thegginthesky

This is not the point. The point is you misquoted the article without understanding the full context, and was corrected. The parents weren't judgmental of an online life, they were just unaware. Matter of fact, on the documentary from one of the replies, it felt that they were glad their son had good friends who really cared about him.

I'm a similar position as you in terms of job, salary and balance.

But this is my 5th job out of college and the longest I've been in a single job. When I hit my 4year mark, I started to think that the grass is greener on the other side and looked for opportunities here and there.

A few years in and I didn't find the right place that would make me jump ship. I have high standards and I can spot redflags based on past experiences in jobs in multiple countries.

I also found out that after I got older and accrue more responsibilities outside work, my job became a much smaller focal point of my life.

I'd rather be employed in an okay place, being paid a competitive enough salary (75% in the curve), and have opportunity to learn new things in the job and out, even if I don't love the field. As opposed to try a new job and risk being put in a toxic work environment and lose the balance I have now.

If you are not growing as an engineer, going to a new job might not help you and could be detrimental. It's much better to learn new things while you have the time to do so. Find an area you want to learn more, try new projects or courses, and have fun at your own pace.

No problem! I have most of the code in very small functions that I'd be willing to contribute.

At my company we have very time sensitive AB tests that we have to run with very few data points (at most 20 conversions per week, after 1000 or so failures).

We found out that using Bayesian A/B testing was excellent for our needs as it could be run with fewer data points than regular AB for the sort of conversion changes we aim for. It gives a probability of group B converting better than A, and we can run checks to see if we should stop the test.

Regular ABs would take too long and the significance of the test wouldnt make much sense because after a few weeks we would be comparing apples to oranges.

I stand corrected! It was my impression that many methods used in ML such as Support Vector Machines, Decision Trees, Random Forests, Boosting, Bagging and so on have very deep roots in Frequentist Methods, although current CS implementations lean heavily on optimizations such as Gradient Descent.

Giving a cursory look into Bishop's book I see that I am wrong, as there's deep root in Bayesian Inference as well.

On another note, I find it very interesting that there's not a bigger emphasis on using the correct distributions in ML models, as the methods are much more concerned in optimizing objective functions.

Most models are derived of Machine Learning principles that are a mix of classic probability theory, Frequentist and Bayesian statistics and lots of Computer Science fundamentals. But there have been advancements in Bayesian Inference and Bayesian Deep Learning, you should check the work of frameworks like Pyro (built on top of PyTorch)

Edit: corrected my sentence, but see 0xdde reply for better info.

Frequentist and Bayesian are correct if both have scientific rigor in their research and methodology. Both can be wrong if the research is whack or sloppy.

It's because practicioners of one says that the other camp is wrong and question each other's methodologies. And in academia, questioning one's methodology is akin to saying one is dumb.

To understand both camps I summarize like this.

Frequentist statistics has very sound theory but is misapplied by using many heuristics, rule of thumbs and prepared tables. It's very easy to use any method and hack the p-value away to get statistically significant results.

Bayesian statistics has an interesting premise and inference methods, but until recently with the advancements of computing power, it was near impossible to do simulations to validate the complex distributions used, the goodness of fit and so on. And even in the current year, some bayesian statisticians don't question the priors and iterate on their research.

I recommend using methods both whenever it's convenient and fits the problem at hand.

I miss the college days where professors would argue endlessly on Bayesian vs Frequentist.

The article is very well succinct and even explains why even my Bayesian professors had different approaches to research and analysis. I never knew about the third camp, Pragmatic Bayes, but definitely is in line with a professor's research that was very through on probability fit and the many iteration to get the prior and joint PDF just right.

Andrew Gelman has a very cool talk "Andrew Gelman - Bayes, statistics, and reproducibility (Rutgers, Foundations of Probability)", which I highly recommend for many Data Scientists

Hey Peter, I'm a foreigner residing abroad with an american spouse of 10 years and we have kids holding American Passports. I'm currently working as a contractor for a company in the US, but I'm thinking of moving to the US and applying for the IR1 visa due to family reasons. I am the primary earner of the household.

What are my limitations in this case? Can I keep the contract I'm currently in while the visa process chugs along? What about proof of income?

That is a much lesser punishment than what the US is doing in this thread. A fine that is a small but non-insignficant percentage of annual revenue is a measured response when you want to punish bad behavior but allow businesses to still operate within the jurisdiction.

Restricting business operation altogether is a response a country gives when they see the other party as extremely adversarial, which is a few orders of magnitude above what EU fines are to Big Tech.

Big difference

I know someone who works on this in Meta. His resume is computer science heavy, with a masters in Machine Learning. On the previous experience side, before getting into Meta, he had about a decade working as a Software Engineer with Machine Learning system in multiple languages, such as Go, C++ and Python.

To get the job he applied for a spot I'm Software Engineer applied in Machine Learning, he went through the multiple step interview process, and then when he got the job he did a few weeks of training and interviewing teams. One of the teams in charge of optimizing ML code in Meta picked him up and now he works there.

Because of Meta's scale, optimizing code that saves a few ms or watts is a huge impact in the bottom line.

In sum:

- Get a formal education in the area - Get work experience somewhere - Apply for a big tech job in Software Engineer applied with ML - Hope they hire you and have a spot in one of the teams in charge of optimizing stuff

I won't be defending Nintendo because their issue with joy-stick drifting, their unwillingness to fix their design flaw, and their constant efforts to stop video game preservation really pisses me off.

But emulators are hit or miss, specially if you aren't tech-savvy enough to fix specific problems, and depends on the people's preferences:

- they need to install and configure an emulator, which isn't fool proof

- go through the hoops of downloading ROMs from sometimes sketchy sources

- configuring each game to work well

- self troubleshooting any issues they might have with some games

- not wanting to play their games online (which in Nintendo's case the online experience is truly lacking)

- not have any moral issues with piracy for games that are currently selling in both physical and digital format and are widely available

Nintendo's Switch is due for a very necessary upgrade, but the console is still widely available, very convenient with minimal setup and troubleshooting, and very plug and play. With the added benefit that you get to support developers to continue making the games.

Freight broker that wanted to use technology to automate the work of finding trucks, managing the communication between truck, shipper and facilities, and auditing the job of moving the load.

But the job is very high revenue (thousands of dollars per cargo depending on the distance) but low profit (5%-10%) in these times.

Like the sibling comment has said.

But what we saw during the peak was crazy, and some investors and execs thought it would last.

At least in regards to what pertains FTL freight, we saw a drop of tougjly 50% in volumes as consumer spending is down when compared to pandemic height. I am pretry sure there will be a seasonal trend upwards, but no where in the way it was before.

If you are in the industry, you can easily check how much cargo is moving around. Maybe by November-December it will be less than a 50% drop, but expect it to return by kid January-February.