We must have a zero tolerance policy for abuse, but bad reporting and bad extrapolation serve no one.
83 karma · joined November 15, 2020
We must have a zero tolerance policy for abuse, but bad reporting and bad extrapolation serve no one.
What sampling method did they use to arrive at this estimate? What is the error range and 95/99% confidence intervals? I haven't been able to find the original report online.
From some articles: The commission found evidence of 2,900 to 3,200 abusers – out of a total of 115,000 priests and other clerics – but said this was probably an underestimation.
We would really need to see what the method they used to arrive at the numbers - this smells like major hyperbole.
UPDATE: a better article is available in French: https://www.lefigaro.fr/actualite-france/les-cinq-chiffres-c...
The number they gave is cases over 70 years, which is a weird way of reporting it. Annually, it is about 2900-3000 priests per year. The article also mention that the current rate in about 1/4 of the rate in the 1970's, showing that some of the church reforms have actually worked. If you compare the French stats to what is being reported here, the anti-church media bias is flagrantly clear.
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.
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.
Other than that, most DDD concepts are a bit dated, and really oriented around JAVA/C# in the early 2000s.
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.
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.
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.
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.
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.
The only I things I manually look for in reviews are overall readability, sensible naming, high level adherence to the architecture/design, and data system/database/API queries.
After 20 years of writing code and leading teams, I've found that most of the stuff developer blogs and the OOP community fret over simply doesn't matter in most cases. The high level interfaces are often more important than the low levels details, and static code analyzers do a decent job of pointing to problems. I've almost never seen bad code that didn't also have many issues in the static analyzer.
It also matters what kind of code you are writing - foundation code or something that will be used by thousands/millions of developers should have a higher bar than some internal one off. However, I've seen enough very profitable companies running on total crap code to believe that code quality is the most important thing.
According to Alan Kay (one of the creators of Smalltalk, the first OOP), message passing is more important to OOP than inheritance. If you listen to his old speeches, what he is describing sounds a lot more like microservices and VMs/containers than what most of the later languages turned it into. (He describes objects as "mini computers" that interact with each other using only public interfaces)
Your co-workers may just be zoned out and collecting a paycheck - at some point the workload can become so impossible that you just don't care anymore. This is especially true if you don't care about a company's mission, product, or customers. Unfortunately the tech boom has led to high turnover at some places, and people just jumping to the next big salary instead of picking a good long term fit.
The tech industry is big enough that you can do what makes you happy - the big name places sometimes aren't a great fit. I've found a pretty good career working at mid-tier Fortune 500 companies. There a lot of good opportunities all across the US if you're willing to forgo SV salaries, know your stuff (and can learn new tech quickly), and focus on helping the business instead of the latest tech fad. (FYI, I make 175k in the mid-west with 40hr work weeks and a 10 min commute, 5 weeks PTO, great healthcare)
Combined with the recent French report (and other government reports in the past) this does seems interesting - this is the first time governments have officially admitted that something unexplained is being seen regularly by experienced aviators.
I'm generally skeptical, but in this case I'm not too convinced by the debunkers either. However we would really need the full data to be released or available for review by multiple groups of experts to know anything.
I would guess it's even worse, maybe 1-5% of doctors truly understand the underlying physiology and will holistically look at all of the information. Most doctors I've met use population heuristics and published guidelines, and if you have something rare they just throw stuff at the wall instead of trying to understand it.
Machine learning and neural networks have prospered due to massive increases in computing speed and data, but I suspect it will also be a dead end in general AI. There will be some niche wins, some quite impressive, but the machine won't have the properties of an intelligent child or even a rat.
There seems to be a big difference in how actual neurons work - I think neural networks are misnamed. The brain is not a big matrix, biology does not appear to reinforce behavior using gradient operations. I think increased research into neuroscience and biomimicry is the most likely approach for general AI, but I think we are still far away and the current hype will just be one more dead end.