthrough is made AI will rapidly become superhuman and humans will not be in control of what it does - it will simply iterate too quickly for us to compete in any way intellectually.

The truth is that artificial intelligence and machine learning today are still aspirational titles - the current tech simple does not learn or reason in any way that is similar to human or even biological reasoning.

Data mining and probabilistic pattern recognition are much more accurate descriptions, but don't sound as exciting.

It's definitely possible that true AI will one day exist, but it may be anywhere from 5 to 1000 years away. I suspect the current approaches will not resemble the final form when it comes to AI.

In my experience the most useful part of DDD is to have a common vocabulary for your projects - developers and end users should have some common terminology. For complex business domains it is good to have a glossary, and for your code to always use those words in the same way the business uses them.

Other than that, most DDD concepts are a bit dated, and really oriented around JAVA/C# in the early 2000s.

This is yet another paper where the title exaggerates the importance of the conclusion.

I found a copy of the original paper - there are several risks to the model that the authors mentioned:

- They only mutated conditionals to test coverage, not any of the other possible errors the test suite may be looking for.

- Equivalent mutations may be miscounted, particularly when developers test a lot of off by one errors.

- There data may not meet the assumptions of the Kendell r correlation used.

- Most of their data had low levels of coverage - previous research shows high coverage is needed before it is related to effectiveness.

- They did not account for object oriented code boilerplate code (getter/setters) which do not need to be tested, causing their counts to be potentially be off. This is major, as they were using only Java projects.

- They had very narrow inclusion criteria, so the results may not be generalizable to all codebases. The projects had to have over 1000 test written; the average LOC was generally in the 100k range.

This honestly sounds like some grad students final project, not advice for real world digestion.

Unfortunately the marketing has gotten ahead of the actual engineering - the features can be useful but it does take an adjustment period to learn its limitations.

I have Toyota Safety Sense 2.5 on a 2021 Camry:

Pre Collision system: pretty good, it did help once when a car suddenly pulled in front of me and rapidly slowed down. Pretty good at detecting pedestrians when backing up. A good safety net, but you should never rely on it.

Dynamic cruise control: pretty good, but you need to take control if traffic is totally stopped or a car suddenly pulls in our out in front of you.

Lane departure / lane tracing assist: almost useless - too many faded lanes and complicated patterns where I live.

Road sight assist: accurate but mostly useless

Blind spot detection: a good first pass, but you still need to look. It can miss motorcycles or bicycles on a slower road. As the article mentioned, it can also miss if something is about to go into your blind spot.

By far the most useful newer feature is the 360 view camera - it makes parking super easy.

I'm not expert is any of this, but what worries me is that political pressures, conflicts of interest, profit motives, rushed timelines, experts who have misled to get a reaction, and many studies with either short terms or small sample sizes are making it so that the average person has no idea what to believe.

"No Silver Bullet" is similar to "Moore's law" - an observation rather than an absolute rule. In both cases, the authors were only projecting into the future 10 years.

It is just measurement, not pessimism or optimism. The fact that "No Silver Bullet" is still true 35 years later show just how hard progress can be.

> Tor has done more harm than good for human rights

Sad but true - it seems like every technology that is made to promote free speech, anonymity, or non-centralization ultimately leads to bad actors using it and more authoritarian laws being passed. I'm not sure what the solution is.

> So, maybe age-ism is a thing, but I'd also argue a lot of age-ism can be reduced to "I never even tried keeping up and hyper-specialized myself into a dying corner even when the signs became clear I'm becoming outdated, so why doesn't anyone hire me?"

My experience has been exactly this. I've read many such complaints in the last 20 years of working, and in every single case the author has pigeonholed themselves into a long obsolete corner of the industry. There is a difference between 30 years of experience and 5 years repeated 6 times.

It is important to get onto the right team to have a long lived tech career. Your business domain should be big enough so you organically have to move with the rest of the industry. By 20+ years, you should be in a senior enough position to help lead the change.

I've never met a tech guru who was up to date and didn't retire or move on to other things on his/her own terms.